ABSTRACT - UCL Discovery



THE VISUAL BASIS OF READING IMPAIRMENT IN POSTERIOR CORTICAL ATROPHYSUBMITTED TO UNIVERSITY COLLEGE LONDON FOR THE DEGREE OF DOCTOR OF PHILOSOPHYKEIR YONG2014INSTITUTE OF NEUROLOGY, UNIVERSITY COLLEGE LONDONDECLARATION STATEMENTI, Keir Yong, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Keir YongABSTRACTThis thesis explores the nature of reading impairment in posterior cortical atrophy (PCA), a degenerative syndrome most commonly caused by Alzheimer’s disease (AD) pathology. PCA is characterised by cognitive deficits associated with posterior brain atrophy, including disruption to various visual domains, with relatively spared episodic memory function. Acquired dyslexia is an early and debilitating symptom of PCA; however, there is a lack of group investigations of reading dysfunction. Through a better understanding of dyslexia, including the contribution of visual deficits, the optimal conditions for PCA patients’ reading might be revealed. A series of studies were conducted to characterise reading and other visual deficits in PCA patients, typical AD (tAD) patients and healthy controls, accompanied by a consistent and comprehensive battery of neuropsychological tests. One form of early visual processing deficit that has been proposed to crucially limit reading in normal peripheral vision is crowding. Behavioural and neuroimaging investigations confirmed the qualitative similarity between crowding and deficits in identifying centrally presented flanked stimuli in PCA. Assessments of single word recognition and passage reading were carried out through behavioural, eye movement and neuroimaging analysis. Perceptual and spatial factors primarily determined single word and passage reading ability in PCA, not tAD. One counter-intuitive finding was how PCA patients demonstrated particular difficulty reading text in large font. Results also identified two patients who demonstrate remarkably preserved reading despite showing grave visual impairment; this discrepancy poses problems for general visual accounts of reading deficits. The two patients were followed longitudinally, revealing how the development of enhanced crowding effects coincided with loss of reading ability. Insights from the thesis informed the development of two interventions which intended to provide the optimal conditions for reading in PCA; both interventions resulted not only in consistent gains in reading accuracy, but also in improvements in self-reported reading ease and comprehension. Table of Contents TOC \o "1-3" \h \z \u ABSTRACT PAGEREF _Toc387766387 \h - 3 -AIMS OF THIS THESIS PAGEREF _Toc387766388 \h - 11 -1.INTRODUCTION PAGEREF _Toc387766389 \h - 12 -1.1.CHAPTER INTRODUCTION PAGEREF _Toc387766390 \h - 12 -1.2.DEMENTIA PAGEREF _Toc387766391 \h - 13 -1.2.1.Alzheimer’s disease (AD) PAGEREF _Toc387766392 \h - 13 -1.2.2.Posterior Cortical Atrophy (PCA) PAGEREF _Toc387766393 \h - 16 -1.3.CHAPTER CONCLUSIONS PAGEREF _Toc387766394 \h - 21 -2.NEUROPSYCHOLOGY OF PCA PAGEREF _Toc387766395 \h - 23 -2.1.CHAPTER INTRODUCTION PAGEREF _Toc387766396 \h - 23 -2.2.VISUAL PROCESSING PAGEREF _Toc387766397 \h - 23 -2.2.1.Early visual processing PAGEREF _Toc387766398 \h - 23 -2.2.2.Visuospatial processing and inverse size effects PAGEREF _Toc387766399 \h - 25 -2.2.3.Visuoperceptual processing PAGEREF _Toc387766400 \h - 27 -2.3.READING PAGEREF _Toc387766401 \h - 28 -2.3.1.Contribution of areas of early visual processing PAGEREF _Toc387766402 \h - 29 -2.3.2.Cognitive models PAGEREF _Toc387766403 \h - 30 -2.3.3.Neuroanatomical models PAGEREF _Toc387766404 \h - 31 -2.4.ACQUIRED DYSLEXIA PAGEREF _Toc387766405 \h - 32 -2.5.NON-VISUAL NEUROPSYCHOLOGICAL FEATURES PAGEREF _Toc387766406 \h - 35 -2.5.1.Language PAGEREF _Toc387766407 \h - 35 -2.5.2.Praxis PAGEREF _Toc387766408 \h - 36 -2.6.CHAPTER CONCLUSIONS PAGEREF _Toc387766409 \h - 36 -3.METHODS OVERVIEW PAGEREF _Toc387766410 \h - 38 -3.1.PARTICIPANTS PAGEREF _Toc387766411 \h - 38 -3.1.1.Patients PAGEREF _Toc387766412 \h - 38 -3.1.2.Healthy controls PAGEREF _Toc387766413 \h - 38 -3.2.CLINICAL ASSESSMENT PAGEREF _Toc387766414 \h - 38 -3.3.INCLUSION CRITERIA PAGEREF _Toc387766415 \h - 39 -3.3.1.PCA PAGEREF _Toc387766416 \h - 39 -3.3.2.Typical AD PAGEREF _Toc387766417 \h - 39 -3.4.NEUROPSYCHOLOGY PAGEREF _Toc387766418 \h - 39 -3.4.1.Background neuropsychology PAGEREF _Toc387766419 \h - 39 -3.4.2.Visual Assessment PAGEREF _Toc387766420 \h - 40 -3.5.RESPONSE LATENCIES PAGEREF _Toc387766421 \h - 41 -3.6.EYETRACKING PAGEREF _Toc387766422 \h - 41 -3.7.IMAGING PAGEREF _Toc387766423 \h - 42 -3.7.1.MRI acquisition PAGEREF _Toc387766424 \h - 42 -3.7.2.Image processing software PAGEREF _Toc387766425 \h - 42 -3.7.3.Image processing PAGEREF _Toc387766426 \h - 42 -4.VISUAL CROWDING EFFECTS IN PCA PAGEREF _Toc387766427 \h - 44 -4.1.CHAPTER INTRODUCTION PAGEREF _Toc387766428 \h - 44 -4.2.METHODS PAGEREF _Toc387766429 \h - 45 -4.2.1.Participants PAGEREF _Toc387766430 \h - 45 -4.2.2.Background neuropsychology PAGEREF _Toc387766431 \h - 46 -4.2.3.Crowding assessment PAGEREF _Toc387766432 \h - 47 -4.2.4.Data analysis PAGEREF _Toc387766433 \h - 49 -4.3.RESULTS PAGEREF _Toc387766434 \h - 51 -4.3.1.Crowding Assessment 1 - flanker and spacing effects PAGEREF _Toc387766435 \h - 51 -4.3.2.Crowding assessment 2 – Polarity effects PAGEREF _Toc387766436 \h - 54 -4.3.3.Error analysis PAGEREF _Toc387766437 \h - 54 -4.3.4.Summary of behavioural data PAGEREF _Toc387766438 \h - 55 -4.3.5.Neuroimaging findings PAGEREF _Toc387766439 \h - 55 -4.4.DISCUSSION PAGEREF _Toc387766440 \h - 56 -4.5.CHAPTER CONCLUSIONS PAGEREF _Toc387766441 \h - 60 -5.READING IN PCA PAGEREF _Toc387766442 \h - 62 -5.1.CHAPTER INTRODUCTION PAGEREF _Toc387766443 \h - 62 -5.2.METHODS PAGEREF _Toc387766444 \h - 63 -5.2.1.Participants PAGEREF _Toc387766445 \h - 63 -5.2.2.Background neuropsychology PAGEREF _Toc387766446 \h - 63 -5.2.3.Reading assessment PAGEREF _Toc387766447 \h - 63 -5.2.4.Data analysis PAGEREF _Toc387766448 \h - 65 -5.3.RESULTS PAGEREF _Toc387766449 \h - 66 -5.3.1.Perceptual corpus PAGEREF _Toc387766450 \h - 66 -5.3.2.Cursive font reading PAGEREF _Toc387766451 \h - 71 -5.3.3.Neuroimaging findings PAGEREF _Toc387766452 \h - 71 -5.4.DISCUSSION PAGEREF _Toc387766453 \h - 72 -5.5.CHAPTER CONCLUSIONS PAGEREF _Toc387766454 \h - 76 -6.CASE STUDIES: INTACT READING IN PCA PAGEREF _Toc387766455 \h - 77 -6.1.CHAPTER INTRODUCTION PAGEREF _Toc387766456 \h - 77 -6.2.METHODS PAGEREF _Toc387766457 \h - 79 -6.2.1.Participants PAGEREF _Toc387766458 \h - 79 -6.2.2.Background neuropsychology PAGEREF _Toc387766459 \h - 81 -6.2.3.Experimental procedures PAGEREF _Toc387766460 \h - 81 -6.2.4.Data analysis PAGEREF _Toc387766461 \h - 84 -6.3.RESULTS PAGEREF _Toc387766462 \h - 85 -6.3.1.Visual assessment PAGEREF _Toc387766463 \h - 85 -6.3.2.Word reading PAGEREF _Toc387766464 \h - 85 -6.3.3.Single letter processing PAGEREF _Toc387766465 \h - 89 -6.4.DISCUSSION PAGEREF _Toc387766466 \h - 89 -6.5.CHAPTER CONCLUSIONS PAGEREF _Toc387766467 \h - 95 -7.CASE STUDIES: LONGITUDINAL ASSESSMENT OF READING IN PCA PAGEREF _Toc387766468 \h - 97 -7.1.CHAPTER INTRODUCTION PAGEREF _Toc387766469 \h - 97 -7.2.METHODS PAGEREF _Toc387766470 \h - 98 -7.2.1.Participants PAGEREF _Toc387766471 \h - 98 -7.2.2.Imaging PAGEREF _Toc387766472 \h - 98 -7.2.3.Experimental procedures PAGEREF _Toc387766473 \h - 98 -7.2.4.Data analysis PAGEREF _Toc387766474 \h - 100 -7.3.RESULTS PAGEREF _Toc387766475 \h - 101 -7.3.1.Reading assessment PAGEREF _Toc387766476 \h - 101 -7.3.2.Visual assessment PAGEREF _Toc387766477 \h - 106 -7.3.3.Crowding assessment PAGEREF _Toc387766478 \h - 106 -7.3.4.Error analysis PAGEREF _Toc387766479 \h - 109 -7.4.DISCUSSION PAGEREF _Toc387766480 \h - 109 -7.5.CHAPTER CONCLUSIONS PAGEREF _Toc387766482 \h - 113 -8.PASSAGE READING IN PCA PAGEREF _Toc387766483 \h - 114 -8.1.CHAPTER INTRODUCTION PAGEREF _Toc387766484 \h - 114 -8.2.METHODS PAGEREF _Toc387766485 \h - 115 -8.2.1.Participants PAGEREF _Toc387766486 \h - 115 -8.2.2.Background neuropsychology PAGEREF _Toc387766487 \h - 116 -8.2.3.Passage reading assessment PAGEREF _Toc387766488 \h - 116 -8.2.4.Data analysis PAGEREF _Toc387766489 \h - 118 -8.3.RESULTS PAGEREF _Toc387766490 \h - 118 -8.3.1.Reading accuracy PAGEREF _Toc387766491 \h - 118 -8.3.2.Reading latency PAGEREF _Toc387766492 \h - 121 -8.3.3.Eye movement data PAGEREF _Toc387766493 \h - 121 -8.4.DISCUSSION PAGEREF _Toc387766494 \h - 121 -8.5.CHAPTER CONCLUSION PAGEREF _Toc387766495 \h - 122 -9.FACILITATING READING IN PCA PAGEREF _Toc387766496 \h - 123 -9.1.CHAPTER INTRODUCTION PAGEREF _Toc387766497 \h - 123 -9.2.METHODS PAGEREF _Toc387766498 \h - 124 -9.2.1.Pilot study 1 PAGEREF _Toc387766499 \h - 124 -9.2.2.Pilot study 2 PAGEREF _Toc387766500 \h - 126 -9.2.3.Main investigation- Single-word and Double-word presentation PAGEREF _Toc387766501 \h - 127 -9.2.4.Data analysis PAGEREF _Toc387766502 \h - 128 -9.3.RESULTS PAGEREF _Toc387766503 \h - 129 -9.3.1.Efficacy of reading intervention PAGEREF _Toc387766504 \h - 129 -9.3.2.Eye movement data PAGEREF _Toc387766505 \h - 133 -9.4.DISCUSSION PAGEREF _Toc387766506 \h - 134 -9.5.CHAPTER CONCLUSIONS PAGEREF _Toc387766507 \h - 136 -10.THESIS CONCLUSIONS PAGEREF _Toc387766508 \h - 138 -10.1.CHAPTER INTRODUCTION PAGEREF _Toc387766509 \h - 138 -10.2.ROLE OF EXCESSIVE CROWDING IN LETTER RECOGNITION PAGEREF _Toc387766510 \h - 139 -10.3.ROLE OF PERCEPTUAL FACTORS IN WORD RECOGNITION PAGEREF _Toc387766511 \h - 139 -10.4.PERCEPTUAL FACTORS IN TEXT READING AND READING INTERVENTIONS PAGEREF _Toc387766512 \h - 141 -10.5.CAUSATIVE ROLE OF VISUAL IMPAIRMENT IN READING PAGEREF _Toc387766513 \h - 142 -10.6.IMPLICATIONS FOR MODELS OF READING PAGEREF _Toc387766514 \h - 143 -10.7.CLINICAL IMPLICATIONS PAGEREF _Toc387766515 \h - 144 -10.8.PCA v tAD PAGEREF _Toc387766516 \h - 145 -10.9.CHAPTER CONCLUSION PAGEREF _Toc387766517 \h - 146 -PUBLICATIONS PAGEREF _Toc387766518 \h - 147 -ACKNOWLEDGEMENTS PAGEREF _Toc387766519 \h - 148 -APPENDIX 1: DUBOIS ET AL. (2007) DIAGNOSTIC CRITERIA FOR PROBABLE AD PAGEREF _Toc387766520 \h - 149 -APPENDIX 2: NINCDS-ADRDA 2011 CRITERIA FOR DEMENTIA AND PROBABLE AD PAGEREF _Toc387766524 \h - 150 -APPENDIX 3: PROPOSED DIAGNOSTIC CRITERIA FOR PCA PAGEREF _Toc387766530 \h - 151 -APPENDIX 4: VISUAL ASSESSMENT NEUROPSYCHOLOGICAL EXAMPLE STIMULI PAGEREF _Toc387766537 \h - 152 -APPENDIX 5: READING CORPORA PERFORMANCE FOR FOL AND CLA PAGEREF _Toc387766541 \h - 157 -GLOSSARY PAGEREF _Toc387766545 \h - 159 -REFERENCES PAGEREF _Toc387766546 \h - 161 -Table of Figures TOC \h \z \c "Figure" Figure 1.1 Grey matter and cortical thickness for PCA relative to tAD and controls. PAGEREF _Toc373759319 \h - 19 - TOC \h \z \c "Figure 4." Figure 4.1 Crowding example stimuli PAGEREF _Toc373758716 \h - 47 -Figure 4.2 Accuracy and latency data for the PCA and tAD groups across spacing and letter, shape and number flanker conditions. PAGEREF _Toc373758717 \h - 52 -Figure 4.3 Accuracy and latency data for the PCA and tAD groups across spacing and same and reverse polarity flanker conditions. PAGEREF _Toc373758718 \h - 55 -Figure 4.4 Statistical parametric maps of grey matter volume associated with crowding effects in the PCA group. PAGEREF _Toc373758719 \h - 57 - TOC \h \z \c "Figure 5" Figure 5.1 Summary of reading and latencies for the PCA, tAD and control groups PAGEREF _Toc373759387 \h - 68 -Figure 5.2 Proportion of participants in each group who show an effect of each variable on either latency or accuracy at the individual level. PAGEREF _Toc373759388 \h - 69 -Figure 5.3 Statistical parametric maps of grey matter volume associated with the difference in accuracy between large and small words in the PCA group. PAGEREF _Toc373759389 \h - 72 - TOC \h \z \c "Figure 6." Figure 6.1 Neuroanatomical features in FOL and CLA PAGEREF _Toc373759021 \h - 80 -Figure 6.2 Mean reading latencies for words of different length across all corpora for FOL, CLA and their control groups. PAGEREF _Toc373759022 \h - 89 -Figure 6.3 Mean response latencies for flanked letter identification for FOL, CLA and their control groups. PAGEREF _Toc373759023 \h - 91 -Figure 6.4 Mean reading latencies for words of different length for FOL, CLA and previously reported letter-by-letter readers PAGEREF _Toc373759024 \h - 93 - TOC \h \z \c "Figure 7" Figure 7.1 MRI sections and voxel-compression maps for FOL and CLA. PAGEREF _Toc374291609 \h - 99 -Figure 7.2 FOL and CLA’s accuracy and latency data across longitudinal assessments. PAGEREF _Toc374291610 \h - 102 -Figure 7.3 FOL and CLA’s latencies for words of different length across longitudinal assessments PAGEREF _Toc374291611 \h - 104 -Figure 7.4 Types of error made across longitudinal assessments PAGEREF _Toc374291612 \h - 106 -Figure 7.5 FOL and CLA’s flanked letter identification perfomance across longitudinal assessments. PAGEREF _Toc374291613 \h - 108 - TOC \h \z \c "Figure 8" Figure 8.1 Heatmap of PCA accuracy data from a sample passage. PAGEREF _Toc374292020 \h - 119 -Figure 8.2 Order of first forty words read by tAD and PCA patients PAGEREF _Toc374292021 \h - 120 - TOC \h \z \c "Figure 9" Figure 9.1 Presentation conditions for Pilots 1 and 2 PAGEREF _Toc374292083 \h - 125 -Figure 9.2 Sequential single-word and sequential double-word presentations PAGEREF _Toc374292084 \h - 128 -Figure 9.3 Accuracy and latency data for the PCA, tAD and control groups PAGEREF _Toc374292085 \h - 130 -Figure 9.4 PCA reading accuracy for baseline and under both reading interventions PAGEREF _Toc374292086 \h - 131 -Figure 9.5 PCA, tAD and control error rates under different presentation conditions PAGEREF _Toc374292087 \h - 131 -Figure 9.6 PCA self-reported measures of reading. PAGEREF _Toc374292088 \h - 133 -Table of Appendix Figures TOC \h \z \c "Figure A" Figure A-i Visual acuity test (CORVIST) subset to scale (Snellen equivalent: 6/18- 6/9) PAGEREF _Toc374292588 \h - 152 -Figure A-ii Shape detection test (VOSP) PAGEREF _Toc374292589 \h - 152 -Figure A-iii Shape discrimination (Efron, 1968): three levels of difficulty PAGEREF _Toc374292590 \h - 153 -Figure A-iv Hue discrimination (CORVIST) PAGEREF _Toc374292591 \h - 153 -Figure A-v Object Decision (VOSP) PAGEREF _Toc374292592 \h - 154 -Figure A-vi Fragmented letter (VOSP) PAGEREF _Toc374292593 \h - 154 -Figure A-vii Unusual and usual views (Warrington and James, 1988) PAGEREF _Toc374292594 \h - 155 -Figure A-viii Number location (VOSP) PAGEREF _Toc374292595 \h - 155 -Figure A-ix Dot counting (VOSP) PAGEREF _Toc374292596 \h - 155 -Figure A-x A Cancellation task PAGEREF _Toc374292597 \h - 156 -Table of Tables TOC \h \z \c "Table 4" Table 4.1 Demographic characteristics of PCA, tAD and normal control groups PAGEREF _Toc374292747 \h - 46 -Table 4.2 Molecular pathology data for PCA and tAD patients. PAGEREF _Toc374292748 \h - 46 -Table 4.3 Neuropsychological scores of patients with PCA and tAD. PAGEREF _Toc374292749 \h - 48 -Table 4.4 Comparisons between PCA and tAD accuracy and latency data PAGEREF _Toc374292750 \h - 53 - TOC \h \z \c "Table 5" Table 5.1 Different levels of reading variables for words from the perceptual corpus. PAGEREF _Toc373759603 \h - 64 - TOC \h \z \c "Table 6." Table 6.1 Neuropsychological scores of FOL/CLA relative to normative data PAGEREF _Toc373759225 \h - 80 -Table 6.2 Accuracy and latency data for FOL, CLA and relevant control groups on the word reading experiments. PAGEREF _Toc373759226 \h - 87 -Table 6.3 Performance on tests of letter processing. PAGEREF _Toc373759227 \h - 90 -Table 6.4 Performance on tests of visuoperceptual function. PAGEREF _Toc373759228 \h - 91 - TOC \h \z \c "Table 7" Table 7.1 Reading and crowding assessment accuracy and latency for FOL/CLA and their matched control groups. PAGEREF _Toc374292664 \h - 103 -Table 7.2 FOL and CLA’s performance on background neuropsychological measures and tests of visual processing. PAGEREF _Toc374292665 \h - 107 - TOC \h \z \c "Table 8" Table 8.1 Demographic information for PCA, tAD and healthy control groups. PAGEREF _Toc374292732 \h - 115 -Table 8.2 Molecular pathology data for PCA and tAD patients. PAGEREF _Toc374292733 \h - 115 -Table 8.3 Neuropsychological scores of patients with PCA and tAD PAGEREF _Toc374292734 \h - 117 -Table 8.4 Eye movement data for PCA, tAD and controls under standard presentation PAGEREF _Toc374292735 \h - 121 - TOC \h \z \c "Table 9" Table 9.1 Accuracy and comprehension performance on Pilot studies 1 and 2. PAGEREF _Toc374292828 \h - 126 -Table 9.2 Correlations between PCA performance on behavioural measures and reading accuracy under different presentation conditions PAGEREF _Toc374292829 \h - 132 -Table 9.3 Eye movement data for PCA, tAD and controls under reading interventions. PAGEREF _Toc374292830 \h - 134 -AIMS OF THIS THESISThe most common cause of dementia is Alzheimer’s disease (AD), defining features of which are the presence of senile plaques, neurofibrillary tangles and severe neuronal and synaptic loss. While in most AD patients these pathological changes tend to be accompanied by memory complaints and medial temporal lobe atrophy, there is considerable variation in the cognitive phenotypes of individuals with AD pathology. Posterior cortical atrophy (PCA) is a syndrome most commonly caused by AD; it is characterised by deficits in visuospatial and higher-order visuoperceptual processing and is associated with structural changes predominantly in parietal, occipital and occipito-temporal regions. An early and particularly life-limiting symptom of PCA is acquired dyslexia; however, there is a scarcity of systematic group studies evaluating reading among PCA patients. Not only would such studies help determine the prevalence, type and heterogeneity of reading impairment in PCA, but they would also clarify the mechanisms through which deficits in basic, higher-order visual function and eye movement control undermine reading. Better understanding of these mechanisms might inform the development of aids and strategies which reduce or eliminate the susceptibility of reading to spatial, perceptual and oculomotor impairment , allowing individuals with PCA to maximise their reading ability over the early course of the disease. AimsTo investigate the role of early visual, visuoperceptual and visuospatial function in limiting reading ability in PCA and AD. To confirm whether flanked letter identification deficits in PCA patients reflect early visual dysfunction in the form of enhanced visual crowding.To characterise the perceptual factors affecting single word recognition in PCA and AD. To characterise the factors affecting passage reading ability in PCA and AD.To identify whether ameliorating or curtailing visual deficits which contribute towards reading dysfunction result in improvements in reading ability. INTRODUCTIONCHAPTER INTRODUCTIONOver a hundred years ago, Alois Alzheimer identified the amyloid plaques and neurofibrillary tangles that have come to be associated with Alzheimer’s disease, the main cause of dementia. Since then, dementia has emerged as one of the greatest challenges to health and social care; there are an estimated 820,000 people in the UK with dementia, with this number expected to double within 30 years. The cost of care in the UK is estimated at ?23 billion a year; the global cost of dementia is estimated to amount to more than 1% of the world’s gross domestic product. An internationally ageing population (Kinsella et al., 2009) accompanied by age-related increases in the prevalence of dementia (Fratiglioni et al., 2001; Corrada et al., 2010) and the lack of a cure or prevention for most causes of dementia demonstrate the incredible burden that progressive dementias have on health care systems, along with the psychological and financial strain placed on family caregivers (Etters et al., 2007). It is estimated that more than half of individuals with dementia live in care homes (Macdonald & Cooper, 2007). Projections by Comas-Herrera et al. (2007), which refer to cognitive impairment in older people but reflect mainly dementia-associated costs, suggest that expenditure on long-term care services in England will rise from 0.60% of GDP in 2002 to 0.96% of GDP in 2031. An important factor in determining admission to care homes is the capacity to perform activities of daily living (ADL). Diminished capacity for ADLs results in reduced independence and increased care demands (Bullock & Hammond, 2003), while increased needs for caregiver assistance with activities is the main predictor of a need for skilled care, the most common reason for the institutionalisation of individuals with dementia by caregivers (Buhr et al., 2006). Furthermore, dependency on others to perform ADLs has been identified as the primary factor underlying quality of life measures in dementia (Andersen et al., 2004). In individuals with dementia, instrumental ADLs, functions which particularly reflect independence, are predicted by higher order visuoperceptual (Glosser et al., 2002; Jefferson et al., 2006) and visuospatial ability (Fukui & Lee, 2009; Hill et al., 1995). PCA patients show dramatic impairment to these aspects of vision, but have relatively preserved insight into difficulties arising from this impairment (Benson et al., 1988; Mendez et al., 2002; Tang-Wai et al., 2004), while patients with an amnestic typical AD (tAD) presentation often have secondary visuospatial and visuoperceptual deficits (Almkvist, 1996; Caine & Hodges, 2001; Quental et al., 2013; Binetti et al., 1998) which become more prominent over the disease course (Grady et al., 1988; Paxton et al., 2007). A better understanding of visual dysfunction in PCA offers a rare and unique perspective into later stages of tAD in which patients are unable to describe or explain the visual problems they face. Consequently, the development of technological aids which minimise deficits in visuoperceptual and visuospatial function might not only benefit PCA patients in the community, day centres or care homes by allowing increased fulfilment of ADLs, prolonging independence, reducing carer burden and improving quality of life, but also patients who are further into the disease course of tAD. DEMENTIADementia is an acquired syndrome involving a persistent impairment of multiple cognitive domains and activities of daily living (Cummings & Benson, 1983; Rossor, 1994; Qiu et al., 2009). The most common cause of dementia is AD pathology; however, other non-AD forms of dementia include dementia with Lewy Bodies (DLB), corticobasal degeneration (CBD), frontotemporal lobar degeneration (FTLD), vascular dementia (VaD), Creutzfeld Jakob disease (CJD) and Acquired Immunodeficiency Syndrome (AIDS) dementia. Comorbidity can exist between dementias, posing problems for clinical diagnosis, and similar symptoms for syndromes such as PCA can arise from different underlying pathological processes. This thesis focuses on patients, both typical amnestic and PCA, who have probable AD based on clinical and neuroimaging data. Alzheimer’s disease (AD)PathologyTwo structural brain changes identified in Alzheimer’s original patient still form the neuropathological basis of AD: extracellular amyloid β-protein deposits and intracellular neurofibrillary tangles (NFT) (Selkoe, 2000). Neurofibrillary changes develop initially in the transentorhinal region, progressing to marked involvement of the transentorhinal and entorhinal regions, including minor changes in the hippocampus, followed by a progressive invasion of the isocortex (Braak & Braak, 1996). Amyloid deposits are first found in basal portions of the frontal, temporal and occipital lobes; deposits are subsequently found dispersed across almost all cortical regions, with only mild involvement of the hippocampal region; finally, dense formations of deposits are found in most of the isocortex (Braak & Braak, 1991). The progressive invasion of the cerebral cortex by the hallmarks of AD pathology is accompanied by neuronal and synaptic loss (Terry et al., 1991; Gomez-Isla et al., 1996) and cerebral atrophy (Chan et al., 2003). There have been suggestions of a selective vulnerability of certain cells to AD, such as cells with long, sparsely myelinated axonal projections, with propagation of AD pathology taking place through cortico-cortical connections (Morrison et al., 1986a; Lewis et al., 1987; Hof et al., 1989; Braak & Tredici, 2011); such suggestions are in line with the notion of early AD manifesting as the loss of interaction between different cortical regions (De Lacoste & White, 1993).EpidemiologyThe strongest risk factor for AD is older age, which not only implicates age-related biological processes in the development of AD, but also raises the possible expression of various lifestyle and environmental effects having accumulated over the lifespan (Qiu et al., 2009). Of the various factors associated with developing AD, vascular risk factors include smoking (Peters et al., 2008), obesity (Kivipelto et al., 2005) and high blood pressure (Kivipelto et al., 2001), while psychosocial protective factors include a higher educational level (Karp et al., 2004), increased physical activity (Rovio et al., 2005) and greater occupational complexity (Andel et al., 2005). While the majority of AD patients are elderly, some individuals develop AD at a younger age. Young-onset AD is conventionally defined as patients with onset before 65 years of age (Rossor et al., 2010); a younger age of onset tends to be more associated with inherited familial AD (Sampson et al., 2004). Genetic factorsWhile the vast majority of AD cases are sporadic, highly-heritable, autosomal dominant forms of AD exist. The main cause of such forms are mutations in the presenilin-(PS) 1 gene on chromosome 21 (Janssen et al., 2003), with some cases caused by mutations in the PS-2 gene (Mann et al., 1997) or mutations on the β-amyloid precursor protein (APP) gene (Chartier-Harlin et al., 1991). The Apolipoprotein E (APOE) ε4 allele is a susceptibility gene for both young- and late-onset AD (Qiu et al 2009); relative to ε3/ε3 carriers, carriers with an increased number of APOE ε4 alleles have been associated with an increased risk of developing AD and a decreased age of onset (Qiu et al., 2004). Previous findings suggest that cognitive decline may be more rapid in APOE ε4 carriers (Cosentino et al., 2008) and that grey matter volume in medial temporal regions is negatively correlated with the number of ε4 alleles carried by participants (Filippini et al., 2009). However, one study of 328 AD patients found no associated with APOE status and rate of cognitive or behavioural decline (Tschanz et al., 2011). Clinical diagnostic criteria for sporadic ADAD is characterised by a progressive intellectual deterioration, with memory disturbance nearly always presenting as the leading symptom (Cummings & Benson, 1983), consistent with early pathological changes in medial temporal regions. The National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA; McKhann et al., 1984) set out criteria for the diagnostic certainty of AD; definite AD could only be established by autopsy or biopsy, probable AD was determined from clinical and neuropsychological examination, while possible AD reflected an atypical presentation in the absence of other sources of dementia. More recently, diagnostic criteria for research have been revised, emphasising a deficit in episodic memory as the leading core diagnostic criterion for typical AD and proposing the use of one or more biomarkers as supportive features. Biomarkers included medial temporal lobe atrophy as identified from structural imaging, markers of pathology such as amyloid positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) samples showing low Aβ42 concentration and high t-tau concentration, reduced glucose metabolism in bilateral temporal parietal regions shown by PET and the presence of a genetic mutation in the PS-1, PS-2 or APP genes (Dubois et al., 2007; Dubois et al., 2010; see REF _Ref372648694 \h Appendix 1). While typical AD is considered as a predominantly amnestic syndrome, current diagnostic criteria also include secondary deficits in visuospatial ability, nonamnestic language and executive function (McKhann et al., 2011; see REF _Ref372648602 \h Appendix 2), with visuospatial and visuoperceptual deficits sometimes presenting early on in the disease course (Almkvist, 1996; Caine & Hodges, 2001; Quental et al., 2013; Binetti et al., 1998). However, atypical AD presentations in which memory impairment is not the primary deficit are becoming increasingly well-recognised. A frontal variant of AD has been proposed, in which patients show a higher proportion of NFTs in the frontal lobes and perform poorly on measures of executive function and performance intelligence quotient (IQ) relative to typical AD patients (Johnson et al., 1999). The logopenic variant of primary progressive aphasia, involving word finding pauses in the absence of major comprehension and syntactic impairment, has been linked with AD pathology (Mesulam et al., 2008). While weak visuospatial ability is not considered particularly atypical in moderate AD (Grady et al., 1988), leading defining features in some patients with underlying AD pathology include dramatic impairments in visuospatial and visuospatial processing, apraxia, acalculia and spelling difficulties, suggesting pathogenesis primarily within posterior regions of the brain (Benson et al., 1988; Hof et al., 1990). The proportion of AD patients who have a non-amnestic clinical presentation increases in young-onset patients; one study found a third of young-onset AD patients presented with impairments in vision, praxis, language or executive function rather than memory (Koedam et al., 2010). Numerous benefits exist from being able to better characterise and differentiate clinical presentations of AD: improving inclusion criteria and outcome measures for intervention trials (Dubois et al., 2010), diagnosis rates and the relevance of support and clinical services to patients (Crutch et al., 2012a), and our understanding of the disease mechanisms underlying AD. Posterior Cortical Atrophy (PCA)NosologyOver the last three decades, there have been an increasing number of reports of patients with progressive and relatively selective visual impairment despite having normal acuity (Cogan, 1985; De Renzi, 1986; Benson et al., 1988; Caine et al., 2004). Benson et al. (1988) was the first to introduce the term PCA, reporting five patients who exhibited signs of Balint’s and Gerstmann’s syndrome and showed abnormal but relatively spared memory until later in the course of their disease. Subsequent neuropathological evaluation found that patients with clinical presentations consistent with PCA had hallmarks of AD in the form of NFTs and senile plaques (Hof et al., 1990; Tang-Wai et al., 2004). The prevalence of AD pathology in PCA cases has led to terms such as ‘the visual variant of AD’ or ‘biparietal AD’ (Levine et al., 1993; Ross et al., 1996; Galton et al., 2000). However, some cases of PCA have been attributable to corticobasal degeneration, dementia with Lewy bodies or prion disease (Renner et al., 2004; Seguin et al., 2011; Mendez, 2000; Tang-Wai et al., 2003). Cases have been made for the classification of PCA as its own distinct nosological entity based on the uniformity of its clinical profile (Tang-Wai & Mapstone, 2006), while others have argued that PCA exists on an continuum of phenotypic variation in AD and does not represent a discrete disease (Stopford et al., 2008). The core behavioural phenotype of PCA involves visuospatial and visuoperceptual impairment, features of Balint’s (simultanagnosia, oculomotor apraxia, optic ataxia) and Gerstmann’s syndrome (acalculia, agraphia, left-right disorientation, finger agnosia), and alexia and apraxia (McMonagle et al., 2006; Crutch et al., 2012a). McMonagle et al. (2006) reported that some of the most common symptoms of PCA were alexia, agraphia, simultanagnosia, acalculia and optic ataxia. Longitudinal studies indicate that the relative preservation of memory, language and executive function observed in early stages of PCA tend to give way to more global cognitive impairment (Levine et al., 1993; McMonagle et al., 2006). Observations of visual hallucinations, noted in up to 25% of PCA patients (Josephs et al., 2006), and dystonia might implicate underlying DLB or corticobasal degeneration (Crutch et al., 2012a). Some phonological impairment has been noted in PCA, and there is an overlap in performance between PCA and logopenic progressive aphasic (LPA) patients on tasks of nonword repetition, phonemic fluency and prosody processing (Crutch et al., 2013). Different subtypes of PCA have been proposed, including distinct parietal (dorsal), occipitotemporal (ventral) and caudal forms (primary visual) (Ross et al., 1996; Galton et al., 2000), based on findings from individual case reports. However, while a group study of PCA which included detailed behavioural measures found deficits associated with occipital and temporal lobe damage, such as achromatopsia, hemianopia, visual agnosia, prosopagnosia, the overall pattern was one of a consistent, disproportionate deficit in dorsal relative to ventral or primary visual processes (McMonagle et al., 2006).PathologyFindings from pathological studies suggest the majority of PCA patients have underlying AD (Hof et al., 1990; Renner et al., 2004; Tang-Wai et al., 2004; Galton et al., 2000; Seguin et al., 2011). However, some cases of PCA have been associated with Lewy body pathology (Tang-Wai et al., 2003; Renner et al., 2004), corticobasal degeneration (Tang-Wai et al., 2004; Seguin et al., 2011; Renner et al., 2004), subcortical gliosis and prion disease (Renner et al., 2004). Reporting pathological data for 21 PCA patients, Renner et al. (2004) found 13 had AD pathology, 2 had AD-Lewy body variant, 1 had DLB with coexisting subcortical gliosis and 2 had prion-associated disease (CJD and fatal familial insomnia). A separate investigation found 7 of 7 PCA patients had AD pathology (Alladi et al., 2007) while a CSF biomarker analysis by Seguin et al. (2011) found that 17 of 22 PCA patients fulfilled biological criteria for typical AD; of the remaining 5 patients, two exhibited a normal CSF profile but showed a PCA corticobasal syndrome.In PCA patients with AD pathology, differences have been noted in the distribution of NFTs (Tang-Wai et al., 2004) or NFTs and senile plaques (Levine et al., 1993; Ross et al., 1996) relative to typical AD patients. In PCA patients, differences have been localised particularly within occipital regions which are relatively unaffected in tAD, with pathological distribution increasing in densities from area V1 to the visual association areas (Hof et al., 1989; Hof et al., 1990; Hof et al., 1997) and lower densities reported in frontal regions (Levine et al., 1993; Hof et al., 1997). However, while Tang-Wai et al. (2004) found lower concentrations of senile plaques in hippocampal regions of PCA relative to tAD patients, similar plaque densities were found in other cortical areas; furthermore, Renner et al. (2004) did not find evidence of differences in parietal burden of senile plaques or NFTs. Pathological observations in PCA have been interpreted to reflect the particular involvement of cortical pathways between the striate cortex, posterior parietal and cingulate cortices. A disproportionate loss of Meynert cells in the striate cortex has been noted in PCA; such cells have long projections to area MT and the superior colliculus (Hof et al., 1989; Hof et al., 1990). High densities of NFTs found in layer III of Brodmann areas 17, 18 and 19 suggests involvement of projections from areas 17 to 18 and feedforward projections from areas 18 and 19 to visual association areas. In addition, feedback projections are likely affected given high densities of NFTs in layers V and VI of area 18 (von Gunten et al., 2006). Such findings not only inform the understanding of AD pathogenesis; the selective vulnerability of certain cells to AD might help explain patterns of cognitive deficits. ImagingConsistent with the terminology of PCA, imaging techniques have identified atrophy particularly in posterior brain regions. Using voxel-based morphometry (VBM), cross-sectional studies have found reductions in grey matter volume in occipital, posterior parietal and posterior temporal regions in PCA relative to healthy controls (Lehmann et al., 2011; Whitwell et al., 2007; Migliaccio et al., 2009; Migliaccio et al., 2012). Comparisons of PCA and tAD patients have found lower grey matter volume in occipital and bilateral posterior parietal regions of the PCA group (Whitwell et al., 2007; Lehmann et al., 2011); Lehmann et al. (2011) also identified lower cortical thickness in the right superior parietal lobule of the PCA group and in the left entorhinal cortex in the tAD group (see REF _Ref371593884 \p \h Figure 1.1). Diffusion tensor imaging studies have identified white matter changes in posterior brain networks (Migliaccio et al., 2012), with one case study suggesting particularly early involvement of the occipital lobe (Duning et al., 2009). However, it is worth mentioning how not all PCA patients show clear posterior atrophy (McMonagle et al., 2006); the inconsistency of prominent atrophy noted at autopsy by Renner et al. (2004) led them to suggest the use of posterior cortical dysfunction rather than PCA.Functional imaging studies of PCA patients using single photon emission computed tomography (SPECT) and fluorodeoxyglucose- (FDG) PET point to hypometabolism in posterior cerebral hemispheres and hypoperfusion in occipital, parietal and temporal cortices relative to healthy controls (Nestor et al., 2003; Kas et al., 2011; Gardini et al., 2011; Rosenbloom et al., 2010). Some of these studies have also identified bilateral hypometabolism in the frontal eye fields compared to controls, which may relate to oculomotor apraxia in PCA (Crutch et al., 2012a), and have observed hypometabolism in occipito-parietal regions relative to tAD patients (Nestor et al., 2003; Kas et al., 2011). Other imaging techniques include using Pittsburgh compound-B (PiB) PET to map amyloid-β deposition. Such techniques have either suggested increased amyloid-β deposits in occipital and parietal regions of PCA patients relative to tAD patients (Tenuovo et al., 2008; Lehmann et al., 2013) or that no differences exist in amyloid plaque burden (De Souza et al., 2011; Rosenbloom et al., 2010). Interestingly, while Rosenbloom et al. (2010) noted hypometabolism in PCA patients’ inferior occipitotemporal regions, no significant evidence was found of differences in amyloid-β See Lehmann et al., 2011Figure SEQ Figure \* ARABIC \s 5 1.1 Grey matter differences between A) controls and PCA patients, B) tAD and PCA patients, and variation in cortical thickness compared between C) controls and PCA patients and D) tAD and PCA patients identified by Lehmann et al. (2011). Colour bar scales for statistical difference represent false discovery rate (FDR) at a p<.05 level of significance. -649605415925deposition; the lack of a strong consensus on differences in deposition might reflect similar inconsistencies in pathological distribution observed in post-mortem studies. The considerable overlap in grey matter volume and cortical thickness in the precuneus, posterior cingulate cortex and left temporoparietal and medial temporal regions between PCA, LPA and early-onset tAD patients may indicate these clinical syndromes represent different points within an AD spectrum (Migliaccio et al., 2009; Ridgway et al., 2012).EpidemiologyPrecise estimates of the prevalence and incidence of PCA are difficult to obtain. This is largely a consequence of variability in diagnostic criteria, misclassification or lack of awareness of PCA. Of 154 cases in a memory clinic, 4% had PCA (Croisile, 2004), while Snowden et al. (2007) reported that out of 523 patients who attended a specialist centre for cognitive disorders, 5% had a visual presentation (labelled as PCA). Regarding Snowden et al.’s (2007) study, it is possible some of the 7% of patients with a language presentation might be considered PCA patients, given how clinical classification included reading, writing, calculation and spelling difficulties. While investigations of PCA suggest it can be considered a young-onset dementia with a mean age of onset in the late 50s (Mendez et al., 2002; McMonagle et al., 2006), PCA patients with a wide range of ages have been reported (40-86 years: Tang-Wai et al., 2004). Some studies have reported women making up a disproportionate number of PCA patients (Tang-Wai et al., 2004; Snowden et al., 2007), while others have found no difference in gender distribution (Mendez et al., 2002; McMonagle et al., 2006; Renner et al., 2004). Genetic factorsTo date, there have been no clear suggestions of the genetic basis of PCA. While a study of 40 PCA patients found 11 had a family history of dementia, none presented with a phenotype akin to PCA, and the incidence of family history in PCA does not significantly differ with that in tAD (Mendez et al., 2002; Tang-Wai et al., 2004). It is possible that APOE ε4 status may differ between PCA and tAD patients; two studies have found around 20-30% of patients with a visual presentation of AD were ε4-positive, relative to over 80% of amnestic AD patients (Schott et al., 2006; Snowden et al., 2007). However, other studies have reported that around 45-55% of PCA patients were ε4-positive and suggested a lower prevalence of APOE ε4-positive tAD patients (55-75%; Tang-Wai et al., 2004; Migliaccio et al., 2009; Rosenbloom et al., 2011). Larger group studies with stringent inclusion criteria are required to investigate the intriguing possibility that reduced APOE ε4 allele load might confer some form of protection to medial temporal regions from AD pathogenesis (Filippini et al., 2009) and also identify novel genetic risk factors for PCA. Recently, a mutation was identified on the PS-1 gene of a 67 year old PCA patient with no family history of dementia (although her father died at 51) (Sitek et al., 2013). PS-1 mutations have been related to disruptions in the amyloid-β 40/42 ratio of familial AD patients through over production of amyloid-β 42 (Growdon & Rossor, 1998; Scheuner et al., 1996); this ratio has been strongly implicated in AD pathogenesis (De Strooper & Annaert, 2010). Proposed diagnostic criteriaThe core features of PCA include an insidious onset and gradual progression, presentation of visual impairment in the absence of significant ocular disease, an absence of stroke or tumour, relatively preserved episodic memory and the presence of any of the following symptoms: simultanagnosia, optic ataxia, oculomotor apraxia, dyspraxia, environmental disorientation or aspects of Gerstmann’s syndrome. Supportive features include alexia, ideomotor apraxia, young age of onset and neuroimaging evidence of parieto-occipital atrophy or hypometabolism (Mendez et al., 2002; Tang-Wai et al., 2004; McMonagle et al., 2006; see Appendix 3). While there is reasonable agreement regarding these criteria, slight differences exist in definitions of PCA, which may result from variations of clinical experience at different centres and the fact that proposed criteria have not been validated in a wider population sample. Such differences limit comparisons between studies, and likely contribute to inconsistencies in pathological or imaging literature. There is a need for a consensus on certain issues, such as whether the term PCA should be applied to patients with a pure visual presentation, with more global cognitive impairment but with visual deficits as the earliest presenting or most prominent feature or with patients who present with apraxia, acalculia or spelling difficulties but do not show early or prominent visual impairment (Crutch, 2013). The lack of pathological confirmation of underlying AD in PCA patients raises questions on whether pharmacological interventions for AD are suitable in PCA, and whether PCA patients who show low Aβ 1-42 and high total tau levels consistent with AD diagnosis (Dubois et al., 2007) should be included in new pharmacological trials for AD given how they may be unsuitable for outcome measures, such as executive function tasks with a visuospatial component (Crutch et al., 2012a). In order to address some of these concerns, the PCA Working Party, consisting of researchers representing 23 institutions in 9 counties, was set up; the party intends to facilitate improvement and harmonisation of the diagnostic criteria for PCA and promote better clinical and research practice (Crutch et al., 2012b).CHAPTER CONCLUSIONSDementia is a debilitating and life-limiting condition; degenerative dementias carry significant social and economic costs to patients, their caregivers and society as a whole. The most common cause of dementia is AD, which can result in different clinical phenotypes. Typical AD involves a primary amnestic presentation accompanied by additional deficits in non-memory domains such as visuospatial ability or language. Additional deficits can be present at diagnosis or can emerge later in the disease course. A form of atypical AD is PCA. PCA patients tend to experience early complex and disruptive visual deficits while exhibiting relatively preserved episodic memory, subsequently progressing to a more global dementia state. NEUROPSYCHOLOGY OF PCACHAPTER INTRODUCTIONThe posterior cortex supports a number of cognitive domains, each of which can be broken down into subcomponent skills. Calculation, spelling, praxis and purportedly reading are abilities that are lateralised to the left hemisphere, while spatial skills, object and face perception are largely lateralised to the right hemisphere. Early visual and spatial processing are not lateralised skills, instead maintaining retinotopic distribution in the occipital lobes.Neuropsychology is of particular relevance to the diagnosis and characterisation of PCA. Standardised neuropsychological measures provide a means to evaluate whether patients fulfil proposed core diagnostic criteria for PCA. Through detailed neuropsychological assessment in PCA, future group studies might clarify whether discrete ventral, dorsal and caudal forms of PCA exist, and could offer ways to discriminate between PCA patients with underlying AD, DLB or CBD pathology. Longitudinal analysis of neuropsychological data might offer suggestions of the biological mechanisms underlying PCA, or could allow a greater insight into the effect of emerging visual deficits on other areas of the visual system. From a practical perspective, better characterisation of patients’ cognitive profiles can have a significant bearing on how they are treated clinically (Crutch, 2013). VISUAL PROCESSINGEarly visual processingWhile deficits in higher-order visual domains, such as spatial or object perception, tend to be reported more frequently than disrupted early visual processing in PCA, it is likely that deficits in early visual processes at least contribute to higher-order visual problems (Crutch et al., 2012). Early visual dysfunction includes visual crowding and deficits in perceiving form, colour, motion and single point localisation and is associated with the occipital lobes (Warrington, 1986; Farah, 2000; Heider, 2000; Qiu & Heydt, 2005; Levi, 2008). Consistent with AD pathology in areas V1, V2, V3 and V4 (Hof et al., 1997), some investigations suggest widespread occipital lobe dysfunction in PCA, occurring as early as V1 (Metzler-Baddeley et al., 2010) while colour after-effects in PCA have been interpreted as showing degeneration of excitatory neurons and relative preservation of inhibitory interneurons in V1 (Chan et al., 2001; Crutch et al., 2011).Visual localisation relates to the inability to localise a single object and is often assessed using single point localisation (McCarthy & Warrington, 1990); such localisation deficits may constitute part of visuospatial impairment. Holmes (1919) gave the classic account of visual disorientation, reporting patients with difficulties in the localisation, maintenance of fixation and visual tracking of objects. The functional impact of visual disorientation is considerable, with many patients being unable to correctly reach for objects or walk across a room unaided (McCarthy & Warrington, 1990; Langdon & Thompson, 2000). Visual disorientation has been associated with damage to the occipital or occipitoparietal region (Yealland, 1916; Godwin-Austen, 1965; Warrington, 1986) and forms one of the features of Balint’s syndrome. Visual disorientation has been identified in PCA in a range of case studies (Kaida et al., 1998; Chan et al., 2001; Galton et al., 2000; Tenuovo et al., 2008; Crutch et al., 2011), with a more recent study of 21 PCA patients finding 60% of them were impaired on a task of single point localisation and 81% were impaired on dot counting (Lehmann et al., 2011). Problems in visual localisation may be variable, with visually-disorientated PCA patients sometimes being able to pinpoint small objects with surprising accuracy or better localise moving stimuli (Crutch, 2013; Midorikawa et al., 2008). Form perception is often assessed using tasks such as the Efron Squares test (Efron, 1969) or figure-ground detection (Warrington & James, 1991) which are measures of shape discrimination or shape detection processes; performance on these tasks has been found to dissociate, leading to suggestions that such processes operate in parallel (Kartsounis & Warrington, 1991; Davidoff & Warrington, 1993). Abnormalities in shape discrimination, shape detection, orientation discrimination, contour integration, form coherence, visual field detection and colour perception have all been noted in PCA (Mendez et al., 1990; Wakai et al., 1994; Chan et al., 2001; Tang-Wai et al., 2004; Lehmann et al., 2011; Metzler-Baddeley et al., 2010; Pelak et al., 2011; Whitwell et al., 2007; McMonagle et al., 2006); studies have identified how 65-80% of PCA patients showed impaired performance on the figure-ground detection task, while 67% showed deficits in discriminating squares and oblongs (Lehmann et al., 2011; Shakespeare et al., 2013). Group studies have also suggested a greatly varying incidence of hemianopia or quadrantanopia in PCA (5%-78%; McMonagle et al., 2006; Whitwell et al., 2007; Pelak et al., 2007); while Pelak et al. (2007) identified visual field defects in 7 of 9 patients, these may reflect confounding visual phenomena such as the inverse size effect (chapter REF _Ref374025486 \r \h 2.2.2), neglect or visual disorientation. PCA participants in Lehmann et al.’s (2011) study, all of whom showed impairment on higher order object and/or space perception tasks, also demonstrated impaired performance on one or more early visually processing tasks. Object and space detection were correlated with figure ground and colour discrimination performance, shape discrimination was correlated with object but not space perception and point localisation was correlated with space but not object perception. These results provide evidence that deficits in higher-order visual domains are frequently related to different patterns of early visual processing deficits in the PCA syndrome. Visual crowdingOne form of prominent early visual processing deficit in PCA is enhanced visual crowding. Crowding is a form of inhibitory interaction which is present in normal peripheral vision, involving the diminishing effect of nearby stimuli (‘flankers’) on identification of a target stimulus (Levi, 2008). The occurrence of crowding when target stimuli and flankers are separately presented to different eyes indicates a cortical locus (Flom et al., 1963; Tripathy & Levi, 1994). In the healthy periphery, the core characteristic of crowding is that it is dependent on eccentricity (distance from fixation), which determines the critical spacing between target and flankers (Pelli et al., 2007). The critical spacing is the distance at which flankers diminish identification of the target stimulus, and has been roughly localised as being half the eccentricity of the target from fixation in peripheral vision (Bouma, 1970). Beyond spacing, increasing visual similarity between target and flanker stimuli exacerbates the crowding effect. While crowding is independent of stimulus type, font and contrast (Pelli et al., 2004; Tripathy & Cavanagh, 2002; Pelli & Tillman, 2008), crowding effects diminish with target and flanker stimuli of opposite polarity (Kooi et al., 1994; Chakravarthi & Cavanagh, 2007). While crowding is most evident in normal peripheral vision, it has been observed over very small distances in healthy individuals’ foveal vision (0.05? to 0.08? of visual angle: Flom et al., 1963; Strasburger et al., 1991). However, PCA patients have exhibited deficits on centrally-presented flanked letter identification tasks that are in line with crowding; such deficits suggest crowding is operating over large distances in foveal vision (Crutch & Warrington, 2007a; Crutch & Warrington, 2009; Mendez et al., 2007). Two case reports of PCA patients have identified beneficial effects of increased spacing on flanked letter identification, erroneous feature integration of flanking letter fragments on target letters, interactions between letter spacing and letter confusability and an ameliorating effect of reverse polarity flankers (Crutch & Warrington, 2007a; Crutch & Warrington, 2009); all of which are characteristic of crowding. While the occipital lobe has been cited most often as the locus of crowding, a divergence of opinion occurs with more specific localisation (Levi, 2008). In PCA, it is possible that the diffuse distribution of AD pathology in the occipital lobe may affect the expression of crowding.Visuospatial processing and inverse size effects Prominent deficits in visuospatial ability are some of the most frequent and striking characteristics of PCA. While visual disorientation emphasises a deficit in the ability to localise a single object, higher-order visuospatial impairment is more associated with deficits in representing the spatial relationships between multiple objects and/or integrating spatial information across modalities (e.g. visuomotor tasks; Warrington et al., 1967; Freedman & Dexter, 1991; Mendez, 2001; Binetti et al., 1998). Visual disorientation can be restricted to one half of the visual field (Cole et al., 1962); in contrast, visuospatial disorders are mostly associated with right parietal damage and are not restricted to the contralateral visual field. Deficits in visuospatial ability can lead to poor performance on tasks such as position discrimination (Taylor & Warrington, 1973), dot counting (Warrington & James, 1967), matching orientation (Benton et al., 1975), cancellation or search tasks (Albert, 1973; De Renzi et al., 1970). Impaired performance on such tasks has been noted in a range of studies of PCA (Benson et al., 1988; Mizuno et al., 1996; Stark et al., 1997; Ross et al., 1996; Delazer et al., 2006; Videaud et al., 2009; Tenuovo et al., 2008; Metlzer-Baddeley et al., 2010; Lehmann et al., 2011). Poor performance on measures of visuospatial ability may reflect visual neglect in some PCA patients; various studies have noted neglect (Cogan, 1985; Wakai et al., 1994; Mendez & Cherrier, 1998; Migliaccio et al., 2012) with one recent group study identifying signs of neglect in 16 of 24 PCA patients (Andrade et al., 2010). Earlier studies had identified much lower rates of neglect (15-20%; Tang-Wai et al., 2004; McMonagle et al., 2006); this discrepancy may be due to these investigations employing clinical examination, rather than neuropsychological measures. However, the line bisection and Bells test (Gauthier et al., 1989) used by Andrade et al., (2010) are not ‘pure’ measures of neglect in PCA; they may be confounded by visuospatial and visuomotor impairment, and the counterintuitive tendency of some PCA patients to misperceive large stimuli. A diminished ability to recognise large rather than small pictures, words and letters has been noted in PCA for some time (Saffran et al., 1990; Coslett et al., 1995; Stark et al., 1997; Crutch et al., 2011); such performance occurs in response to stimuli presented in isolation and is not restricted to one half of space. This deficit has been referred to as the inverse size effect (Coslett et al., 1995), and likely contributes to problems in scene perception in PCA (Shakespeare et al., 2013) and clinical complaints of patients who report having particular difficulty in reading large font, such as newspaper headlines (Crutch et al., 2011). This inverse size effect may reflect a reduced effective field of vision, which has been associated with parietal and parieto-occipital damage (Michel & Henaff, 2004; Russell et al., 2004, 2013). Disproportionate deficits in perceiving stimuli presented in peripheral vision have been attributed to an inability to cope with high attentional demands (Russell et al., 2013), although they may also relate to poor eye movement control, inefficient scanning strategies (Shakespeare et al., 2013) or visual field defects (Delaj et al., 2010; Pelak et al., 2011). Visuoperceptual processingProgressive and disproportionate deficits in object recognition were some of the earliest noted features of PCA (De Renzi, 1986; Benson et al., 1988). Lissauer (1890) proposed a two stage model of object recognition: the apperceptive stage, involving a “conscious awareness of a sensory impression”, and the association stage, involving “associating other notions with the content of apperception”. This model was based on observations of patient GL who could perceive and discriminate visual stimuli, but could not recognise objects. Subsequent models of object recognition have specified that a deficit arising at the level of perceptual categorisation subsequent to visual sensory processing presents as an apperceptive agnosia, while a deficit in semantic categorisation presents as an associative agnosia (Warrington, 1985). The term apperceptive agnosia refers to patients with impaired object recognition; such patients demonstrate intact visual sensory functions and their impairment does not relate to a semantic deficit. Apperceptive agnosia has been linked to right parietal damage (Warrington & James, 1967; Warrington & Taylor, 1973) and has been assessed using stimuli that degrade visual complexity, including everyday objects shown from different angles (Warrington & Taylor, 1973; Warrington & James, 1988), silhouette drawings of objects (Warrington & James, 1988), overlapping drawings (De Renzi et al., 1969), incomplete line drawings (Gollin, 1960) or degraded letters (Faglioni et al., 1969; Warrington & James, 1967). Apperceptive agnosia has been noted in various case studies of PCA (Hof & Bouras, 1991; Wakai et al., 1994; Aharon-Peretz et al., 1999; Galton et al., 2000); one group study observed that patients with apperceptive agnosias tended to have longer disease durations (McMonagle et al., 2006). Two group studies have placed the incidence of visual agnosia at around 45-65% and have attributed these to deficits in apperception (Mendez et al., 2002; McMonagle et al., 2006); however, neither of these studies assessed performance on measures of early visual processing such as shape detection or discrimination. All three PCA patients with visuoperceptual impairment from Sala et al. (1996) had difficulties in form and line discrimination, emphasising the likely contribution of early visual impairment towards higher-order visual deficits. In a study of 21 PCA patients, Lehmann et al. (2011) found all 21 showed impaired performance on recognition tasks of fragmented letters and objects shown from unconventional angles; however, all patients also showed deficits on at least one measure of early visual processing. Associative agnosia refers to patients who can construct a sufficient perceptual representation of an object, but the representation suffers from a loss of meaning (McCarthy & Warrington, 1990). Such patients often have bilateral temporal lesions (Capitani et al., 2003), although associative agnosia has been found in a patient with unilateral posterior temporal damage (McCarthy & Warrington, 1986). Selective associative agnosias tend to be rare in PCA, with group observations of visual agnosias in PCA not being attributed to semantic deficits (Mendez et al., 2002; McMonagle et al., 2006). While some patients have been described as having associative agnosias (NS: Aharon-Peretz et al., 1999; RM: Giovagnoli et al., 2009), patient RM also demonstrated problems perceiving overlapping and degraded figures and discriminating lines. Pathological distribution may relate to the incidence of associative agnosia; in AD patients, NFT densities in occipito-temporal regions have been correlated with poor performance on tests of conceptual knowledge, while a lack of neuroanatomic correlation with tests involving overlapping or hidden figures has been interpreted as apperceptive agnosia resulting from more diffuse AD pathology (Giannakopoulos et al., 1999). Another form of visuoperceptual deficit is prosopagnosia, which has been defined as a disproportionate impairment in recognising faces relative to other objects (Bodamer, 1947). Prosopagnosia can result from damage to right occipito-temporal regions, although it is not a common consequence of such damage (De Renzi et al., 1994). Problems identifying faces can be an early (Wakai et al., 1994; Aharon-Peretz et al., 1999) and common (20-25%: Mendez et al., 2002; Tang-Wai et al., 2004; McMonagle et al., 2006) symptom of PCA, leading to suggestions of prosopagnosia as a supportive diagnostic feature for PCA (Tang-Wai et al., 2004). However, disorders of face perception in PCA are rarely very selective; they have been accompanied by visual agnosias (Wakai et al., 1994; Aharon-Peretz et al., 1999; McMonagle et al., 2006) and deficits in early visual processing (Wakai et al., 1994; Sala et al., 1996). READINGA multitude of studies demonstrate literate adults’ impressive ability to rapidly recognise words of varying script, size, font and case, and even words in highly unfamiliar presentations such as mixed case (Besner, 1989; Mayall et al., 1997). Such studies have led to the notion that an aspect of visual word recognition is the generation of an invariant word representation which is remarkably unsusceptible to variations in visual input. The notion of word recognition relying on the perception of words via letters integrated into a word form has existed for some time. Cattell’s (1886) study demonstrated a word superiority effect, in which letters were read more rapidly within words than in random combinations of the same letters. Subsequent investigations identified how, when controlling for participants’ better memory for words than nonsense letter strings and their ability to guess word identities, performance for identifying single letters was superior when letters were embedded in words versus letter strings (Reicher, 1969; Wheeler, 1970). The pseudoword superiority effect refers to how letters are identified more efficiently in orthographically regular, pronounceable nonwords (or pseudowords) relative to irregular, unpronounceable nonwords (McClelland, 1976; Paap et al., 1982). Other evidence which has been interpreted as supporting the existence of word form and/or parallel letter processing includes healthy readers’ consistency of reading latencies for words of 3-6 letters (Behrmann et al., 1998; Nazir et al., 1998), the perception of words despite variations in size, case and font, and priming effects occurring across such variations (Rayner & Pollatsek, 1989). Whether words possess properties not only exceeding their basic perceptual aspects but also differing from their phonological and semantic properties is a theme that has shaped various neuropsychological and neuroimaging investigations.Contribution of areas of early visual processingThe transition of simple feature detection to font invariant letter and word recognition is classically considered a hierarchical process, with simple features forming progressively more abstract visual representations. Cell responses measured in the cat striate cortex have found that both simple and complex cells strongly respond to oriented bars; however, while simple cells have small receptive fields with distinct inhibitory and excitatory subfields (strong phase dependence), complex cells have large receptive fields with no phase dependence (Hubel & Wiesel, 1962). Authors of this study proposed a model in which complex cells are fed information from simple cells with neighbouring receptive fields, generating phase invariant feature representations. A more recent model of object recognition proposed by Riesenhuber and Poggio (1999) suggests that simple visual features processed by simple cells are progressively pooled across different locations (through complex cells) and combinations of features (through composite cells). In both models, moving up the visual hierarchy leads to increasingly abstract and location invariant representations, which in the context of letter recognition culminates in shape-specific letter cells (for example, cells selectively responding the letter ‘a’ in either lower, upper-case or possibly italicized font) and ultimately more abstract shape invariant complex cells (i.e. cells responding the letter ‘a’ regardless of font; Grainger et al., 2008). A functional anatomical model of word recognition proposed by McCandliss et al. (2003) attributes processing of letters to retinotopically distributed occipital regions. The model suggests pathways responsible for generating letter representations in the left and right hemisphere are modulated by visuospatial attention governed by parietal regions. These pathways are resolved in the left fusiform gyrus, an area which the authors maintain is the site of a region dedicated to the processing of abstract visual word forms, the purported visual word form area (VWFA; see Chapter 2.3.3).Cognitive modelsDual-route modelsThe dual-route model offered by Marshall and Newcombe (1973) has often been employed as a framework within which to understand the deficits underlying deep dyslexia, surface dyslexia and visual dyslexia. It was termed the dual-route model as it involves two processing routes from orthography to speech; a semantic route and a route dependent on reading via grapheme-phoneme correspondences. A dual-route approach has also been proposed to explain orthographic processing, in which location specific letter detectors generate two forms of location invariant sublexical representations; i) informative letter combinations and ii) frequently co-occurring multi-letter graphemes (Grainger & Ziegler, 2011), with both routes supporting whole word orthographic representations or route ii) feeding into a phonological route. The more recent Dual-Route Cascaded (DRC) model (Coltheart et al., 2001) is a computational implementation of the dual-route theory, maintaining that words are read aloud via two routes. The first, a direct, lexical route, operates by parallel cascaded processing, in which activation spreads from letter features to letters and the orthographic lexicon, subsequently activating appropriate phonological output; irregular words, being reliant on the orthographic lexicon, are read via this route. The second is an indirect, non-lexical route, in which orthographic input is parsed into graphemes which are serially converted into phonemes using grapheme-phoneme correspondences; nonword words can only be read using this route as they do not exist in the orthographic lexicon. Critics of the DRC model cite findings which show nonword reading does not always result in responses predicted by rules of grapheme-phoneme correspondences (Andrews & Scarratt, 1998); more generally, the model has troubling accounting for effects of orthographic neighbourhood size, including facilitating effects of increased neighbourhood size on reading speed (Ziegler et al., 2001), as it is insufficiently sensitive to orthographic bodies (Grainger & Dufau, 2012). In addition, a prediction of the DRC, in which accurate reading aloud of known words may be achieved without referring to semantic knowledge, is questioned by the high prevalence of surface dyslexia among individuals with semantic dementia (Woollams et al., 2007) and imageability effects on normal readers’ reading of low-frequency exception words (Cortese et al., 1997; Strain et al., 1995).Triangle modelTriangle models provide a framework for a connectionist approach to reading, in which cognitive processes exists as competitive or cooperative interactions between units. Processing is determined by weights on connections between units; weighted connections are a consequence of the accumulated experience of the reading system; in this way, a connectionist approach emphasises the adaptive nature of the reading system. Aspects of reading which are particularly reflected in connectionist models include how children learn to read an increasing number of words over a long period, through learning, children may read novel items, and brain damage may result in graded and sometimes inconsistent declines in reading performance (Plaut, 2005). The computational model proposed by Seidenberg and McClelland (1989) is formed of three interconnected domains; orthography, semantics and phonology. The model features two routes from orthography to speech; a direct route from orthography to phonology, and an indirect route from orthography, through semantics, to phonology; it suggests the direct route is specialised for representing more frequent and consistent mappings between orthography and phonology, with correct readings of exception words depending more on the indirect route. The Seidenberg and McClelland (1989) triangle model emphasised parallel interactions between semantic and phonological information rather than distinguishing lexical and sublexical procedures. While the model accounted for various aspects of reading performance in healthy individuals, it was criticised for its poor reading performance for orthographically regular nonwords (Besner et al., 1990). A model proposed by Plaut et al. (1996) aimed to address this limitation by applying constraints on orthographic and phonological representations based on grapheme-phoneme correspondences; implementations of the phonological pathway produced nonword reading of comparable competency to skilled readers.Neuroanatomical modelsA controversial and enduring debate that has arisen from neuroimaging studies of reading is the existence of the visual word form area (VWFA; Cohen et al., 2000), an area supposedly specialised for processing of visual word forms. The anatomical site of the purported VWFA is often placed in the left fusiform gyrus, in the junction between inferior temporal and fusiform gyri on the occipitotemporal sulcus (Jobard et al., 2003). This region has been proposed as the critical lesion site for pure alexia, a dyslexia classically considered a consequence of a specific deficit in acquiring word form representations (Binder & Mohr, 1992; Leff et al., 2001; P?ugshaupt et al., 2009). The VWFA has been found to selectively respond to printed and handwritten words rather than chequerboards or consonant strings (Cohen et al., 2000), objects of matched visual complexity (Qiao et al., 2010; Szwed et al., 2011), letters in upper and lower case (Dehaene et al., 2004) and activation in this region has been found to increase proportionately with sentence reading rate (Dehaene et al., 2010). Furthermore, the VWFA shows reduced activation to conscious words which have been subliminally primed; these priming effects occur independently of letter case (Dehaene et al., 2001). While the region has been found to be disproportionately active in response to real words versus orthographically illegal letter strings (Cohen et al., 2000), differences in activation have not been found in response to words versus pseudowords (Jobard et al., 2003); such results have been interpreted as demonstrating a preference for processing sublexical units, such as graphemes, within the VWFA. While proponents of the VWFA suggest that it governs efficient parallel word recognition, words presented in unfamiliar formats, such as rotated words, words in mixed-case or with excessive inter-letter spacing, might demand involvement of serial reading strategies mediated by dorsal systems (Vinckier et al., 2006; Cohen et al., 2008). Given how writing as a cultural invention has not existed long enough to promote an innate specialisation for visual word recognition, how might the proposed specialisation of the VWFA come about? McCandliss et al. (2003) suggested that the inferotemporal region is an area with a particular affinity for complex object recognition and a great capacity for plasticity; the developmental process of accumulating reading experience would provoke the progressive specialisation of this region for processing of orthographic stimuli. However, critics of the VWFA have dismissed its proposed specialisation for recognising word or word like stimuli on several grounds. Price and Devlin (2003) question the neuropsychological evidence base for the purported role of the left fusiform gyrus in processing visual word forms by referring to both the extensive lesions of previously cited pure alexics and the co-occurrence of deficits in non-orthographic processing. They also cite studies showing VWFA activation in response to object recognition (Murtha et al., 1999; Etard et al., 2000), to auditory words and descriptions of objects (Price et al., 2002; Thompson-Schill et al., 1999) and when making rhyming judgments (Booth et al., 2002). The suggestion that VWFA activation is not specific to word stimuli is echoed by other authors, who suggest that this region is involved in shape processing of objects and false fonts as well as words (Ben-Shachar et al., 2007) or processing of information of high spatial frequencies (Roberts et al., 2013). Such findings are not necessarily inconsistent with McCandliss et al.’s (2003) point on functional specialisation of the VWFA; from their perspective, this specialisation is most likely a consequence of a recycled area of visual cortex which has evolved for other purposes, such as processing of complex visual stimuli. As such, the left fusiform cortex would only be partially specialised for letter, grapheme or word form recognition in skilled readers; only very local regions of the cortex would selectively respond to orthographic stimuli, with such regions often eluding the spatial resolution of imaging techniques, particular PET imaging.ACQUIRED DYSLEXIAAcquired dyslexias are often divided into two broad categories: those relating to the visual attributes of written words and those involving subsequent phonological and semantic stages of reading (McCarthy & Warrington, 1990). Peripheral dyslexias involve impairment to prelexical and/or word form processing, while central dyslexias involve impairments to lexical/post-lexical processes (Riddoch, 1990). Peripheral dyslexias include neglect dyslexia, hemianopic dyslexia and pure alexia (sometimes referred to as “word form dyslexia” or “letter-by-letter” (LBL) reading). LBL reading involves intact letter identification and relatively accurate, but slow, reading, whereby response latencies increase in a linear manner proportionate to word length (Shallice and Warrington, 1980; Farah and Wallace, 1991; Binder and Mohr, 1992; Warrington and Langdon, 1994; Hanley and Kay, 1996; Cohen et al., 2000). Central dyslexias include surface dyslexia, phonological dyslexia and deep dyslexia. Characteristics of surface dyslexia include the production of phonological nonwords in oral reading due to phoneme omissions, substitutions or additions and regularisation errors when reading irregular words. Phonological dyslexia involves poor nonword reading in the absence of semantic errors. Deep dyslexia involves poor reading of abstract words in particular, an inability to read nonwords and the occurrence of semantic errors and visual and morphological effects in errors (Plaut & Shallice, 1993; Schattka et al., 2010). Acquired dyslexia often presents as an early (Benson et al., 1988; Freedman et al., 1991; Berthier et al., 1991) and common (80-95%; Mendez et al., 2002; McMonagle et al., 2006) symptom of PCA, and alexia is a proposed supportive feature of a diagnosis of PCA (Tang-Wai et al., 2004; Mendez et al., 2002). PCA patients often find spatial aspects of reading particularly challenging; various reports exist of patients having difficulty following text along a printed line or moving from one line to the next (Rogelet et al., 1996; Mendez, 2001; Crutch et al., 2011), seeing lines of text in ‘false order’ (Tenuovo et al., 2008) and losing their place on a page or even on reading cards (Crutch et al., 2011; Kirshner & Lavin, 2006). Such difficulties may arise from posterior brain damage; Levine et al. (1985) identified a patient with parieto-occipital lesions, who could read isolated words in paragraphs but in a disordered manner, and would move irregularly between or within lines of text. A combination of visual disturbances likely contribute to text reading difficulties in PCA, including a restriction in the effective field of vision, enhanced crowding, neglect (Andrade et al., 2010), visual field defects (Pelak et al., 2011) and perceived motion of static stimuli (Crutch et al., 2011).Most previous studies on reading in PCA have investigated single word recognition and have identified a range of dyslexias: neglect dyslexia (Catricala et al., 2011), attentional dyslexia (Saffran & Coslett, 1996), pure alexia (Price & Humphreys, 1995; Freedman et al., 1991; Berthier et al., 1991), crowding dyslexia (Crutch & Warrington, 2009) and word form access dyslexia (Crutch & Warrington, 2007b). Increased perceptual complexity most likely adversely affects word recognition in PCA, given poorer performance for reading words in cursive font (De Renzi, 1986), stylised font (Mendez, 2001) or words with crosshatched letters (Mendez & Cherrier, 1998). PCA patients’ poor reading for nonwords might suggest the presence of phonological deficits, including the ability to use print-to-sound correspondences (Mendez, 2001); however, PCA patients have also been noted to make a disproportionate number of visual errors when reading nonwords (Mendez et al., 2007). PCA case studies tend to give the impression that reading dysfunction manifests as a peripheral rather than central dyslexia, although O’Dowd and de Zubicaray (2003) interpreted the quality of dysgraphia and spelling impairment in one PCA patient as indicative of an intermediate deficit in a graphemic buffer (Kay & Hanley, 1994). Given the complexity of visual impairment in PCA, it is important to exercise caution when making inferences of the specificity of reading deficits. For example, while Catricala et al. (2011) considered one patient (RR) to have neglect dyslexia, there was some heterogeneity in error responses, with RR misreading initial and terminal parts of words (bombabombardamento, palazzolazzo). It is possible that a reduced effective visual field might result in errors that superficially seem a consequence of neglect dyslexia, although such errors would likely not consistently show a bias to one visual half. This reduction might also contribute to length effects on reading latencies given how words of increased length present as perceptually larger. Group studies of reading in PCA would be better placed to not only disentangle and gauge the heterogeneity of patterns of reading deficit, but also reveal the contributions of other parts of the visual system in supporting reading ability. The only group study of reading dysfunction in PCA to date identified frequent visual errors in response to regular, irregular words and nonwords, an absence of regularisation errors and disproportionate difficulty reading nonwords (Mendez et al., 2007). Along with deficits on flanked letter identification tasks, these data led the researchers to suggest the term “apperceptive alexia” to reflect the contribution of deficits in visuoperception and visuospatial attention. However, while Mendez et al. (2007) stressed the role of attentional deficits as evidenced by performance on a flanker letter task, performance on this task is more suggestive of crowding rather than attentional effects. Results demonstrated an effect of the visual similarity of flankers on target letter identification; unlike standard definitions of attentional dyslexia, this flanker effect occurred regardless of flanker category (numbers [e.g.55S55], letters [e.g. KKXKK]).Excessive crowding is a promising candidate for a form of early visual processing deficit that may particularly impinge on reading. Bouma and Legein (1977) found that dyslexics’ letter identification in peripheral vision was disproportionately inhibited by the presence of flankers. Our uncrowded vision corresponds to the visual span (Pelli et al., 2007); the visual span is the extent to which we can read without moving our eyes, and been suggested to limit our reading rate (Chung et al., 2004; Legge et al., 2001). Increased inter-letter spacing has produced benefits to dyslexics’ reading ability (Spinelli et al., 2002; Zorzi et al., 2012), while letter confusability, a measure of the visual similarity of letters, has been suggested to account for word length effects in LBL readers (Arguin et al., 2002; Fiset et al., 2005). In PCA, beneficial effects of moderate inter-letter spacing and lower summed letter confusability have been identified on whole-word reading, leading to the proposal that observed reading deficits may be attributed to “crowding dyslexia” (Crutch & Warrington, 2009).NON-VISUAL NEUROPSYCHOLOGICAL FEATURESCharacteristic deficits of PCA include those in non-visual domains associated with dominant parietal function, including calculation, spelling and praxis (Delazer et al., 2006; Aharon-Peretz et al., 1999; Kas et al., 2011; McMonagle et al., 2006). Relative preservation of memory and semantics has been noted in PCA (Mendez et al., 2002; McMonagle et al., 2006; Tang-Wai et al., 2004); the majority of patients do not show early episodic memory impairment as assessed clinically or through neuropsychological testing (Whitwell et al., 2007; Lehmann et al., 2011) and demonstrate competent performance on semantic fluency and synonym discrimination tasks relative to tAD patients (Mendez et al., 2002) and healthy individuals (Lehmann et al., 2011). Some studies have suggested that executive function is relatively preserved in PCA compared to tAD (Mendez et al., 2002), while others have found no evidence of differences between PCA and tAD patients (Rosenbloom et al., 2011). An important caveat is that visual impairment can confound tasks with a visual component that intend to assess performance IQ or executive function, which likely accounts for PCA patients’ performance IQ being lower than verbal IQ (McMonagle et al., 2006). Language While verbal language has been considered relatively intact in PCA compared to visual domains (Mendez et al., 2002; McMonagle et al., 2006), there is evidence that some language skills are impaired in PCA. Benson et al. (1988) noted how all five of the PCA patients he reported developed transcortical sensory aphasia, which included anomia and comprehension disorder. Subsequent studies have observed some prevalence of early language difficulties in PCA; out of 14 PCA patients, over a third presented with language complaints at disease onset (Migliaccio et al., 2009), 7 of 9 presented signs of anomia in spontaneous speech (Magnin et al, 2013) while another investigation found 24 of 27 PCA patients showed signs of anomia (Tang-Wai et al., 2004). However, classification of language complaints in Migliaccio et al.’s (2009) study includes reading difficulties, while it is not possible to discern whether anomia was assessed visually or verbally in Tang-Wai et al.’s (2004) investigation. Studies comparing PCA with LPA patients have found overlapping deficits in phonemic fluency and word retrieval in response to verbal descriptions (Crutch et al., 2013; Magnin et al., 2013). In the context of poor repetition of nonwords and comprehension of longer sentences in the absence of syntactic deficits, it is possible that such performance could reflect weaknesses in phonological processing and short term memory, particularly given the purported role of parietal systems in mediating the short-term phonological storage and retrieval of verbal information (Jonides et al., 1998), and/or damage to early stages of auditory processing (Goll et al., 2010). PraxisA common symptom in PCA is limb apraxia, which previous group studies have observed in around 95% of patients (McMonagle et al., 2006; Kas et al., 2011). Previous reports exist of individuals presenting with progressive apraxia and symptoms suggestive of posterior cortical dysfunction but with relatively intact visual and memory function. Green et al. (1995) identified how one AD patient whose recognition memory was within the normal range presented with prominent apraxia, along with difficulties performing mental arithmetic and hypoperfusion particularly within the posterior parietal cortex. Another case involves a patient who performed within the normal range on four tests of verbal and spatial memory and measures of early visual and visuoperceptual processing; the primary deficit was a progressive loss of manual praxis, with additional dyscalculia (De Renzi, 1986). These studies raise questions as to whether the term PCA might be used for patients with progressive apraxia, acalculia and/or spelling difficulties who do not demonstrate early and prominent visual deficits; if so, 47 patients defined as having a language or apraxic presentation in Snowden et al. (2007) may actually be considered to have PCA. Some investigations have been explicit in describing patients with apraxia as the leading symptom as having PCA (Aharon-Peretz et al., 1999; Goethals & Santens, 2001); Aharon-Peretz et al. (1999) proposed that PCA represents two clinically related behavioural phenotypes involving either predominant visuospatial impairment or apraxia. An apraxic presentation of PCA may reflect an overlap with corticobasal degeneration (Goethals & Santens, 2001; McMonagle et al., 2006) which has been noted as the underlying pathological cause of PCA in other patients (Mendez, 2000; Tang-Wai et al., 2003; Seguin et al., 2011). CHAPTER CONCLUSIONSThe cognitive phenotype of PCA includes various neuropsychological deficits associated with posterior function, most prominent of which are a range of visual disturbances. Deficits in early visual, visuoperceptual and visuospatial function have been documented, with the leading impairment usually arising within the dorsal system. The nature of reading difficulties is suggestive of a peripheral deficit, although the precise manner in which visual dysfunction relates to acquired dyslexia in PCA is unclear. Visual deficits in PCA might predict particular forms of reading difficulty; for example, the inverse size effect could restrict recognition of large words, visuospatial impairment might limit word localisation within sentences or passages and enhanced crowding might disrupt parallel letter processing or identification of words flanked by adjacent words. More generally, visuoperceptual impairment and alexia are likely to coincide given the role of the ventral system in object and word recognition.METHODS OVERVIEWPARTICIPANTSPatientsThe majority of patients had attended the Cognitive Disorders Clinic at the National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London, UK. This is a secondary/tertiary referral centre, with attending patients often presenting with younger or atypical presentations of dementia. All patients underwent a clinical and neuropsychological assessment. Participants recruited from the clinic included PCA and sporadic AD patients of an amnestic presentation. Participants who had evidence of an ischaemic stroke or brain tumour were not recruited into the investigation. Ethical approval for the investigation was provided by the National Research Ethics Service London-Queen Square ethics committee and informed consent was obtained from all participants. Disease duration was defined as the time elapsed since first onset of cognitive symptoms. Healthy controlsControl participants were neurologically healthy and lacked a family history of dementia or contraindications to Magnetic Resonance Imaging (MRI) scanning. Demographic information was collected for controls; controls with hearing or visual problems were excluded from testing. CLINICAL ASSESSMENTAll participants who attend the Cognitive Disorders clinic completed a full clinical assessment involving the following:Recording of the full clinical history from patient and an informant.Detailed neuropsychological assessment to establish the form and severity of the behavioural phenotype.Blood tests to exclude other causes of cognitive problems. Electroencephalography (EEG) to exclude seizures or identify brain activation patterns suggestive of types of dementia. MRI scanning to exclude other causes of cognitive problems such as tumours, ischaemic strokes or subdural haematomas or identify ischaemic damage and/or patterns of brain atrophy.Some patients also underwent the following:Blood tests to screen for known genetic mutations may be carried out on symptomatic individuals with a family history and/or age of onset suggestive of an autosomal dominant inheritance, or individuals whose family history indicates that they are at risk of inheriting a known mutation.Lumbar punctures in order to collect and analyse CSF samples, giving a measure of levels of tau and Aβ 1-42 proteins. INCLUSION CRITERIAInformed consent was obtained using procedures approved by the National Hospital for Neurology and Neurosurgery.PCAPCA patients all fulfilled clinical criteria for a diagnosis of posterior cortical atrophy (summarised in REF _Ref371937685 \r \h 1.2.2.6). Core features of diagnostic criteria included visuospatial and visuoperceptual impairments, features of Balint’s (simultanagnosia, oculomotor apraxia, optic ataxia) and Gerstmann’s syndrome (acalculia, agraphia, finger agnosia, left-right disorientation) with relatively preserved memory (Tang-Wai et al., 2004; Mendez et al., 2002; McMonagle et al., 2006). While PCA patients fulfilled these criteria at some point in their clinical history, by time of experimental testing some patients had progressed to more global cognitive impairment. PCA patients fulfilled research criteria for probable Alzheimer’s disease (Dubois et al., 2007, 2010). Typical ADTypical AD patients had a clinical history of an amnestic AD presentation and fulfilled NINCDS-ADRDA criteria, including recent revisions, summarised in chapter REF _Ref371594198 \r \h 1.2.1.4 (McKhann et al., 1984; McKhann et al., 2011; Dubois et al., 2007). All typical AD patients performed below 5th %ile on verbal and/or visual recognition memory tests (Warrington, 1984; Warrington, 1996). NEUROPSYCHOLOGYBackground neuropsychologyShort Recognition Memory Test for faces/words (Warrington, 1996): task involved 1) learning of faces through visual presentation and words through joint auditory and visual presentation and 2) subsequent identification of faces/words from 1) paired with distractor faces/words. Concrete Synonyms (Warrington et al., 1998): for each target word participants were requested to identify which of two semantically-related words were closest in meaning to the target. All words were of high concreteness. Graded Naming Test: all participants were requested to name objects of decreasing frequency from their verbal description.Cognitive estimates (Shallice & Evans, 1978): this task involved participants estimating answers to questions which can be effectively guessed using general knowledge. Calculation (Graded Difficulty Arithmetic: Jackson & Warrington, 1986)Spelling (Graded Difficulty Spelling Test: Baxter & Warrington, 1994)Gesture production test (Crutch, unpublished): this task involved pantomiming the use of objects (joint auditory and visual presentation) and replicating experimenter’s gestures.Digit Span (forwards/backwards)Single Word recognition from the Cortical Visual Screening Test (CORVIST; James et al., 2001)Visual AssessmentSee Appendix 4 for example stimuli of measures of visual processing.Early visualVisual acuity test (CORVIST): task required discrimination of squares, circles and triangles at decreasing stimulus sizes corresponding to Snellen form acuity levels ranging from visual acuity of 6/9 to 6/36.Shape detection test from the Visual Object and Shape Perception battery (VOSP; Warrington and James, 1991): Figure-ground discrimination task involving random black pattern stimuli (N=20), half with a degraded ‘X’ superimposed. Patients were requested to state whether an “X” was present.Shape discrimination: The stimuli (N = 60) for this boundary detection task, adapted from Efron (1968), were a square (50 x 50 mm) or an oblong matched for total flux. There were three levels of difficulty: oblong edge ratio 1:1.63 (Level I), 1:1.37 (Level II), and 1:1.20 (Level III). The task was to discriminate whether each shape presented was a square or an oblong.Hue discrimination (CORVIST): The stimuli (N=4) comprised nine colour patches, eight of the same hue but varying luminance and one target colour patch of a different hue.VisuoperceptualObject Decision (VOSP): Stimuli (N=20) each comprise of four silhouette images, one of a real object (target) plus three non-object distractors.Fragmented Letters (VOSP): Participants were asked to identify visually degraded letters (N=20).Unusual and usual views (Warrington and James, 1988): Participants were asked to identify photographs of real objects (N=20) pictured from an ‘unusual’, non-canonical perspective. Items not identified from the non-canonical perspective are subsequently re-presented photographed from a more ‘usual’, canonical perspective.VisuospatialNumber location (VOSP): Stimuli (N=10) consist of two squares, the upper square filled with Arabic numerals in different positions, and the lower square with a single black dot. Participants are requested to identify the Arabic numeral whose spatial position corresponds to that of the target dot.Dot counting (VOSP): Stimuli (N=10) are arrays of 5-9 black dots on white background. Participants were asked to count the dots as quickly as possible without touching stimuli. A Cancellation (Willison and Warrington, 1992): Participants were requested to mark as quickly as possible with a pencil the location of 19 targets (letter As) presented among distractors (letters B-E) in a grid on an A4 sheet.RESPONSE LATENCIESReading/letter naming responses were recorded using an Olympus DS-40 digital voice recorder; reading latencies were manually determined from the onset of each word/letter/passage using the digital audio editor Audacity (). Latency data for erroneous responses and responses where participants had become overtly distracted from the task were removed from the analysis.EYETRACKINGEye movements were recorded using the head-mounted Eyelink II system, an infrared video-based eye tracker. The Eyelink II recorded gaze location at 250Hz. Fixations and saccades used in the present analysis were parsed by the Eyelink system, using standard velocity and acceleration thresholds (30o/s and 8000o/s2). Blinks were removed using Eyelink’s automated blink detection. Five point calibration was carried out at the start of the experiment, and a single point centrally-presented drift-correct was carried out prior to each passage. Fixations and saccades that spatially preceded the first word in passage experiments were discounted from analysis.IMAGINGMRI acquisitionT1-weighted volumetric magnetic resonance images (MRIs) were acquired on a Siemens Trio TIM 3T scanner (Siemens Medical Systems) for 20 PCA patients. Images were acquired using a 3D magnetization prepared rapid gradient echo (MP-RAGE) sequence producing 208 contiguous 1.1?mm thick sagittal slices with 28-cm field of view and a 256?×?256 acquisition matrix, giving approximately isotropic 1.1×1.1×1.1?mm voxels; a 32-channel head coil was used.Image processing softwareMIDASThe MIDAS (Medical information Display and Analysis System) software (Freeborough et al., 1996; Freeborough et al., 1997) allows simultaneous display of 3D imaging data. MIDAS allows semiautomatic MRI segmentation into brain and nonbrain regions (Freeborough et al., 1997). The MIDAS allows the overlaying of images, such as voxel-compression maps or region of interest masks, on top of MRI scans.MatlabMatlab is a high-level language and interactive environment for numerical computation, visualisation, and programming. Matlab 2012? is developed by MathWorks (Sherborn, Massachusetts) and was used to implement of software packages such as SPM8 (see REF _Ref371594002 \r \h 3.7.3.1).Image processingVoxel-based morphometryFor the voxel-based morphometry (VBM) analysis, magnetic resonance brain images were preprocessed using SPM8 software (Statistical Parametric Mapping, Version 8; ) running on MatLab 2012?. Images were converted to NIFTI format () and rigidly orientated to standard space based on the international consortium for brain mapping template using the “New segment” function in SPM8. Rigidly-orientated scans were segmented into native space grey matter, white matter and cerebrospinal fluid. The DARTEL toolbox (Ashburner, 2007) was used to perform spatial normalization, modulating the grey matter and white matter volumes according to the deformation fields and smoothing at 6mm full-width half-maximum. Total intracranial volume, age, gender and Mini Mental State Examination (MMSE) score were included as covariates. Total intracranial volume was calculated by adding the volumes derived from the native-space cerebrospinal fluid, grey and white matter segments. An explicit mask was applied in all analyses to include voxels for which the intensity was >.1 in at least 80% of the images; this has been shown to reduce anatomical bias in participants with greater cortical atrophy (Ridgway et al., 2009). A voxel-wise statistical threshold of p<?0.05, family-wise error (FWE) corrected for multiple comparisons was applied in all analyses. A more liberal threshold (p<0.001 uncorrected) was applied in figures for better visualization of additional areas where grey matter differences may be present.Fluid registrationFluid-based non-rigid image registration (Freeborough & Fox, 1998) was used to identify local volumetric changes in grey matter, white matter and cerebrospinal fluid between paired images from different time points. It uses a viscous fluid model to calculate the warping or deformation needed to achieve correspondence of both images at the voxel level (Scahill et al., 2002). The Jacobian determinants of the deformation fields represent the location and extent of warping, and can be displayed as voxel-compression maps which show longitudinal expansion and contraction of local brain regions. The MIDAS was used to overlay voxel-compression maps on rigidly aligned MRI scans for visualisation.VISUAL CROWDING EFFECTS IN PCACHAPTER INTRODUCTIONChapter REF _Ref374535107 \r \h 2.2.1.1 outlined a specific early visual processing deficit, crowding, which limits identification of target stimuli in the presence of adjacent flanker stimuli. Crowding effects are dependent on critical spacing, the distance at which flankers reduce identification of target stimuli, and are independent from the category of target/flankers, font of letter flankers and contrast (Pelli et al., 2004; Tripathy & Cavanagh, 2002; Pelli & Tillman, 2008). Crowding effects are greater with target and flankers of increased visual similarity, and are reduced with target and flankers of opposite polarity (Kooi et al., 1994; Bernard & Chung, 2011). Although some theories of crowding suggest that it may be a consequence of poor resolution of attention (Intriligator & Cavanagh, 2001), crowding tends to be considered a preattentive process related to the interaction between simple visual features. Three main types of theories have been proposed: the first, a classic lateral masking perspective, associates crowding with low level masking, a consequence of competition between a finite amount of feature detectors (Townsend et al., 1971; Wolford & Chambers, 1984). This masking is said to occur at the level of the retina, lateral geniculate nucleus, or the primary visual cortex (V1) (Chakravarthi & Cavanagh, 2007). The second theory suggests that crowding arises as a problem of excessive feature integration; when cells responsible for pooling information over a large area encounter flankers, flanker stimuli information is assimilated with the target stimulus (Levi et al., 2002; Pelli et al., 2004; Greenwood et al., 2010). The third, source confusion theory, proposes that features from flankers are mistaken to be target features (Wolford, 1975; Krumhansl & Thomas, 1977); this theory has been associated with accounts of crowding which emphasise spatial attention (Strasburger et al., 2005) or spatial uncertainty as a consequence of larger receptive fields (Dayan & Solomon, 2010). Some researchers have discriminated between the first two theories by suggesting that crowding limits identification of target stimuli, while lateral masking limits both identification and detection (Parkes et al., 2001; Pelli et al., 2004), while others have proposed how both may represent the same effect (Pernet et al., 2006). Levi’s (2008) review of crowding suggests that there is a growing consensus that crowding involves a two-stage process, encompassing both detection of simple features, possibly in V1, and integration of features downstream from V1. While the current investigation refers to visual crowding, some of the findings and conclusions may be equally applicable to lateral masking.Previous studies on crowding place the occipital cortex as the anatomical locus (Levi, 2008; Bi et al., 2009; Fang & He, 2008; Anderson et al., 2012). More precise localisation of crowding varies between studies, ranging from V1 (Blake et al., 2006), V2 (Chung et al., 2007) and V4 (Liu et al., 2009), while a two-stage model of crowding including both feature detection and integration might respectively involve V1 (Levi, 2008) and the extrastriate cortex (Robertson, 2003). Crowded stimuli have been shown to provoke increased fMRI activation from areas V1-V4 (Bi et al., 2009; Anderson et al., 2012), suggesting that crowding may be a multistage process.In healthy individuals, crowding only tends to inhibit perception in parafoveal vision; in PCA patients, prominent flanked letter identification deficits in line with crowding are evident in central vision (Crutch & Warrington, 2007a; Crutch & Warrington, 2009; Mendez et al., 2007). However, there have been no group investigations that have set out to specifically test crowding effects in PCA. While a range of visual deficits have been noted in PCA, we are currently unable to evaluate whether these deficits may in fact be contributed towards by enhanced crowding, and if so, what the incidence of such crowding is within PCA and tAD. This study aims to confirm whether patterns of flanked letter identification deficits in PCA are in line with crowding, and if so, gauge its prevalence in our PCA cohort and compare it to performance on the same tasks within a tAD group. The administration of a set of reading tasks to the same participants, reported in chapter REF _Ref374541838 \r \h 5, intends to clarify the nature between enhanced crowding and acquired dyslexia in PCA. Given the attenuating effects of increased spacing (Bouma, 1970; Townsend et al., 1971) and reverse polarity on crowding in normal peripheral vision (Kooi et al., 1994; Chakravarthi & Cavanagh, 2007), we hope that the manipulation of these variables will facilitate letter identification in such a way that might suggest means of supporting diminished reading ability in PCA. METHODSParticipantsThe study participants were 26 PCA patients, 17 typical AD patients and 14 healthy controls. The PCA patients all fulfilled clinical criteria for a diagnosis of posterior cortical atrophy (see chapter REF _Ref371937685 \r \h 1.2.2.6; Tang-Wai et al., 2004; Mendez et al., 2002; McMonagle et al., 2006) . Both PCA and tAD patients fulfilled research criteria for probable Alzheimer’s disease (see chapter REF _Ref371594198 \r \h 1.2.1.4; Dubois et al., 2007, 2010). Molecular pathology (18F amyloid imaging or CSF) was available for 7/26 PCA and 11/17 tAD patients (see REF _Ref374114693 \h Table 4.2); results for all tAD patients and 6/7 PCA patients were consistent or borderline consistent with AD pathology (positive amyloid scan on standard visual rating or CSF Aβ1-42 ≤450 and/or tau/Aβ ratio >1) The healthy controls were matched to the PCA and tAD groups on age and years of education, with the PCA and tAD participants additionally matched disease duration and Mini-Mental State Examination (MMSE; see REF _Ref371938204 \h Table 4.1).Table 4. SEQ Table_4 \* ARABIC 1 Demographic characteristics of PCA, tAD and normal control groups?PCATypical Alzheimer's diseaseControlNumber of participants261714Gender (male/female)10/1612/55/9Age (years)61.4 ± 7.765.0 ± 5.162.7 ± 5.0Education level (years)14.6 ± 2.314.9 ± 2.416.1 ± 2.4Disease duration (years)4.7 ± 3.15.0 ± 1.7-MMSE1 (/30)17.7 ± 5.017.5 ± 4.9-β-Amyloid PET/ CSF consistent with AD6/711/11Table 4. SEQ Table_4 \* ARABIC 2 Molecular pathology data for PCA and tAD patients; interpretation symbols indicate where results do not support AD pathology (-), are borderline consistent with AD pathology (+) and are >85% specific for AD pathology (++). DiagnosisAmyloid F18 imagingCSF total tauCSF Aβ 1-42CSF Tau:Aβ ratioCSF interpretationPCA-3104880.64-PCA-9316251.49+PCApositive10721268.51++PCA-1511471.03+PCApositive---++PCApositive10823652.96++PCApositive---++tAD-2892801.03+tAD-7572852.66++tAD-9403482.70++tAD-9521954.88++tAD-9773223.03++tAD-6252772.26++tAD->1200313>3.83++tAD-9131914.78++tAD->1200217>5.52++tAD-10991955.64++tAD-8503622.35++ Background neuropsychology PCA and tAD participants were administered a battery of background neuropsychological tests (chapter REF _Ref371594258 \r \h 3.4.1) and tests examining early visual, visuoperceptual and visuospatial processing (chapter REF _Ref371594267 \r \h 3.4.2). Mean scores for the PCA and tAD groups and an estimate of their performance relative to normative data sets appropriate for the mean age of each group are shown in Table 4.3.On tasks without a core visual component, the performance of the PCA group was mostly equivalent to (Concrete Synonyms, Naming, Digit Span forwards) or better than (Short Recognition Memory Test: words) that of the tAD group. PCA patients had lower scores than tAD patients on tests sensitive to parietal dysfunction (Calculation, Digit Span backwards, Cognitive estimates). Visual assessment identified how PCA patients showed greater impairment than the tAD group on all tests of basic visual function (except colour discrimination and single letter naming), visuoperceptal function (except unusual [non-canonical] object perception) and visuospatial processing.Crowding assessmentAll participants were requested to name target stimuli (upper-case letters excluding I, J, O, Q, W and X) under the following conditions (example stimuli are shown in REF _Ref376251080 \h Figure 4.1):Task 1 - Unflanked letter identification (N=20): The target stimuli were alphabetic items presented in isolation. Letters were resented in random order for 6000ms in a fixation box (3.2° in width, 2.9° in height) at the centre of the screen.Task 2 - Letter flankers (N=24): Target letters were flanked on each side by a letter, forming a 3-letter non-word combination.Task 3 - Shape flankers (N=24): Target letters were flanked on each side by a triangle presented at different orientations. Triangles were of equal height and line thickness to target letters.Task 4 - Number flankers (N=24): Target letters were flanked on each side by an Arabic numeral, chosen from a range between 2 and 9. Task 5 - Same-polarity flankers (N=24): Target letters were flanked on each side by black letters; this condition was the same as Task 2 except that items were presented on a grey background to match Task 6 (see below).-157480448945Task 6 - Reverse-polarity flankers (N=24): Target black letters were flanked on each side by white letters, all presented on a grey background.Figure 4. SEQ Figure_4. \* ARABIC 1 Example stimuli used in the letter, shape, and number experiments and same/reverse polarity experiment under condensed and spaced spatial conditions.TestMax ScoreRaw ScorePCA(mean age: 61.0)AD(mean age: 65.0)?meanmin|maxmeanmin|maxDifferenceNorms/commentBackground NeuropsychologyShort Recognition Memory Test1 for words*(joint auditory/visual presentation)2519.5 ± 3.714|2514.7 ± 1.513|19p<.0001PCA: 5th-10th%ile, AD: ~<5th %ile (Cut off: 19)Short Recognition Memory Test for faces*2517.8 ± 4.010|2416.8 ± 3.012|22p>.3Both ~<5th %ile (Cut off: 18)Concrete Synonyms test22520.0 ± 3.711|2420.9 ± 2.517|25p>.4Both 10th-25th %ileNaming (verbal description)2011.4 ± 6.62|2013.7 ± 6.44|25p>.2Both ~<5th%ile (Cut off: 15)Cognitive estimates3 (error score)3014.6 ± 7.51|2710.6 ± 5.02|20p=.074Both ~<1st%ile (Cut off: 9)Calculation (GDA4)*241.6 ± 2.94.9 ± 5.3p<.05PCA: ~<5th%ile, AD:5th-25th%ileSpelling (GDST5- Set B, first 20 items)*208.9 ± 6.50|1910.8 ± 5.60|19p>.3Both 10th-25th%ileGesture production test61512.7 ± 3.42|1514.1 ± 1.410|15p>.1-Digit span (forwards)126.0 ± 2.61|116.1 ± 1.43|8p>.8Both 25th-50th%ileMax forwards85.6 ± 1.81|85.5 ± 0.84|7p>.9-Digit span (backwards)122.6 ± 1.70|73.6 ± 1.91|9p=.078Both 5th-10th%ile?Max backwards72.3 ± 1.30|43.3 ± 1.12|6p<.05-CORVIST7 reading test1613.8 ± 3.06|1615.7 ± 0.813|16p<.05-Visual AssessmentEarly visual processingVisual acuity (CORVIST): Snellen6/9(median 6/9)6/9|6/36(median 6/9)6/9|6/12-Normal acuityFigure-ground discrimination (VOSP8)2016.3 ± 3.010|2018.6 ± 1.316|20p<.01PCA: ~<5th%ile, AD: 5th-10th%ileShape discrimination92012.6 ± 3.96|2017.2 ± 3.29|20p<.0005Healthy controls do not make any errorsHue discrimination (CORVIST)42.6 ± 1.10|43.0 ± 1.30|4p>.3-Visuoperceptual processingObject Decision (VOSP)*2010.0 ± 4.13|1615.9 ± 2.43|20p<.0001PCA: ~<5th%ile, AD: 10th-25th%ileFragmented letters (VOSP)202.9 ± 3.90|1713.5 ± 6.61|20p<.0001Both ~<5th%ile (Cut off: 16)Unusual and usual views10: Unusual206.6 ± 6.80|199.9 ± 5.12|16p>.1Both ~<1st%ile (Cut off: 12)Unusual and usual views10: Usual208.4 ± 5.50|2016.5 ± 4.04|20p<.0001Both ~<1st%ile (Cut off: 18)Visuospatial processingNumber location (VOSP)*101.8 ± 2.50|85.7 ± 3.80|10p<.005Both ~<5th%ile (Cut off: 6)Dot counting (VOSP)103.4 ± 3.20|108.1 ± 3.10|10p<.0001Both ~<5th%ileA Cancellation11: Completion time90s79.5s ± 17.448s|111s36.3s ± 15.717s|69sp<.0001Both ~<5th%ile (Cut off: 32s)A Cancellation11: Number of letters missed196.6 ± 5.10|180.53 ± 1.10|4p<.0005-Table 4. SEQ Table_4 \* ARABIC 3 Neuropsychological scores of patients with PCA and tAD relative to normative data *Behavioural screening tests supportive of PCA diagnosis. 1 Warrington (1996). 2 Warrington, McKenna and Orpwood (1998). 3 Shallice and Evans (1978). 4 Graded Difficulty Arithmetic test (GDA; Jackson & Warrington, 1986). 5 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 6 Crutch (unpublished). 7 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001). 8 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 9 Efron (1968): oblong edge ratio 1:1.20. 10 Warrington and James (1988). 11 Willison and Warrington (1992).In each flanking condition, target letter identification was probed under two spatial conditions, condensed and spaced. The edge-to-edge distance between the target letter and flankers was 0.875mm in the condensed condition and 8.75mm in the spaced condition, with the height of stimuli (10.5mm) corresponding to a visual angle of 1.20° at a viewing distance of 50cm. On each trial, if participants named the flanker, they were given one prompt to name the letter in the middle; this prompt was intended to limit errors resulting from visual disorientation. The same combination of flankers was used for each target letter under both spatial conditions within each flanker condition. Alphabetic items excluded the letters I, J, O, Q, W and X, and occurred with equal frequency within each task. The stimuli were presented in blocks of 6 items, with blocks being administered in an ABBA design. All flanked stimuli were presented in the centre of the screen within a fixation box (6.4° in width, 2.9° in height) which was intended to limit visual disorientation. All 26 PCA patients completed tasks 1, 2 and 4; 24 completed task 3 and 22 completed tasks 5-6. Data analysisBackground NeuropsychologyDifferences between the PCA and tAD groups were calculated using a t-test.Behavioural CovariatesTests of early visual, visuoperceptual and visuospatial processing (see Visual Assessment) were transformed and averaged to form composite scores for each visual domain. Raw scores were transformed into a standardised range (0-100) in which 0 and 100 corresponded to the minimum and maximum score achieved by any patient (irrespective of PCA and tAD group membership) respectively. The following raw scores were also transformed into a standardised range (0-100) for the PCA v tAD regression analysis: unflanked letter identification, digit span (backwards), A cancellation time. MMSE and disease duration were also used as behavioural covariates.Crowding indices for VBM analysis: Scores on shape and number flankers (Tasks 3-4) were used as crowding indices, based on the rationale that errors with non-letter flankers were less likely to reflect attentional or executive deficits. Indices were based on raw score differences:Spacing (shape): difference in accuracy between spacing conditions (spaced – condensed) in task 3 (shape flankers).Spacing (number): difference in accuracy between spacing conditions (spaced – condensed) in task 4 (number flankers).Spacing (shapes/numbers): difference in accuracy between spacing conditions (spaced – condensed) in tasks 3 & 4 combined.Polarity: difference in accuracy between tasks 5 & 6 (reverse polarity – same polarity).Polarity (condensed): difference in accuracy between tasks 5 & 6 (condensed condition only).Naming LatenciesSee chapter REF _Ref371594131 \r \h 3.5 for details of latency recording and determination. Latency data greater than 2 standard deviations from the mean of each participant were removed. Prior to latency regression analysis, latency data were transformed using an inverse transformation due to non-normal distribution of residuals. Naming latency data were only analysed for participants who made no errors or too few errors to permit meaningful error analysis using logistic regression or chi squared tests (Crowding assessment 1: PCA: N=12, MMSE=18.8, disease duration=3.9 yrs; Crowding assessment 2: N=9, MMSE=20.0, disease duration=3.0yrs; all tAD and control participants).Statistical AnalysisA one-way repeated measures ANOVA was used to examine overall differences between letter, shape and number flankers. Analysis of accuracy and latency data was conducted using logistic and linear models respectively; both models included spacing and flanker type as predictor variables, with the linear model also including accuracy rate as a covariate. Between patient group (PCA v tAD) regression analyses used the same logistic and linear models but also included diagnosis and one of the behavioural covariates listed above. Models were used to test for interactions between spacing and flanker type. Differences between PCA and tAD groups were calculated using a Wilcoxon rank-sum test and differences within groups were calculated using a Wilcoxon signed-rank test.Neuroimaging AnalysisMRI acquisition and preprocessing were carried out as described in chapters REF _Ref371594153 \r \h 3.7.1 and REF _Ref371594002 \r \h 3.7.3.1 respectively. Associations between regional grey matter volume and crowding indices (see chapter REF _Ref371941216 \r \h 4.2.4.2) were assessed using voxel-wise linear regression models. In addition to the explicit mask outlined in chapter REF _Ref371594002 \r \h 3.7.3.1, a region of interest (ROI) mask covering the occipital lobe was applied given prior anatomical hypotheses about the locus of crowding. The ROI mask was created using the Hammers atlas, which was warped to the groupwise average image created from the bias-corrected T1-weighed images of all 20 PCA participants using the DARTEL toolbox (Ashburner & Friston, 1997). Associations between grey matter volume and behavioural performance were assessed over the whole brain and within the occipital ROI.RESULTSThe mean and standard deviation percentage accuracy and naming latency results for each group on Tasks 1-6, plus group comparisons, are shown in REF _Ref371942645 \h Table 4.4. Crowding Assessment 1 - flanker and spacing effectsTask 1 - Unflanked letter identification: There was no significant difference in unflanked letter identification accuracy between the PCA and tAD or control groups. One PCA patient made one error, while the tAD and control groups did not make any errors. There was a trend towards the PCA group having longer naming latencies than the tAD group. Both PCA and tAD groups were slower than the control group. Tasks 2-4 - Letter, shape and number flankers: A summary of the PCA and AD accuracy and naming latency data is shown in REF _Ref376251782 \h Figure 4.2. The PCA patients were consistently worse than both tAD patients and controls in terms of both naming accuracy and latency on Tasks 2-4. The accuracy of tAD patients was not significantly different from that of controls on any Task, but they were slower on Task 4.Effects of spacingPCA patients (N=26) showed significantly poorer accuracy for target letter identification in the condensed than spaced condition (z=7.81, p<.001). Analysis of latency data (N=10) identified longer naming latencies in the condensed condition (t=3.33, p<.01). At the individual patient level, 18/26 (69.2%) showed a spacing effect in accuracy and/or latency; all but three of those (15/26: 57.7%) showed this spacing effect even when analysis was restricted to non-letter flankers. tAD patients did not demonstrate an effect of spacing (p>.5) on accuracy but latencies were significantly longer in the condensed condition (t=4.73, p<.001). Controls made no errors, but did show longer latencies in the condensed condition (t=2.89, p<.05). Analysis of combined accuracy data from the PCA and tAD groups revealed a significant interaction between diagnosis and spacing (z=-2.77, p<.01), with PCA patients showing a greater spacing effect; no such interaction was found for naming latencies (p>.1). Analysis of combined latency data for the tAD and control groups found no evidence of an interaction between diagnosis and spacing (p>.4). Effects of flanker categoryPCA patients showed significantly poorer accuracy for letters relative to other flanker categories (vs shapes: z=2.68, p<.01; vs numbers: z=2.61, p<.01). However, this between-category difference only held in the spaced condition (vs shapes: z=3.65, p<.001; v numbers: z=3.55, p<.001); in the condensed condition, there was no significant difference between letters and other flankers (v shapes/numbers: p>.1). PCA patients showed significantly slower naming latencies for letters relative to other flanker categories (v shapes: z=3.74, p<.005; v numbers: t=2.60, p<.05): this between-category difference was consistent across spacing conditions. tAD patients did not show poorer accuracy for letters (v shapes/numbers: p>.1) but did show longer latencies (v shapes: t=6.13, p<.001; v numbers: t=5.13, p<.001). Controls made no errors, but showed longer latencies for letter flankers (v shapes: t=2.59, p<.05; v numbers: t=3.55, p<.005). Unlike the PCA group, both tADs and controls showed longer latencies for letter flankers in both spaced and condensed conditions.Post-hoc Analysis of CovariatesFigure 4. SEQ Figure_4. \* ARABIC 2 Accuracy and naming latency data for the PCA and AD group for letter, shape and number flankers in both spatial conditions (* = p<0.05; **= p<.005, ***= p<.0005).None of the behavioural covariates (early visual, visuoperceptual and visuospatial processing, unflanked letter identification, Digit Span (backwards), A Cancellation time, MMSE or disease duration) could account for the spacing effect within the PCA or tAD groups. None of the covariates could account for the overall group difference in naming accuracy between the PCA and tAD groups. Similarly, none of the covariates could account for group differences when considering the condensed condition alone, however, visuoperceptual (z=2.13, p<.05; diagnosis: p>.2) and visuospatial (z=3.60, p<.001; diagnosis: p>.1) function did account for the group difference in the spaced condition. Linear regression analysis did found that none of the covariates could account for the group difference between the PCA and tAD groups for naming speed overall or in either spacing condition.-3232151270Table 4. SEQ Table_4 \* ARABIC 4 Comparisons between PCA and tAD accuracy and latency data. Asterisks denote where the PCA or tAD group significantly differs from each other or the control group (vs controls: * = p<0.05; **= p<.005; vs tAD: ^=p<.05; ^^=p<.005).?Naming accuracy (%)GroupsNaming latency (ms)Groups??TaskPCAtADControlsPCAtADControlsNmeanmin|maxmeanmin|maxmeanmin|maxmeanmin|maxmeanmin|maxmeanmin|max1. Unflanked letter identification2099.8 ± 0.295.0|100100 ± 0100|100100 ± 0100|100785 ± 315**360|3390583 ± 95*390|1050497 ± 53340|7502. Letter flankers2475.8 ± 25.1**^^12.5|10099.3 ± 1.695.8|100100 ± 0100|1002726 ± 3196**^^470|23560634 ± 128260|2610570 ± 113210|9003. Shape flankers2483.5 ± 18.6**^^33.3|10099.7 ± 1.095.8|100100 ± 0100|1001631 ± 867**^^400|11870546 ± 86210|1160538 ± 95280|8304. Number flankers2483.6 ± 23.5**^^33.3|10099.7 ± 1.095.8|100100 ± 0100|1002072 ± 1842**^^290|23760548 ± 92*220|1610469 ± 119230|8605. Same polarity letter flankers2478.8 ± 22.5**^^29.2|10098.5 ± 2.991.7|100100 ± 0100|1001836 ± 1167**^^340|9320609 ± 128280|2100518 ± 124230|11306. Reverse polarity letter flankers2486.5 ± 15.6**^^54.2|10099.3 ± 2.291.7|100100 ± 0100|1002133 ± 1635**^^310|11550591 ± 91*280|1610506 ± 131240|890?Summary dataTotal (Tasks 2-4)7281.3 ± 19.7**^^20.8|10099.6 ± 1.195.8|100100 ± 0100|1002054 ± 1694**^^290|23760573 ± 94210|2610524 ± 100210|900Total condensed (Tasks 2-4)3672.0 ± 26.7**^^11.1|10099.7 ± 0.997.2|100100 ± 0100|1002510 ± 2481**^^420|23760591 ± 98210|2610537 ± 100250|900Total spaced (Tasks 2-4)3690.0 ± 16.0**^^30.6|10099.5 ± 1.594.4|100100 ± 0100|1001659 ± 1073**^^290|20820555 ± 95220|1610515 ± 101210|880Total (Tasks 5-6)4882.7 ± 18.3**^^50.0|10098.9 ± 2.2*91.7|100100 ± 0100|1002004 ± 140**^^310|11550597 ± 104280|2100512 ± 126230|1130Crowding assessment 2 – Polarity effectsTasks 5 & 6 - Same- and reverse-polarity flankers: A summary of the PCA and AD accuracy and naming latency data is shown in REF _Ref376251794 \h Figure 4.3 REF _Ref371942975 \h . The PCA patients were consistently worse than both tAD patients and controls in terms of both naming accuracy and latency on Tasks 5-6. Differences in accuracy and latency between the tAD and control groups did not reach formal levels of significance, except for tAD patients being slower on Task 6.Effects of polarityPCA patients (N=22) showed significantly poorer accuracy for target letter identification with same- rather than reverse-polarity flankers (z=-3.07, p<.005). This polarity effect only occurred for condensed flankers (condensed: z=4.82, p<.001; spaced: p>.8). Analysis of latency data (N=10) found a significant effect of spacing (t=2.66, p<.05) but not polarity (p>.9) on naming speed. In the tAD group, there was no significant effect of polarity on naming speed (p>.8) or accuracy (p>.1); however, there was an interaction between spacing and polarity (t=-2.63, p<.05), with condensed flankers of same polarity having longer naming latencies. While none of the controls made any errors, there was a trend towards longer naming latencies with same polarity flankers (t=-2.18, p=.050), though there was no interaction between spacing and polarity (p>.1). Error analysisOf all error responses in the PCA group (overall error rate: 17.7%), 22.9% were from the target being unidentified, which could result from participants being unable to either detect or identify the target, and 32.2% were from flanker identification (e.g. ZNHZ). However, in 44.9% of errors, the response was neither the target nor a flanker: the majority of these errors were suggestive of perceptual integration of flanker and target stimuli (YMTV, 3T6C). This was despite accurate unflanked letter identification (overall error rate: 0.2%), with only one PCA patient making one error (CG).229870-355600Figure 4. SEQ Figure_4. \* ARABIC 3 Accuracy and naming latency data for the PCA and AD group for same and reverse polarity flankers in both spatial conditions (* = p<0.05; **= p<.005, ***= p<.0005).Summary of behavioural dataIn the PCA group, there was a consistent effect of spacing, regardless of flanker type, on accuracy and an inconsistent effect of flanker type of accuracy; there were also effects of spacing and flanker type on naming speed. At the individual level of patients, effects of spacing on naming speed or accuracy were more prevalent than effects of flanker type. However, the spacing effect on accuracy is ameliorated by reverse polarity flankers. These findings suggest that enhanced crowding is the primary factor in determining letter identification. None of the behavioural covariates account for the difference in naming speed or accuracy between the PCA and tAD groups in the condensed condition. The pattern of performance was similar in both the tAD and healthy control groups.Neuroimaging findingsT-values for neuroanatomical associations of performance on tasks 3-4 in the PCA group are shown in REF _Ref376251925 \h Figure 4.4. No significant associations between indices of crowding (crowding (shapes), crowding (numbers), crowding (shapes/numbers) and grey matter volume were found when correcting for multiple comparisons over whole brain volume. When restricting analysis to the pre-specified occipital region, a significant negative correlation was found between crowding (shapes/numbers) and grey matter volume in the right collateral sulcus, between the fusiform and lingual gyri after correcting for multiple comparisons (p<.05); a more pronounced crowding effect for letters surrounded by shapes and numbers was associated with reduced grey matter volume in this region. In tasks 5-6, there were no significant associations between the discrepancy in accuracy between flankers of opposite polarity (polaritydiff, polaritydiff (condensed)) and grey matter over whole brain volume or within the pre-specified occipital region.DISCUSSIONThese findings support previous reports of crowding in PCA, in that performance on centrally-presented flanker letter identification tasks reflect a phenomenon qualitatively similar to that of typical crowding in healthy individuals but to a much greater degree. In the PCA group, spacing, not flanker type, primarily determined accuracy in tasks of flanked letter identification. Results also indicate a high prevalence of enhanced crowding in PCA, with 58% of the PCA group exhibiting a spacing effect on speed or accuracy in a non-letter flanker condition. A particularly interesting phenomenon observed in the PCA group is how reverse polarity flankers ameliorated the crowding effect, consistent with observations of crowding in normal peripheral vision.Despite similar accuracy in naming letters presented in isolation (Task 1), the PCA group were considerably less accurate than the tAD and the healthy control group in all tasks involving flanked letter identification. The tAD group were not significantly less accurate than controls on any of the tasks. The PCA group showed a trend towards being slower on unflanked letter identification than the tAD group but were considerably slower on flanked letter identification tasks. The tAD group were slower than the control group on unflanked letter identification, but not flanked letter identification, tasks. Tasks 2-4 demonstrated a consistent effect of spacing on accuracy across flanker type and an inconsistent effect of flanker type on accuracy across spacing in the PCA group. Tasks 5 and 6 demonstrated how the spacing effect occurred with same but not reverse polarity flankers. There was a similar pattern of performance in both naming speed and accuracy in the tAD and healthy control groups in both experiments. The current findings demonstrate that the PCA group has disproportionate deficits in speed and accuracy on tasks of flanked letter identification relative to control groups; however, it is necessary to rule out other factors in being able to account for this poor performance, particularly (i) attentional dyslexia and (ii) poor visuospatial processing. Attentional dyslexia involves deficits in the recognition of multiple, concurrently presented stimuli of the same category, for example letters presented with other letters (Humphreys & Mayall, 2001; Warrington et al., 1993); these deficits, however, are not as pronounced when multiple stimuli are of different categories. In tasks 2-4, spacing, not flanker type, consistently -587375431800Figure 4. SEQ Figure_4. \* ARABIC 4 Statistical parametric maps of grey matter volume associated with a measure of crowding (crowding (shapes/numbers)). The statistical parametric maps are displayed on axial (A), coronal (B) and sagittal (C) sections of the mean normalized bias-corrected images in MNI space: the right hemisphere is shown on the right on coronal and axial sections. When restricting analysis to a pre-specified region of interest (see region below in blue), there was an association between a greater degree of crowding and lower grey matter volume in the collateral sulcus (FWE corrected: p<.05; peak location: x=30 y=-58 z=-8): uncorrected t-values for this association are displayed below in a colourmap.determined naming accuracy. While there was an interaction between spacing and flanker type, this may be a consequence of perceptual similarity, and hence crowding, as opposed to the category-specific deficit previously linked with attentional dyslexia. Another possibility is that poor visuospatial processing may account for the current findings, especially given the prominence of deficits in spatial localisation in PCA. In tasks 2-4, performance on tasks involving unflanked letter identification, early visual, visuoperceptual or visuospatial processing, digit span backwards or the A cancellation task did not account for the overall difference in accuracy between the PCA and tAD groups. However, measures of visuospatial and visuoperceptual ability did account for the group difference in the spaced, but not condensed, condition. This suggests that poor accuracy in the spaced condition may be contributed towards by visuospatial and visuoperceptual impairment, while poor accuracy in the condensed condition is a consequence of enhanced crowding. The types of errors made by the PCA group have implications for accounts of crowding. A classic lateral masking perspective would predict errors such as being unable to detect the presence of target stimuli (Polat & Sagi, 1993); deficits in the feature detection process within two-stage models of crowding may also lead to such errors (Levi, 2008; Pelli et al., 2004), although it is plausible that, in either case, a lack of target detection might result in errors involving misidentifying flanker stimuli. Source confusion theories would predict errors to arise from misreporting flanker instead of target stimuli (Wolford, 1975). While not by a large margin, the greatest proportion of error responses did not involve identification of target or flanker stimuli; the majority of these errors are consistent with an averaging of flanker and target information, in line with feature integration and compulsory pooling theories. With enhanced crowding being the most likely candidate in being able to account for poor letter identification in the PCA group, what might be the locus for this phenomenon in the visual system? Previous studies have suggested the neural correlates of crowding as being somewhere within the occipital lobe (see chapter REF _Ref374535107 \r \h 2.2.1.1). Our imaging data suggests that, within the occipital region, scores indicative of prominent crowding effects were associated with lower grey matter volume within the right collateral sulcus, between the fusiform and lingual gyri. Without retinotopic mapping, it is difficult to be confident of the exact correspondence between anatomical location and visual area. In previous studies, similar regions have been classed as area V4 (Gallant et al., 2000; Sereno et al., 1995; DeYoe et al., 1996; Hadjikhani et al., 1998), V3 (Yeatman et al., 2013) and V3a (Grill-Spector & Malach, 2004). Interestingly, V4 fulfils a variety of criteria that make it a promising locus for crowding. Receptive field size and anisotropy in V4 are similar in orientation and size to the radial/tangential anisotropy of crowding (Pinon et al., 1998; Toet & Levi, 1992), while studies have suggested that V4 is an area in which information from different stimulus types, orientation and spatial frequencies converge (Ferrera et al., 1992; Ferrera et al., 1994; Logothetis & Charles, 1990; David et al., 2006) and estimates of V4 receptive field size overlap with the extent of crowding in peripheral vision (Smith et al., 2001; Chung et al., 2007). Bias competition, in which patterns within receptive fields compete to determine the firing rate of individual neurons, has been localised in areas V4 and higher (Chelazzi et al., 2001; Desimone & Duncan, 1995; Reynolds et al., 1999); this may underlie crowding as a possible consequence of competitive feature-integration processing (Nandy & Tjan, 2007). Anderson et al. (2012) cite how there is a significant increase in population-based receptive field size from V1 to V4 (Amano et al., 2009; Smith et al, 2001), and suggest that crowding effects might accumulate from pooling of target and flanker stimuli over receptive fields of increasing size. While V4 lesions in macaques have been found to impair boundary discrimination (De Weerd et al., 1996) and induce a spacing effect on orientation thresholds surrounded by distractors (De Weerd et al., 1999), one study has found that V4 lesions in macaques did not alter the amplitude of the crowding effect (Merigan, 2000). The clear effect of polarity observed in these results (tasks 5 and 6) might suggest which regions govern some aspects of crowding. Reverse polarity has been shown to segregate information via ON and OFF pathways at the level of bipolar cells in the outer retina, which continues to stay relatively distinct until reaching the early visual cortex (Schiller, 1992). This segregation of information between target stimuli and flankers of reverse polarity presumably is what alleviates the crowding effect, although there is evidence of interaction between the two pathways (Harris & Parker, 1995; Wassle & Boycott, 1991). Regarding the neural correlates of where information from ON and OFF pathways is integrated, Zhou et al. (2000) found that 48% of V1 and 20% V2 and V4 neurons in macaques encoded local contrast polarity, while the majority of neurons in V2 and V4 encoded direction-specific contrast polarity edges. Motoyoshi and Kingdom (2007) proposed a two stream model of 2nd-order processing, with the first stream composed of complex V1 cells sensitive to orientation and the second composed of lateral geniculate nucleus or V1 blob cells sensitive to polarity. However, similar temporal limits of the polarity advantage and attention (Chakravarthi & Cavanagh, 2007) contest the notion of this effect being an exclusively low-level process. Our imaging data do not provide a means to discriminate between low- and high- theories of the polarity effect, as measures of this effect were not significantly associated with grey matter volume. Integration and bottom-up pooling models of crowding (Parkes et al., 2001; Wilkinson et al., 1997; Greenwood et al., 2009) propose that, while isolated contours are processed by simple cells, a high concentration of flanking contours in a small region, or “integration field” (Pelli et al., 2004), leads to a greater response of complex cells which then suppress simple cell activity within their receptive field area. Histopathological reports of PCA (see chapter REF _Ref371945253 \r \h 1.2.2.2) have observed a selective vulnerability of certain neurons to AD, particularly cells with long axonal projections (Morrison et al.,1986a; Lewis et al., 1987) such as V1 Meynert cells in PCA (Hof et al., 1990), and extensive disruption of feedforward and feedback projections by NFT and SP in Brodmann areas 17 to 19 (von Gunten et al., 2006), while some have suggested that AD pathogenesis might spread through cortico-cortical connections (see chapter REF _Ref371945439 \r \h 1.2.1.1). In the context of previous pathological findings in PCA and the current data, it is possible that, in these patients, simple cell activity in areas such as the primary visual cortex may be less disrupted by AD pathology than complex cells in more downstream visual areas, such as V4. Cells in V4 which suppress simple cell activity through connections with areas earlier in the visual system may be particularly susceptible to AD pathology; this would result in a diminished ability to suppress signals of high contour concentrations within an integration field. The polarity advantage suggests that visual information in opposite polarity is being either successfully segregated and resolved within early parts of the visual system or discriminated through attentional mechanisms. While the current crowding and imaging data do not favour low- or high-level explanations of the polarity effect, the high distribution of AD pathology in the posterior parietal relative to primary visual cortex noted by Hof et al. (1997) and relatively intact visual acuity compared to gravely impaired visuospatial ability demonstrated by the PCA group on neuropsychological measures, tentatively suggests that the polarity advantage would more likely be conferred by a relatively preserved early visual system than parietal-mediated attentional systems (Intriligator & Cavanagh, 2001). However, the primary purpose of this study was to explore crowding from a particular perspective; neurodegenerative patients who exhibit prominent flanked letter identification deficits. Given how PCA patients have distributed brain atrophy particularly in the occipital lobe and parietal cortex (Lehmann et al., 2011); caution should be exercised in drawing inferences about the precise neural correlates of crowding in healthy individuals from the current data.CHAPTER CONCLUSIONSThese findings demonstrate the grave inhibitory effect in spatial vision that is of qualitative similarity to crowding in PCA, as evidenced by consistent effects of spacing across different categories of flankers and the ameliorating effect of reverse polarity flankers. Performance on tasks of early visual, visuoperceptual or visuospatial processing cannot account for the difference in overall letter naming accuracy between the PCA and tAD groups. In addition, we found an association between measures of crowding and the right collateral sulcus, an anatomical region that may correspond to area V4. Future investigations include assessing the evolution of crowding in longitudinal studies; such studies might reveal how emerging enhanced crowding effects impact other aspects of visual processing, including the reading system. READING IN PCACHAPTER INTRODUCTIONReading difficulties are a characteristic and debilitating symptom of PCA (see chapter REF _Ref374200298 \r \h 2.3). Various forms of dyslexia have been identified in case studies of PCA; however, group studies are required to gauge the extent and heterogeneity of reading dysfunction in PCA, and in particular to clarify the role of other aspects of visual function in influencing reading ability. Authors of the only group study of reading in PCA (Mendez et al., 2007) stressed that further studies analysing not only reading accuracy, but reading latency, were necessary to precisely assess differences in word processing.The primary focus of the current investigation is upon the effect of perceptual variables on single word reading ability in PCA. Two perceptual attributes of words - inter-letter spacing and font size – merit particular consideration given previous evidence of their potential impact on reading in some individuals with PCA. First, the manipulation of inter-letter spacing in letter identification paradigms is well known to modulate the size of the so-called ‘crowding’ effect. Crowding is implicated in reading dysfunction by previous observations that increased inter-letter spacing facilitates reading ability in dyslexics (Spinelli et al., 2002; Zorzi et al., 2012) and letter confusability predicts performance in LBL readers (Arguin et al., 2002; Fiset et al., 2005). If enhanced crowding is a component of dyslexia in PCA, this would raise the possibility that the conditions in which crowding effects are diminished in flanked letter identification tasks (increased spacing, reverse polarity flankers (Kooi et al., 1994) might be applied in order to facilitate whole word reading.The second perceptual attribute of particular interest in the current investigation is font size. Many PCA patients describe greater difficulty perceiving large than small objects (perhaps most strikingly by a patient who was unable to read the headlines of his newspaper but could read those of another passenger reading the same paper further down the train carriage on which he was travelling; see Crutch, 2013). Such ‘inverse size effects’ have been formally documented in a small number of patients with progressive visual disturbance who exhibited more impaired identification for large relative to small pictures, words and letters presented in isolation (Saffran et al., 1990; Coslett et al., 1995; Stark et al., 1997). This common clinical complaint in PCA has been attributed to a reduction in the effective visual field (Russell et al., 2004; Crutch et al., 2011). However the magnitude, prevalence and specificity of this effect in PCA remain unknown.The aim of this chapter was to improve the characterisation of single word reading in PCA by manipulating the perceptual properties of words in a manner predicted to influence reading accuracy and speed. The perceptual properties examined included inter-letter spacing, font size, length, case, font type and confusability, and the performance of PCA patients was compared directly with that of tAD patients and healthy controls. It was hypothesised that perceptual properties would be a primary determinant of reading ability in the PCA but not tAD or healthy control groups. A secondary aim was to consider the role of basic visual, visuoperceptual and visuospatial processing in PCA and tAD patients in order to improve our understanding of the causal and associative relationships between these different aspects of visual function and reading ability in PCA.METHODSParticipantsThe study participants were the same patients and controls from chapter 4. The healthy controls were matched to the PCA and tAD groups on mean age and years of education, with the PCA and tAD participants additionally matched for mean disease duration and Mini-Mental State Examination score (MMSE; see REF _Ref371938204 \h Table 4.1).Background neuropsychologyPCA and tAD participants were administered a battery of background neuropsychological tests (chapter REF _Ref371594258 \r \h 3.4.1) and tests examining early visual, visuoperceptual and visuospatial processing (chapter REF _Ref371594267 \r \h 3.4.2). Mean scores for the PCA and tAD groups and an estimate of their performance relative to normative data sets appropriate for the mean age of each group are shown in Table 4.3. For a summary of visual assessment and performance on background neuropsychological tests, see chapter REF _Ref371941617 \r \h 4.2.2.Reading assessmentAll words in the main and subsidiary reading experiments were presented for an unlimited duration on a Dell Latitude E5420 laptop at a viewing distance of 50cm. Words were presented at the centre of the screen within a rectangular fixation box (22.5° in width, 4.3° in height); the fixation box remained on the screen throughout the experiment (including the inter-stimulus interval) to help maintain participant fixation within an area proximate to the word stimuli.Perceptual corpusAll participants read aloud a total of 192 single words which involved simultaneous manipulations of five different perceptual properties:Inter-letter spacing (2 levels: no spaces and 2 blank s p a c e s)Font Size (2 levels: small and large): words were presented with a visual angle of letter height subtending 0.5° for small words vs 2° for large words Case (2 levels: UPPER CASE and lower case)Length (3 levels: 3-, 5- and 7-letter words) Mean letter confusability (2 levels: high and low): upper case ratings for each letter were averaged from the confusability matrices of Van der Heijden et al., (1984), Gilmore et al., (1979), Townsend, (1971), and Fisher et al., (1969). Lower case ratings were averaged from the confusability matrices of Geyer, (1977), and Boles and Clifford, (1989). The stimulus pool of 192 words was constructed from 24 8-word sets matched for mean frequency (CELEX: Baayen et al., 1995), age of acquisition (AoA: Gilhooly & Logie, 1980) and concreteness (Coltheart, 1981) (see REF _Ref374542667 \h Table 5.1). The structure of the reading sets was such that the effect of each individual perceptual property upon reading performance could be directly compared as all other properties and variables were matched. For example, the font size effect could be readily examined as the small (N=96) and large (N=96) font words were matched for all background variables and contained an equal number of spaced and unspaced (N=48 each), upper and lower case (N=48 each), 3-, 5- and 7-letter words (N=32 each) and high and low confusability words (N=48 each). All words were presented in fixed random order, divided into two blocks with a break of approximately 20mins between blocks. All 192 words were presented in Arial Unicode MS. Table 5. SEQ Table_5 \* ARABIC 1 Different levels of reading variables for words from the perceptual corpus (N=192) matched for AoA, Concreteness and Frequency.VariableLevelNAoAConcreteFreqConfusabilityHigh9637348636Low9635849836SpacingSpaced9636449335Unspaced9636749137SizeLarge9636549137Small9636649335CaseUpper9636449842Lower9636748630Length364319528445643574993276441945631Cursive font readingA subset (N=12) of items were selected from the perceptual corpus fulfilling an equal number of levels of reading variables; these were re-presented in a cursive font (Wrexham Script) to 22 PCA patients, who were requested to read them aloud. The words were drawn from the no letter spacing condition and were presented in random order. Data analysisBackground neuropsychologyDifferences between the PCA and tAD groups were calculated using a t-test.Behavioural covariatesComposite scores: All raw scores from the Visual Assessment were transformed into a standardised range (0-100) in which 0 and 100 corresponded to the minimum and maximum score achieved by any patient (irrespective of PCA and tAD group membership). Transformed scores in each visual assessment test were averaged within three visual processing domains in order to give composite scores for the following covariates of interest:Early visual processing (Early: chapter REF _Ref371594314 \r \h 3.4.2.1): Shape discrimination, Figure-ground discrimination and Crowding (mean difference in accuracy for number and shape flankers between spacing conditions [chapter 4]). Visuoperceptual processing (chapter REF _Ref371594320 \r \h 3.4.2.2): Object decision, Fragmented letters and Usual and Unusual views.Visuospatial processing (chapter REF _Ref371594329 \r \h 3.4.2.3): Number location and Dot counting The raw scores for the following nuisance variables were also transformed into a standardised range for the PCA v tAD regression analysis: Single letter accuracy, Digit Span (backwards), A Cancellation time (Willison & Warrington, 1992).Reading latenciesSee chapter REF _Ref371594131 \r \h 3.5 for details of latency recording and determination. Latency data greater than 2 standard deviations from the mean of each participant were removed. Prior to latency regression analysis, latency data were transformed using a log transformation due to non-normal distribution of residuals. In order to examine reading latency data we divided participants into 2 groups based on accuracy of reading words presented in a normal manner (small, unspaced words). As latency analysis was restricted to correct responses, reading latency data were difficult to interpret where there was a high error rate, resulting in a large proportion of missing data. For this reason, we divided participants into 2 groups based on accuracy of reading words under normal condition (small, unspaced words). Group 1 (PCA: N=10, mean MMSE=20.7, mean disease duration=3.0yrs; tAD: N=16, mean MMSE=17.7, mean disease duration=5.1yrs) made no errors on these items, or did not make enough reading errors to produce significant effects at the individual level using logistic regression or chi squared tests. The low proportion of errors allowed for analysis of latency data in this group.Group 2 (PCA N=16, mean MMSE=16, mean disease duration= 5.8yrs; tAD N=1, MMSE=14, disease duration= 3.3yrs) made enough errors to allow for meaningful error analysis. The high proportion of error prevented analysis of latency data in this group.Statistical analysisAnalyses of accuracy and latency data were conducted using logistic and linear mixed models respectively; both models used random subject effects and fixed effects of size, spacing, case, length, confusability, AoA, concreteness, frequency, orthographic neighbourhood size and word order, with the linear model of latency data also including accuracy rate as a fixed effect. Analysis of accuracy and latency data was carried out first on each of the PCA, tAD and control groups. Subsequently, group comparisons between PCA and tAD performance were conducted using similar logistic and linear mixed models but including only reading variables that were significant at the PCA and tAD group level, diagnosis and each of following behavioural covariates: Early visual processing, Early visual processing [excluding crowding], visuoperceptual processing, visuospatial processing, MMSE, Disease duration, digit span backwards, A cancellation, single letter naming. Differences in cursive font reading between PCA and tAD groups were calculated using a Wilcoxon rank-sum test and differences within groups were calculated using a Wilcoxon signed-rank test.Neuroimaging analysisMRI acquisition and preprocessing were carried out as described in chapters REF _Ref371594153 \r \h 3.7.1 and REF _Ref371594002 \r \h 3.7.3.1 respectively. Associations between regional grey matter volume and reading performance were assessed using voxel-wise linear regression models. Differences in accuracy between levels of reading variables which were significant at the group level were used in VBM regression models (see chapter REF _Ref371594002 \r \h 3.7.3.1 for covariates). RESULTSPerceptual corpusOverall summaryThe mean percentage error rates and reading latencies are shown in REF _Ref376252200 \h Figure 5.1. The PCA group was, on average, significantly less accurate and slower than both the AD group (t=3.5, p<.005 and t=-2.8, p<.01, respectively) and the control group (t=3.5, p<.005 and t=-3.2, p<.005, respectively). The AD group showed a trend towards being less accurate than the control group and was significantly slower (t=-2.0, p=.051 and t=3.2, p<.005, respectively). Response accuracy in each groupPCA: PCA patients (N=26; overall accuracy=76.8%, SD=47.1) were less accurate for words with increased inter-letter spacing (z=-10.2, p<.001), large font size (z=-7.9, p<.001), increased length (z=-2.8, p<.01), higher AoA (z=-6.9, p<.001) and lower frequency (z=4.5, p<.001). There were also trends towards lower accuracy for words with greater orthographic neighbourhood size (z=-1.8, p=.077) and higher concreteness (z=-1.8, p=.084). There were no significant effects of case (p>.9), letter confusability (p>.3) or word order (p>.8) on accuracy. tAD: tAD patients (N=17; overall accuracy=98.0%, SD=6.6) were less accurate for words with higher AoA (z=-4.5, p<.001), lower frequency (z=2.6, p<.01) and for words which were read later in the assessment (z=-2.8, p<.01).Controls: There was no effect of any of the variables on reading accuracy at either the group level (N=13; overall accuracy=99.8%, SD=.04) or individual level.Response latency in each groupPCA: PCA patients (N=10; overall mean RT=1.17s, SD=.56) were slower to read words with increased inter-letter spacing (z=11.8, p<.001), large font size (z=5.8, p<.001), and higher AoA (z=4.4, p<.001). Overall reading accuracy was also a significant predictor of reading speed (z=-3.9, p<.001). tAD: tAD patients (N=16; overall RT=.73s, SD=.16) were slower to read words with increased inter-letter spacing (z=4.8, p<.001) and higher AoA (z=4.4, p<.001) that were read earlier in the assessment (z=-2.9, p<.005). There was a trend towards words of lower frequency being read more slowly (z=-1.8, p=.073). Overall reading accuracy was also a significant predictor of reading speed (z=-3.9, p<.001). Controls: The control group (N=14; overall mean RT=.59s, SD=.08) were slower to read words with higher AoA (z=5.1, p<.001), increased inter-letter spacing (z=3.3, p<.005), lower letter confusability (z=-2.6, p<.01), decreased font size (z=-2.0, p<.05) that were read earlier in the assessment (z=-8.2, p<.001). There was also a trend towards smaller words being read more slowly than larger words (z=-1.9, p=.055). Overall reading accuracy was also a significant predictor of reading speed (z=-2.4, p<.05).-337185181610Figure 5. SEQ Figure_5 \* ARABIC 1 Summary of reading accuracy and latencies for the PCA, tAD and Control groups. Asterisks denote a significant effect of each reading variable on reading speed or accuracy (* = p<0.05; **= p<.005). Error bars show standard error for each group mean.Between-group comparisonsThe proportion of participants in each group whose reading accuracy or speed was predicted by one or more variables at the individual level is shown in REF _Ref374542108 \h Figure 5.2. Increased font size reduced reading accuracy or speed in 46% of the PCA group, but increased reading speed in 18% of the tAD and 7% of the control group. Figure 5. SEQ Figure_5 \* ARABIC 2 Proportion of participants in each group who show an effect of each variable on either latency or accuracy at the individual level.Between-group accuracyAs described above, differences in accuracy between the PCA and tAD groups were modelled using mixed-effects logistic regression including as covariates reading variables that were statistically significant at the group level for either PCA or tAD groups. These variables were spacing, size, order, AoA, frequency and length. There were significant interactions between diagnosis and spacing (accuracy: z=2.5, p<.05; latency: z=-8.6, p<.001) and diagnosis and size (accuracy: z=2.8, p<.01; latency: z=2.8, p<.01), with increased spacing and size leading to lower accuracy in the PCA group; none of these interactions could be accounted for by any of the behavioural correlates.There was no evidence of a group difference in overall reading accuracy after adjusting for participants’ composite scores of the following covariates of interest: visuoperceptual, visuospatial or early visual function, or the A cancellation task; these scores were better predictors of reading accuracy than diagnosis whether included individually or simultaneously in a regression model. The following nuisances variables, including markers of disease severity (MMSE scores, disease duration), nonvisual indicators of executive function (digit span backwards) or single letter recognition performance could not account for group differences in accuracy. This suggests that the between-group differences in overall accuracy were driven particularly by poor early visual, visuoperceptual and visuospatial abilities.Given the possible role of crowding in limiting reading ability (Crutch & Warrington, 2009; Yong et al., 2013), we conducted a post hoc analysis evaluating the extent to which crowding measures accounted for the group difference relative to other measures of early visual processing. A composite (labelled Early visual processing [excluding crowding]) was calculated with the omission of the crowding task score; unlike the composite score for Early visual processing which included measures of crowding, this composite did not account for the between-group difference. In addition, a likelihood ratio test demonstrated that including the crowding measure resulted in an improved fit of the model to the data relative to other measures of early visual processing (Figure-ground discrimination, Shape discrimination), while including measures of early visual processing other than crowding did not improve how well the model fitted the data.Between-group latencyDifferences in latency were modelled using a mixed-effects linear regression analysis of latency data for the PCA and tAD groups including as covariates reading variables that were significant at the group level for either PCA or tAD groups (spacing, size, order, AoA). There was no evidence of a group difference in overall reading speed after adjusting for participants’ composite scores on tests of visuoperceptual function. None of the nuisance variables (disease duration, composite scores, MMSE, digit span backwards, A cancellation, single letter processing tasks) could account for group differences in overall reading latency.Individual differences in accuracy and latencyThere was a great degree of variability in reading accuracy within the PCA group (range: 19.8% to 99.5%). 23/26 (88.5%) of the PCA patients performed below the 5th%ile of the control group’s accuracy and latency data. Of the three patients whose reading ability was within the normal range of the control group, one patient’s performance could be attributed to his relatively mild visual symptoms and short disease duration. However, the other two PCA patients’ performance (FOL & CLA) was achieved despite showing grave deficits in almost all measures of visual processing, including some additional single letter processing tasks, suggesting these types of visual processing do not critically determine reading ability (see chapter REF _Ref371599193 \r \h 6 for a detailed analysis of FOL and CLA’s reading ability and visual impairment). Error analysisAn analysis of PCA error types revealed 68.9% visual errors, 19.3% miscellaneous errors, 9.6% phonological errors and 2.1% derivational errors. In 23/26 participants the most common errors were visual errors; the other three participants only made one error each, with one making a phonological error and the other two making derivational errors. Within the 23 participants making visual errors, the highest proportions of any other single error type were observed in the following patients: Participant 8: 57 miscellaneous vs 71 visual errors; Participant 5: 15 phonological vs 30 visual errors; Participant 4: 3 derivational vs 18 visual errors.Of the visual errors, 52.2% of letters read incorrectly were substitution errors, 23.6% were deletion errors and 24.2% were addition errors. 17.2% of visual errors were neglect errors (Ellis et al., 1987). Participant 15 made the most errors in the left (n=7) relative to the right (n=1) side of words, while Participant 24 made the most errors in the right (n=12) relative to the left (n=3) side of words.Cursive font readingThe PCA group (N=22) made, on average, more errors reading words in cursive than non-cursive font (cursive: Mean=68.6%, SD=32.4; non-cursive: Mean=89.3%, SD=15.8: z=-3.71, p<.0005). The tAD group scored too near ceiling to reveal any such differences (cursive: Mean=96.1%, SD=7.3; baseline: Mean=97.1%, SD=5.0: p>.8). The PCA group was significantly worse than the tAD group reading cursive font (z=3.29, p<.005).Neuroimaging findingsNeuroanatomical associations of reading performance in the PCA group are shown in REF _Ref374542135 \h Figure 5.3. In order to identify grey matter associations with reading ability, accuracy discrepancy scores between levels of reading variables which significantly predicted overall reading accuracy in PCA (Large vs Small, Spaced vs Unspaced, High vs Low AoA, High vs Low Frequency) were used as behavioural indices. In the PCA group, a greater inverse size effect (lower accuracy for reading large rather than small font size words)was associated with lower grey matter volume in t the right superior parietal lobule after correcting for multiple comparisons over whole brain volume (p<.05). There was no evidence of statistically significant associations between grey matter volume and the other three variables tested (spacing, age of onset, frequency) in this group.-75565275590Figure 5. SEQ Figure_5 \* ARABIC 3 Statistical parametric maps of grey matter volume associated with the difference in accuracy between large and small words in the PCA group. The statistical parametric maps are displayed on coronal (A), sagittal (B) and axial (C) sections of the mean normalized bias-corrected images in MNI space; the right hemisphere is shown on the right on coronal and axial sections. Whole-brain analysis found that, within the PCA group, a greater discrepancy in accuracy between large and small words was associated with reduced grey matter volume in the right superior parietal lobule: t-values are displayed below (p<.001 uncorrected) with the FWE corrected (p<.05) peak circled in blue (peak location: x=18, y=-75, z=44). The colour bar shows the t value.DISCUSSIONThis chapter aimed to better characterise single word reading in PCA and understand the relationship between reading and other visual processes by examining reading of words in which inter-letter spacing, font size, length, font type, case and confusability were varied systematically. On average, the PCA group was considerably less accurate and slower than the tAD or healthy control group, with the tAD group demonstrating slower but not significantly less accurate performance than controls. PCA reading accuracy was predicted by the perceptual variables of letter spacing, size and length plus the lexical variables of age of acquisition and frequency. Similarly, PCA reading speed was predicted by letter spacing, size and age of acquisition. The perceptual complexities of cursive font also had an adverse effect on PCA reading performance whilst overall case and confusability effects were not detected. In contrast, no perceptual variables were predictive of reading accuracy in the tAD or control groups (with high or ceiling level performance in most individuals). Letter spacing, age of acquisition and word order were the only variables which predicted reading speed in both tAD and control groups. A further prominent difference between the PCA and tAD groups was the direction of the size effect. Increasing font size significantly reduced accuracy and/or slowed reading for half the PCA participants (50%), whilst larger text improved reading speed overall in the healthy control group and for the minority of tAD participants who showed a size effect (18%). VBM whole-brain analysis within the PCA group found that this size effect (less accurate reading of large than small font size print) was associated with lower grey matter volume in the right superior parietal lobule. The impact of perceptual variables on reading performance and preponderance of visual errors (69%) are unsurprising given that visual impairment is the defining feature of the PCA syndrome. Of greater neuropsychological interest is the determination of which aspects of visual processing are associated with this pattern of reading dysfunction, and the interaction between these processes and text manipulations employed in the current investigation. We attempted to evaluate which behavioural covariates (including those derived from the detailed visual assessment) might contribute towards reading dysfunction by accounting for the discrepancy in performance between the PCA and tAD groups. PCA patients’ inferior reading accuracy relative to tAD patients could not be accounted for by generic markers of disease severity (MMSE, disease duration) but was significantly associated with performance on all three visual covariates (early visual, visuoperceptual and visuospatial processing). However the early visual processing covariate only predicted accuracy when this composite score included a measure of visual crowding. By contrast, only poor visuoperceptual ability could account for PCA patients’ increase in reading latencies relative to tAD patients. The specific effects of letter spacing and size also could not be accounted for by any of the behavioural covariates, suggesting it is the combination of visual deficits at multiple levels of the visual system which give rise to the observed and distinctive pattern of reading seen in PCA. Before considering the overall classification of reading impairment in PCA, we discuss possible explanations for the considerable impact firstly of letter spacing and secondly of font size upon patients’ reading of the current set of perceptually manipulated words. First, letter spacing was included as one of the perceptual text manipulations in the current investigation because previous case studies had shown its influence upon both single letter and word identification (Crutch and Warrington, 2009). This study revealed optimal letter spacing is partially task dependent. With flanked letter identification, performance was significantly improved by inserting 2 spaces between letters (mean centre-to-centre spacing = 1.52?) as compared with normal presentation text (0 spaces; mean centre-to-centre spacing = 0.86?). With word-reading a U-shaped function was obtained; performance improved when inter-letter spacing was increased from 0.78? to 1.21?, an effect attributed to a reduction in crowding, but declined again when spacing increased to 2.27?, because increasing spacing past a given point damages whole-word form and parallel letter processing. In the current investigation, values of 0.86? (unspaced) and 1.52? (spaced) were selected to maximise individual letter identification ability. However the results, which show significantly worse PCA reading performance in the spaced condition, suggest that any benefits in reduced crowding of individual letter identities was outweighed by inevitable increases in the visual angle subtended by the outmost letters within perceptually longer words. Nonetheless, PCA patients showed significantly greater spacing effects than the tAD or control groups, raising questions about the mechanism underpinning the ability to read spatially distributed words. It has been proposed that failure to achieve parallel letter processing due to presentation of text in unfamiliar formats invokes involvement of dorsally-mediated reading strategies such as serial letter scanning (Hall et al., 2001; Braet & Humphreys, 2007). Reading words with increased inter-letter spacing has been associated with the engagement of parietal lobes in healthy individuals (Cohen et al., 2008), and double spacing has been found to disrupt reading in a patient with occipitoparietal lesions (Vinckier et al., 2006). It is possible that reading of spaced words in PCA patients demands support from dorsally-mediated reading strategies and involves greater visuospatial demands; if so, the vulnerability of dorsal systems in PCA (e.g. McMonagle et al., 2006; Lehmann et al., 2011) would help to account for these patients’ particularly poor reading ability for spaced words. The failure of dorsal-parietal systems in reading unfamiliar text may also contribute towards disproportionately poor reading of cursive font in PCA (chapter REF _Ref372465172 \r \h 5.3.2; De Renzi, 1986), especially as difficult-to-read handwriting has been shown to activate parietal networks in healthy individuals (Qiao et al., 2010), and may account for previous reports of poorer reading of words of greater perceptual complexity (Mendez & Cherrier, 1998; Mendez, 2001). Another possibility is that impaired reading of words with increased inter-letter spacing (or in cursive font) might result from a ventral deficit, possibly a disrupted word-form system, which could accommodate word processing under familiar but not unfamiliar presentation.Turning secondly to the impact of font size, the PCA group’s better reading performance with small rather than large words was not only counter-intuitive but also in direct contrast to size effects seen overall in the control group and in a small number of tAD patients. This size effect may be attributable to what has been termed a (spatial) restriction in the effective visual field, which occurs in right-brain-damaged individuals when the processing demands of more centrally-presented stimuli/tasks exhaust available attentional capacity (Russell et al., 2004, 2013). In the current task, though matched for overall form, large font words extend further into the periphery than small print words. (This is also the case for spaced as compared with unspaced words as varied in the inter-letter spacing condition). As noted above, grey matter volume analysis in the PCA group found an association between the discrepancy in accuracy between large and small words and grey matter volume in the right superior parietal lobule. This localisation is in keeping with previous studies of peripheral spatial attention. Parieto-occipital damage has been associated with reduced perception and localisation within the visual periphery (Michel & Henaff, 2004; Rossetti et al., 2005; Pisella et al., 2009), and greater activation in the superior parietal lobule has been found for stimuli in peripheral vision which were actively attended during an orientation discrimination task (Vandenberghe et al., 1996) or when participants shifted attention towards peripheral vision relative to maintaining attention at fixation (Corbetta et al., 1993). A potentially complementary explanation of the size effect in PCA is that reading larger words increases the demand for multiple saccades, spatial shifts in attention and/or visuospatial ability. fMRI studies have identified saccade-related activation in the superior parietal lobule (Sereno et al., 2001; Medendorp et al., 2005; Merriam et al., 2003), while the superior parietal cortex has been associated with shifting rather than sustained attention (Molenberghs et al., 2007; Kelley et al., 2008; Vandenberghe et al., 2001a), and has been suggested as a possible anatomical locus for visuospatial attention along with the supramarginal gyrus and intraparietal areas (Pierrot-Deseilligny et al., 2004). As previous studies have identified reaching, perceptual and localisation deficits in the peripheral vision of superior parietal lobule lesion patients maintaining central fixation (Wolpert et al., 1998; Pisella et al., 2009; Rosetti et al., 2005), it is unlikely that deficits in integrating information across multiple saccades can completely account for the inverse size effect. Beyond the impact on single word recognition in PCA, the inverse size effect documented in these patients also has implications for reading at and above the sentence level. Any restriction in the effective visual field would limit the perceptual span and parafoveal preview benefit (Hyona et al., 2004; Rayner, 1998; McDonald, 2006) and might inhibit the ability to move between consecutive lines of text, as has been previously observed in PCA (Ross et al., 1996) and in a patient with Balint’s syndrome (Michel & Henaff, 2004). An interesting comparison group is patients with retinitis pigmentosa, a condition involving a progressive pigmentary degeneration of the retina, often resulting in restricted central area of vision, or “tunnel vision” (Madreperla et al., 1990). Increased reading speed has been observed in patients with retinitis pigmentosa when reading words of reduced font size (Sandberg et al., 2006) and words presented in negative polarity, i.e. white text on a black background (Ehrlich, 1987). Reverse polarity presentation may be a particularly promising manipulation, given its ameliorating effect on crowding in both PCA patients and healthy individuals (Crutch & Warrington, 2007a; Crutch & Warrington, 2009; Kooi et al., 1994; Chakravarthi & Cavanagh, 2007). Presentation methods that reduce the need for visuospatial processing in reading, such as rapid serial visual presentation or horizontally scrolling text (Leff & Behrmann, 2008) may be also beneficial in limiting visual disorientation.CHAPTER CONCLUSIONSThe current findings suggest that not one but a combination of deficits are associated with the acquired peripheral dyslexia observed in PCA. Overall, poor reading accuracy is associated with deficits in early visual processing, particularly including enhanced visual crowding, and poor visuoperceptual and visuospatial ability. However, these deficits are not causally related to a universal impairment of reading (as shown by preserved reading for small, unspaced words in some patients) but rather are (con)text specific (being particularly evident for large, spaced or crowded lengthy words). The vulnerability of dorsal systems in PCA may account for disproportionate difficulties reading text which eludes ventrally-mediated parallel letter processing; that is, words written in unfamiliar formats, such as text with double spacing or cursive font. Poor visuospatial ability and restrictions in the effective visual field as a consequence of parietal atrophy may also explain the inverse size effect. The profile of reading impairment in PCA does not align with any classical subtypes of peripheral dyslexia (e.g. pure alexia, neglect dyslexia), underlining why previous investigators have coined the term “apperceptive alexia” to capture the combination of contributory deficits (Mendez et al., 2007). However, further to the suggestions of Mendez et al. (2007) that apperceptive alexia might be attributable to visuoperceptual and visuospatial deficits, the current findings also indicate the role of early visual processing deficits, particularly enhanced visual crowding, in contributing towards poor reading. CASE STUDIES: INTACT READING IN PCACHAPTER INTRODUCTIONWhile the previous chapter demonstrated the prevalence of reading impairment in PCA, it also identified the heterogeneity of reading performance (see chapter REF _Ref371599585 \r \h 5.3.1.3). Interestingly, two individuals with PCA (FOL & CLA) exhibited remarkably preserved whole word and letter reading despite demonstrating profound visual deficits on a range of neuropsychological tests. Patterns of reading ability in FOL and CLA provide a means to evaluate whether impairments in specific domains of visual processing domains necessitate reading dysfunction. Whether visual processing deficits play a causal role in acquired dyslexia has implications for different accounts of LBL reading. A classic view of LBL reading is that it indicates destruction or inaccessibility of a visual word form system (Warrington and Shallice, 1980). Subsequent imaging studies have identified an area within the left fusiform gyrus which is specialised for letter and word recognition and which may constitute the visual word form area (VWFA; Cohen et al., 2000). This area has been found to selectively respond to words rather than objects of matched visual complexity (Szwed et al., 2011); it invariantly responds to letters in upper and lower case (Dehaene et al., 2004), printed and handwritten words (Qiao et al., 2010) and damage to this area often results in LBL reading (Binder and Mohr, 1992; Leff et al., 2001; Cohen et al., 2004; McCandliss et al., 2003; P?ugshaupt et al., 2009). More recently, the attribution of LBL reading to a specific word form deficit has been challenged on two main grounds, namely that the condition and its characteristic word length effects can be accounted for by a general visual deficit and/or a letter identification deficit.A general visual account of LBL reading suggests that reading, as a complex behaviour, can be disrupted by even the most subtle low-level visual deficits (Friedman and Alexander, 1984; Farah and Wallace, 1991; Price and Devlin, 2003), which propagate by a cascade process to the level of lexical and semantic representations within the visual system (Behrmann et al., 1998). A number of single case and case series studies of LBL readers have reported associated impairments on a range of perceptual tasks involving non-orthographic stimuli. For example, Friedman and Alexander (1984) identified a LBL patient who was impaired on tasks of letter identification, object recognition and had an elevated threshold relative to controls in detecting briefly presented pictures. Furthermore, Farah and Wallace’s (1991) patient TU performed poorly on tasks involving the perception of non-orthographic stimuli under time constraints; these results were replicated by Sekuler and Behrmann (1996). More recently, Mycroft et al. (2009) found that seven LBL readers were similarly impaired for both linguistic and non-linguistic stimuli on tasks of visual search and matching, and the LBL group as a whole performed worse than the control group on a task of visual complexity. By contrast, there are documented cases of LBL readers with no discernible impairment in letter identification speed or the identification of rapidly displayed letters (Warrington and Langdon, 2002; Rosazza et al., 2007) or in a range of tasks assessing visual processing, such as complex picture analysis, visual short term memory and picture recognition from unusual views (Warrington and Shallice, 1980). However, proponents of pre-lexical theories of LBL reading tend to dismiss such cases as reflecting insufficiently sensitive assessment of visual processing skills or the use of non-reading tasks which are not making demands comparable to those involved in reading (Behrmann et al., 1998; Patterson, 2000).Alternative accounts attribute LBL reading to an impairment of letter activation. Some accounts suggest that the critical letter processing deficits may be restricted to the identification of individual letters (e.g. Arguin and Bub, 1992, 1993; Reuter-Lorenz and Brunn, 1990; Behrmann and Shallice, 1995). Other accounts ascribe LBL reading to a deficit in the mechanisms responsible for rapid, parallel processing of letters, leading to the less efficient serial encoding of the component letters of a word (Patterson and Kay, 1982; Behrmann et al., 2001; Cohen et al., 2003). One such possible mechanism is the inability to use the optimal spatial frequency band for letter and word recognition, with letter confusability effects emerging at lower spatial frequencies (Fiset et al., 2006). It should also be noted that some authors have argued that deficits in letter processing are common to all LBL readers, while speculating that such deficits may be due to a more basic visual impairment (Behrmann et al., 1998).One observation regarding the general visual account of LBL reading is that the evidence base is largely associative in nature; that is, most studies claim that the co-occurrence of the characteristics of LBL reading (i.e. accurate but slow reading, with prominent word length effects) and a particular deficit (e.g. impaired perception of non-lexical stimuli) confers support for their chosen position. In addition, proponents of the general visual impairment account have claimed support for their position from control brain-damaged patients who show the complementary association of no perceptual deficit and no impairment of reading (e.g. patient OL; Mycroft et al., 2009). By contrast, in the current study it is argued that such evidence does not prove a causal link between general visual deficits and LBL reading behaviour. This is achieved by presenting evidence from two patients who exhibit profound visual dysfunction in the presence of accurate and rapid word reading. Rather than demonstrating a selective impairment to the visual word form system in the absence of general visual dysfunction, these patients’ reading abilities are remarkably preserved despite grave and diffuse impairments to their visual system.The main aim of this study was to evaluate the hypothesis that general visual dysfunction necessarily leads to LBL reading. The general visual account predicts that basic visual impairments should be associated with slow, inefficient reading, with prominent word length effects characterised by considerable increases in reading latency with each additional constituent letter. Contrary to these predications, we report two PCA patients who demonstrate highly accurate and rapid reading with equivocal or absent word length effects despite profound visual dysfunction. This preservation of reading skills was observed despite significantly impaired performance on non-lexical chequerboard perception and rapid serial visual letter presentation tasks, failure on which has previously been linked to LBL reading by proponents of the general visual accounts. The reported distinction between intact reading and impoverished visual function raises questions as to whether the evidence cited for general visual accounts of LBL reading truly reflects causation, or merely the association of deficits elicited by damage to contiguous brain regions.METHODSParticipantsStudy participants were selected for the current study following the observation of visuoperceptual and visuospatial impairment but preserved performance on a screening test for reading (CORVIST- see background neuropsychology) and when reading small, unspaced words (see chapter REF _Ref371599585 \r \h 5.3.1.3). FOL is a 58 year old right-handed retired administrator for the NHS who was referred to the Specialist Cognitive Disorders Clinic at the National Hospital of Neurology and Neurosurgery in 2010 with a 4-year history of progressive visual impairment. When seen at clinic she described “looking but not being able to see”, with early symptoms of visual dysfunction including difficulty in locating objects in front of her and problems reading clocks. FOL fulfilled the PCA behavioural criteria (failing tests of arithmetic and spatial and object perception) but her spelling was well preserved. Her memory ability, while not robust, was still within normal limits. Her general neurological examination was normal. Brain MRI (see REF _Ref371603546 \h Figure 6.1) showed predominantly biparietal atrophy somewhat more marked on the right with relative preservation of the hippocampi, medial temporal lobe structures and no significant vascular burden. CLA is an 86 year-old right-handed retired classics teacher who was first seen at the National Hospital in January 2011 as part of a clinical assessment. Presenting symptoms included being unable to judge depth and movement and failing to see objects in front of her. CLA fulfilled the PCA criteria, failing tests of spatial and object perception, but spelling and arithmetic were well preserved and she demonstrated strong performance on a test of verbal memory. Her general neurological examination was normal. Brain MRI (see REF _Ref371603546 \h Figure 6.1) Figure 6. SEQ Figure_6. \* ARABIC 1 Neuroanatomical features in FOL and CLA: representative brain MRI sections for each patient show the distribution of atrophy in each case. The left hemisphere is shown on the left for all coronal and axial sections (left panels in each case). Sagittal sections (right panels in each case) are through the left (Lh) and right (Rh) cerebral hemispheres. For purposes of comparison with previous functional imaging studies of the visual word form area (VWFA), these brain volumes have been transformed into MNI standard streotactic space; the white arrow indicates the mean activation peak of the VWFA (x=-44, y=-58, z=-15) constituted from 17 functional imaging studies (Jobard et al., 2003). revealed bilateral atrophy of both posterior cerebral hemispheres, more prominent on the right with anterior extension into bilateral peri-Sylvian cortices and the inferior and medial right temporal lobe but relative sparing of the left inferior temporal lobe; additional mild frontal lobe atrophy was evident bilaterally, and there was a mild to moderate degree of small vessel ischaemic damage. The distribution of brain atrophy in FOL and CLA was compared with the location of the visual word form area (VWFA ) in the left posterior fusiform gyrus, determined from previous functional imaging studies (Jobard et al., 2003). There was sparing of grey matter in the region of the VWFA relative to more dorsal brain regions (FOL) and the contralateral temporal lobe (CLA) .Nine control participants completed all tasks administered to the PCA patients. The controls were split into two groups appropriate for each patient, matched as closely as possible for age, gender and years of education (FOL controls [N=4]: mean age 58.4yrs [range 56-60], all female, mean education: 16 yrs; CLA controls [N=5]: mean 83.5yrs [range 81-84], all female, mean education: 14.8 yrs).Background neuropsychologyIn addition to the behavioural screening tests, CLA and FOL completed a battery of background neuropsychological tests. Their scores on each task and an estimate of their performance relative to appropriate normative data sets are shown in REF _Ref371605347 \h Table 6.1. On the Mini Mental State Examination (MMSE), FOL performed below the normal range. She performed well on tests of concrete synonyms, cognitive estimates and naming, and her praxic skills were only mildly impaired to verbal command. She made no errors on a screening test for reading and one error on a nonword reading task. CLA performed within the normal range on the MMSE. Her concrete synonym comprehension performance was within normal limits but she was impaired on tests of cognitive estimates and naming. CLA had some difficulties on a test of praxic skills, specifically in pantomiming using a toothbrush and hammer. CLA made no errors on a screening test for reading and three errors on a nonword reading task.Experimental proceduresVisual assessmentFOL and CLA were administered measures of early visual, visuoperceptual and visuospatial processing (see chapter REF _Ref371605620 \r \h 3.4.2). In addition, both FOL/CLA and their respective control groups TestMax ScoreRaw Score??FOLCLANorms/commentBackground NeuropsychologyMMSE 1302427FOL: ImpairedShort Recognition Memory Test2 for words*(joint auditory/visual presentation)252124Within normal rangeConcrete Synonyms test3252024Within normal rangeNaming (verbal description)201911CLA: <1st %ile; FOL: normal limitsCognitive estimates4 (error score)30117CLA: <1st %ile; FOL: normal limitsCalculation (GDA5)*2408FOL: <1st %ile; CLA: normal limitsSpelling (GDST6- Set B, first 20 items)*201819Within normal rangeGesture production test715149-Digit span (forwards)12910FOL: 25th-50th %ile; CLA >50th %ileMax forwards877Digit span (backwards)1245Within normal range?Max backwards734CORVIST8 reading test161616-Visual AssessmentEarly visual processingVisual acuity (CORVIST): Snellen6/96/96/18CLA: near-normal; FOL: normalFigure-ground discrimination (VOSP9)201714<5th %ileShape discrimination10Easy (oblong edge ratio 1:1.63)201920Healthy participants with normal vision make no errors on difficult versionModerate (oblong edge ratio 1:1.37)201919Difficult (oblong edge ratio 1:1.20)20914Hue discrimination (CORVIST)422ImpairedVisuoperceptual processingObject Decision (VOSP)*20157CLA: <5th%ile; FOL: 10th-25th%ileFragmented letters (VOSP)2050<5th %ileUnusual and usual views11: Unusual2050<1st %ileUnusual and usual views11: Usual201810<1st %ileVisuospatial processingNumber location (VOSP)*1055<1st %ileDot counting (VOSP)10710FOL: <5th%ile; CLA: normal limitsA Cancellation12: Completion time90s60s50<5th %ileA Cancellation12: Number of letters missed1910-Graded nonword reading test13252422-Table 6. SEQ Table_6. \* ARABIC 1 Neuropsychological scores of FOL/CLA relative to normative data*Behavioural screening tests supportive of PCA diagnosis. 1 Mini-Mental State Examination (MMSE: Folstein, Folstein & McHugh, 1975). 2 Warrington (1996). 3 Warrington, McKenna and Orpwood (1998). 4 Shallice and Evans (1978). 5 Graded Difficulty Arithmetic test (GDA; Jackson & Warrington, 1986). 6 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 7 Crutch (unpublished). 8 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001). 9 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 10 Efron (1968): oblong edge ratio 1:1.20. 12 Warrington and James (1988). 11 Willison and Warrington (1992). 13 Snowling et al., (1996).completed an experiment involving 24 chequerboard patterns developed by Ichikawa (1985) and employed in previous investigations of pure alexia (Mycroft et al., 2009). Chequerboards were composed of either 3×3 or 4×4 grids with the height/width of individual grid squares being kept constant (subtending 0.5° of visual angle at a viewing distance of 50cm). Each chequerboard comprised a pattern of white and black squares, constructed so as to avoid obvious patterns and many squares of the same colour being adjacent to one another (see REF _Ref376252666 \h Table 6.4) Each chequerboard pattern was paired once with itself and once with another pattern that differed by a single square. This produced a total of 48 pairs, with each pair consisting of chequerboards being presented one above the other at the centre of the screen. Each pair of chequerboards was preceded by a fixation point presented for 1000ms.Participants were asked to decide whether the chequerboards in each pair were the same or different as quickly and accurately as possible by verbal response. The pairs remained on screen until a response was given and there was a 1000ms inter-trial interval. One block of 6 practice trials preceded 2 blocks of 24 test trials. Each block contained an equal number of 3×3 and 4×4 chequerboards.Word readingAll participants were requested to read aloud 3 corpora yielding a total of 250 words. Each corpus was as follows:Brown and Ure words (Brown & Ure, 1969): 72 words taken from the Brown and Ure (1969) corpus, which was composed of a subset of words at three levels of length (4, 6 and 8 letters) matched on two levels of frequency and two levels of concreteness.Schonell reading list (Schonell & Goodacre, 1971): 100 words of decreasing frequency, ranging in length from 3 to 14 letters.Coltheart regular/irregular words (Coltheart et al., 1979): 39 pairs of regular and irregular words ranging from 3 to 8 letters long, matched for word frequency (Kucera & Francis, 1967), concreteness, part of speech and number of letters, syllables and morphemes.All words were presented in Arial Unicode MS for an unlimited duration within a rectangular fixation box at the centre of the screen; letter height corresponded to a visual angle of 1.2° from a viewing distance of 50cm.Single letter processingA series of letter processing tasks were administered, with all stimuli presented within a central fixation box to ameliorate the effects of visual disorientation:Letter naming - All participants were requested to read the letters of the alphabet, excluding I, J, O, Q, W and X, in upper case. Letter height corresponded to a visual angle of 1.2° from a viewing distance of 50cm.Rapid serial visual presentation (RSVP) letter/number identification - Letter strings of six letters each were presented serially in the same central spatial position, without an interval between successive letters, as described by previous studies in LBL reading (Warrington and Langdon, 2002; Behrmann and Shallice, 1995). There were three exposure durations of 150, 200 and 250 ms per letter; all participants were tested in nine blocks of 10 strings, with three blocks at each of the three durations arranged in a Latin square design. Before the presentation of each letter string, a target letter was named; participants were asked to decide whether the target letter was present in each string. The target item occurred randomly in positions two to five in each string, with the target item being present in half of all trials. In a subsequent experiment, a similar test was administered using Arabic numeral strings rather than letter strings. The number of trials was halved, resulting in nine blocks of 5 strings. Flanked letter identification - All participants were requested to name letters under letter, shape and number flanker conditions. Flanked letter stimuli were the same as in chapter 4 (see tasks 2-4: REF _Ref376251080 \h Figure 4.1).Data analysis Reading latency analysisSee chapter REF _Ref371594131 \r \h 3.5 for details of latency recording and determination. Analyses of the Brown and Ure (1969) and Schonell (Schonell and Goodacre, 1971) corpora were conducted using multiple linear regression, as neither FOL nor CLA made enough errors to allow the use of a logistic regression model. The regression model was used to relate response latencies to the effects of frequency and length. Overall regression analysis was conducted using a linear mixed model, which was fitted to reaction times with random subject and item effects and fixed effects of length, diagnosis, their interaction and parisons between both patients and their matched control groups were conducted using a modified t-test developed by Crawford and Garthwaite (2002) specifically to identify abnormality of test scores in single case studies. Comparisons between differences in a patient’s scores on two tasks and differences between the control groups’ performance on the same two tasks were conducted the Revised Standardised Difference Test (RSDT) developed by Crawford and Garthwaite (2005). All reported p values represent one-way probability.RESULTSVisual assessmentThe results of patients FOL and CLA on each early visual, visuoperceptual and visuospatial processing task are shown in REF _Ref371605347 \h Table 6.1, together with the corresponding normative data. FOL failed every single early visual, visuoperceptual and visuospatial task administered with the following two exceptions: visual acuity and Object decision. On the chequerboard Experiment, FOL exhibited significantly poorer performance than controls (t=-32.7, p<.001) on 3×3 and 4×4 chequerboards (15/24 v 14/24, respectively) and disproportionately identified chequerboards as being the same (96%) rather than different (25%) (d prime score=1.057).CLA was also impaired on all tests of early visual processing except for only mild weakness on a test of visual acuity. She was also impaired on all visuoperceptual tasks and all but one visuospatial task (Dot counting). On the chequerboard experiment, CLA exhibited significantly poorer performance than controls (t=-27.7, p<.001) on 3×3 and 4×4 chequerboards (16/24 v 15/24, respectively) and was more likely to identify chequerboards as being the same (71%) rather than different (58.5%) (d prime score=.759).Word readingThe total (and percentage) correct responses and mean (and SD) reading latency data for word reading performance by FOL, CLA and their relevant control samples are shown in Table 6.2. Brown and Ure words - FOL made no error responses, while her control group made one error overall. There was no significant difference between FOL’s response latencies and those of the control group. Regression analysis found a significant effect of length (t=2.2, p<.05), but not of frequency (t=-.89, p>.3) or concreteness (t=-1.54, p>.1) on FOL’s response latencies. When examining control responses at the group level, neither frequency nor length was significantly related to response latencies, although length was related to response latencies in one individual control. Neither CLA nor her control group made any error responses. There was no significant difference between CLA and her control group’s response latencies. Regression analysis found no significant effects of length, frequency or concreteness on the response latencies of CLA or her controls. Schonell reading list - FOL made three error responses; two of these were regularisation errors (colonel, homonym), with the remaining error being a visually-based neologism (ineradicableinerascible). The control group overall made three errors. FOL showed a trend towards being less accurate and having longer latencies relative to controls; however, neither of these effects reached formal levels of significance. Regression analysis found a significant effect of length but not of frequency on response latencies for FOL (t=4.01, p<.001) and at the group level for her matched controls (t=4.18, p<.001).CLA again made no error responses; the control group made a total of five errors between 3 participants. There was no significant difference in response accuracy between CLA and her control group. When examining response latencies, CLA was significantly slower than controls. Regression analysis found a significant effect of length but not of frequency on response latencies for both CLA (t=2.11, p<.05) and, at the group level, her matched controls (t=5.4, p<.001).Coltheart regular/irregular words - FOL made only one visual error response reading irregular words (GAUGEGAUCHE). The control group made no errors; consequently it was not possible to use a modified t-test for error analysis. There was no significant difference between FOL and her control group in the size of regularity effect (Revised Standardized Difference Test: t=0.4, p>0.4). Neither CLA nor the control group made any errors. CLA’s response latencies were significantly longer than those of controls for both regular and irregular words. The Revised Standardized Difference Test identified CLA as being significantly slower for irregular than regular words relative to her control group (t=5.1, p<0.005).Table 6. SEQ Table_6. \* ARABIC 2 Accuracy and latency data for FOL, CLA and relevant control groups on the word reading experiments.??Reading Skills??FOLControl GroupdifferenceCLAControl Groupdifference1. Brown and Ure wordsTotal correct72/72(100%)71.8/72 ± .4(99.7% ±.6)-72/72(100%)72/72(100%)-RT.60 ± .11.51 ± .04t=1.9, p=.08.64 ± .12.57 ± .06t=1.2, p>.12. Schonell wordsTotal correct97/100(97%)99.3/100 ± 1.0(99.3% ± 1.0)t=-2.1, p=.063100/100(100%)99/100 ± 1.2(99% ± 1.2)t=2.8, p<.05Mean rt.72 ± .22.54 ± .07t=2.2, p=.056.78 ± 0.31.60 ± .06t=2.8, p<.053. Coltheart wordsTotal correct77/78 (98.7%)78/78(100%)-78/78(100%)78/78(100%)-RT (Regular).54 ± .08.48 ± .04t=1.2, p>.1.72 ± .34.53 ± .05t=10.5, p<.001RT (Irregular).59 ± .14.51 ± .05t=1.3, p>.1.92 ± .81.55 ± .05t=10.5, p<.0011Brown and Ure (1969). 2Coltheart et al. (1979). 4Schonell and Goodacre (1971).Overall reaction time and word length analysis - Reading latencies for words of up to 12 letters, summing across the 3 reading corpora, are shown in REF _Ref371610486 \h Figure 6.2. When examining the response latencies of FOL and her control group, there was a main effect of length (z=2.5, p<.05) but not diagnosis (p>.3). There was a significant interaction between diagnosis and length (z=2.3, p<.05). However, there was significant variation in the size of word length effect within the control group; this was demonstrated by fitting the same model to the control data, plus a second model extended to allow length effects to vary by control participant. Comparison of the two models by a likelihood ratio test identified a highly significant difference in length effects between controls (p<.0001).When examining reading latencies of CLA and her control group, there was a main effect of length on reading latencies (z=3.1, p<.005), but only a trend towards a main effect of diagnosis (z=1.9, p=.06). There was no interaction between diagnosis and length (p>.2). 121920-128270Figure 6. SEQ Figure_6. \* ARABIC 2 Mean reading latencies for words of different length across all corpora for (A) Patient FOL and her matched controls, and (B) Patient CLA and her matched controls, with estimated upper and lower control confidence intervals.Single letter processingThe total (and percentage) correct responses and mean (and SD) latency data for letter processing performance by FOL, CLA and their relevant control samples are shown in REF _Ref376252333 \h Table 6.3.Letter naming - Neither FOL nor her control group made any error responses. There was no significant difference between FOL’s reading latencies and those of her control group. Neither CLA nor her control group made any error responses. However, CLA was significantly slower than her control group. Rapid letter/number identification: Letters – Overall letter identification was significantly lower for FOL than her controls; this overall effect reflected significantly lower performance when stimuli were presented for 150ms but not 200 or 250ms. CLA also made significantly more errors overall, and specifically when stimulus duration was 150ms or 250ms but not 200ms.Numbers – Overall, FOL scored significantly lower than her control group. This difference was significant for numbers being displayed for 150ms, but ceiling effects in the other temporal conditions prevented analysis using a modified t-test. There was no significant difference between CLA and her controls for stimuli at any of the tested exposure durations.Flanked letter identification – See REF _Ref374542319 \h Figure 6.3 for FOL and CLA’s naming latencies. Neither FOL nor her control group made any errors on the flanked letter identification tasks. Summing across all conditions, FOL was slower than her control group. Target-flanker spacing had a significant effect on response latency in only one flanker condition, where target letters were named slower with spaced than condensed number flankers (z=-2.2, p<.05). There was a trend towards there being an interaction between flanker condition and spatial condition (t=1.9, p=0.08). As with FOL, neither CLA nor her control group made any errors. Summing across all conditions, CLA was slower than her control group. Target-flanker spacing had a significant effect upon response latency in one flanker condition, where target letters were named slower with condensed than in spaced letter flankers (z=2.0, p<.05). There was also one main effect of flanker type, with CLA’s responses in the letter flanker condition significantly slower than in the number flanker condition (z=2.5, p<.05). Overall, there was a significant interaction between the group x spacing condition, with target letters being named more slowly with condensed rather than spaced flankers relative to controls (t=7.5, p<.001).Table 6. SEQ Table_6. \* ARABIC 3 Performance on tests of letter processing.??Letter Identification?Example stimuli?FOLControl GroupDifferenceCLAControl GroupDifferenceSingle letter readingTotal correct20/20(100%)20/20(100%)-20/20(100%)20/20(100%)-89979572390Mean RT.59 ± .09.48 ± .06t=1.5, p>.1.82 ± .17.56 ± .04t=5.4, p<.005Temporal MaskingTotal correct25/35 (71.4%)31.5/35 ± .6 (90% ± 1.6)t=-10.1, p<.00522/35 (62.9%)30.6/35 ± .9 (87.4% ± 2.6)t=-8.8, p<.00138671570485Recognition threshold62ms16ms_62ms22ms ± 8.8_Rapid Identification: letters150ms25/2928.5/30 ± .60t=-3.9, p<.052527.8/30 ± .46t=-5.5, p<.005200ms28/2928.25/30 ± .78t=.8, p>.22728.2 ± .74t=-1.5,p >.1250ms28/2928.25/30 ± .78t=.4, p>.32628.8 ± .42t=-6.1,p<.005Total correct82/88 (93.2%)88/90 ± 1.4 (97.8% ± 1.6)t=-2.7, p<.0578/90 (86.7%)87.2/90 ± .4 (97.8% ± .5)t=18.8, p<.001Rapid Identification: numbers150ms13/15 14.75 ± .50 t=-3.1, p<.0514/1514.6/15 ± .89t=-.6, p>.2200ms14/1515/15 -15/1514.4/15 ± .89-250ms15/1515/15-12/1514.6/15 ± .89t=-2.6, p<.05Total correct42/45 (93.3%)44.8/45 ±.5 (99.6% ± 1.1)t=-2.9, p<.0541/45 (91.1%)43.6/45 ± 2.6 (96.9% ± 5.8)t=-0.9, p>.2Flanked letter IdentificationTotal correct72/72(100%)72/72(100%)-72/72(100%)72/72(100%)-Mean RT1.20 .48 ± .12t=5.3, p<.011.14.50 ± .05t=11.2, p<.001Group by Spacing interactiont=1.9, p=.08t=7.5, p<.001Table 6. SEQ Table_6. \* ARABIC 4 Performance on tests of visuoperceptual function.110998069215Visuoperceptual SkillsExample stimuliFOLControl GroupDifferenceCLAControl GroupDifferenceChequerboard ExperimentTotal correct29/48 (60.4%)47.3/48 ± .5 (98.4% ± 1.0)t=-32.7, p<.00131/48 (64.6%)47.6/48 ± .6 (99.2% ± 1.1)t=-27.7, p<.00120637559055Figure 6. SEQ Figure_6. \* ARABIC 3 Mean response latencies for target letters under different flanking conditions (letter, shape and number) and spatial conditions (crowded and spaced) for (A) Patient FOL and her matched controls, and (B) Patient CLA and her matched controls.DISCUSSIONThis chapter describes two PCA patients, FOL and CLA, who demonstrate preserved reading ability in spite of profoundly impaired visual function. Both patients were impaired on neuropsychological tests of early visual, visuoperceptual and visuospatial processing. Despite these grave visual impairments, both patients were able to read aloud words with perfect to near-perfect accuracy. Reading performance was also rapid, with FOL’s latencies not significantly different to controls on any of the 3 tests of reading, and CLA significantly slower on 2/3 sets but showing only a trend to slower reading overall once frequency was taken into account. In addition, word length effects were equivocal or absent, with FOL showing a modestly increased length effect relative to controls (amongst whom effects of length upon reading latency were also evident) and CLA showing no increase in word length effect. In further contrast to their gravely impaired visual processing, at the single letter level there was only minimal evidence of impaired processing, with patient CLA showing slow (but accurate) single letter identification under normal viewing conditions. Considering each patient’s performance in more detail, FOL’s results seem to indicate her reading ability is almost entirely spared. In each reading corpus, FOL did not differ from her control group in either accuracy or reading latency. Regression analyses conducted on all 250 reading responses (summing across tasks A1, A2 and A3) did reveal a diagnosis (FOL vs controls) x length (number of letters) interaction. However, the same analyses found effects of length on reading latencies within matched controls, and length has been shown previously to influence reading speed in normal readers (O’Regan and Jacobs, 1992; Spieler and Balota, 1997). More importantly, the absolute increase in mean reading latency for each additional letter as estimated from the regression model was 36ms per letter, a small increase which is comparable to that of controls (control mean: 13ms/letter; control 4: 32ms/letter) and an order of magnitude different to the increases of 90-7000ms per additional letter reported in previous descriptions of LBL reading (e.g. Fiset et al., 2005; McCarthy and Warrington, 1990; Mycroft et al., 2009; see REF _Ref376252385 \h Figure 6.4). It should also be noted that the trend towards a difference between FOL and the control group’s reading latencies for the Schonell reading test may reflect the particularly low frequency of various words in this corpus (‘somnambulist’, ‘ineradicable’) and FOL’s marginally lower educational level. The reading accuracy of patient CLA was also excellent, with not a single error recorded on any of the reading corpora. For example, her faultless performance on the demanding Schonell reading test conveys an estimated IQ of at least 118 (Nelson and McKenna, 1975). Her reading latencies did not differ from controls on the Brown and Ure words (A1), but reading speed did fall below that of controls on the Coltheart and Schonell tests (A2 and A3), with a significant regularity effect (irregular words slower than regular words) on the Coltheart set. Despite this, the overall difference in latencies across all 250 words failed to reach formal levels of significance. There was also no significant difference between CLA and her controls in the effect of increasing word length. -3790951910080The main aim of the current investigation was to evaluate the claim that general visual dysfunction can account for the acquired peripheral dyslexic syndrome known as LBL reading. General visual function accounts propose that even minor low-level perceptual deficits propagate to or limit activation of lexical representations, ultimately resulting in impaired reading behaviour. One specific prediction of such accounts is that pronounced word length effects are an inevitable consequence of deficits in general pre-lexical processing (e.g. Farah and Wallace, 1991; Behrmann et al., 1998; Mycroft et al., 2009). The data presented in the Figure 6. SEQ Figure_6. \* ARABIC 4 Mean reading latencies for words of different length compared to 5 example letter-by-letter readers reported by Mycroft et al. (2009).current study fail to support this prediction. Apart from demonstrating accurate and,particularly in the case of FOL, rapid word reading, word length effects were equivocal (FOL) or absent (CLA). This was despite the inclusion of very long words (up to 14 letters) which should maximise any chance of eliciting abnormal word length effects. This failure to detect the dramatic word length effects routinely observed in LBL readers cannot be attributed to preserved visual function, as both patients exhibited dramatic impairments on a wide variety of perceptual tasks. These included a chequerboard task previously used to support the claim that LBL readers have a perceptual impairment that extends beyond alphanumeric stimuli (Mycroft et al., 2009, chapter REF _Ref374202986 \r \h 6.2.3.1). However, in asserting that such general visual accounts of LBL reading are incompatible with the data presented here for FOL and CLA, we would wish to state unambiguously that we are not denying that some forms of visual impairment may have an inevitable cost for reading function. Rather we would argue against (i) the pejorative and under-specified use of terms such as ‘general visual impairment’, and (ii) the assumption that any form of visual impairment can cause reading impairment. We have previously proposed that enhanced visual crowding (the excessive integration of visual features, sometimes referred to as lateral masking) may be one of several specific visual deficits which can cause a particular form of dyslexia (Crutch & Warrington, 2007a, 2009). Indeed, we predicted that any patient demonstrating enhanced visual crowding on flanked letter identification tasks would also show some form of visual dyslexia. In line with this prediction, neither FOL nor CLA (whose reading is largely preserved) showed enhanced crowding; CLA did show slowed target letter identification particularly with condensed rather than spaced flankers (Task B4), but unlike visual crowding, this flanking effect was only present for flankers of the same category (letter flankers but not number or shape flankers). Given the degenerative nature of the PCA syndrome, we would predict that FOL and CLA’s reading skills will eventually become affected; the task going forward will be to identify any components of visual dysfunction that play a causative role in this predicted deterioration.The other aim of the investigation was to evaluate the hypothesis that impaired letter processing plays a causal role in LBL reading. Such accounts posit that whole reading requires fast parallel letter identification, and that deficits in letter processing inevitably give rise to reading dysfunction and word length effects (e.g. Bub et al., 1989; Howard, 1991; Behrmann and Shallice, 1995; Hanley and Kay, 1996; Price and Devlin, 2003). While both FOL and CLA were significantly less accurate than controls at identifying rapidly serially presented single letters, it is likely that this performance reflects a combination of their basic visual deficits rather than a specific problem of letter processing, particularly as FOL also demonstrated poorer accuracy on an equivalent task looking at rapidly presented numbers. The absence of strong evidence of a deficit in single letter processing suggests that intact parallel letter identification may account for their preserved reading in both patients.To adequately counter the general visual processing difficulties position it needs to be shown that any visual processing difficulty of the patients shown on some other perceptual task plausibly arises from impairment to a processing system necessary for word reading and not some potentially unrelated visual process. Naturally this is a very difficult point to disprove absolutely. However on these grounds one can make the extremely strong statement that none of the component visual processes required for normal performance on any of the 10 visual tasks evaluated in this investigation (which examine different levels of the visual system and involve different levels of task difficulty: figure-ground discrimination, shape discrimination, hue discrimination, number location, dot counting, object decision, fragmented letters, canonical and non-canonical view perception, grid experiment), are necessary for intact reading because our patients failed every single task. Furthermore, the impaired processes highlighted by these tasks also do not fall into the poorly-defined category of ‘general visual dysfunction’ which advocates of the general visual account claim cause LBL reading. However, at the much more relative level, the crashing visual deficits highlighted in our patients are an order of magnitude greater than the often subtle deficits claimed for patients cited in support of the general visual account.Having documented grave visual impairments, it remains to be established what mechanisms support reading in FOL and CLA. The accurate and rapid reading shown by both patients suggests preservation of word form representations or parallel letter processing mechanisms. This notion cannot be verified by the available structural imaging data. However, we note both MRI scans of FOL and CLA ( REF _Ref371603546 \h Figure 6.1) indicate relative preservation of the left fusiform gyrus, commonly cited as the locus of the VWFA (Cohen et al., 2000) and an area in which lesions often result in letter-by-letter reading (Binder and Mohr, 1992; Leff et al., 2001; Cohen et al., 2004; McCandliss et al., 2003; P?ugshaupt et al., 2009). This area perhaps provides an anatomical substrate for preserved reading ability in these patients, with one possibility being that strong reading performance is supported by preservation of certain inputs to the VWFA that bypass other impaired aspects of early visual processing. Support for this notion centres on evidence that the VWFA has connections to the primary visual cortex (Rockland and Van Hoesen, 1994; Tanaka, 1997; Haynes et al., 2005) whose relative integrity in FOL and CLA may be indicated by their continued strong or adequate performance on tests of visual acuity. However this suggestion involves the visual word form system maintaining its efficacy, even in the presence of widespread dysfunction at lower levels of the visual system. CHAPTER CONCLUSIONSThe performance of FOL and CLA represents an impressive demonstration of the resilience and efficiency of the reading system in the face of profound visual dysfunction, irrespective of whether the observed reading is attributable to preservation of the word form and/or aspects of parallel letter processing. The reading ability of these patients suggests that many types of early visual, visuoperceptual and visuospatial impairment are not necessarily causally linked to reading dysfunction, and question general visual accounts of conditions such as letter-by-letter reading. Notably, both the patients concerned remained intact on centrally-presented tests of visual crowding at the time of the reading assessment; longitudinal follow-up will determine whether the (presumed eventual) emergence of excessive crowding will finally herald the onset of reading difficulties for all types of text.CASE STUDIES: LONGITUDINAL ASSESSMENT OF READING IN PCACHAPTER INTRODUCTIONThe previous chapter identified two patients, FOL and CLA, who showed a compelling divergence between profound visual impairment and preserved reading ability. FOL and CLA achieved rapid reading and perfect to near-perfect reading accuracy despite showing deficits on ten measures of early visual, visuoperceptual and visuospatial processing (figure-ground discrimination, shape discrimination, hue discrimination, number location, dot counting, object decision, fragmented letters, canonical and non-canonical view perception, grid experiment). The previous findings established that impaired performance on these ten tasks does not necessitate reading dysfunction. These results, in combination with the lack of evidence of letter-by-letter (LBL) reading in these patients, pose problems for proponents of general visual accounts of reading (Friedman and Alexander, 1984; Farah and Wallace, 1991; Price and Devlin, 2003; Mycroft et al., 2009), which predict that deficits in pre-lexical visual processing result in disruption to reading in the form of prominent word length effects. Notably, both patients showed intact visual acuity and did not show evidence of enhanced crowding deficits on flanked letter identification tasks by making errors or showing consistent spacing effects on task performance. The previous chapter raised the question as to how their efficient reading was maintained, and it was predicted that any subsequent development of prominent crowding deficits might be associated with an eventual deterioration in reading ability.The previous chapter suggested that intact reading in both FOL and CLA might be a consequence of the relative preservation of the left fusiform gyrus, including its connections to the primary visual cortex, allowing for the continued efficacy of the visual word form system. The left fusiform gyrus is often referred to as the anatomical site of the visual word form area (VWFA; Cohen et al., 2000; Jobard et al., 2003). This region has been found to selectively respond to printed and handwritten words (Qiao et al., 2010; Szwed et al., 2011), letters in upper and lower case (Dehaene et al., 2004) and activation in this region has been found to increase proportionately with sentence reading rate (Dehaene et al., 2010).Damage to the visual word form system has been implicated in LBL readers (Warrington & Shallice, 1980); consistent with this view, lesions to the VWFA have been shown to result in pure alexia (P?ugshaupt et al., 2009).The current chapter presents findings from longitudinal assessments of reading ability in FOL and CLA which show deterioration in reading speed and accuracy over two years. The main aim of this study was to investigate the evolving relationship between crowding and word recognition. It was hypothesised that any emergence of crowding would be associated with deficits in reading ability. One subsidiary hypothesis was that if crowding and reading deficits did become apparent, the impact of crowding on word recognition would manifest through greater difficulty reading words of higher letter confusability, given how visual similarity exacerbates the crowding effect. A second subsidiary hypothesis was that decrements in reading accuracy and speed would be most evident for longer words, whose number of letters exceed the visual span and/or exhibit flanker effects on parallel letter processing, given that reductions in the uncrowded window limit the visual span and elevated numbers of flankers increase the magnitude of crowding effects (Poder & Wagemans, 2007). In the event, both patients began to exhibit prominent flanked letter identification deficits at follow-up, and the relationship between these enhanced crowding effects and reading is described below.METHODSParticipantsThe study participants were the same two individuals with PCA as in chapter REF _Ref371599193 \r \h 6 (Yong et al., 2013), FOL and CLA.ImagingNon-linear registrations of serial imaging to both patients’ baseline scans and the resultant voxel-compression maps (see chapter REF _Ref374195784 \r \h 3.7.3.2) are shown in REF _Ref374197633 \h Figure 7.1. The white arrow indicates the mean activation peak of the visual word form area (x=-44, y=-58, z=-15) constituted from 17 functional imaging studies (Jobard et al., 2003).FOL: Maps suggest relative sparing of left posterior fusiform (iii) and more extensive involvement of the right than the left occipital lobe. CLA: While maps indicate diffuse atrophy, with extensive involvement of the occipital lobe, they also indicate the relative preservation of the left relative to the right inferior temporal lobe, particularly in the posterior inferotemporal region (iii).Experimental proceduresSubsequent to the initial baseline assessment in chapter REF _Ref371599193 \r \h 6, the patients each completed two longitudinal follow-up assessments (first follow-up [FU1] and second follow-up [FU2]), yielding a total of three assessments. FOL was assessed 16 and 25 months after her initial visit, while CLA was assessed 18 and 27 months after her initial visit. -1440180-719455Figure 7. SEQ Figure_7 \* ARABIC 1 (i) Coronal (ii) axial and (iii) left and (iv) right sagittal MRI sections for FOL and CLA at baseline and colour coded voxel-compression maps produced from subsequent scans (FOL: 25 months; CLA: 24 months), fluid-registered to baseline scans. A region within the boundaries of the VWFA as constituted by a functional imaging meta-analysis (Jobard et al., 2003) is indicated by the white arrows.Reading assessmentParticipants were requested to read aloud the same three corpora administered at baseline: the Brown and Ure (1969) corpus, the Schonell reading list (Schonell and Goodacre, 1971) and the Coltheart regular/irregular words (Coltheart et al., 1979). Mean letter confusability was based on upper case ratings averaged from the confusability matrices of Van der Heijden et al., (1984), Gilmore et al., (1979), Townsend, (1971), and Fisher et al., (1969), with lower case ratings averaged from the confusability matrices of Geyer, (1977), and Boles and Clifford, (1989).Visual assessmentParticipants were administered the same measures of early visual, visuoperceptual and visuospatial processing administered at baseline.Crowding assessmentParticipants were administered the same centrally-presented single letter naming and flanked letter identification tasks administered at baseline.Data analysisResponses were recorded using an Olympus DS-40 digital voice recorder; response latency determination, analysis and accuracy and latency comparisons were consistent with chapter REF _Ref371594131 \r \h 3.5. While neither FOL nor CLA made enough errors at baseline to allow for meaningful analysis of accuracy data, overall reading accuracy and latency analyses were conducted using logistic regression and linear mixed models respectively. The linear mixed model used random word order effects and fixed effects of word length, mean letter confusability, word frequency, case (upper or lower) and assessment (baseline, FU1 or FU2), while the logistic model included as covariates all random and fixed effect variables from the linear mixed model, clustered by word order. Post hoc analysis of CLA’s reading latency data was conducted using the linear mixed model but including orthographic neighbourhood size (Nsize) as an additional covariate. Prior to latency regression analysis, latency data were transformed using an inverse transformation due to non-normal distribution of residuals. We used a sign test to identify letter naming differences in accuracy between spacing conditions. Overall flanked letter identification accuracy analysis was conducted using logistic regression, including spacing, flanker category and assessment as covariates, clustered by letter order. As latency analysis was restricted to correct responses, latency data were not analysed for crowding follow-up assessments owing to high error rates. Data collected from FOL and CLA’S first and second follow-up assessments was compared with control group data collected at baseline. All reported p values represent one-way probability. RESULTSReading assessmentMean percentage error rates and reading latencies for overall performance (summing across reading corpora) at baseline and follow-up assessments are shown in REF _Ref374197769 \h Figure 7.2. A more detailed breakdown of number and percentage correct responses and mean and SD latency data for reading performance by FOL, CLA and their relevant control samples on the individual reading corpora are shown in REF _Ref374199365 \h Table 7.1.Overall reading accuracyFOL: Overall analysis of FOL’s accuracy data across the three assessments found accuracy decreased in subsequent assessments (z=-3.22, p<.005). While there was a slight decline in FOL’s overall accuracy between baseline (98.4%) and FU1 (97.2%), this did not reach formal levels of significance (p>.1). However, there was a significant decline between baseline and FU2 (92.8%; z=-3.38, p<.005) and between FU1 and FU2 (z=-2.26, p<.05). Across the three assessments, words of increased length were read less accurately (z=-2.60, p<.01); there was no significant difference in length effects on accuracy at different assessments (all p>.1). There were no significant effects of mean letter confusability (p>.4), frequency (p>.3) or case (p>.1) on reading accuracy.CLA: Overall analysis of CLA’s accuracy data across the three assessments also found a decrease in accuracy in subsequent assessments (z=-6.02, p<.001). There was a decline in CLA’s overall accuracy between baseline (100%) and FU1 (94.4%; z=-2.51, p<.05) and FU2 (90.4%; z=-3.20, p<.005), with decline also evident between FU1 and FU2 (z=-1.96, p<.05). Across the three assessments, words in upper case were read less accurately (z=-1.98, p<.05) and similarly to FOL there was a trend towards longer words being read less accurately (z=-1.78, p=.075). There was no significant difference in length effects on accuracy between follow-up assessments (p>.6). There were no significant effects of mean letter confusability (p>.4) or frequency (p>.2) on reading accuracy.356235114935Figure 7. SEQ Figure_7 \* ARABIC 2 Overall reading accuracy and latency data across three longitudinal assessments. Error bars show standard deviation for control groups.?A: Reading assessment?maxFOL/control raw scores?CLA/control raw scores???Control GroupFOL?Control GroupCLA???baselinebaselinefollow up 1follow up 2?baselinebaselinefollow up 1follow up 2?1. Brown and Ure wordsTotal7271.8, SD=.4 (99.7%)72 (100%)71 (99%)71 (99%)?1. Brown and Ure words72 (100%)72 (100%)70 (97%)69 (96%)??RT?0.51, SD=.040.601.05**1.66**??0.57, SD=.060.640.91**data missing?2. SchonellTotal10099, SD=1 (99%)97 (97%)97 (97%)92 (92%)**?2. Schonell99, SD=1.2 (99%)100 (100%)94 (94%)*88 (88%)**??RT?0.54, SD=.070.721.04**1.58**??0.60, SD=.060.78*0.92**6.52**?3. Coltheart wordsTotal7878 (100%)77 (99%)75 (96%)69 (89%)?3. Coltheart words78 (100%)78 (100%)72 (92%)69 (89%)?RegularTotal3939(100%)39 (100%)37 (95%)35 (90%)?Regular39(100%)39 (100%)36 (92%)35 (90%)??RT?0.48, SD=.040.560.97**1.37**??0.53, SD=.050.91**0.88**6.62**?IrregularTotal3939 (100%)38 (97%)38 (97%)34 (87%)?Irregular39 (100%)39 (100%)36 (92%)34 (87%)??RT?0.51, SD=.050.591.15**1.51**??0.55, SD=.051.1**1.1**7.87**?B: Crowding assessmentSingle letter namingTotal2020 (100%)20 (100%)20 (100%)20 (100%)?Single letter reading20 (100%)20 (100%)20 (100%)20 (100%)??RT?0.48 ± .060.59not recorded1.08**??0.56 ± 0.40.82**0.87**3.81**?Flanked letter identificationTotal72/4872 (100%)72 (100%)37/48 (77%)58 (81%)?Flanked letter identification72 (100%)72 (100%)64 (89%)57 (79%)?CondensedTotal36/2436 (100%)36 (100%)16/24 (67%)25 (69%)?Condensed36 (100%)36 (100%)29 (81%)25 (69%)?SpacedTotal36/2436 (100%)36 (100%)21/24 (88%)33 (92%)?Spaced36 (100%)36 (100%)35 (97%)32 (89%)?Table 7. SEQ Table_7 \* ARABIC 1 A) Reading assessment and B) Crowding assessment accuracy and latency for FOL/CLA and their matched control groups; highlighted figures indicate where FOL/CLA’s performance was poorer than their respective control groups (*=p<.05; **=p<.005).Overall reading latencyOverall reading latencies for words of up to 12 letters read at baseline, FU1 and FU2 are shown in REF _Ref374199542 \h Figure 7.3.55181510160Figure 7. SEQ Figure_7 \* ARABIC 3 Mean overall reading latencies for words of different length read by patient FOL and CLA at baseline, first and second follow-up and baseline latencies for their respective matched controls, with estimated upper and lower control confidence intervals. FOL: Overall analysis of FOL’s latency data across the three assessments found that reading speed was slower in subsequent assessments (t=-16.82, p<.001). There was a decline in FOL’s reading speed between baseline (633ms) and FU1 (1048ms; z=-14.59, p<.001) and FU2 (1493ms: z=-17.27, p<.001), with further decline in reading speed between FU1 and FU2 (z=-3.11, p<.005). Across the three assessments, increased word length led to slower reading speed (length: z=-6.18, p<.001); length effects were most pronounced at second follow-up, with a mean increase of 116ms per letter. There was an interaction between word length and assessment, with words of increased length being read slower at follow-up assessments relative to baseline (FU1: z=3.27, p<.005; FU2: z=2.18, p<.05); there was no significant interaction between follow-up assessments (p>.3). Increased letter confusability led to slower reading speed (letter confusability: z=-1.97, p<.05). There was an interaction between letter confusability and assessment, with words of lower confusability being read slower at FU2 (z=2.47, p<.05) and baseline (z=1.99, p<.05) relative to FU1; there was no significant interaction between baseline and FU2 (p>.5). There was no overall effect of frequency or case (both p>.1). CLA: Overall analysis of CLA’s latency data also found that reading speed was slower in subsequent assessments (z=-36.23, p<.001). There was also a decline in CLA’s reading speed between baseline (751ms) and FU1 (940ms; z=-14.59, p<.001) and FU2 (6854ms; z=-17.27, p<.001), with further decline between FU1 and FU2 (z=-3.11, p<.005). Across the three assessments, CLA was also slower reading words of increased length (z=-5.17, p<.001). There was an interaction between word length and assessment: in contrast to FOL, words of decreased length were read slower relative to baseline at FU2 (z=3.16, p<.005), and a trend towards words of decreased length being read slower at FU2 relative to FU1 (z=1.94, p=.052). There was no significant difference in length effects between FU1 and FU2 (p>.1). A post hoc analysis of CLA’s reading latencies was conducted in order to determine whether slower reading speed for shorter words at FU2 was a consequence of such words having more orthographic neighbours (Weekes, 1997): however, neighbourhood size did not account for this effect (length: z=3.15, p<.005; Nsize: p>.1). CLA was slower reading words in upper case font (z=-3.07, p<.005). Also in contrast to FOL’s reading, there was no significant effect of mean letter confusability or word frequency on reading speed (both p>.1). A detailed account of FOL and CLA’s performance upon the three individual reading corpora which were combined to give the overall performance data described above can be found in Appendix 5.Error analysisFigure 7. SEQ Figure_7 \* ARABIC 4 Number of types of error made across longitudinal assessments-1911351183005See REF _Ref374199743 \h Figure 7.4 for a summary of errors made by FOL and CLA at different assessments. Visual errors made up 50% of FOL’s error responses at baseline, 57% of error responses at FU1 and 80% at FU2. CLA did not make any errors at baseline, whilst visual errors made up 60% of her error responses at FU1 and 65% of her error responses at FU2.Visual assessmentFOL and CLA’s performance on measures of early visual, visuoperceptual and visuospatial processing and background neuropsychology is shown in REF _Ref374199857 \h Table 7.2. Consistent with baseline performance, both FOL and CLA demonstrated impairments in a range of visual domains.Crowding assessmentThe number and percentage correct responses and mean and SD latency data for letter naming performance in unflanked and flanked conditions by FOL, CLA and their relevant control samples are shown in REF _Ref374199365 \h Table 7.1Single letter namingFOL/CLA: Neither FOL nor CLA made any error responses at baseline, FU1 or FU2.Flanked letter identificationFor mean percentage error rates across baseline and follow-up assessments, see REF _Ref374200016 \h Figure 7.5.Table 7. SEQ Table_7 \* ARABIC 2 FOL and CLA’s performance on background neuropsychological measures and tests of visual processing (not tested: NT). Shaded numbers indicate task performance is within normal limits. TestMax ScoreFOLCLABaselineFU1FU2BaselineFU1FU2Background NeuropsychologyMMSE1302423152713NTShort RMT words2252114162421NTConcrete synonyms3252021202020NTSpelling (oral) 42018661911NTDigit span (forwards)8987102NTDigit span (backwards)741054NTVisual AssessmentEarly visual processing?Visual acuity (CORVIST5): Snellen6/96/96/96/126/186/18NTFigure-ground (VOSP6)201716171411NTShape discrimination720101771013NTVisuospatial processing?Number location (VOSP)1050NT5NTNTDot counting (VOSP)10730101NTVisuoperceptual processing?Object decision (VOSP)201514137NTNTFragmented letters (VOSP)208510NTNTUsual views8201820NT5NTNTUnusual views20106NT0NTNT1 Mini-Mental State Examination (MMSE: Folstein, Folstein & McHugh, 1975). 2 Warrington (1996). 3 Warrington, McKenna and Orpwood (1998). 4 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 5 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001). 6 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 7 Efron (1968): oblong edge ratio 1:1.20. 8 Warrington and James (1988).Figure 7. SEQ Figure_7 \* ARABIC 5 Accuracy for flanked letter identification tasks in condensed and spaced conditions across three longitudinal assessments (*=p<.05).-43243599060FOL: FOL did not complete letter identification tasks in the number flanker condition at FU1. Overall analysis of FOL’s accuracy data found that flanked letter identification was less accurate at subsequent assessments (z=-3.28, p<.005). There was an overall effect of spacing, with letters being identified less accurately in the condensed condition (z=2.98, p<.005). There was no significant interaction between spacing and assessment (p>.3). There was no significant effect of flanker category on naming accuracy (all p>.1). At FU1, there was a trend towards poorer overall naming accuracy in the condensed relative to the spaced condition across letter and shape flanker conditions (66.7% vs 87.5%; p=.090); there was a significant effect of spacing for letters flanked by shapes (58.3% vs 100.0%; p<.05). At FU2, overall naming accuracy was poorer in the condensed relative to the spaced condition across all flanker conditions (69.4% vs 91.7%; p<.05).CLA: Similar to FOL, overall analysis of CLA’s accuracy data found that flanked letter identification was less accurate at subsequent assessments (z=-3.53, p<.001). There was also an overall effect of spacing, with letters being identified less accurately in the condensed condition (z=2.29, p<.05). There was no significant interaction between spacing and assessment (p>.9). There was no significant effect of flanker category on naming accuracy (letter vs shape flankers: p>.4; letter vs number flankers: p>.1; shape vs number flankers: p>.5). At FU1, overall naming accuracy was poorer in the condensed relative to the spaced condition across all flanker conditions (80.6% vs 97.2%; p<.05). At FU2, while overall naming accuracy was poorer in the condensed relative to the spaced condition, this difference did not reach formal levels of significance (72.2% vs 86.1%; p>.1).Error analysisFOL: Of FOL’s total error responses made over both follow-up assessments (21.2%), 43.3% were from flanker identification (e.g. ZNHZ), 35.8% were from the target being unidentified and 20.8% were due to neither target nor flanker being identified. CLA: Of CLA’s total error responses made over both follow-up assessments (16.0%), 46.1% were from flanker identification, 2.8% were from the target being unidentified and 51.1% were due to neither target nor flanker being identified. Errors following the target being unidentified could result from both FOL and CLA being unable to either detect the target or provide a response. Responses in which neither target nor flanker were identified were often suggestive of feature substitution or perceptual averaging of target and flanker stimuli (e.g. YMTV; 6F2T). DISCUSSIONThe current paper reports a two year follow-up evaluation of the relationship between visual processing and reading ability in two PCA patients, FOL and CLA. At the baseline assessment, both patients demonstrated remarkably preserved reading ability despite showing impaired performance on ten tasks of visual processing. At subsequent assessments, their reading ability showed signs of deterioration; whilst still impressive given the gravity of their early visual, perceptual and spatial impairments, it could not be considered normal in either patient. The emergence of these reading deficits coincided with deterioration in their performance on flanked letter identification tasks, pointing to the evolution of an enhanced visual crowding deficit. By contrast, visual acuity remained relatively well-preserved even at follow-up. In this discussion, we consider how characteristics of the emergent reading deficit, namely effects of word length and letter confusability, may provide insight into the process through which crowding disrupts word recognition.To first summarise the data, the investigation revealed a significant decline in FOL and CLA’s reading ability. At baseline assessment, reading ability for FOL and CLA was within normal limits, with CLA achieving 100% reading accuracy. By first follow-up, both patients’ reading speed had declined relative to both their own baseline performance and their age- and gender-matched control groups. In addition, CLA was not only slower but also less accurate, making errors on high-frequency words (e.g., FREETREE, GLOVECLOVE). By second follow-up, reading accuracy for both patients was well below that of controls. Follow-up assessments of both FOL and CLA identified impaired performance on tests sensitive to crowding, involving the identification of centrally-presented target letters flanked by letter, shape and number flankers. Without prompting, FOL commented explicitly on how flankers were “interfering… pushing in”. Poor performance was not more pronounced with letter (i.e. same category) flankers as might be expected with attentional dyslexia (Humphreys & Mayall, 2001; Warrington et al., 1993); instead, consistent effects of spacing were observed in different flanker conditions, with elevated error rates resulting from targets with condensed flankers. This pattern of deficit is strongly indicative of visual crowding, and mirrors flanked letter identification deficits observed in other PCA patients (Crutch & Warrington, 2007a; Crutch & Warrington, 2009; Mendez et al., 2007). With regard to the effect of word length, increases in FOL’s mean latencies per additional letter were modest at her first two assessments (FOL baseline: 36ms/letter; FOL follow-up 1: 25ms/letter; control 4: 32ms/letter). However, increases per letter at second follow-up were within the range of previous descriptions of LBL reading (FOL follow-up 2: 116ms/letter; cf Behrmann et al., 1998). In contrast, following comparable increases in mean reading latency per additional letter between CLA and controls at baseline and first follow-up (CLA baseline: 19ms/letter; CLA follow-up 1: 15ms/letter: control mean: 13ms/letter), CLA was actually slower reading shorter words at second follow-up assessment, although her differences in mean reading speed/letter between assessments were not as pronounced as those observed in FOL (CLA follow-up 2: -31ms/letter). The data raise the question of why FOL but not CLA showed an increase in length effects over subsequent assessments. Another difference between FOL and CLA’s reading performance was the emergence of effects of letter confusability on FOL’s reading latencies, with more visually similar letters being read more slowly. As greater visual similarity increases the magnitude of crowding, any influence of letter confusability might reflect the impact of crowding on parallel letter processing. Fiset et al. (2005) proposed that higher letter confusability within words or subsets of words reduces LBL readers’ ability to achieve successful parallel letter processing. They suggested that such words might provoke switching to a compensatory serial letter processing strategy, and emphasised how switching to such a strategy varies depending on patients’ reading ability. It is possible that, when attempting to read words of higher letter confusability, FOL was quicker to adopt a serial reading strategy than CLA, resulting in more prominent length effects. This may be because FOL was more susceptible to letter confusability effects or was more conscious of her difficulties when reading words of high letter confusability. CLA may have been more reliant on processing whole-word information rather than adopting a serial reading strategy. In line with this suggestion, larger effects of frequency and reduced effects of word length have been identified in older adults, which have been interpreted as showing an increased propensity towards representing words as single units based on the greater reading experience of elderly readers (Spieler & Balota, 2000).Variations in patterns of reading performance between the two patients may also relate to differences in the quality of crowding effects between FOL and CLA. A greater proportion of FOL’s errors arose from her being unable to provide a response to target stimuli, while CLA showed a greater tendency towards making error responses that named neither the target nor flanker. If flanked letter identification errors arise from competition between feature detectors, as proposed in lateral masking accounts (Townsend et al., 1971; Wolford & Chambers, 1984), crowding may limit letter detection as well as identification (Parkes et al., 2001; Pelli et al., 2004). By contrast, feature integration accounts suggest pooling of information over multiple features of flanker and target stimuli, inhibiting identification but not detection (Pelli et al., 2004; Greenwood et al., 2010). FOL’s lack of responses may reflect a low-level deficit in feature detection, whereas CLA’s error responses may stem from a higher-level deficit of excessive integration between features of target and flanker stimuli. With regard to the possible anatomical substrates of these deficits, proposals of a two-stage process of crowding involve a lower-level feature detection stage, possibly in V1, and a higher-level integration of features downstream from V1 (Levi, 2008). Whilst we cannot know the current distribution of pathology in our patients in sufficient detail, crowding as a failure of feature detection in FOL would predict greater pathological involvement of the striate cortex, while crowding as a consequence of excessive feature integration in CLA would predict disproportionate involvement of the extrastriate cortex. A reduced visual span might account not only for FOL’s slower reading for longer words, but also for both patients’ poorer accuracy for words of increased length. The “shrinking visual span hypothesis” (Legge et al., 1997) proposes that words whose lengths exceed the size of the visual span demand increases in eye movements, leading to length effects on reading latency. It is possible that prominent crowding effects in FOL are reducing her visual span, subsequently requiring an increase in the number of fixations needed to achieve word recognition, which result in reduced speed when reading longer words. The tendency towards less accurate reading of longer words in both patients may be a result of poor integration of information from multiple fixations, or might also be due to increased numbers of letters exhibiting greater inhibitory flanker effects on parallel letter identification (Poder & Wagemans, 2007).What implications do these findings have for our interpretation of results in Chapter 6? In the previous chapter, we proposed that efficient reading in both patients was a consequence of preserved word form/parallel letter processing, maintained by the integrity of early aspects of the visual system (as suggested by strong performance on tests of visual acuity), the VWFA and interconnecting projections. In the current investigation, while both patients showed diffuse brain atrophy, we argued for the relative preservation of the region corresponding to the VWFA at subsequent assessment. Given the dissociation between crowding and acuity (Song et al., 2014), it is possible that even the continued efficacy of both the early visual system and the VWFA is undermined by enhanced crowding and any accompanying occipital atrophy. Alternatively, the progression of occipital atrophy may have compromised the structural integrity of the i) early visual system, ii) inputs to the VWFA or iii) the VWFA itself. FOL’s acuity, while relatively well-preserved, was slightly diminished over subsequent assessments; this, along with the notion that her enhanced crowding may be a particular consequence of a deficit in feature detection, might be a consequence of pathological involvement of the striate cortex. In contrast, we have proposed CLA’s enhanced crowding arose from a higher-level deficit in feature integration; such a deficit might result from extrastriate involvement, accompanied by a deteriorating condition of inputs to the VWFA. Despite the purportedly different loci of deficits in possibilities i) and ii), both would likely constrain visual representations which form the basis of orthographic inputs in both patients. As previously suggested, this may have provoked FOL to adopt serial reading strategies when reading words of high confusability (i.e. Fiset et al., 2005). In contrast, CLA may have been better able to represent whole word form and/or parallel letters based on her greater reading experience, and so would not have had to resort to serial reading, but may instead have difficulty selecting appropriate orthographic units (Patterson, 1978; Crutch & Warrington, 2007b). Another interpretation is that FOL’s emerging length effects might reflect the increasing pathological involvement of her VWFA due to disease progression, leading to a diminished ability to achieve word form/parallel letter processing. However, it is necessary to acknowledge the limitations of the current investigation in evaluating the above hypotheses given the difficulty of drawing precise conclusions about neuroanatomical localization based on two patients with diffuse atrophy.While the current investigation suggests enhanced crowding is particularly associated with acquired dyslexia, the patterns of reading and visual deficits exhibited by FOL and CLA are still inconsistent with general visual accounts which maintain a deficit in general processing of visual material causally underlies acquired dyslexia (Mycroft et al., 2009). The present findings support the notion of excessive crowding, a specific prelexical deficit, undermining reading. The locus of this deficit most likely lies earlier in the visual system than accounts which propose peripheral dyslexia as a consequence of selective damage to orthographic processing (Warrington & Shallice, 1980; Patterson & Kay, 1982; Rosazza et al., 2007). In contrast to these accounts which maintain that such selective damage results in specific reading deficits, in the form of pure alexia, disruptive effects of excessive crowding would not be limited to orthographic stimuli. It is worth emphasising, however, that the emergence of FOL’s length effects does not mean that she should necessarily be considered a pure alexic. Our primary interpretation of the current findings involves excessive crowding, a low-level deficit, restricting reading ability; Leff et al. (2001) proposed that low-level perceptual deficits might underlie length effects in some previously reported ‘pure alexics’, while Montant and Behrmann (2001) suggested that pure alexia is a term that should be applied to readers with a specific word length effect in the absence of other symptoms. CHAPTER CONCLUSIONSThe current investigation highlights the co-occurrence of crowding effects and diminished reading ability in FOL and CLA. Although the concurrent emergence of reading and crowding deficits in these individuals represents evidence of association rather than causation, the data further underline the potential role of enhanced crowding in hindering word recognition. Questions remain about differences between both patients, including the quality of crowding effects and the employment of serial reading strategies. However, the current findings provide an insight into how crowding may limit reading ability, and demonstrate a novel neurodegenerative approach towards understanding the relationship between one specific form of basic visual deficit and the reading system.PASSAGE READING IN PCA CHAPTER INTRODUCTIONVarious reports exist of PCA patients having difficulty following lines of text, moving between one line to another and losing their place on a page (see chapter REF _Ref374200298 \r \h 2.3). Although these difficulties have been recognised clinically and anecdotally, they have yet to be empirically examined. The current chapter characterises the spatial and oculomotor properties of text reading behaviour in PCA; the ultimate goal being to apply any resulting insight in the development of a reading aid. The poor ability of PCA patients in coping with the spatial demands of reading multiple lines of text likely relates to a range of deficits: single point localisation (visual disorientation), spatial analysis (spatial agnosia), visual search (spatial attentional impairments) and eye fixation stability (Mizuno et al., 1996; Stark et al., 1997; Delazer et al., 2006; Lehmann et al., 2011; Crutch et al., 2011).Restrictions in the effective field of vision as evidenced by poorer recognition of larger stimuli (Stark et al., 1997; Crutch et al., 2011) may also contribute to the reading impairment, frustrating attempts to locate subsequent lines of text. Crowding may play a particular role in text reading where letters not only within a word (as in single word reading) but also from preceding and subsequent words within a line and from lines of text above and below exhibit inhibitory flanking effects and create a highly cluttered visual scene. Furthermore, both inverse size and enhanced crowding effects would be expected to limit the perceptual span and parafoveal preview benefit in reading (Pelli et al., 2007; Hyona et al., 2004; Rayner, 1998; McDonald, 2006), leading to increased demands in fixations and consequent reductions in reading speed.The current chapter aimed to identify the spatial, lexical and oculomotor characteristics of passage reading in PCA patients using measures of reading ability and eye movements, compared with tAD patients and healthy controls. This investigation intended to examine the hypothesis that spatial factors are the primary determinants of PCA passage reading ability by demonstrating that spatial attributes (position within line, position within paragraph, position within page) are better predictors of reading accuracy than non-spatial attributes (e.g. word frequency, class). Revealing the core components of poor passage reading in PCA will inform the design of subsequent reading interventions which minimise or evade weak aspects of spatial, perceptual and/or oculomotor function. METHODSParticipants15 PCA patients, 6 tAD patients and 6 healthy controls participated in the main experiment. The PCA patients all fulfilled clinical criteria for a diagnosis of posterior cortical atrophy (Tang-Wai et al., 2004; Mendez et al., 2002; McMonagle et al., 2006) and research criteria for probable Alzheimer’s disease (McKhann et al., 2011). The tAD patients fulfilled research criteria for a diagnosis of typical amnestic Alzheimer’s disease (McKhann et al., 2011). Diagnoses were made based on clinical and neuroimaging data. The healthy controls were matched to the PCA and tAD groups on age and years of education, with the PCA and tAD participants additionally matched for disease duration and Mini-Mental State Examination score (MMSE; see REF _Ref374278225 \h Table 8.1). Molecular pathology (18F amyloid imaging or CSF) was available for 5/15 PCA and 4/6 tAD patients; all results were consistent with AD pathology (positive amyloid scan on standard visual rating or CSF Aβ1-42 ≤450 and/or tau/Aβ ratio >1; see REF _Ref374278233 \h Table 8.2). Ethical approval for the study was provided by the local ethics committee and informed consent was obtained from all participants.Table 8. SEQ Table_8 \* ARABIC 1 Demographic information for PCA, tAD and healthy control groups.?PCAAlzheimer's diseaseControlNumber of participants1566Gender (male:female)6:92:42:4Age (years)61.0 ± 6.662.0 ± 7.561.0 ± 4.6Education level (years)13.6 ± 2.013.8 ± 4.513.8 ± 2.7Disease duration (years)4.2 ± 1.75.7 ± 2.3-MMSE (/30)119.0 ± 4.222.8 ± 5.3-β-Amyloid PET/ CSF consistent with AD5/54/4-Table 8. SEQ Table_8 \* ARABIC 2 Molecular pathology data for PCA and tAD patients; interpretation symbols indicate where results do not support AD pathology (-), are borderline consistent with AD pathology (+) and are >85% specific for AD pathology (++).Diagnosisamyloid F18 imagingCSF total tauCSF abeta 1-42CSF Tau:Abeta ratioCSF interpretationPCApositive10721268.51++PCApositive10823652.96++PCApositive---++PCApositive---++PCApositive---++tAD-2892801.03+tAD-7572852.66++tAD-9403482.70++tAD-9521954.88++Background neuropsychologyPCA and AD patients were administered a battery of neuropsychological tests. Mean scores on each task and an estimate of their performance relative to appropriate normative data sets are shown in REF _Ref374278463 \h Table 8.3. Overall PCA performance was consistent with the cognitive characteristics outlined by McMonagle et al. (2006), with most prominent symptoms being higher order visual deficits with relatively less impaired memory ability. PCA patients were borderline impaired but within normal limits on tests of recognition memory (Short Recognition Memory Test words/faces), whilst tAD patients were impaired and showed a trend towards worse performance than PCA patients. PCA performance on most tests without a core visual component was equivalent to that of the tAD group (Concrete Synonyms, Naming, Digit Span). However, PCA patients were significantly more impaired than tAD patients on tests of numeracy and literacy (Calculation, Cognitive Estimates, Spelling) and on all tests of early visual, visuoperceptual and visuospatial function except the CORVIST colour screening test.Passage reading assessmentStimuliParticipants read aloud 6 passages (mean word count: 107.0, SD=5.2) in a standard presentation block-of-text format (mean number of lines: 14.8, SD=1.2), with each passage split into 3 paragraphs. Passages were selected from the BBC news archive in order to reduce priming from current events. Words were in black Arial Unicode MS font, with a visual angle of letter height subtending 0.45° when viewed from a distance of 50cm, presented on a grey background.Procedure Participants were given a maximum of 300 seconds to read each passage. Participants who took more than 10 seconds to provide a response for a word were prompted to move onto the next word. Participants were not discouraged from using their finger to maintain their place when reading. Passages were administered through a repeated-measures design that included presentation conditions from chapter 9. Words read correctly were marked as accurate, regardless of word order. For details of latency measurement and eye movement recording, see chapters REF _Ref371594131 \r \h 3.5 and REF _Ref374273439 \r \h 3.6 respectively.Table 8. SEQ Table_8 \* ARABIC 3 Neuropsychological scores of patients with PCA and tAD TestMax ScoreRaw Score??PCA (mean age: 61.0)AD(mean age: 62.0)DifferenceNorms/commentmeanmin|maxmeanmin|maxBackground NeuropsychologyShort Recognition Memory Test2 for words*2519.0 ± 3.813|2415.1 ± 3.19|16p=.082PCA: 5th%ile, AD: ~<5th %ile (Cut off: 19)(joint auditory/visual presentation)Short Recognition Memory Test for faces*2519.8 ± 3.414|2517.3 ± 4.612|21p>.2PCA: 5th-10th%ile, AD: ~<5th %ile (Cut off: 18)Calculation (GDA3)*241.5 ± 3.70|144.2 ± 5.10|13p<.05PCA: ~<5th%ile, AD:5th-25th%ileSpelling (GDST4- Set B, first 20 items)*209.3 ± 5.02|1713.8 ± 5.38|19p=.081Both 5th-25th%ileDigit span (max forwards)85.3 ± 1.04|76.2 ± 1.24|7p>.1Both 25th-50th%ileDigit span (max backwards)?72.9 ± 1.30|74.0 ± 1.32|8p>.1Both 5th-10th%ileVisual AssessmentEarly visual processingVisual acuity (CORVIST5): Snellen6/9(median 6/9)6/9|6/12(median 6/9)6/9|6/12-Both within normal limitsFigure-ground discrimination (VOSP6)2016.4 ± 2.911|2018.8 ± 1.118|20p=.057PCA: ~<5th%ile, AD: 5th-10th%ileShape discrimination – Efron squares7Difficult (oblong edge ratio 1:1.20)2011.9 ± 4.56|2017.8 ± 3.512|20p<.05Healthy controls do not make any errorsHue discrimination (CORVIST)42.6 ± 1.10|43.4 ± 1.31|4p>.1-Healthy controls do not make any errorsCrowding107.9 ± 2.42|101010|10p<.001Visuoperceptual processingObject Decision (VOSP)*2010.6 ± 4.34|1717.0 ± 2.214|20p<.005PCA: ~<5th%ile, AD: 25th-50th%ileFragmented letters (VOSP)203.7 ± 4.20|1518.4 ± 1.316|19p<.001PCA: ~<5th%ile, AD: 25th-50th%ileUnusual and usual views8: Unusual204.0 ± 3.90|1213.8 ± 1.312|15p<.005PCA: ~<1st%ile, AD:5th-25th%ileUnusual and usual views8: Usual2013.8 ± 5.33|2019.8 ± 0.519|20p<.01PCA: ~<1st%ile, AD: within normal limitsVisuospatial processingNumber location (VOSP)*101.4 ± 2.30|67.7 ± 2.92|10p<.001PCA: ~<5th%ile, AD: 5th-10th%ileDot counting (VOSP)102.6 ± 3.00|98.8 ± 1.37|10p<.005PCA: ~<5tht%ile, AD: within normal limitsA Cancellation9: Completion time90s83.2 ± 14.154s|111s30.6 ± 12.320|45p<.001Both ~<5th%ile (Cut off: 32s)A Cancellation9: Number of letters missed197.5 ± 5.01|160.6 ± 0.50|1p<.005*Behavioural screening tests supportive of PCA diagnosis. 1 Mini-Mental State Examination (MMSE: Folstein, Folstein & McHugh, 1975). 2 Warrington (1996). 3 Graded Difficulty Arithmetic test (GDA; Jackson & Warrington, 1986). 4 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 5 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001).6 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 7 Efron (1968). 8 Warrington and James (1988). 9 Willison and Warrington (1992). Data analysisBetween-group differences in reading accuracy, reading speed and performance on neuropsychological measures were calculated using a Wilcoxon rank-sum test. Analysis of spatial factors on reading accuracyTwo-way crossed random-effects models were used to assess the effect of spatial aspects of text on reading accuracy; all models used random participant and passage effects, with random participant effects nested within the passage variable, and fixed-effects of the following nuisance variables: number of repeats, word length, frequency and order. Regression analysis was restricted to content words. Three separate models were used, including fixed-effects of the following variables:?Categorical text position: paragraph (1, 2 or 3), line (3-6) and peripheral (paragraph perimeter vs paragraph interior).?Continuous page coordinate position: x-coordinates and y-coordinates from the centre of the screen. ?Continuous paragraph coordinate position: x-coordinates and y-coordinates from the centre of each paragraph.Eye movement dataDifferences between PCA, tAD and control measures of eye tracking data were assessed using a Wilcoxon rank-sum test. Fixation measures were the overall number of fixations and fixation duration. Saccadic measures were the overall number of saccades, number of left/right saccades and saccade amplitude. Disinhibition in one tAD participant meant the Eyelink head-mounted system was poorly secured; eyetracking data from this participant was removed from analysis. RESULTSReading accuracyA heatmap showing the effect of word location on reading accuracy in the PCA group for an example passage is shown in REF _Ref374356754 \h Figure 8.1. PCA patients were less accurate (overall accuracy: 57.2%, SD=21.7) than tAD patients (overall accuracy: 97.5%, SD=2.4; z=-3.43, p<.001) and controls (overall accuracy: 99.4%, SD=.01; z=3.51, p<.001); there was a trend towards tAD patients reading less accurately than controls (z=1.93, p=.054). Overall accuracy would have been lower if taking word order into account, particularly in the PCA group, as words were marked correct regardless of when they were read in each passage (see REF _Ref374356762 \h Figure 8.2).Figure 8. SEQ Figure_8 \* ARABIC 1 Heatmap of PCA accuracy data from a sample passage.-20955-311150Analysis of spatial factors on reading accuracy: In terms of categorical text position, PCA patients were more accurate reading words located on paragraph perimeters (z=5.98, p<.001) or on lines at the beginning of paragraphs (z=-2.34, p<.05). Words were read more accurately in the first or second relative to the third paragraph (first vs third: z=2.08, p<.05; second vs third: z=2.86, p<.005). In terms of continuous coordinate position analyses, page centred analysis found words were read more accurately when positioned further from the centre of the screen on the x (z=-3.07, p<.005) and y (z=-5.65, p<.001) axes. Paragraph centred analysis found words were read more accurately when positioned further from the centre of each paragraph on the x (z=-3.39, p<.005) and y (z=-5.11, p<.001) axes. In all spatial factors models, longer words (p<.05) and words occurring earlier in each passage (p<.005) were read more accurately. Words of lower frequency were read more accurately in the coordinate based (p<.05) but not text position (p>.1) analyses. In contrast to the impact of spatial factors on PCA reading, there were no significant effects of any of the spatial variables on tAD patients’ reading accuracy. The control group did not make enough errors to allow for accuracy analysis.Figure 8. SEQ Figure_8 \* ARABIC 2 Order of first forty words read by 1) a tAD patient and in two PCA patients with MMSE scores of 2) thirteen and 3) twenty-two. Numbers refer to where words were repeated, each ‘-‘ in statements indicates a pause of 3 seconds. Under our marking scheme, words were marked as correct regardless of word order: in this way participant 3) was considered to have read thirty-eight of her first forty words correct.-1822453175Reading latencyPCA patients (overall latency: 174.0s, SD=73.9) took longer to read passages than tAD patients (overall latency: 52.0s, SD=18.3; z=3.50, p<.001) and controls (overall latency: 36.2s, SD=5.2; z=3.50, p<.001). tAD patients were slower than controls (z=2.08, p<.05).Eye movement dataA summary of mean and standard deviation saccade and fixation data and group comparison statistics is shown in REF _Ref374279431 \h Table 8.4. The PCA group made significantly more saccades, more fixations and longer fixation durations compared to the tAD and control groups. In addition, the PCA group made significantly more horizontal saccades than the control group and left saccades than the tAD group, but group differences in saccade amplitude did not reach formal levels of significance. There were no significant differences in any eye movement measures between the tAD and control group.Table 8. SEQ Table_8 \* ARABIC 4 Eye movement data for PCA, tAD and control groups. Asterisks denote where the PCA group significantly differs from the tAD or control group (vs controls: * = p<0.05; **= p<.005; vs tAD:^=p<.05 ).Standard paragraph presentation (Baseline)SaccadesFixationsOverall NLeftRightAmplitudeOverall NDurationPCA(N=6)mean384 ± 13**^118 ± 29**^166 ± 19**2.0 ± 0.5405 ± 125**^349 ± 42**^min|max265|55283|180106|2391.5|3.0280|579291|411tAD(N=5)mean180 ± 7247 ± 1998 ± 332.1 ± 0.5190 ± 70254 ± 34min|max116|29526|7569|1441.7|2.9120|302209|290control(N=6)mean134 ± 2234 ± 1180 ± 92.3± 0.4141 ± 22239 ± 33min|max92|15122|5464|892.0|3.1100|161176|267DISCUSSIONThis investigation demonstrated the debilitating effects of spatial location of text on reading ability in PCA. All measures of word position (line number, paragraph perimeter/interior, distance from centre of screen and distance from centre of paragraph) were significant predictors of reading accuracy, whereas word frequency and word category were not. Not only were PCA patients slower and less accurate than tAD and control groups, they also made longer fixations and more saccades and fixations overall.While almost all previous empirical investigations of acquired dyslexia in PCA have focused on single-word recognition, the current study set out to characterise reading deficits that operate at the passage level. These deficits may underlie comments made by PCA patients of having difficulty following, or moving between, lines of text; comments of this nature were made by participants in-between testing, along with complaints of text moving (described as “a gentle sliding” by one participant) consistent with previous reports of PCA patients’ perceived motion of static stimuli (Crutch et al., 2011). PCA patients were less accurate at reading words positioned within rather than at the edge of paragraphs, words at the end of paragraphs and words in the second or third rather than the first paragraph of each passage; PCA patients were less accurate at reading words positioned horizontally and vertically towards the centre of each paragraph or the centre of the overall passage. The diminished ability to read words positioned within blocks of text may be a consequence of visual disorientation, spatial agnosia, spatial attentional impairment and/or enhanced crowding, with multiple adjacent words increasing the probability of mislocalisation and/or disrupted word recognition. The propensity for making omission errors illustrates deficits in word localisation that greatly hinder passage reading in PCA. Eyetracking data further emphasises the disordered and inefficient quality of passage reading in PCA. Despite having much poorer reading accuracy than tAD or control participants, PCA patients made more fixations and saccades and showed increases in fixation duration. These additional fixations and saccades likely relate to the slower reading speed of PCA patients, and could arise from a reduced ability to both localise and subsequently correctly perceive relevant words. The absence of the parafoveal preview benefit (Rayner, 1998), possibly due to diminished perception of peripheral vision (Crutch et al., 2011; Crutch, 2013), might account for the increase in fixation duration in the PCA group. CHAPTER CONCLUSIONThe current investigation demonstrates the grave difficulties faced by PCA patients in reading passage text, most prominent of which is a drastic inability to adequately localise words. The current chapter confirmed the hypothesis that spatial factors are the primary determinants of PCA passage reading ability; of seven spatial variables, all were found to predict passage reading accuracy in PCA. Spatial variables did not predict reading accuracy in tAD or healthy control participants, who read passages at ceiling or near ceiling levels. Compared to tAD and control participants, PCA patients were slower, less accurate and made highly inefficient eye movement and fixation patterns. FACILITATING READING IN PCACHAPTER INTRODUCTIONFollowing the previous chapter, the current investigation intended to create the optimal conditions for passage reading in PCA by applying perceptual manipulations that minimise or evade visual deficits in PCA (visual disorientation, spatial agnosia, spatial attentional deficits, fixation instability, reduced effective field of vision, enhanced crowding). It was hypothesised that presenting words in a single, central location would reduce the challenge of localising words within sentences or identifying the onset of subsequent lines of text. Marking this location with a fixation box might also serve as a permanent cue to location and hence aid fixation stability. Reports of PCA patients being able to better localise moving stimuli (Crutch, 2013; Midorikawa et al., 2008) further suggested that rather than using rapid serial visual presentation (RSVP) paradigm to present words to a single location, successively moving words to fixation may act as a motion cue to assist disorientated readers. Such movement might also elude potential temporal masking effects occurring with traditional RSVP (Broadbent & Broadbent, 1987). It was further predicted that restricting text presentation to individual words would remove crowding effects from adjacent words, and that the risk of the horizontal and vertical line features of the fixation box themselves crowding the target text could be attenuated by using opposite contrast polarity (Kooi et al., 1994; Chakravarthi & Cavanagh, 2007). Two interventions were developed on these bases; both moved words successively into a fixation box of opposite polarity to text. In addition, one intervention presented the subsequent word in each passage to the right of the fixation box in order to assess whether participants’ reading would benefit from the presence of words to the right of fixation, as in healthy individuals (Hyona et al., 2004; Rayner, 1998).The current investigation evaluated the efficacy of an intervention designed to ameliorate the impact of spatial, perceptual and oculomotor impairments upon their reading ability by assessing reading performance and eye movement patterns and comparing them with tAD patients and healthy controls. The study tested the hypothesis that reducing the spatial, perceptual and oculomotor demands of passage reading would significantly improve reading ability in PCA. It was predicted that sequential presentation of words to a single location marked by a permanent fixation box and motion cues (to reduce the impact of visual disorientation, spatial agnosia, spatial attentional deficits, fixation instability, reduced effective field of vision, and enhanced crowding) would yield improvements in both reading performance (accuracy, latency and independent ratings of global comprehension) and self-rated measures of reading experience (ease and comprehension). METHODSWords for both pilots and the main experiment were presented in Arial Unicode MS font with a visual angle of letter height subtending 0.5° at a distance of 50cm. All passages had a cut-off time of 300 seconds.Pilot study 1 Pilot stimuli10 passages from the Readright? website (Leff et al., 2012) consisting of 49 words each under the following presentation conditions (see REF _Ref376252408 \h Figure 9.1):Baseline: black text in standard presentation. Reverse polarity (RP): as 1 but with words in alternating polarity (black and white)Serial single word (SSW): black text was presented one word at a time in a white fixation box (width: 5.7°, height: 2.3°) at the centre of the screen. Moving window (MW): black text was presented one sentence at a time through a fixation box (same dimensions as in presentation 3). Only text in the fixation box was visible. Moving window- Reverse polarity (MW-RP): as presentation 4; however all text in each sentence was visible, with text inside and outside the fixation box presented in opposite polarity.Each passage was assigned to 1 of the 5 conditions, for a total of 2 passages for each condition. Passages in each condition were matched on the time taken to read them by healthy controls. Pilot procedure4 PCA participants were asked to read aloud all passages. Order of presentation was arranged in an ABBA design. The first 5 passages were preceded by a practice sentence under the same presentation conditions (“This is practice text”). The rate of moving text presentation for MV and MV-RP was varied to select a speed that participants were comfortable with.Pilot resultsSee REF _Ref374280540 \h Table 9.1 for mean accuracy data across the different presentation conditions. Performance was varied; in the baseline passages, one participant did not make any errors, while another only read 45% of words correctly. There was a trend overall towards serially presented single words being read more accurately than in the baseline condition (z=-1.91, p=.057).Figure 9. SEQ Figure_9 \* ARABIC 1 Presentation conditions for Pilots 1 and 2 (i: baseline, ii: reverse polarity, iii: serial single word, iv: moving window, v: moving window- reverse polarity, vi: sequential triple word presentation)-62230-472440Table 9. SEQ Table_9 \* ARABIC 1 Accuracy and comprehension performance on Pilot studies 1 and 2.?Pilot Study 1 (N=4)Pilot Study 2 (N=7)?Accuracy (%)Accuracy (%)Comprehension (%)i. Baselinemean78.6 ± 23.856.0 ± 30.541.7 ± 25.8min|max44.9|10021.7|91.90|75ii. Reverse Polarity mean73.3 ± 26.0--min|max38.8|96.9--iii. Serial Single wordmean98.6 ± 1.796.8 ± 2.654.2 ± 24.8min|max95.9|10091.2|98.825|75iv. Moving Windowmean89.0 ± 13.275.0 ± 16.170.8 ± 29.3min|max66.3|10043.6|94.125|100v. Moving Window- Reverse Polaritymean90.4 ± 9.3--min|max74.5|100--vi. Sequential Triple Wordmean-94.4 ± 5.062.5 ± 13.7min|max-84.1|98.350|75Pilot study 2 Pilot stimuliFour articles (mean length= 234.8 words, SD=18.8) each composed of 4 paragraphs (mean length= 58.6 words, SD=10.1) were selected from the BBC news archive. All articles were presented twice: 1) entirely in the baseline (standard presentation) condition, and 2) with each paragraph presented in each of conditions i (baseline), iii and iv from Pilot study 1 (chapter REF _Ref372895673 \r \h 9.2.1). In addition, a sequential triple word (vi: SQTW) condition was included, in which text was presented three words at a time, with the central word positioned in the fixation box (see REF _Ref376252408 \h Figure 9.1). Words were presented to the left and right of the central word by a centre-to-centre distance of 5.7°. After each response, successive words were moved from right to left into the fixation box at a velocity of 22.8°/s.Pilot procedure 7 PCA participants were asked to read aloud all articles. Order of presentation was arranged in an ABBA design, with an equal distribution of presentation conditions across paragraphs. In order to assess comprehension, statements were generated by taking one sentence from each paragraph; in half the statements, part of the sentence was replaced with semantically-related information. Following reading each paragraph, corresponding statements were read aloud and participants were asked to judge whether they had seen the statement in the previous text. Pilot resultsSee REF _Ref374280540 \h Table 9.1 for mean accuracy and comprehension data across the different presentation conditions. Mean accuracy was 46.2% overall (SD=34.5) for 1) article entirely presented in the baseline condition. Paragraphs presented in serial single and sequential triple words were read more accurately than in the baseline condition (SSW: z=-2.37, p<.05; SQTW: z=-2.37, p<.05). There was a significant increase in comprehension performance for moving sentences (z=-2.33, p<.05) and a trend towards an increase in comprehension accuracy for sequentially presented triple words (z=-1.72, p=.086) relative to the baseline condition.Main investigation- Single-word and Double-word presentationStimuliTo contrast with the baseline condition of standard passage reading (as described in chapter REF _Ref374203169 \r \h 8) two reading interventions were designed to provide the optimal conditions for reading in PCA by minimising the spatial, perceptual and oculomotor demands of reading.1.Sequential Single Word (single-word): passages presented one word at a time in a central fixation box. 2.Sequential Double Word (double-word): identical to the single-word condition except that each target word was accompanied by the subsequent word, displayed parafoveally to the right of the fixation box (mean centre-to-centre distance between target/parafoveal word: 5.7°). In both the single-word and double-word conditions, words were successively moved at a velocity of 22.8°/s from a location 5.7° degrees to the right of the fixation box into the centre of the fixation box where they then remained stationary until read (see REF _Ref374279856 \h Figure 9.2). Both interventions were presented within a fixation box (height: 2.1°; width: 4.3°) to limit visual disorientation; the box was in opposite polarity to text, to limit any crowding effects exhibited by the box on word identification.Figure 9. SEQ Figure_9 \* ARABIC 2 Sequential single-word and sequential double-word presentations: words appear in the fixation box (1); following participants’ responses, successive words move rapidly (2) into the fixation box (3).-675005-390525ProcedureParticipants read aloud the 6 passages described in chapter REF _Ref374273491 \r \h 8.2.3.1 under 3 conditions of presentation: baseline (standard presentation: chapter REF _Ref374203169 \r \h 8), single-word and double-word. A repeated-measures design was used for PCA and tAD groups, with each passage read in every condition by each participant to control for lexical and syntactic differences between passages. Passages were administered in the same order to limit variability in time differences between sets of repeated passages. Presentation conditions were arranged in an ABBA design within blocks, with block order arranged in an ABBA design between participants and presentation condition always differing between trials; in this way order effects were controlled both within and between participants. The healthy control group was only administered the first block, with presentation order varying between controls. After each passage, PCA patients were asked to rate “How easy was it to read the passage?”, “How well did you understand the passage?” and “How pleasant was it to read the passage?” on a 4-point auditory-verbal scale (Very, Quite, Not really, Not at all). For the first 3 passages, PCA and tAD patients were also asked a global comprehension question (“Can you tell me the gist of that article?”) to ensure participants were extracting semantic information from each passage. General aspects of test administration, measurement of reading latencies, and eye movement recording and measures were consistent with chapter REF _Ref374203169 \r \h 8.Data analysisEfficacy of interventionsRegression models were identical to those used in chapter REF _Ref374273658 \r \h 8.2.4.1 except for the replacement of spatial variables with presentation condition (baseline versus single- or double-word) as the variable of interest. Pairwise correlations were used to calculate strength of associations between PCA accuracy data and performance on measures of visual processing (Crowding [letters with 4 number flankers, all unspaced: i.e. 48G23], spatial analysis [VOSP Dot Counting, VOSP Number Location] and object perception [VOSP Object Decision]) and disease severity (MMSE score and disease duration). Measures of visual processing were transformed into a standardised range (0-100) in which 0 and 100 corresponded to the minimum and maximum score achieved by any patient. Global comprehension question responses were read by blinded raters (N=14) and assigned to the paragraph that they felt corresponded most to each response. Differences between the number of correctly assigned comprehension questions and self-reported ease, comprehension and pleasantness measures were assessed using a Wilcoxon signed-ranks test. Errors were classified according to criteria outlined in Crutch and Warrington (2009). Eye movement dataDifferences in saccade and fixation measures between baseline and intervention conditions within the PCA group were assessed using a Wilcoxon signed-ranks test. Strength of associations with reading accuracy was calculated using a pairwise correlation test. RESULTSEfficacy of reading interventionReading accuracy and latencyPercentage error rates and reading latency data for the PCA, tAD and control groups on the baseline, single-word and double-word conditions are shown in REF _Ref374279990 \h Figure 9.3. There was an interaction between patient groups and reading intervention; compared to tAD patients, PCA patients performed significantly more accurately in the single- (z=3.62, p<.001) and double-word (z=5.81, p<.001) relative to the baseline condition. There was no significant interaction between group (PCA versus controls) and the reading intervention owing to the near-ceiling level of performance in the control group (overall error rate: 0.3%). PCA: Compared with baseline reading performance, the PCA group showed significant improvements in response accuracy with both the single-word (67% improvement; z=38.17, p<.001) and double-word interventions (64% improvement; z=34.82, p<.001). These group level effects were mirrored at the individual patient level, with all PCA patients showing significant improvement with both single- and double-word interventions; one participant’s accuracy increased from 24% to 93% using the single-word intervention (see REF _Ref374280221 \h Figure 9.4). The PCA group performed more accurately in the single- than in the double-word condition (z=-5.61, p<.001). Lower reading accuracy was revealed for words read later in each passage (z=-11.35, p<.001) and words of higher frequency (z=-2.3, p<.05). There were no significant effects of repeats (p>.6) or word length (p>.2). There was no significant difference in reading latency between the baseline condition and single- (p>.3) or double-word (p>.7) conditions.-301625-376555Figure 9. SEQ Figure_9 \* ARABIC 3 Summary of reading accuracy and latencies for the PCA, tAD and control groups. Error bars show standard error for each group mean. tAD: Compared with baseline reading performance, the tAD group showed modest increments in accuracy with the single-word (2.3%; z=4.60, p<.001) and double-word interventions (1.7%; z=3.24, p<.005). At the single patient level, there were effects of the single-word intervention in 2/6 and effects of double-word intervention in 1/6 of the tAD participants; the greatest increase in accuracy for a tAD patient was from 93% to 99%. The tAD group performed more accurately in the single-word than in the double-word condition (z=2.00, p<.05). There were no effects of repeated passages (p>.1), word length (p>.3), frequency (p>.3) or word order (p>.4) on reading accuracy. Reading speed was fastest in the baseline relative to both the single- and double-word conditions (both z=2.20, p<.05); reading speed was faster in the double- relative to the single-word condition (z=1.99, p<.05). Controls: Reading accuracy rates were at ceiling in all three conditions, therefore there were no significant effects upon accuracy of presentation condition or any of the nuisance variables. As with the tAD patients, reading speed was fastest in the baseline relative to both the single- and double-word conditions (both z=2.20, p<.05), and faster in the double-relative to the single-word condition (z=2.21, p<.05).Figure 9. SEQ Figure_9 \* ARABIC 4 PCA participants’ reading accuracy for baseline (standard presentation) and under both reading interventions.-300355160655Error analysis226695753110Error rates for PCA, tAD and control groups under the different presentation conditions are shown in REF _Ref374357169 \h Figure 9.5. Associations between behavioural measures and reading accuracy under the different presentation conditions are shown in REF _Ref374357198 \h Table 9.2. Figure 9. SEQ Figure_9 \* ARABIC 5 Error rates on a logarithmic scale for PCA, tAD and control groups under different presentation conditions. ‘Other’ errors include phonological, derivational and miscellaneous errors. PCA: By far the biggest proportion of errors made by PCA patients in the baseline condition were omission errors, with patients missing words or whole sections of each passage. Lower numbers of omission errors were associated with better performance on tests of spatial analysis (VOSP Dot Counting: r=-.58, p<.05; VOSP Number Location: r=-.55, p<.05) and, to a lesser extent, crowding (r=-.50, p=.059). Higher numbers of visual errors were associated with better performance on tests of dot counting (r=.65, p<.01) or number location (r=.47, p=.077); no significant associations were found between the number of visual errors and measures of crowding (p>.1). In the single-word condition, no significant associations were found between numbers of omission errors and measures of dot counting (p>.4), number location (p>.7) or crowding (p>.7). Lower numbers of visual errors were significantly associated with better performance on measures of crowding (r=-.52, p<.05) but not dot counting (p>.3) or number location (p>.4). In the double-word condition, lower numbers of omission errors were associated with better performance on dot counting (r=-.64, p<.05), and to a lesser extent, number location (r=-.49, p=.071); there was no significant association between omission errors and performance on crowding measures (p>.2). Numbers of visual errors were not significantly associated with measures of crowding (p>.4) or spatial analysis (Dot Counting: p>.4; Number Location: p>.2).Table 9. SEQ Table_9 \* ARABIC 2 Correlations between PCA performance on behavioural measures and reading accuracy under different presentation conditions (*=p<.05).Measures of visual processingDisease severityCrowdingDot countingNumber LocationObject DecisionMMSEDisease durationBaseline accuracy.57*.61*.60*.36.11-.27Single-word accuracy.53*.36.28.43.04-.01Double-word accuracy.34.61*.54*.23.41-.25Self-reported measuresPCA patients’ self-reported measures of reading experience in the three reading conditions are shown in REF _Ref374280634 \h Figure 9.6. Overall, the PCA group rated reading passages in both intervention conditions to be significantly easier (single-word: z=3.41, p<.001; double-word: 3.30, p<.005), more pleasant (single-word: z=3.24, p<.005; double-word: z=2.58, p<.05) and more readily understood (single-word: z=3.38, p<.001; double-word: z=3.14, p<.005) than in the baseline condition. The PCA group also considered passages in the sequential single-word condition to be significantly easier (z=2.18, p<.05) and more pleasant to read (z=2.15, p<.05), but not better understood (p>.2) than in the double-word condition. Figure 9. SEQ Figure_9 \* ARABIC 6 Proportion of passages under the different presentation conditions that were considered easy, pleasant or well understood by PCA patients.-384810-506730Global comprehensionPCA: 90.9% of responses were correctly assigned to passages in the baseline condition; 96.2% of responses were correctly assigned in the single-word and 88.3% in the double-word conditions. There were no significant differences between measures of global comprehension between any of the presentation conditions. tAD: 86.9% of responses were correctly assigned to passages in the baseline condition; 72.6% of responses were correctly assigned in the single-word and 82.1% in the double-word conditions. There were no significant differences between measures of global comprehension between any of the presentation conditions. There was a trend towards PCA patients having better global comprehension than tAD patients in the single-word condition (z=1.82, p=.068). There was no significant between-group differences in the baseline (p>.2) or double-word conditions (p>.5).Eye movement dataThe mean and standard deviation saccade and fixation data for the baseline, single-word and double-word conditions and group comparison statistics are shown in REF _Ref374357212 \h Table 9.3. As with the baseline condition (as reported in chapter REF _Ref374274306 \r \h 8.3.3), PCA patients made significantly more saccades and more fixations than either the tAD patients or the controls in both the single-word and double-word reading interventions. However, the single-word condition was the only reading condition in which the duration of PCA patients’ fixations did not differ Table 9. SEQ Table_9 \* ARABIC 3 Eye movement data for PCA, tAD and control groups in the reading intervention conditions. Asterisks denote where the PCA group significantly differs from the tAD or control group (vs controls: * = p<0.05; **= p<.005; vs tAD:^=p<.05).Single-word presentationSaccadesFixationsOverall NLeftRightAmplitudeOverall NDurationPCA(N=6)mean336 ± 52**^111 ± 21**^131 ± 53*1.2 ± 0.5^355 ± 49**^467 ± 71min|max276|38588|14473|1990.8|2.0288|410375|577tAD(N=5)mean197 ± 5650 ± 1969 ± 330.8 ± 0.2214 ± 55544 ± 168min|max142|26031|7815|920.5|0.9148|275362|785control(N=6)mean152 ± 5036 ± 2369 ± 280.8 ± 0.2176 ± 52533 ± 152min|max99|22813|7727|1070.5|1.1106|237390|783Double-word presentationSaccadesFixationsOverall NLeftRightAmplitudeOverall NDurationPCA(N=6)mean383 ± 63**^141 ± 42*^168.0 ± 33.2**2.1 ± 0.8402 ± 68**^393 ± 65^min|max327|46470|190128|2130.9|3.4332|501324|499tAD(N=5)mean255 ± 6891 ± 31132 ± 322.4 ± 1.0264 ± 65305 ± 52min|max168|33539|11988|1691.5|4.0185|344216|347control(N=6)mean186 ± 4665 ± 25102 ± 202.5 ± 0.6199 ± 47360 ± 81min|max146|26742|10971|1221.7|3.2147|284277|473significantly from that of either tAD patients or controls, representing an increase in fixation duration relative to the baseline condition (z=2.20, p<.05). The single-word condition also led to saccades of decreased amplitude relative to the baseline condition (z=-1.99, p<.05). DISCUSSIONWhile chapter REF _Ref374203169 \r \h 8 implicated spatial, perceptual and oculomotor factors in determining passage reading ability in PCA, the current investigation applied perceptual manipulations to text in an effort to circumvent these factors along with associated deficits. Two reading interventions, single-word and double-word presentation, were evaluated; both interventions intended to lessen the spatial demands of passage reading. The interventions resulted in dramatic improvements in the reading accuracy of PCA patients; single-word presentation resulted in a mean increase of 67.3% in reading accuracy, with one participant’s accuracy almost tripling from 24% to 93%. Single-word presentation led to increases in PCA patients’ fixation duration and decreases in saccadic amplitude, suggesting improved fixation stability and reduced saccadic demands under this condition. This investigation demonstrated how two reading interventions resulted in considerable and consistent gains in PCA patients’ reading accuracy. These gains were accompanied by improvements in the self-reported ease of reading, reading comprehension and pleasantness. Evidently, the serial presentation used in both interventions eliminates frequent difficulties experienced by PCA patients in repeating or skipping lines of text. By reducing the spatial and oculomotor demands of reading, this serial presentation may have reduced the susceptibility of PCA patients’ reading ability to deficits in early or spatial vision. Neuropsychological findings suggest poor reading in the baseline condition is primarily linked to visuospatial impairment; findings also associate enhanced visual crowding with poor passage reading ability, although it is probable that such crowding is not only inhibiting word localisation but also word recognition. Associations between neuropsychological measures of visuospatial ability/visual disorientation and reading accuracy were found in the baseline and double-word, but not single-word condition, while associations between crowding effects and reading accuracy were found in the baseline and single-word, but not double-word condition. In addition, omission errors were associated with measures of visuospatial ability and visual disorientation in baseline and double-word, but not single-word conditions. One possibility is that the detrimental effects of deficits such as visual disorientation and enhanced crowding on reading are mitigated under intervention presentations; this might be due to the reduced amount of visual information that can compete or interfere with a target word. However, associations between enhanced crowding and reading ability under single-word presentation and visual disorientation and reading ability under double-word presentation suggest that text read under either intervention did not wholly evade weaknesses in other forms of visual processing. While eyetracking measures identified increases in PCA participants’ fixation duration under the single-word condition, with the resulting duration being near equivalent to that of tAD and control participants, elevated numbers of saccades and fixations in the PCA relative to tAD or control groups were not restricted to the baseline condition. While the single-word intervention did not result in a decrease in the number of eye movements, the decrease in saccade amplitude under the single-word condition might suggest a reduction in saccadic demands.While the single- or double- word presentation might provide the basis of a reading application, other directions might be available which further improve upon PCA patients’ performance. Eye movement abnormalities in PCA patients may reflect similar deficits to those observed in dyslexic children who have unsteady eye control and report stationary text to be moving (Stein, 2003). Such deficits have been linked to impairments of the magnocellular system (Ray et al., 2005) leading to a reduced capacity to inhibit the parvocellular system following saccades; Lovegrove et al. (1990) suggested this may result in a lessened ability to erase visual images from previous eye movements. As the magnocellular pathway is inhibited by certain light wavelengths (Roorda & Williams, 1999), coloured filters might facilitate magnocellular function in PCA patients. Coloured filters have already been shown to improve the reading ability of dyslexic children (Solman et al., 1991; Robinson & Foreman, 1999), fluent adult readers (Chase et al., 2003) and have resulted in a reduction in visual disorientation in a PCA patient (Crutch et al., 2011). Another visual deficit that might be addressed in a reading application is the inverse size effect, which limits single word recognition in PCA and has plausible roles in limiting the parafoveal preview benefit and exacerbating visual disorientation. If this effect is contributed towards by visual field defects, it is possible that visual field expansion resulting from prism adaptation might provide further support to PCA patients. Finally, while both interventions likely moderate crowding effects by restricting the potential for adjacent words to act as inhibitory flankers, it is still possible that enhanced crowding is somehow interfering with reading at the single-word level (Crutch & Warrington, 2009). By varying letter contrast polarity within words without compromising the word form, it may be possible to further limit crowding disrupting reading ability.There are several limitations regarding interpretations of the current data. Lacking measures of the inverse size effect, it is not possible to investigate its role in limiting passage reading ability. Near-ceiling accuracy and small numbers of participants in the tAD group prevent the identification of factors which are reducing reading ability in tAD relative to control participants. While each passage was preceded by a drift correct, the time taken to read passages meant that eye movements were often not aligned with interest areas by the end of each passage, which meant that word-based or interest area analyses were not feasible. CHAPTER CONCLUSIONSPoor passage reading in PCA is likely a consequence of various deficits: poor visuospatial ability and spatial attention, fixation instability, a reduced effective field of vision and to a lesser extent, enhanced crowding. This investigation outlines two reading intervention techniques, both of which adopt text presentation methods that are less susceptible to these deficits. Both interventions result in clinically-meaningful increases in reading ability, and subjective increases in reading ease, comprehension and pleasantness. Eyetracking data suggests that, in particular, the single-word intervention promotes a greater efficiency of eye movements. These encouraging findings provide the foundations for novel software-based assistive technology that will support reading ability in PCA patients.THESIS CONCLUSIONSCHAPTER INTRODUCTIONPCA is a debilitating condition involving progressive visual impairment; such impairment prevents PCA sufferers from carrying out a range of ADLs, reducing their quality of life and demanding increased assistance from their carers. An instrumental ADL which PCA patients have frequent and early difficulty with is reading (Benson et al., 1988; Freedman et al., 1991; Berthier et al., 1991; Mendez et al., 2002; McMonagle et al., 2006). The importance of reading is highlighted by elderly participants rating reading as one of the top three instrumental ADLs required for maintaining community living (Fricke & Unsworth, 2001). This thesis aimed to investigate the nature of reading impairment in PCA by assessing the impact of perceptual factors and the contribution of various visual deficits towards reading dysfunction (chapter REF _Ref372389238 \r \h 2.2). One form of early visual processing deficit which may particularly limit visuoperceptual and reading ability is crowding (chapter REF _Ref374535107 \r \h 2.2.1.1); however, previous observations of enhanced crowding effects in PCA are based on a handful of case reports. Case reports form almost all previous investigations of acquired dyslexia in PCA (chapter REF _Ref374200298 \r \h 2.3); these tend to focus on single word recognition, and have not attempted quantitative analysis of reading above the single word level. Visual assessment of PCA patients can be challenging given how such patients often demonstrate impairments in multiple domains; not controlling for deficits such as a reduced effective visual field (chapter REF _Ref374025486 \r \h 2.2.2) or visual disorientation (chapter REF _Ref374541239 \r \h 2.2.1) may confound specific measures of visual or reading ability.This thesis conducted systematic group investigations of visual crowding, single word recognition and passage reading in PCA. These investigations attempted to gauge the prevalence and quality of visual and reading deficits through analysis of accuracy, latency, eye movement and self-reported data, and to understand the brain basis of specific aspects of visual and reading functions through selected VBM neuroimaging analyses. A consistent and comprehensive battery of neuropsychological measures was employed to assess relationships between different aspects of visual function and identify confounding visual deficits. Findings were contrasted with healthy controls and AD patients with an amnestic presentation. A case series of two patients was also studied and followed longitudinally, exploring the causative role of visual deficits in acquired dyslexia. Having identified aspects of reading which are particularly vulnerable to visual impairment in PCA, two reading interventions were developed, based on the notion that navigating such impairment might facilitate reading. The relationships between different forms of visual processing have implications for our understanding of how perception is limited in PCA and moderate to severe tAD. Improving this understanding will inform the design of technological aids which will maximise weak visual ability or emphasise the use of relatively spared aspects of vision, ideally resulting in greater independence, quality of life and a reduction in carer burden.ROLE OF EXCESSIVE CROWDING IN LETTER RECOGNITIONPrevious case reports of PCA have revealed performance on flanked letter identification tasks in line with prominent crowding effects (chapter REF _Ref374535107 \r \h 2.2.1.1). Chapter REF _Ref372557747 \r \h 4 identified group effects of spacing (chapter REF _Ref373489243 \r \h 4.3.1) and ameliorating effects of opposite polarity flankers (chapter REF _Ref373489261 \r \h 4.3.2), confirming the qualitative similarity between centrally-presented flanked letter identification deficits in PCA and flanked letter identification deficits in normal peripheral vision. The current neuroimaging findings support the anatomical locus of crowding as being within the right collateral sulcus in these patients, a region which may correspond to area V4 (chapter REF _Ref372630732 \r \h 4.3.5). Area V4 fulfils various criteria for the locus of crowding (see chapter REF _Ref374284976 \r \h 4.4), while fMRI investigation of healthy individuals has found that, of areas V1-V4, crowding effects provoked greatest activation in area V4 (Anderson et al., 2012). In the context of proposals of a model of crowding involving feature detection and higher-level feature integration stages (Levi, 2008), this locus, along with behavioural data suggesting errors do not predominantly arise from a failure to detect target stimuli, evokes crowding in the current PCA patient group as a consequence of deficits in feature integration. These investigations suggest PCA may provide a novel perspective into the nature of crowding; a neurodegenerative approach offers an opportunity to track the evolution of crowding deficits and assess how such deficits may affect other parts of the visual system. Future research will further clarify the nature of enhanced crowding effects in PCA through pathological investigation, in particular, associating occipital distribution of AD pathology to flanked letter identification deficits and investigating whether the susceptibility of certain cell populations to AD relates to the expression of crowding as a deficit in feature integration versus one of feature detection. Behavioural investigations will examine the contribution of crowding towards higher level deficits in word, object and scene perception in PCA. Future neuroimaging studies will aim to identify neural correlates of the polarity effect, which will improve our understanding of crowding and provide suggestions how flankers of opposite polarity are less susceptible to spacing effects. ROLE OF PERCEPTUAL FACTORS IN WORD RECOGNITIONResults highlight a curious visual phenomenon in PCA, in which patients were slower and less accurate reading words in larger font. The magnitude of this inverse size effect (chapter REF _Ref374025486 \r \h 2.2.2) was associated with grey matter volume in the right superior parietal lobule (chapter REF _Ref372545601 \r \h 5.3.3). The inverse size effect may reflect diminished ability to perceive peripheral stimuli and/or to execute spatial shifts of attention or multiple saccades. Chapter REF _Ref372466719 \r \h 5.3 did not find overall patterns characteristic of neglect dyslexia in PCA, with less than a fifth of errors being consistent with neglect errors (chapter REF _Ref372537124 \r \h 5.3.1.4). The lack of LBL reading from chapters REF _Ref372462822 \r \h 0 and REF _Ref371599193 \r \h 6 question general visual accounts of LBL reading, which propose that deficits in prelexical visual processing lead to length effects on reading latencies. Despite deficits in word recognition, results also outline the surprising efficacy of single word reading given the myriad of visual difficulties experienced in PCA. Mendez et al. (2007) suggested the term “apperceptive alexia” to describe the contribution of visuoperceptual and visuospatial deficits towards poor reading ability in PCA. Chapter REF _Ref372462887 \r \h 5.3.1.2 identified how differences in reading ability between PCA and tAD patients could be accounted for in particular by visuoperceptual, but also visuospatial and early visual processing. The relationship between visuoperceptual ability and word reading is unsurprising, given associations between ventral systems and parallel letter processing (Vinckier et al., 2006) and between deficits in object perception and alexia (Farah, 1991; Glosser et al., 2002). From a neuroanatomical perspective, it is possible that damage to occipito-temporal ventral pathways might result in alexia coinciding with visual agnosia (Rumiati & Humphreys, 1997). Chapter REF _Ref372466855 \r \h 5.3.1.2.1 outlined the contribution of visuospatial processing towards poor word recognition in PCA relative to tAD patients. Poor visuospatial ability may underlie PCA patients’ poor reading ability for words with inter-letter spacing or words in cursive font, given suggestions of words in unfamiliar presentations demanding support from dorsal systems (chapter REF _Ref372652459 \r \h 5.4) which are greatly disrupted in PCA. The dysfunctional involvement of dorsally-mediated reading strategies might account for previous observations of PCA patients having difficulty reading words with crosshatched letters (Mendez & Cherrier, 1998), in stylised font (Mendez, 2001) or cursive font (De Renzi, 1986). Visuospatial impairment may also directly contribute to, or at least compound, the inverse size effect; reading larger words might place greater demands on visuospatial ability and/or increase the number of spatial shifts in attention. Chapter REF _Ref372466855 \r \h 5.3.1.2.1 also demonstrated how, among measures of early visual processing, prominent crowding deficits were particularly important in accounting for differences in accuracy between PCA and tAD patients. Chapter REF _Ref371599193 \r \h 6 identified how two patients with preserved reading demonstrated impairments on all measures of visual function apart from tests of acuity and crowding, while chapter REF _Ref372467125 \r \h 7 documented how the evolution of crowding effects in these patients was accompanied by the emergence of reading dysfunction. Mendez et al.’s (2007) term “apperceptive alexia” also referred to difficulties in perceptual integration; while authors were referring to such difficulties relating to deficits in attention, they could also reflect the excessive feature integration observed in studies of crowding (Levi et al., 2002; Pelli et al., 2004). Future investigations will examine the precise mechanism through which crowding disrupts word recognition and/or parallel letter processing; for example, does crowding lead to a loss of letter feature detection, excessive feature integration across letters and does the nature of crowding deficits vary between PCA patients. PERCEPTUAL FACTORS IN PASSAGE READING AND READING INTERVENTIONSDramatic visual disorientation and visuospatial impairment are characteristic symptoms of PCA (chapter REF _Ref371937685 \r \h 1.2.2.6; chapter REF _Ref374541254 \r \h 2.2.1; chapter REF _Ref374025486 \r \h 2.2.2). Such deficits may not necessarily limit individual word recognition but may still result in gross disruptions of text reading. Mendez (2001) reported a PCA patient with poor visuospatial ability who had difficulty in localising the beginning of lines of text, but whose single word reading was significantly preserved. Previous descriptions of sentence and passage reading in PCA outlined how patients lose their place in a block of text, within sentences or even on reading cards (chapter REF _Ref372546741 \r \h 2.3); however, there have been no attempts to empirically investigate the nature of these reading difficulties in PCA. This thesis quantitatively assessed passage reading in PCA, and identified how spatial aspects of text were the crucial determinants of reading ability. Words located towards the centre of paragraphs or passages were read less accurately, words on the edges of paragraphs or lines of text were read more accurately (chapter REF _Ref374288154 \r \h 8.3.1). The overwhelming majority of errors related to patients missing whole lines of text or individual words within lines. Eye movement recordings revealed how PCA patients made an excessive number of saccades and fixations, which likely reflect a combination of deficits in spatial vision and relate to reduced reading speed in PCA (chapter REF _Ref374274306 \r \h 8.3.3).Assessment of passage reading was accompanied by two reading interventions which intended to reduce the vulnerability of reading in PCA to the following deficits: visual disorientation, spatial agnosia, spatial attentional deficits, fixation instability, a reduced effective field of vision and enhanced crowding (chapter 9). The mechanism of both interventions involved limiting the spatial, perceptual and oculomotor demands of reading by presenting passages one word at a time within a fixation box. Reading interventions resulted in consistent gains in reading accuracy for PCA patients; the efficacy of these interventions is a direct demonstration of how reducing these factors benefits text reading in PCA. This is not to say that the effect of deficits in spatial vision is completely abolished under intervention conditions; relative to controls and tAD patients, PCA patients read slowly and made excessive eye movements. In addition, findings still associate single-word presentation reading ability with performance on measures of crowding and double-word presentation reading ability with measures of visuospatial ability (chapter REF _Ref374288504 \r \h 9.3.1.2), suggesting the continued influence of certain visual processing deficits on text perception under both interventions. Future investigations will assess and discriminate the relative contributions of visual disorientation, spatial agnosia, spatial attentional deficits, fixation instability, a reduced effective field of vision and enhanced crowding.CAUSATIVE ROLE OF VISUAL IMPAIRMENT IN READINGThe notion of reading as a highly-specialised process is controversial (chapter REF _Ref372555188 \r \h 6.1). Classic studies of pure alexia emphasised the discrepancy between word or letter recognition and other forms of visual processing (Kinsbourne & Warrington, 1962; Warrington & Shallice, 1980; Arguin & Bub, 1993), with more recent neuroimaging studies suggesting a region termed the VWFA is functionally specialised for letter and word processing (Cohen et al., 2000). Given the recency of written relative to spoken language as a cultural invention, it is unlikely that a VWFA would have evolved specifically for reading. However, one suggestion is that accumulated reading experience promotes the specialization of a pre-existing inferotemporal pathway for higher-order visual processing (McCandliss et al., 2003).A general visual account perspective on LBL reading is difficult to reconcile with findings from two patients, FOL and CLA, who show profound visual dysfunction, yet demonstrate remarkably preserved whole word and letter reading and an absence of the length effects observed in previous reports of LBL readers (chapter REF _Ref371599193 \r \h 6). Results are interpreted as showing preservation of orthographic processing despite impaired prelexical processing, underlining the resilience of the reading system and indicating that deficits on ten measures of visual processing do not necessitate poor word recognition. The notion of PCA patients bypassing perceptual deficits in order to achieve access to word forms has been previously suggested by Mendez et al. (2007), and receives support from two PCA patients demonstrating a word superiority effect (Mendez & Cherrier, 1998). While both patients in chapter 6 read both accurately and rapidly, longitudinal assessment in chapter 7 revealed decreases in their reading ability; these decreases were accompanied by the emergence of spacing effects on flanked letter identification tasks which are characteristic of visual crowding (chapter REF _Ref374535107 \r \h 2.2.1.1). This is consistent with the inhibitory effects of crowding on reading in normal peripheral vision (Chung et al., 2004; Legge et al., 2001; Pelli et al., 2007). However, FOL, not CLA, showed effects of letter similarity on reading speed; such effects might be predicted given crowding is exacerbated by flankers of increased visual similarity. Findings strongly implicate enhanced crowding in limiting FOL and CLA’s reading ability and provide further evidence of the relationship between crowding and word recognition. Questions are raised regarding variations in the use of serial reading strategies and the locus of crowding in both patients. Subsequent pathological investigation may identify different crowding mechanisms; from the perspective of a two-stage model of crowding, striatal involvement would predict deficits in feature detection, and extrastriatal involvement would predict deficits in feature integration. IMPLICATIONS FOR MODELS OF READINGWhile our findings emphasise the role of perceptual factors in determining reading ability, the disruption of word recognition arising from PCA patients’ visual impairment is likely compounded by deficits in phonology. While this investigation only assessed phonological ability to a very limited extent, phonological deficits have been previously reported in PCA (Crutch et al., 2013; Magnin et al., 2013). PCA patients’ tendency to make visual errors in chapter 5 might be exacerbated by a diminished ability to monitor their phonological output, allowing such errors to go unchecked (Crutch & Warrington, 2007b). A connectionist approach might predict that such phonological deficits would result in a greater reliance on an indirect route via semantic knowledge; in chapter 5, slightly more accurate reading for words of greater concreteness in PCA but not tAD patients lends tentative support to this perspective. Evaluating a dual-route approach is particularly difficult in the context of PCA; the indirect route involves serial processing and phonology, both of which are likely vulnerable to impairment of dorsal systems and parietal atrophy in these patients. However, two attributes of the DRC model, a lack of involvement of the semantic pathway in reading aloud of normal words and an insensitivity to orthographic bodies (Woollams et al., 2007; Grainger & Dufau, 2012) might limit the ability of the model to account for subtle effects of both concreteness and orthographic neighbourhood size on PCA patients’ single word recognition (chapter 5). Future directions might include a more comprehensive investigation of concurrent visual and phonological impairment in PCA to clarify the extent to which poor reading ability might be a result of a multicomponent dyslexic syndrome.From a neuroanatomical perspective, our results do not directly address the purported role of the VWFA; PCA patients did not show evidence of LBL reading, and the evolution of length effects on reading speed in FOL (chapter 7) was attributed to the emergence of crowding rather than the compromised integrity of the VWFA leading to a specific deficit in orthographic processing. However, the current data add to previous findings on the contribution of dorsal systems towards serial processing of words presented in unfamiliar formats (Vinckier et al,. 2006; Cohen et al., 2008). Results suggest that the involvement of parietal-mediated spatial-attentional processes in reading unfamiliar words results in a particularly drastic reduction in reading ability, not only for words with an abnormally great extent of inter-letter spacing, but also possibly for words of increased font size, given how reading words at greater eccentricities provokes greater posterior parietal fMRI activation (Cohen et al., 2008). Furthermore, chapters 8 and 9 emphasise the role of dorsal impairment in undermining reading ability above the single-word level. Despite this notion of dorsal systems supporting recognition of unfamiliar words, it is not unlikely that the early visual and visuoperceptual difficulties experienced by PCA patients are provoking dorsal involvement for words under conditions that normal readers would consider sufficient for parallel processing; the extent of this involvement would be clarified through functional imaging studies of normally-presented single word recognition in individuals with PCA.CLINICAL IMPLICATIONSThis thesis applied findings from studies of early visual processing, word recognition and passage reading in PCA to inform the basis of two interventions founded on a similar principle: that minimising the susceptibility of word localisation and recognition to deficits in spatial, perceptual and oculomotor function might result in benefits to reading ability. Being able to maximise reading in patients with early to moderate stage PCA would help maintain professional and recreational activities, likely resulting in greater independence for patients and benefits in quality of life for patients and carers. PCA participants showed consistent gains in accuracy (chapter REF _Ref374289442 \r \h 9.3.1.1) and strong preference for passages read under both interventions (chapter REF _Ref374289461 \r \h 9.3.1.3), supporting their clinical utility. A concern was that the presentation format of the interventions would result in each trial becoming an exercise in word, not passage, reading, so that the global aspects of passages would elude participants. However, beyond PCA patients’ rating of their own comprehension as being better in the intervention conditions, measures of global comprehension as judged by independent raters did not detect any loss in comprehension in the intervention conditions (chapter REF _Ref374289479 \r \h 9.3.1.4). While both reading interventions were promising, some notable caveats must be acknowledged in order to shape future development of a patient-friendly reading application. Despite improvements in reading accuracy, neither intervention resulted in increases in reading speed (chapter REF _Ref374289442 \r \h 9.3.1.1). As the experimenter controlled the rate of text presentation in both interventions, it is possible that, if either intervention formed the basis of a reading application, rates of presentation maintained by users themselves might be more efficient. As apraxia is a common symptom of PCA (Tang-Wai et al., 2004; McMonagle et al., 2006) any potential reading application would have to be able to determine the rate of presentation based on auditory in addition to motor information. Some degree of carer input would likely be required to set up a future reading application, after which subsequent activation of the device, text selection and optimal rate of presentation could be achieved through voice recognition software. While both interventions likely moderate crowding effects by restricting the potential for adjacent words to act as inhibitory flankers, it is still possible that enhanced crowding is interfering with reading at the single-word level (Crutch & Warrington, 2009). By varying letter contrast within words without compromising the word form, it may be possible to limit the disruptive effects of crowding on individual words.The current findings may inform future remedial approaches that are relevant not only to reading difficulties, but also the wider context of impaired vision in PCA. Facilitation of magnocellular function using coloured filters may help address perceived motion of static stimuli and visual disorientation in PCA (Crutch et al., 2011), while visual field expansion using prism adaptation may attenuate the inverse size effect. Such approaches might result in benefits to safety, including reduced numbers of falls, and neuropsychiatric symptoms stemming from visual impairment. PCA v tADBackground neuropsychological assessment in studies of excessive crowding, single word recognition and passage reading revealed cognitive phenotypes in line with previous descriptions of PCA and tAD (chapter REF _Ref371937685 \r \h 1.2.2.6; chapter REF _Ref374290923 \r \h 4.2.2; chapter REF _Ref374290896 \r \h 8.2.2) Relative to tAD patients, PCA patients performed better overall on tests of recognition memory, with a greater proportion of PCA patients performing within a normal range for these tests. PCA patients performed worse on non-visual tests associated with parietal function, such as calculation or cognitive estimates. On tests of visual function, PCA patients consistently demonstrated impairments relative to healthy individuals and tAD patients. tAD patients did show weak performance on tests of visuospatial function, although this is not uncharacteristic of AD patients with a predominantly amnestic presentation (Caine & Hodges, 2001; Quental et al., 2013).Findings suggest that the pattern of performance in tAD on tasks of flanked letter identification, single word recognition and passage reading is more in line with controls than PCA patients. Almost without exception, group differences in reading or letter identification accuracy between tAD and controls did not reach statistical significance. However, the development of visuospatial deficits over the course of tAD (Grady et al., 1988; Almkvist, 1996; Salmon & Bondi, 2009) may coincide with poor reading for reading words in unfamiliar formats or words presented in passages, given the proposed contributions of dorsal systems towards word and passage reading (chapter REF _Ref372562500 \r \h 10.3, chapter REF _Ref372562509 \r \h 10.4). It is possible that pathological involvement of the occipital lobe may lead to the development of prominent crowding effects in tAD; while some post-mortem studies of tAD patients have found little to no evidence of atrophy or AD related pathology in the occipital region (Brun & Gustafson, 1976; Galton et al., 2000), other studies have found considerable deposits of neuritic plaques in the occipital lobes (Arnold et al., 1991). The current study matched PCA and tAD groups on two markers of disease severity: MMSE score and disease duration. While differences in visual function were apparent between groups, decline in visuoperceptual and visuospatial ability in later stages of tAD (Grady et al., 1988; Paxton et al., 2007) may result in visual deficits that echo those in early stage PCA. Improving our understanding of visual ability and developing approaches and technological aids that support vulnerable aspects of vision in PCA may have implications for a wider AD population. CHAPTER CONCLUSIONInvestigations in this thesis not only outline the considerable contribution of visual dysfunction to reading difficulties in PCA, but also differentiate the contributions of specific visual processing deficits when reading words presented normally, in unfamiliar formats, in isolation or in passages. Results also demonstrate how various visual processing deficits do not necessitate poor reading. Findings clarify the quality and prevalence of prominent visual crowding in PCA and emphasise its role in limiting reading ability. Reading interventions are developed on the premise that limiting the spatial, perceptual and oculomotor demands of text reading supports reading ability in PCA. Better understanding of the complex nature of visual impairment in PCA will provide the foundation for approaches towards supporting or circumnavigating particularly weak aspects of vision; such approaches may also benefit patients in later stages of amnestic AD.PUBLICATIONSThe following section refers to investigations which have been published or submitted based on the current findings along with other publications which I contributed towards during my PhD. Chapter 5 Yong K. X., Shakespeare T. J., Cash D., Henley S. M. D., Warren J. D., Crutch S. J. (2013) (Con)text-specific effects of visual dysfunction on reading in posterior cortical atrophy.Chapter 6Yong, K. X., Warren, J. D., Warrington, E. K., & Crutch, S. J. (2013). Intact reading in patients with profound early visual dysfunction. Cortex.Other publicationsShakespeare, T. J., Yong, K. X., Frost, C., Kim, L. G., Warrington, E. K., & Crutch, S. J. (2013). Scene perception in posterior cortical atrophy: categorization, description and fixation patterns. Frontiers in human neuroscience, 7, 621 doi: 10.3389/fnhum.2013.00621.Witoonpanich, P., Cash, D. M., Shakespeare, T. J., Yong, K. X., Nicholas, J. M., Omar, R., ... & Warren, J. D. (2013). Olfactory impairment in posterior cortical atrophy. Journal of Neurology, Neurosurgery & Psychiatry, 84(5), 588-590.ACKNOWLEDGEMENTSI would like to thank all the patients and their carers for all their patience and goodwill and the considerable time and effort they volunteered towards research at the Dementia Research Centre. This thesis was contributed to in no small part by various members of the Dementia Research Centre. In particular, I would like to thank my supervisors, Seb and Jason and my colleague Tim for their generous support and expertise. I would like to thank Nick Fox, Jonathan Bartlett, Jenny Nicholas, Manja Lehmann, Susie Henley, Dave Cash, Kelvin Leung, Elizabeth Warrington and Alex Leff for their invaluable input regarding presentation skills, statistical methods, imaging techniques, neuropsychology and eyetracking. I would also like to thank people at work who have helped out with various issues that have cropped up over the last three years: Anne Parnell, Suzie Barker, Nicole Schmitz, Laura Downey, Hannah Golden, Felix Woodward, Kirsty Macpherson, Aisling Buckley, Laila Ahsan, Liz Gordon, Emily Manning, Ian Malone, Casper Nielsen, Shona Clegg, Kate MacDonald, Jane Douglas, Katy Judd, Amanda Haines, Laura Monje Garcia, Ayesha Khatun, Deepali Patel, Natalie Ryan, Yuying Liang, Tom Yeatman, Ross Paterson, Catherine Slattery and Kishan Rajdev. I would particularly like to thank Edith Tan, who should take most of the credit for me completing my PhD. I would also like to thank Juanita Hoe, Martin Orrell, Elisa Aguirre, Amy Streater, Lauren Yates, Alex Feast, Nadia Crellin, Cathy Forbes and Fiona Horton for their support and supervision as a member of the SHIELD team.This work was supported by an Alzheimer’s Research UK Senior Research Fellowship to Sebastian Crutch. Jason Warren is supported by a Wellcome Trust Senior Clinical Fellowship. This work was supported by the NIHR Queen Square Dementia Biomedical Research Unit. This work was undertaken at University College London Hospital/University College London which received a proportion of funding from the Department of Health’s NIHR Biomedical Research Centres funding scheme.APPENDIX 1: DUBOIS ET AL. (2007) DIAGNOSTIC CRITERIA FOR PROBABLE ADCore diagnostic criteria:Presence of an early and significant episodic memory impairment that includes the following features:Gradual and progressive change in memory function Objective evidence of significantly impaired episodic memory on testingAssociation or isolation of episodic memory impairment at AD onset or advancementSupportive featuresPresence of medial temporal lobe atrophy (hippocampi, entorhinal cortex, amygdala) on MRIAbnormal CSF tau/Aβ 1-42 ratio: low Aβ 1-42, increased total tau concentrations, increased phosphor-tau concentrations or a combination of the threeReduced metabolism in bilateral temporoparietal regions on PET imagingAD autosomal dominant mutation within immediate familyExclusion criteriaSudden onset, occurrence of gait disturbances, seizures and behavioural changesFocal neurological features and early extrapyramidal signsPresence of medical disorders that can account for memory and related symptomsAPPENDIX 2: NINCDS-ADRDA 2011 CRITERIA FOR DEMENTIA AND PROBABLE ADCRITERIA FOR ALL-CAUSE DEMENTIA: CORE CLINICAL CRITERIADementia is diagnosed when there are cognitive or behavioural (neuropsychiatric) symptoms that:Interfere with the ability to function at work or at usual activitiesRepresent a decline from previous levels of functioning and performingAre not explained by delirium or major psychiatric disorderCognitive impairment is detected and diagnosed through a combination of (1) history-taking from the patient and a knowledgeable informant and (2) an objective cognitive assessmentThe cognitive or behavioural impairment involves a minimum of two of the following domains:Impaired ability to acquire and remember new informationImpaired reasoning and handling of complex tasks, poor judgmentImpaired visuospatial abilitiesImpaired language functions Changes in personality, behaviour, or comportmentPROBABLE AD DEMENTIACore clinical criteria:Probable AD dementia is diagnosed when the patient meets criteria for dementia described earlier in the text, and in addition has the following characteristics:Insidious onsetClear-cut history of worsening of cognition by report or observationThe initial and most prominent cognitive deficits in one of the following categories:Amnestic presentationNonamnestic presentations (Language, Visuospatial, Executive)No evidence of cerebrovascular disease, Dementia with Lewy bodies, frontotemporal dementia or other concurrent active neurological disease.Increased level of certainty:Documented cognitive decline on informant and cognitive testingProbable AD dementia in a carrier of a causative AD genetic mutation (APP, PS-1, PS-2)Evidence of AD pathophysiological processAmyloid PET imagingElevated CSF tau, both total tau and phosphorylated tauFDG uptake on PET in temporoparietal cortexDisproportionate atrophy in medial, basal and lateral temporal lobe and medial parietal cortexAPPENDIX 3: PROPOSED DIAGNOSTIC CRITERIA FOR PCAMENDEZ ET AL. (2002) PROPOSED CLINICAL DIAGNOSTIC CRITERIA Core diagnostic features (all must be present)Insidious onset and gradual progressionPresentation with visual complaints with intact primary visual functionsEvidence of predominant complex visual disorder on examination: elements of Balint’s syndrome, visual agnosia, dressing apraxia, environmental disorientationProportionally less impaired deficits in memory and verbal fluencyRelatively preserved insight with or without depressionSupportive diagnostic features Presenile onsetAlexiaElements of Gerstmann’s syndromeIdeomotor apraxiaPhysical examination within normal limitsPredominant impairment of visual functionPredominantly occipitoparietal abnormality with relative sparing of frontal and medial temporal regionsTANG-WAI ET AL. (2004) PROPOSED CLINICAL DIAGNOSTIC CRITERIA Core featuresInsidious onset and gradual progressionPresentation of visual complaints in the absence of significant primary ocular disease explaining the symptomsRelative preservation of anterograde memory and insight early in the disorderDisabling visual impairment throughout the disorderAbsence of stroke or tumourAbsence of early parkinsonism and hallucinationsAny of the following:Simultanagnosia with or without optic ataxia or ocular apraxiaConstructional dyspraxiaVisual field defectEnvironmental disorientationAny of the elements of Gerstmann syndromeSupportive featuresAlexiaPresenile onsetIdeomotor or dressing apraxiaProsopagnosiaNeuropsychological deficits referable to parietal and/or occipital regionsFocal or asymmetric atrophy in parietal and/or occipital regions on structural imagingFocal or asymmetric hypoperfusion/hypometabolism in parietal and/or occipital regions on functional imagingAPPENDIX 4: VISUAL ASSESSMENT NEUROPSYCHOLOGICAL EXAMPLE STIMULI-1467485430530EARLY VISUAL PROCESSINGFigure A- SEQ Figure_A \* roman i Visual acuity test (CORVIST) subset to scale (Snellen equivalent: 6/18- 6/9)Figure A- SEQ Figure_A \* roman ii Shape detection test (VOSP) Figure A- SEQ Figure_A \* roman iii Shape discrimination (Efron, 1968): three levels of difficultyFigure A- SEQ Figure_A \* roman iv Hue discrimination (CORVIST)VISUOPERCEPTUAL PROCESSINGFigure A- SEQ Figure_A \* roman v Object Decision (VOSP)Figure A- SEQ Figure_A \* roman vi Fragmented letter (VOSP)-18415357505Figure A- SEQ Figure_A \* roman vii Unusual and usual views (Warrington and James, 1988)VISUOSPATIAL PROCESSINGFigure A- SEQ Figure_A \* roman viii Number location (VOSP)Figure A- SEQ Figure_A \* roman ix Dot counting (VOSP)DAEEBACCBEDEABEABBDBCBEABBACDDBBBCDAAABDEECEBAEDDDAACCEBBCDCDEDECAAADABCDEEDEBBCDDACCAEAFigure A- SEQ Figure_A \* roman x A Cancellation taskAPPENDIX 5: READING CORPORA PERFORMANCE FOR FOL AND CLABROWN AND URE CORPUS (Brown & Ure, 1969)FOL: While FOL did not make any errors at baseline, she made one error at first follow-up and one error at second follow-up: there was a trend towards FOL being less accurate at both follow-up assessments (both t=-1.8; p=.086). Despite not being significantly slower than controls at baseline, FOL was significantly slower at both follow-up assessments (FU1: t=12.1, p<.005; FU2: t=25.7, p<.001). CLA: While CLA did not make any errors at baseline, she made two errors at first follow-up and three errors at second follow-up. The control group made no errors; consequently it was not possible to use a modified t-test for error analysis. While CLA was not significantly slower than controls at baseline, she was significantly slower at first follow-up assessment (t=5.2, p<.005). Reading latency data were missing for second follow-up due to technical difficulties.SCHONELL READING LIST (Schonell & Goodacre, 1971)FOL: FOL made three errors at baseline assessment; she showed trends towards being slower and less accurate than her control group. FOL made the same amount of errors at first follow-up. FOL made eight errors at second follow-up, making her significantly less accurate than her control group (t=-6.5, p<.005). There was a trend towards FOL reading slower than controls at baseline: she was significantly slower at first (t=6.4, p<.005) and second (t=13.3, p<.001) follow-up.CLA: CLA did not make any errors at baseline, making her significantly more accurate than her control group. However, she made six errors at first follow-up and twelve errors at second follow-up, making her significantly less accurate than her control group (FU1: t=3.8, p<.05; FU2: t=8.4, p<.005). CLA was significantly slower than controls at baseline and both follow-up assessments (FU1: t=4.9, p<.005; FU2: t=90.1, p<.001).COLTHEART REGULAR/IRREGULAR WORDS (Coltheart et al., 1979)FOL: FOL made one visual error at baseline, three errors at first follow-up and nine errors at second follow-up assessment: it was not possible to use a modified t-test for error analysis as controls did not make any errors. FOL’s latencies did not significantly differ from her control group’s at baseline; however, she was significantly slower than controls at both follow-up assessments (FU1: t=11.2, p<.005; FU2: t=18.8, p<.001). While there was no significant difference between FOL and her control group in the size of regularity effect on latencies at baseline (p>.4) or first follow-up (p>.2), irregular words were read disproportionately slower at second follow-up relative to controls (Revised Standardized Difference Test: t=2.61, p<.05).CLA: CLA did not make any errors at baseline assessment but made six errors at first follow-up and nine errors at second follow-up: it was not possible to use a modified t-test for error analysis as controls did not make any errors. CLA was significantly slower than her control group at baseline and follow-up assessments (FU1: t=8.2, p<.005; FU2: t=122.4, p<.001). CLA was disproportionately slower at reading irregular words relative to her control group at baseline and both follow-up assessments (Revised Standardized Difference Test: FU1: t=5.68, p<.005; FU2: t=15.0, p<.0005).GLOSSARYAD: Alzheimer’s DiseaseADL: Activities of Daily LivingAIDS: Acquired Immunodeficiency SyndromeANOVA: Analysis of VarianceAoA: Age of AcquisitionAPOE: Apolipoprotein EAPP: Amyloid Precursor ProteinCBD: Corticobasal DegenerationCJD: Creutzfeld Jakob DiseaseCORVIST: Cortical Visual Screening TestCSF: Cerebrospinal FluidDLB: Dementia with Lewy BodiesEEG: ElectroencephalographyFDG: FluorodeoxyglucoseFDR: False Discovery RateFTLD: Frontotemporal DementiaFEW: Family-Wise ErrorGDA: Graded Difficulty ArithmeticGDST: Graded Difficulty Spelling TestIQ: Intelligence QuotientLBL: Letter-by-LetterLPA: Logopenic Progressive AphasiaMIDAS: Medical information Display and Analysis SystemMMSE: Mini-Mental State ExaminationMNI: Montreal Neurological InstituteMP-RAGE: Magnetization Prepared Rapid Gradient EchoMRI: Magnetic Resonance ImageNFT: Neurofibrillary TanglesNHNN: National Hospital for Neurology and NeurosurgeryNINCDS-ADRDA: National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders AssociationNsize: Orthographic Neighbourhood SizePCA: Posterior Cortical AtrophyPET: Positron Emission TomographyPiB: Pittsburgh compound-BPS: PresenilinRSDT: Revised Standardised Difference TestRSVP: Rapid Serial Visual PresentationSD: Standard DeviationSPECT: Single Photon Emission Computed TomographySPM: Statistical Parametric MappingtAD: typical Alzheimer’s DiseaseVaD: Vascular DementiaVBM: Voxel-Based MorphometryVOSP: Visual Object and Space Perception BatteryVWFA: Visual Word Form AreaREFERENCESAharon-Peretz, J., Israel, O., Goldsher, D., & Peretz, A. (1999). Posterior cortical atrophy variants of Alzheimer's disease. Dement Geriatr Cogn Disord, 10(6), 483-487. doi: 17194 [pii]17194Albert, M. L. (1973). A simple test of visual neglect. Neurology, 23(6), 658-664. Alladi, S., Xuereb, J., Bak, T., Nestor, P., Knibb, J., Patterson, K., & Hodges, J. R. (2007). Focal cortical presentations of Alzheimer's disease. Brain, 130(Pt 10), 2636-2645. doi: 130/10/2636 [pii]10.1093/brain/awm213Almkvist, O. (1996). Neuropsychological features of early Alzheimer's disease: preclinical and clinical stages. Acta Neurol Scand Suppl, 165, 63-71. Amano, K., Wandell, B. A., & Dumoulin, S. O. (2009). Visual field maps, population receptive field sizes, and visual field coverage in the human MT+ complex. Journal of Neurophysiology, 102(5), 2704-2718. doi: 10.1152/jn.00102.200900102.2009 [pii]Andel, R., Crowe, M., Pedersen, N. L., Mortimer, J., Crimmins, E., Johansson, B., & Gatz, M. (2005). Complexity of work and risk of Alzheimer's disease: A population-based study of Swedish twins. Journals of Gerontology Series B-Psychological Sciences and Social Sciences, 60(5), P251-P258. Andersen, C. K., Wittrup-Jensen, K. U., Lolk, A., Andersen, K., & Kragh-Sorensen, P. (2004). Ability to perform activities of daily living is the main factor affecting quality of life in patients with dementia. Health Qual Life Outcomes, 2, 52. doi: 10.1186/1477-7525-2-521477-7525-2-52 [pii]Anderson, E. J., Dakin, S. C., Schwarzkopf, D. S., Rees, G., & Greenwood, J. A. (2012). The Neural Correlates of Crowding-Induced Changes in Appearance. Current Biology, 22(13), 1199-1206. doi: DOI 10.1016/j.cub.2012.04.063Andrade, K., Samri, D., Sarazin, M., de Souza, L. C., Cohen, L., Thiebaut de Schotten, M., . . . Bartolomeo, P. (2010). Visual neglect in posterior cortical atrophy. Bmc Neurology, 10. doi: Artn 68Doi 10.1186/1471-2377-10-68Andrews, S., & Scarratt, D. R. (1998). Rule and analogy mechanisms in reading nonwords: Hough dou peapel rede gnew wirds?. Journal of Experimental Psychology: Human Perception and Performance, 24(4), 1052.Arguin, M., & Bub, D. N. (1993). Single-Character Processing in a Case of Pure Alexia. Neuropsychologia, 31(5), 435-458. doi: Doi 10.1016/0028-3932(93)90059-9Arguin, M., Fiset, S., & Bub, D. (2002). Sequential and parallel letter processing in letter-by-letter dyslexia. Cognitive Neuropsychology, 19(6), 535-555. doi: Doi 10.1080/02643290244000040Arnold, S. E., Hyman, B. T., Flory, J., Damasio, A. R., & Van Hoesen, G. W. (1991). The Topographical and Neuroanatomical Distribution of Neurofibrillary Tangles and Neuritic Plaques in the Cerebral Cortex of Patients with Alzheimer's Disease. Cerebral Cortex, 1(1), 103-116. doi: DOI 10.1093/cercor/1.1.103Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95-113. doi: S1053-8119(07)00584-8 [pii]10.1016/j.neuroimage.2007.07.007Ashburner, J., & Friston, K. J. (2009). Computing average shaped tissue probability templates. Neuroimage, 45(2), 333-341. doi: DOI 10.1016/j.neuroimage.2008.12.008Baxter, D. M., & Warrington, E. K. (1994). Measuring dysgraphia: a graded-difficulty spelling test. Behavioural Neurology.Behrmann, M., Nelson, J., & Sekuler, E. B. (1998). Visual complexity in letter-by-letter reading: "Pure" alexia is not pure. Neuropsychologia, 36(11), 1115-1132. Behrmann, M., Plaut, D. C., & Nelson, J. (1998). A literature review and new data supporting an interactive account of letter-by-letter reading. Cognitive Neuropsychology, 15(1-2), 7-51. Behrmann, M., & Shallice, T. (1995). Pure Alexia - a Nonspatial Visual Disorder Affecting Letter Activation. Cognitive Neuropsychology, 12(4), 409-454. Behrmann, M., Shomstein, S. S., Black, S. E., & Barton, J. J. (2001). The eye movements of pure alexic patients during reading and nonreading tasks. Neuropsychologia, 39(9), 983-1002. doi: S0028-3932(01)00021-5 [pii]Ben-Shachar, M., Dougherty, R. F., Deutsch, G. K., & Wandell, B. A. (2007). Differential sensitivity to words and shapes in ventral occipito-temporal cortex. Cerebral Cortex, 17(7), 1604-1611.Benson, D. F., Davis, R. J., & Snyder, B. D. (1988). Posterior Cortical Atrophy. Archives of Neurology, 45(7), 789-793. Benton, A., Hannay, H. J., & Varney, N. R. (1975). Visual perception of line direction in patients with unilateral brain disease. Neurology, 25(10), 907-910. Berthier, M. L., Leiguarda, R., Starkstein, S. E., Sevlever, G., & Taratuto, A. L. (1991). Alzheimers-Disease in a Patient with Posterior Cortical Atrophy. Journal of Neurology Neurosurgery and Psychiatry, 54(12), 1110-1111. doi: DOI 10.1136/jnnp.54.12.1110Besner, D., & Johnston, J. C. (1989). Reading and the mental lexicon: On the uptake of visual information.Besner, D., Twilley, L., McCann, R. S., & Seergobin, K. (1990). On the association between connectionism and data: Are a few words necessary?.Bi, T., Cai, P., Zhou, T., & Fang, F. (2009). The effect of crowding on orientation-selective adaptation in human early visual cortex. Journal of Vision, 9(11), 13.Binder, J. R., & Mohr, J. P. (1992). The topography of callosal reading pathways. A case-control analysis. Brain, 115 ( Pt 6), 1807-1826. Binetti, G., Cappa, S. F., Magni, E., Padovani, A., Bianchetti, A., & Trabucchi, M. (1998). Visual and spatial perception in the early phase of Alzheimer's disease. Neuropsychology, 12(1), 29-33. Blake, R., Tadin, D., Sobel, K. V., Raissian, T. A., & Chong, S. C. (2006). Strength of early visual adaptation depends on visual awareness. Proceedings of the National Academy of Sciences of the United States of America, 103(12), 4783-4788. doi: DOI 10.1073/pnas.0509634103Bodamer, J. (1947). Die prosop-agnosie. Archiv für Psychiatrie und Nervenkrankheiten, 179(1-2), 6-53.Boles, D. B., & Clifford, J. E. (1989). An Uppercase and Lowercase Alphabetic Similarity Matrix, with Derived Generation Similarity Values. Behavior Research Methods Instruments & Computers, 21(6), 579-586. doi: Doi 10.3758/Bf03210580Bouma, H. (1970). Interaction Effects in Parafoveal Letter Recognition. Nature, 226(5241), 177-&. Bouma, H., & Legein, C. P. (1977). Foveal and Parafoveal Recognition of Letters and Words by Dyslexics and by Average Readers. Neuropsychologia, 15(1), 69-80. doi: Doi 10.1016/0028-3932(77)90116-6Braak, H., & Braak, E. (1991). Neuropathological Staging of Alzheimer-Related Changes. Acta Neuropathologica, 82(4), 239-259. Braak, H., & Braak, E. (1996). Evolution of the neuropathology of Alzheimer's disease. Acta Neurologica Scandinavica, 93, 3-12. Braak, H., & Del Tredici, K. (2011). The pathological process underlying Alzheimer's disease in individuals under thirty. Acta Neuropathologica, 121(2), 171-181. doi: DOI 10.1007/s00401-010-0789-4Braet, W., & Humphreys, G. (2007). A selective effect of parietal damage on letter identification in mixed case words. Neuropsychologia, 45(10), 2226-2233. doi: DOI 10.1016/j.neuropsychologia.2007.02.016Broadbent, D. E., & Broadbent, M. H. P. (1987). From Detection to Identification - Response to Multiple Targets in Rapid Serial Visual Presentation. Perception & Psychophysics, 42(2), 105-113. doi: Doi 10.3758/Bf03210498Brown, W. P., & Ure, D. M. J. (1969). 5 Rated Characteristics of 650-Word Association Stimuli. British Journal of Psychology, 60, 233-&. Brun, A., & Gustafson, L. (1976). Distribution of Cerebral Degeneration in Alzheimers-Disease - Clinicopathological Study. Archiv Fur Psychiatrie Und Nervenkrankheiten, 223(1), 15-33. doi: Doi 10.1007/Bf00367450Bub, D. N., Black, S., & Howell, J. (1989). Word Recognition and Orthographic Context Effects in a Letter-by-Letter Reader. Brain and Language, 36(3), 357-376. Buhr, G. T., Kuchibhatla, M., & Clipp, E. C. (2006). Caregivers' reasons for nursing home placement: Clues for improving discussions with families prior to the transition. Gerontologist, 46(1), 52-61. Bullock, R., & Hammond, G. (2003). Realistic expectations - The management of severe Alzheimer disease. Alzheimer Disease & Associated Disorders, 17, S80-S85. doi: Doi 10.1097/00002093-200307003-00004Caine, D. (2004). Posterior cortical atrophy: A review of the literature. Neurocase, 10(5), 382-385. doi: Doi 10.1080/13554790490892239Caine, D., & Hodges, J. R. (2001). Heterogeneity of semantic and visuospatial deficits in early Alzheimer's disease. Neuropsychology, 15(2), 155-164. Capitani, E., Laiacona, M., Mahon, B., & Caramazza, A. (2003). What are the facts of semantic category-specific deficits? A critical review of the clinical evidence. Cognitive Neuropsychology, 20(3-6), 213-261. doi: Doi 10.1080/02643290244000266Catricala, E., Della Rosa, P. A., Ortelli, P., Ginex, V., Marcone, A., Perani, D., & Cappa, S. F. (2011). The evolution of alexia in two cases of posterior cortical atrophy. Behavioural Neurology, 24(3), 229-236. doi: Doi 10.3233/Ben-2011-0334Cattell, J. M. (1886). The time it takes to see and name objects. Mind, (41), 63-65.Chakravarthi, R., & Cavanagh, P. (2007). Temporal properties of the polarity advantage effect in crowding. J Vis, 7(2), 11 11-13. doi: 10.1167/7.2.117/2/11 [pii]Chakravarthi, R., & Cavanagh, P. (2007). Temporal properties of the polarity advantage effect in crowding. J Vis, 7(2), 11 11-13. doi: 10.1167/7.2.117/2/11 [pii]Chan, D., Crutch, S. J., & Warrington, E. K. (2001). A disorder of colour perception associated with abnormal colour after-images: a defect of the primary visual cortex. J Neurol Neurosurg Psychiatry, 71(4), 515-517. Chan, D., Janssen, J. C., Whitwell, J. L., Watt, H. C., Jenkins, R., Frost, C., . . . Fox, N. C. (2003). Change in rates of cerebral atrophy over time in early-onset Alzheimer's disease: longitudinal MRI study. Lancet, 362(9390), 1121-1122. doi: S0140-6736(03)14469-8 [pii]10.1016/S0140-6736(03)14469-8Chartier-Harlin, M. C., Crawford, F., Houlden, H., Warren, A., Hughes, D., Fidani, L., . . . et al. (1991). Early-onset Alzheimer's disease caused by mutations at codon 717 of the beta-amyloid precursor protein gene. Nature, 353(6347), 844-846. doi: 10.1038/353844a0Chase, C., Ashourzadeh, A., Kelly, C., Monfette, S., & Kinsey, K. (2003). Can the magnocellular pathway read? Evidence from studies of color. Vision Res, 43(10), 1211-1222. doi: S0042698903000853 [pii]Chelazzi, L., Miller, E. K., Duncan, J., & Desimone, R. (2001). Responses of neurons in macaque area V4 during memory-guided visual search. Cerebral Cortex, 11(8), 761-772. Chung, S. T. (2004). Reading speed benefits from increased vertical word spacing in normal peripheral vision. Optometry and vision science: official publication of the American Academy of Optometry, 81(7), 525.Chung, S. T. L., Li, R. W., & Levi, D. M. (2007). Crowding between first- and second-order letter stimuli in normal foveal and peripheral vision. Journal of Vision, 7(2). doi: Artn 10Doi 10.1167/7.2.10Cogan, D. G. (1985). Visual Disturbances with Focal Progressive Dementing Disease. American Journal of Ophthalmology, 100(1), 68-72. Cogan, D. G. (1985). Visual Disturbances with Focal Progressive Dementing Disease. American Journal of Ophthalmology, 100(1), 68-72. Cohen, L., Dehaene, S., Naccache, L., Lehericy, S., Dehaene-Lambertz, G., Henaff, M. A., & Michel, F. (2000). The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain, 123 ( Pt 2), 291-307. Cohen, L., Dehaene, S., Vinckier, F., Jobert, A., & Montavont, A. (2008). Reading normal and degraded words: Contribution of the dorsal and ventral visual pathways. Neuroimage, 40(1), 353-366. doi: DOI 10.1016/j.neuroimage.2007.11.036Cohen, L., Henry, C., Dehaene, S., Martinaud, O., Lehericy, S., Lemer, C., & Ferrieux, S. (2004). The pathophysiology of letter-by-letter reading. Neuropsychologia, 42(13), 1768-1780. doi: 10.1016/j.neuropsychologia.2004.04.018 S002839320400096X [pii]Cohen, L., Martinaud, O., Lemer, C., Lehericy, S., Samson, Y., Obadia, M., . . . Dehaene, S. (2003). Visual word recognition in the left and right hemispheres: Anatomical and functional correlates of peripheral alexias. Cerebral Cortex, 13(12), 1313-1333. doi: DOI 10.1093/cercor/bhg079Cole, S., & Warrington, EK. (1962). Visual disorientation in homonymous half-fields. Neurology, 12, 257-263.Colombo, A., Derenzi, E., & Faglioni, P. (1976). Occurrence of Visual Neglect in Patients with Unilateral Cerebral Disease. Cortex, 12(3), 221-231. Coltheart, M. (1981). The Mrc Psycholinguistic Database. Quarterly Journal of Experimental Psychology Section a-Human Experimental Psychology, 33(Nov), 497-505. Coltheart, M., Besner, D., Jonasson, J. T., & Davelaar, E. (1979). Phonological Encoding in the Lexical Decision Task. Quarterly Journal of Experimental Psychology, 31(Aug), 489-507. Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: a dual route cascaded model of visual word recognition and reading aloud. Psychological review, 108(1), as-Herrera, A., Wittenberg, R., Pickard, L., & Knapp, M. (2007). Cognitive impairment in older people: future demand for long-term care services and the associated costs. International Journal of Geriatric Psychiatry, 22(10), 1037-1045. doi: Doi 10.1002/Gps.1830Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., . . . Raichle, M. E. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences of the United States of America, 96(18), 10422-10427. Corbetta, M. (1993). Positron Emission Tomography as a Tool to Study Human Vision and Attention. Proceedings of the National Academy of Sciences of the United States of America, 90(23), 10901-10903. doi: DOI 10.1073/pnas.90.23.10901Corrada, M. M., Brookmeyer, R., Paganini-Hill, A., Berlau, D., & Kawas, C. H. (2010). Dementia Incidence Continues to Increase with Age in the Oldest Old The 90+Study. Annals of Neurology, 67(1), 114-121. doi: Doi 10.1002/Ana.21915Cortese, M. J., Simpson, G. B., & Woolsey, S. (1997). Effects of association and imageability on phonological mapping. Psychonomic Bulletin & Review, 4(2), 226-231.Cosentino, S., Scarmeas, N., Helzner, E., Glymour, M. M., Brandt, J., Albert, M., . . . Stern, Y. (2008). APOE epsilon 4 allele predicts faster cognitive decline in mild Alzheimer disease. Neurology, 70(19), 1842-1849. Coslett, H. B., Stark, M., Rajaram, S., & Saffran, E. M. (1995). Narrowing the spotlight: A visual attentional disorder in presumed Alzheimer's disease. Neurocase, 1(4), 305-318. doi: Doi 10.1080/13554799508402375Crawford, J. R., & Garthwaite, P. H. (2002). Investigation of the single case in neuropsychology: confidence limits on the abnormality of test scores and test score differences. Neuropsychologia, 40(8), 1196-1208. doi: S002839320100224X [pii]Crawford, J. R., & Garthwaite, P. H. (2005). Testing for suspected impairments and dissociations in single-case studies in neuropsychology: evaluation of alternatives using monte carlo simulations and revised tests for dissociations. Neuropsychology, 19(3), 318-331. doi: 2005-05103-006 [pii]10.1037/0894-4105.19.3.318Croisile, D. B. (2004). Benson’s syndrome or Posterior Cortical Atrophy. Orphanet Encyclopedia. September, 1-4.Crutch, S. J. (2013). Seeing why they cannot see: Understanding the syndrome and causes of posterior cortical atrophy. J Neuropsychol. doi: 10.1111/jnp.12011Crutch, S. J., Lehmann, M., Gorgoraptis, N., Kaski, D., Ryan, N., Husain, M., & Warrington, E. K. (2011). Abnormal visual phenomena in posterior cortical atrophy. Neurocase, 17(2), 160-177. doi: Pii 926575951Doi 10.1080/13554794.2010.504729Crutch, S. J., Lehmann, M., Schott, J. M., Rabinovici, G. D., Rossor, M. N., & Fox, N. C. (2012). Posterior cortical atrophy. Lancet Neurology, 11(2), 170-178. Crutch, S. J., Lehmann, M., Warren, J. D., & Rohrer, J. D. (2013). The language profile of posterior cortical atrophy. Journal of Neurology Neurosurgery and Psychiatry, 84(4), 460-466. doi: DOI 10.1136/jnnp-2012-303309Crutch, S. J., Schott, J. M., Rabinovici, G. D., Boeve, B. F., Cappa, S. F., Dickerson, B. C., . . . Fox, N. C. (2013). Shining a light on posterior cortical atrophy. Alzheimers & Dementia, 9(4), 463-465. doi: DOI 10.1016/j.jalz.2012.11.004Crutch, S. J., Schott, J. M., Rabinovici, G. D., Boeve, B. F., Cappa, S. F., Dickerson, B. C., . . . Fox, N. C. (2013). Shining a light on posterior cortical atrophy. Alzheimers Dement, 9(4), 463-465. doi: 10.1016/j.jalz.2012.11.004S1552-5260(12)02522-8 [pii]Crutch, S. J., & Warrington, E. K. (2007). Foveal crowding in posterior cortical atrophy: A specific early-visual-processing deficit affecting word reading. Cognitive Neuropsychology, 24(8), 843-866. doi: Doi 10.1080/02643290701754240Crutch, S. J., & Warrington, E. K. (2009). The relationship between visual crowding and letter confusability: Towards an understanding of dyslexia in posterior cortical atrophy. Cognitive Neuropsychology, 26(5), 471-498. doi: Doi 10.1080/02643290903465819Cummings, J. L., & Benson, D. F. (1983). Dementia, a clinical approach. Boston: Butterworths.David, S. V., Hayden, B. Y., & Gallant, J. L. (2006). Spectral receptive field properties explain shape selectivity in area V4. Journal of Neurophysiology, 96(6), 3492-3505. doi: 00575.2006 [pii]10.1152/jn.00575.2006Davidoff, J., & Warrington, E. K. (1993). A Dissociation of Shape-Discrimination and Figure Ground Perception in a Patient with Normal Acuity. Neuropsychologia, 31(1), 83-93. doi: Doi 10.1016/0028-3932(93)90083-CDayan, P., & Solomon, J. A. (2010). Selective Bayes Attentional load and crowding. Vision Research, 50(22), 2248-2260. doi: DOI 10.1016/j.visres.2010.04.014De Lacoste, M. C., & White, C. L., 3rd. (1993). The role of cortical connectivity in Alzheimer's disease pathogenesis: a review and model system. Neurobiology of Aging, 14(1), 1-16. De Lacoste, M. C., & White, C. L., 3rd. (1993). The role of cortical connectivity in Alzheimer's disease pathogenesis: a review and model system. Neurobiology of Aging, 14(1), 1-16. De Renzi, E. 1986. Slowly progressive visual agnosia or apraxia without dementia. Cortex, 22: 171–180.De Renzi, E., Faglioni, P., & Scotti, G. (1970). Hemispheric contribution to exploration of space through the visual and tactile modality. Cortex, 6(2), 191-203. De Renzi, E., Perani, D., Carlesimo, G. A., Silveri, M. C., & Fazio, F. (1994). Prosopagnosia can be associated with damage confined to the right hemisphere--an MRI and PET study and a review of the literature. Neuropsychologia, 32(8), 893-902. doi: 0028-3932(94)90041-8 [pii]De Renzi, E., Scotti, G., & Spinnler, H. (1969). Perceptual and associative disorders of visual recognition. Neurology.de Souza, L. C., Corlier, F., Habert, M. O., Uspenskaya, O., Maroy, R., Lamari, F., . . . Sarazin, M. (2011). Similar amyloid-beta burden in posterior cortical atrophy and Alzheimer's disease. Brain, 134(Pt 7), 2036-2043. doi: 10.1093/brain/awr130awr130 [pii]De Strooper, B., & Annaert, W. (2010). Novel research horizons for presenilins and gamma-secretases in cell biology and disease. Annu Rev Cell Dev Biol, 26, 235-260. doi: 10.1146/annurev-cellbio-100109-104117De Weerd, P., Desimone, R., & Ungerleider, L. G. (1996). Cue-dependent deficits in grating orientation discrimination after V4 lesions in macaques. Vis Neurosci, 13(3), 529-538. doi: S0952523800008208 [pii]De Weerd, P., Peralta, M. R., 3rd, Desimone, R., & Ungerleider, L. G. (1999). Loss of attentional stimulus selection after extrastriate cortical lesions in macaques. Nature Neuroscience, 2(8), 753-758. doi: 10.1038/11234Dehaene, S., & Cohen, L. (2011). The unique role of the visual word form area in reading. Trends Cogn Sci, 15(6), 254-262. doi: 10.1016/j.tics.2011.04.003S1364-6613(11)00073-8 [pii]Dehaene, S., Jobert, A., Naccache, L., Ciuciu, P., Poline, J. B., Le Bihan, D., & Cohen, L. (2004). Letter binding and invariant recognition of masked words: behavioral and neuroimaging evidence. Psychological Science, 15(5), 307-313. doi: 10.1111/j.0956-7976.2004.00674.x PSCI674 [pii]Delaj, L., D’Alessandro, R., Stracciari, A., Fonti, C., Cretella, L., & Lodi, R. (2010). Long-lasting hemianopia due to PCA. Journal of neurology, 257(9), 1562-1564.Delazer, M., Karner, E., Zamarian, L., Donnemiller, E., & Benke, T. (2006). Number processing in posterior cortical atrophy--a neuropsycholgical case study. Neuropsychologia, 44(1), 36-51. doi: S0028-3932(05)00173-9 [pii]10.1016/j.neuropsychologia.2005.04.013Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193-222. doi: 10.1146/annurev.ne.18.030195.001205DeYoe, E. A., Carman, G. J., Bandettini, P., Glickman, S., Wieser, J., Cox, R., . . . Neitz, J. (1996). Mapping striate and extrastriate visual areas in human cerebral cortex. Proceedings of the National Academy of Sciences of the United States of America, 93(6), 2382-2386. doi: DOI 10.1073/pnas.93.6.2382Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., DeKosky, S. T., Barberger-Gateau, P., . . . Scheltens, P. (2010). Revising the definition of Alzheimer's disease: a new lexicon. Lancet Neurology, 9(11), 1118-1127. doi: Doi 10.1016/S1474-4422(10)70223-4Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., Dekosky, S. T., Barberger-Gateau, P., . . . Scheltens, P. (2010). Revising the definition of Alzheimer's disease: a new lexicon. Lancet Neurology, 9(11), 1118-1127. doi: 10.1016/S1474-4422(10)70223-4S1474-4422(10)70223-4 [pii]Dubois, B., Feldman, H. H., Jacova, C., Dekosky, S. T., Barberger-Gateau, P., Cummings, J., . . . Scheltens, P. (2007). Research criteria for the diagnosis of Alzheimer"s disease: revising the NINCDS-ADRDA criteria. Lancet Neurology, 6(8), 734-746. doi: Doi 10.1016/S1474-4422(07)70178-3Duning, T., Warnecke, T., Mohammadi, S., Lohmann, H., Schiffbauer, H., Kugel, H., . . . Deppe, M. (2009). Pattern and progression of white-matter changes in a case of posterior cortical atrophy using diffusion tensor imaging. Journal of Neurology Neurosurgery and Psychiatry, 80(4), 432-436. doi: DOI 10.1136/jnnp.2008.153148Efron, R. (1969). What is perception?. In Proceedings of the Boston Colloquium for the Philosophy of Science 1966/1968 (pp. 137-173). Springer Netherlands.Ehrlich, D. (1987). A comparative study in the use of closed-circuit television reading machines and optical aids by patients with retinitis pigmentosa and maculopathy. Ophthalmic Physiol Opt, 7(3), 293-302. Elliott, D. B., TrukoloIlic, M., Strong, J. G., Pace, R., Plotkin, A., & Bevers, P. (1997). Demographic characteristics of the vision-disabled elderly. Investigative Ophthalmology & Visual Science, 38(12), 2566-2575. Etard, O., Mellet, E., Papathanassiou, D., Benali, K., Houdé, O., Mazoyer, B., & Tzourio-Mazoyer, N. (2000). Picture naming without Broca's and Wernicke's area. Neuroreport, 11(3), 617-622.Etters, L., Goodall, D., & Harrison, B. E. (2008). Caregiver burden among dementia patient caregivers: a review of the literature. Journal of the American Academy of Nurse Practitioners, 20(8), 423-428.Faglioni, P., Scotti, G., & Spinnler, H. (1969). Impaired recognition of written letters following unilateral hemispheric damage. Cortex, 5(2), 120-133. Fang, F., & He, S. (2008). Crowding alters the spatial distribution of attention modulation in human primary visual cortex. Journal of Vision, 8(9), 6.Farah, M. J. (2000). The cognitive neuroscience of vision. Blackwell Publishing.Farah, M. J., & Wallace, M. A. (1991). Pure Alexia as a Visual Impairment - a Reconsideration. Cognitive Neuropsychology, 8(3-4), 313-334. Ferrera, V. P., Nealey, T. A., & Maunsell, J. H. (1992). Mixed parvocellular and magnocellular geniculate signals in visual area V4. Nature, 358(6389), 756-761. doi: 10.1038/358756a0Ferrera, V. P., Nealey, T. A., & Maunsell, J. H. (1994). Responses in macaque visual area V4 following inactivation of the parvocellular and magnocellular LGN pathways. Journal of Neuroscience, 14(4), 2080-2088. Filippini, N., Rao, A., Wetten, S., Gibson, R. A., Borrie, M., Guzman, D., . . . Matthews, P. M. (2009). Anatomically-distinct genetic associations of APOE epsilon 4 allele load with regional cortical atrophy in Alzheimer's disease. Neuroimage, 44(3), 724-728. doi: DOI 10.1016/j.neuroimage.2008.10.003Fiset, D., Arguin, M., Bub, D., Humphreys, G. W., & Riddoch, M. J. (2005). How to make the word-length effect disappear in letter-by-letter dyslexia - Implications for an account of the disorder. Psychological Science, 16(7), 535-541. Fiset, D., Arguin, M., Bub, D., Humphreys, G. W., & Riddoch, M. J. (2005). How to make the word-length effect disappear in letter-by-letter dyslexia - Implications for an account of the disorder. Psychological Science, 16(7), 535-541. doi: DOI 10.1111/j.0956-7976.2005.01571.xFiset, D., Gosselin, F., Blais, C., & Arguin, M. (2006). Inducing letter-by-letter dyslexia in normal readers. Journal of Cognitive Neuroscience, 18(9), 1466-1476. Fisher, D. F., Monty, R. A., & Glucksbe.S. (1969). Visual Confusion Matrices - Fact or Artifact. Journal of Psychology, 71(1), 111-&. Flom, M. C., Heath, G. G., & Takahashi, E. (1963). Contour Interaction and Visual Resolution: Contralateral Effects. Science, 142(3594), 979-980. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res, 12(3), 189-198. doi: 0022-3956(75)90026-6 [pii]Fratiglioni L, Rocca WA. Epidemiology of Dementia. In: Boller F, Cappa S, editors. Handbook of Neuropsychology. 2nd ed. Volume 6. Amsterdam: Elsevier; 2001. pp. 193–215Freeborough, P. A., & Fox, N. C. (1998). MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease. Ieee Transactions on Medical Imaging, 17(3), 475-479. doi: Doi 10.1109/42.712137Freeborough, P. A., Fox, N. C., & Kitney, R. I. (1997). Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. Comput Methods Programs Biomed, 53(1), 15-25. doi: S0169260797018038 [pii]Freeborough, P. A., Woods, R. P., & Fox, N. C. (1996). Accurate registration of serial 3D MR brain images and its application to visualizing change in neurodegenerative disorders. J Comput Assist Tomogr, 20(6), 1012-1022. Freedman, L., & Dexter, L. E. (1991). Visuospatial ability in cortical dementia. Journal of clinical and experimental neuropsychology, 13(5), 677-690.Freedman, L., Selchen, D. H., Black, S. E., Kaplan, R., Garnett, E. S., & Nahmias, C. (1991). Posterior Cortical Dementia with Alexia - Neurobehavioral, Mri, and Pet Findings. Journal of Neurology Neurosurgery and Psychiatry, 54(5), 443-448. doi: DOI 10.1136/jnnp.54.5.443Fricke, J., & Unsworth, C. (2001). Time use and importance of instrumental activities of daily living. Australian Occupational Therapy Journal, 48(3), 118-131.Friedman, R. B., & Alexander, M. P. (1984). Pictures, Images, and Pure Alexia - a Case-Study. Cognitive Neuropsychology, 1(1), 9-23. Fukui, T., & Lee, E. (2009). Visuospatial function is a significant contributor to functional status in patients with Alzheimer's disease. Am J Alzheimers Dis Other Demen, 24(4), 313-321. doi: 10.1177/15333175093339031533317509333903 [pii]Gallant, J. L., Shoup, R. E., & Mazer, J. A. (2000). A human extrastriate area functionally homologous to macaque V4. Neuron, 27(2), 227-235. doi: Doi 10.1016/S0896-6273(00)00032-5Galton, C. J., Patterson, K., Xuereb, J. H., & Hodges, J. R. (2000). Atypical and typical presentations of Alzheimer's disease: a clinical, neuropsychological, neuroimaging and pathological study of 13 cases. Brain, 123, 484-498. doi: DOI 10.1093/brain/123.3.484Gardini, S., Concari, L., Pagliara, S., Ghetti, C., Venneri, A., & Caffarra, P. (2011). Visuo-spatial imagery impairment in posterior cortical atrophy: A cognitive and SPECT study. Behavioural Neurology, 24(2), 123-132. doi: Doi 10.3233/Ben-2011-0279Gauthier, L., Dehaut, F., & Joanette, Y. (1989). The Bells Test - a Quantitative and Qualitative Test for Visual Neglect. International Journal of Clinical Neuropsychology, 11(2), 49-54. Geyer, L. H. (1977). Recognition and Confusion of Lowercase Alphabet. Perception & Psychophysics, 22(5), 487-490. doi: Doi 10.3758/Bf03199515Giannakopoulos, P., Gold, G., Duc, M., Michel, J. P., Hof, P. R., & Bouras, C. (1999). Neuroanatomic correlates of visual agnosia in Alzheimer's disease - A clinicopathologic study. Neurology, 52(1), 71-77. Gilhooly, K. J., & Logie, R. H. (1980). Age-of-Acquisition, Imagery, Concreteness, Familiarity, and Ambiguity Measures for 1,944 Words. Behavior Research Methods & Instrumentation, 12(4), 395-427. doi: Doi 10.3758/Bf03201693Gilmore, G. C., Hersh, H., Caramazza, A., & Griffin, J. (1979). Multidimensional letter similarity derived from recognition errors. Percept Psychophys, 25(5), 425-431. Giovagnoli, A. R., Aresi, A., Reati, F., Riva, A., Gobbo, C., & Bizzi, A. (2009). The neuropsychological and neuroradiological correlates of slowly progressive visual agnosia. Neurological Sciences, 30(2), 123-131. doi: DOI 10.1007/s10072-009-0019-9Glosser, G., Gallo, J., Duda, N., de Vries, J. J., Clark, C. M., & Grossman, M. (2002). Visual perceptual functions predict instrumental activities of daily living in patients with dementia. Cognitive and Behavioral Neurology, 15(3), 198-206.Godwin-Austen, R. B. (1965). A case of visual disorientation. J Neurol Neurosurg Psychiatry, 28(5), 453-458. Goethals, M., & Santens, P. (2001). Posterior cortical atrophy. Two case reports and a review of the literature. Clinical Neurology and Neurosurgery, 103(2), 115-119. doi: Doi 10.1016/S0303-8467(01)00114-7Goll, J. C., Crutch, S. J., Loo, J. H. Y., Rohrer, J. D., Frost, C., Bamiou, D. E., & Warren, J. D. (2010). Non-verbal sound processing in the primary progressive aphasias. Brain, 133, 272-285. doi: Doi 10.1093/Brain/Awp235Gollin, E. S. (1960). Developmental Studies of Visual Recognition of Incomplete Objects. Perceptual and Motor Skills, 11(3), 289-298. Gomez-Isla, T., Price, J. L., McKeel, D. W., Jr., Morris, J. C., Growdon, J. H., & Hyman, B. T. (1996). Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer's disease. Journal of Neuroscience, 16(14), 4491-4500. Grady, C. L., Haxby, J. V., Horwitz, B., Sundaram, M., Berg, G., Schapiro, M., . . . Rapoport, S. I. (1988). Longitudinal study of the early neuropsychological and cerebral metabolic changes in dementia of the Alzheimer type. J Clin Exp Neuropsychol, 10(5), 576-596. doi: 10.1080/01688638808402796Grainger, J., Bouttevin, S., Truc, C., Bastien, M., & Ziegler, J. (2003). Word superiority, pseudoword superiority, and learning to read: a comparison of dyslexic and normal readers. Brain Lang, 87(3), 432-440. doi: S0093934X03001457 [pii]Grainger, J., & Dufau, S. (2012). 8 The front end of visual word recognition. Visual Word Recognition Volume 1: Models and Methods, Orthography and Phonology, 1, 159.Grainger, J., & Ziegler, J. C. (2011). A dual-route approach to orthographic processing. Frontiers in psychology, 2.Goethals, M., & Santens, P. (2001). Posterior cortical atrophy. Two case reports and a review of the literature. Clinical Neurology and Neurosurgery, 103(2), 115-119. doi: Doi 10.1016/S0303-8467(01)00114-7Greenwood, J. A., Bex, P. J., & Dakin, S. C. (2010). Crowding changes appearance. Current Biology, 20(6), 496-501. doi: 10.1016/j.cub.2010.01.023 S0960-9822(10)00062-X [pii]Grill-Spector, K., & Malach, R. (2004). The human visual cortex. Annual Review of Neuroscience, 27, 649-677. doi: DOI 10.1146/annurev.neuro.27.070203.144220Growdon, J. H., & Rossor, M. (1998). The dementias (Vol. 1). Butterworth-Heinemann Medical.Hadjikhani, N., Liu, A. K., Dale, A. M., Cavanagh, P., & Tootell, R. B. H. (1998). Retinotopy and color sensitivity in human visual cortical area V8. Nature Neuroscience, 1(3), 235-241. doi: Doi 10.1038/681Hall, D. A., Humphreys, G. W., & Cooper, A. C. G. (2001). Neuropsychological evidence for case-specific reading: Multi-letter units in visual word recognition. Quarterly Journal of Experimental Psychology Section a-Human Experimental Psychology, 54(2), 439-467. doi: Doi 10.1080/02724980042000165Harris, J. M., & Parker, A. J. (1995). Independent neural mechanisms for bright and dark information in binocular stereopsis. Nature, 374(6525), 808-811. doi: 10.1038/374808a0Haynes, J. D., Driver, J., & Rees, G. (2005). Visibility reflects dynamic changes of effective connectivity between V1 and fusiform cortex. Neuron, 46(5), 811-821. doi: DOI 10.1016/j.neuron.2005.05.012Heider, B. (2000). Visual form agnosia: Neural mechanisms and anatomical foundations. Neurocase, 6(1), 1-12. Hill, R. D., Backman, L., & Fratiglioni, L. (1995). Determinants of functional abilities in dementia. J Am Geriatr Soc, 43(10), 1092-1097. Hof, P. R., & Bouras, C. (1991). Object recognition deficit in Alzheimer's disease: possible disconnection of the occipito-temporal component of the visual system. Neurosci Lett, 122(1), 53-56. doi: 0304-3940(91)90191-U [pii]Hof, P. R., Bouras, C., Constantinidis, J., & Morrison, J. H. (1989). Balint's syndrome in Alzheimer's disease: specific disruption of the occipito-parietal visual pathway. Brain Res, 493(2), 368-375. Hof, P. R., Bouras, C., Constantinidis, J., & Morrison, J. H. (1990). Selective Disconnection of Specific Visual Association Pathways in Cases of Alzheimers-Disease Presenting with Balints Syndrome. Journal of Neuropathology and Experimental Neurology, 49(2), 168-184. doi: Doi 10.1097/00005072-199003000-00008Hof, P. R., Vogt, B. A., Bouras, C., & Morrison, J. H. (1997). Atypical form of Alzheimer's disease with prominent posterior cortical atrophy: a review of lesion distribution and circuit disconnection in cortical visual pathways. Vision Res, 37(24), 3609-3625. doi: S0042-6989(96)00240-4 [pii]10.1016/S0042-6989(96)00240-4Hof, P. R., Vogt, B. A., Bouras, C., & Morrison, J. H. (1997). Atypical form of Alzheimer's disease with prominent posterior cortical atrophy: a review of lesion distribution and circuit disconnection in cortical visual pathways. Vision Res, 37(24), 3609-3625. doi: S0042-6989(96)00240-4 [pii]10.1016/S0042-6989(96)00240-4Holmes, G., & Horrax, G. (1919). Disturbances of spatial orientation and visual attention, with loss of stereoscopic vision. Archives of Neurology and Psychiatry, 1(4), 385.Humphreys, G. W., & Mayall, K. (2001). A peripheral reading deficit under conditions of diffuse visual attention. Cognitive Neuropsychology, 18(6), 551-576. Humphreys, G. W., & Mayall, K. (2001). A peripheral reading deficit under conditions of diffuse visual attention. Cognitive Neuropsychology, 18(6), 551-576. Hyona, J., Bertram, R., & Pollatsek, A. (2004). Are long compound words identified serially via their constituents? Evidence from an eye-movement-contingent display change study. Mem Cognit, 32(4), 523-532. Ichikawa, S. (1985). Quantitative and structural factors in the judgment of pattern complexity. Percept Psychophys, 38(2), 101-109. Intriligator, J., & Cavanagh, P. (2001). The spatial resolution of visual attention. Cogn Psychol, 43(3), 171-216. doi: 10.1006/cogp.2001.0755S0010-0285(01)90755-8 [pii]Jack, C. I. A., Smith, T., Neoh, C., Lye, M., & Mcgalliard, J. N. (1995). Prevalence of How Vision in Elderly Patients Admitted to an Acute Geriatric Unit in Liverpool - Elderly People Who Fall Are More Likely to Have Low-Vision. Gerontology, 41(5), 280-285. Jackson, M., & Warrington, E. K. (1986). Arithmetic Skills in Patients with Unilateral Cerebral-Lesions. Cortex, 22(4), 611-620. Jackson, M., & Warrington, E. K. (1986). Arithmetic Skills in Patients with Unilateral Cerebral-Lesions. Cortex, 22(4), 611-620. James, M., Plant, G. T., & Warrington, E. K. (2001). CORVIST: Cortical Vision Screening Test: Manual & Test Materials. Thames Valley Test Company.Janssen, J. C., Beck, J. A., Campbell, T. A., Dickinson, A., Fox, N. C., Harvey, R. J., . . . Collinge, J. (2003). Early onset familial Alzheimer's disease - Mutation frequency in 31 families. Neurology, 60(2), 235-239. Jefferson, A. L., Barakat, L. P., Giovannetti, T., Paul, R. H., & Glosser, G. (2006). Object perception impairments predict instrumental activities of daily living dependence in Alzheimer's disease. Journal of clinical and experimental neuropsychology, 28(6), 884-897.Jobard, G., Crivello, F., & Tzourio-Mazoyer, N. (2003). Evaluation of the dual route theory of reading: a metanalysis of 35 neuroimaging studies. Neuroimage, 20(2), 693-712. doi: Doi 10.1016/S1053-8119(03)00343-4Johnson, J. K., Head, E., Kim, R., Starr, A., & Cotman, C. W. (1999). Clinical and pathological evidence for a frontal variant of Alzheimer disease. Arch Neurol, 56(10), 1233-1239. Jonides, J., Schumacher, E. H., Smith, E. E., Koeppe, R. A., Awh, E., Reuter-Lorenz, P. A., . . . Willis, C. R. (1998). The role of parietal cortex in verbal working memory. Journal of Neuroscience, 18(13), 5026-5034. Josephs, K. A., Whitwell, J. L., Boeve, B. F., Knopman, D. S., Tang-Wai, D. F., Drubach, D. A., . . . Petersen, R. C. (2006). Visual hallucinations in posterior cortical atrophy. Archives of Neurology, 63(10), 1427-1432. doi: DOI 10.1001/archneur.63.10.1427Kaida, K., Takeda, K., Nagata, N., & Kamakura, K. (1998). Alzheimer's disease with asymmetric parietal lobe atrophy: a case report. J Neurol Sci, 160(1), 96-99. doi: S0022510X98002214 [pii]Karp, A., Kareholt, I., Qiu, C. X., Bellander, T., Winblad, B., & Fratiglioni, L. (2004). Relation of education and occupation-based socioeconomic status to incident Alzheimer's disease. American Journal of Epidemiology, 159(2), 175-183. doi: Doi 10.1093/Aje/Kwh018Kartsounis, L. D., & Warrington, E. K. (1991). Failure of Object Recognition Due to a Breakdown of Figure Ground Discrimination in a Patient with Normal Acuity. Neuropsychologia, 29(10), 969-980. doi: Doi 10.1016/0028-3932(91)90061-CKas, A., de Souza, L. C., Samri, D., Bartolomeo, P., Lacomblez, L., Kalafat, M., . . . Sarazin, M. (2011). Neural correlates of cognitive impairment in posterior cortical atrophy. Brain, 134, 1464-1478. doi: Doi 10.1093/Brain/Awr055Kay, J. A. N. I. C. E., & Hanley, R. I. C. H. A. R. D. (1994). Peripheral disorders of spelling: The role of the graphemic buffer. Handbook of spelling: Theory, process and intervention, 295-315.Kelley, T. A., Serences, J. T., Giesbrecht, B., & Yantis, S. (2008). Cortical mechanisms for shifting and holding visuospatial attention. Cerebral Cortex, 18(1), 114-125. doi: DOI 10.1093/cercor/bhm036Kinsella K, Wan H. U.S. Census Bureau: International Population Reports, P95/09-1, An Aging World: 2008. US Census Bureau. Washington, DC: U.S. Government Printing Office; 2009.Kirshner, H. S., & Lavin, P. J. (2006). Posterior cortical atrophy: a brief review. Curr Neurol Neurosci Rep, 6(6), 477-480. Kivipelto, M., Helkala, E. L., Laakso, M. P., Hanninen, T., Hallikainen, M., Alhainen, K., . . . Nissien, A. (2001). Midlife vascular risk factors and Alzheimer's disease in later life: longitudinal, population based study. British Medical Journal, 322(7300), 1447-1451. doi: DOI 10.1136/bmj.322.7300.1447Kivipelto, M., Ngandu, T., Fratiglioni, L., Viitanen, M., Kareholt, I., Winblad, B., . . . Nissinen, A. (2005). Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol, 62(10), 1556-1560. doi: 62/10/1556 [pii]10.1001/archneur.62.10.1556Koedam, E. L. G. E., Lauffer, V., van der Vlies, A. E., van der Flier, W. M., Scheltens, P., & Pijnenburg, Y. A. L. (2010). Early-Versus Late-Onset Alzheimer's Disease: More than Age Alone. Journal of Alzheimers Disease, 19(4), 1401-1408. doi: Doi 10.3233/Jad-2010-1337Kooi, F. L., Toet, A., Tripathy, S. P., & Levi, D. M. (1994). The Effect of Similarity and Duration on Spatial Interaction in Peripheral-Vision. Spatial Vision, 8(2), 255-279. Kooi, F. L., Toet, A., Tripathy, S. P., & Levi, D. M. (1994). The Effect of Similarity and Duration on Spatial Interaction in Peripheral-Vision. Spatial Vision, 8(2), 255-279. doi: Doi 10.1163/156856894x00350Krumhansl, C. L., & Thomas, E. A. C. (1977). Effect of Level of Confusability on Reporting Letters from Briefly Presented Visual-Displays. Perception & Psychophysics, 21(3), 269-279. doi: Doi 10.3758/Bf03214239Kucera, Henry, & Francis, W. Nelson. (1967). Computational analysis of present-day American English. Providence,: Brown University Press.Langdon, D. W., & Thompson, A. J. (2000). Relation of impairment to everyday competence in visual disorientation syndrome: Evidence from a single case study. Archives of Physical Medicine and Rehabilitation, 81(5), 686-691. Leff, A. P., & Behrmann, M. (2008). Treatment of reading impairment after stroke. Current Opinion in Neurology, 21(6), 644-648. doi: 10.1097/WCO.0b013e3283168dc700019052-200812000-00006 [pii]Leff, A. P., Crewes, H., Plant, G. T., Scott, S. K., Kennard, C., & Wise, R. J. (2001). The functional anatomy of single-word reading in patients with hemianopic and pure alexia. Brain, 124(Pt 3), 510-521. Legge, G. E., Ahn, S. J., Klitz, T. S., & Luebker, A. (1997). Psychophysics of reading .16. The visual span in normal and low vision. Vision Research, 37(14), 1999-2010. doi: Doi 10.1016/S0042-6989(97)00017-5Legge, G. E., Mansfield, J. S., & Chung, S. T. L. (2001). Psychophysics of reading XX. Linking letter recognition to reading speed in central and peripheral vision. Vision Research, 41(6), 725-743. doi: Doi 10.1016/S0042-6989(00)00295-9Lehmann, M., Crutch, S. J., Ridgway, G. R., Ridha, B. H., Barnes, J., Warrington, E. K., . . . Fox, N. C. (2011). Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer's disease. Neurobiology of Aging, 32(8), 1466-1476. doi: DOI 10.1016/j.neurobiolaging.2009.08.017Lehmann, M., Ghosh, P. M., Madison, C., Laforce, R., Corbetta-Rastelli, C., Weiner, M. W., . . . Rabinovici, G. D. (2013). Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer's disease. Brain, 136, 844-858. doi: Doi 10.1093/Brain/Aws327Levi, D. M. (2008). Crowding - An essential bottleneck for object recognition: A mini-review. Vision Research, 48(5), 635-654. doi: DOI 10.1016/j.visres.2007.12.009Levi, D. M., Hariharan, S., & Klein, S. A. (2002). Suppressive and facilitatory spatial interactions in amblyopic vision. Vision Res, 42(11), 1379-1394. doi: S0042698902000615 [pii]Levi, D. M., Hariharan, S., & Klein, S. A. (2002). Suppressive and facilitatory spatial interactions in peripheral vision: peripheral crowding is neither size invariant nor simple contrast masking. J Vis, 2(2), 167-177. doi: 10:1167/2.2.32/2/3 [pii]Levi, D. M., & Klein, S. A. (1985). Vernier Acuity, Crowding and Amblyopia. Vision Research, 25(7), 979-991. doi: Doi 10.1016/0042-6989(85)90208-1Levi, D. M., Song, S. A., & Pelli, D. G. (2007). Amblyopic reading is crowded. Journal of Vision, 7(2). doi: Artn 21Doi 10.1167/7.2.21Levine, D. N., Lee, J. M., & Fisher, C. M. (1993). The visual variant of Alzheimer's disease: a clinicopathologic case study. Neurology, 43(2), 305-313. Levine, D. N., Warach, J., & Farah, M. (1985). 2 Visual Systems in Mental-Imagery - Dissociation of What and Where in Imagery Disorders Due to Bilateral Posterior Cerebral-Lesions. Neurology, 35(7), 1010-1018. Lewis, D. A., Campbell, M. J., Terry, R. D., & Morrison, J. H. (1987). Laminar and regional distributions of neurofibrillary tangles and neuritic plaques in Alzheimer's disease: a quantitative study of visual and auditory cortices. Journal of Neuroscience, 7(6), 1799-1808. Lipsitz, L. A., Jonsson, P. V., Kelley, M. M., & Koestner, J. S. (1991). Causes and Correlates of Recurrent Falls in Ambulatory Frail Elderly. Journals of Gerontology, 46(4), M114-M122. Lissauer, H. (1890). Ein Fall von Seelenblindheit nebst einem Beitrage zur Theorie derselben. European Archives of Psychiatry and Clinical Neuroscience, 21(2), 222-270.Liu, T. T., Jiang, Y., Sun, X. H., & He, S. (2009). Reduction of the Crowding Effect in Spatially Adjacent but Cortically Remote Visual Stimuli. Current Biology, 19(2), 127-132. doi: DOI 10.1016/j.cub.2008.11.065Liu, T. T., Jiang, Y., Sun, X. H., & He, S. (2009). Reduction of the Crowding Effect in Spatially Adjacent but Cortically Remote Visual Stimuli. Current Biology, 19(2), 127-132. doi: DOI 10.1016/j.cub.2008.11.065Logothetis, N. K., & Charles, E. R. (1990). The minimum motion technique applied to determine isoluminance in psychophysical experiments with monkeys. Vision Res, 30(6), 829-838. doi: 0042-6989(90)90052-M [pii]Lorusso, M. L., Facoetti, A., Pesenti, S., Cattaneo, C., Molteni, M., & Geiger, G. (2004). Wider recognition in peripheral vision common to different subtypes of dyslexia. Vision Research, 44(20), 2413-2424. doi: DOI 10.1016/j.visres.2004.05.001Lovegrove, W. J., Garzia, R. P., & Nicholson, S. B. (1990). Experimental evidence for a transient system deficit in specific reading disability. J Am Optom Assoc, 61(2), 137-146.Macdonald, A., & Cooper, B. (2007). Long-term care and dementia services: an impending crisis. Age and Ageing, 36(1), 16-22. doi: DOI 10.1093/ageing/afl126Madreperla, S. A., Palmer, R. W., Massof, R. W., & Finkelstein, D. (1990). Visual acuity loss in retinitis pigmentosa. Relationship to visual field loss. Arch Ophthalmol, 108(3), 358-361. Magnin, E., Sylvestre, G., Lenoir, F., Dariel, E., Bonnet, L., Chopard, G., . . . Rumbach, L. (2013). Logopenic syndrome in posterior cortical atrophy. Journal of Neurology, 260(2), 528-533. doi: DOI 10.1007/s00415-012-6671-7Mann, D. M. A., Iwatsubo, T., Nochlin, D., Sumi, S. M., LevyLahad, E., & Bird, T. D. (1997). Amyloid (A beta) deposition in chromosome 1-linked Alzheimer's disease: The Volga German families. Annals of Neurology, 41(1), 52-57. doi: DOI 10.1002/ana.410410110Marshall, J. C., & Newcombe, F. (1973). Patterns of paralexia: A psycholinguistic approach. Journal of psycholinguistic research, 2(3), 175-199.Mayall, K., Humphreys, G. W., & Olson, A. (1997). Disruption to word or letter processing? The origins of case-mixing effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(5), 1275.McCandliss, B. D., Cohen, L., & Dehaene, S. (2003). The visual word form area: expertise for reading in the fusiform gyrus. Trends Cogn Sci, 7(7), 293-299. doi: S1364661303001347 [pii]McCarthy, R. A., & Warrington, E. K. (1986). Visual associative agnosia: a clinico-anatomical study of a single case. Journal of Neurology, Neurosurgery & Psychiatry, 49(11), 1233-1240.McCarthy, R. A., & Warrington, E. K. (1990). Cognitive neuropsychology: A clinical introduction. Academic press.McClelland, J. L. (1976). Preliminary letter identification in the perception of words and nonwords. Journal of Experimental Psychology: Human Perception and Performance, 2(1), 80.McDonald, S. A. (2006). Parafoveal preview benefit in reading is only obtained from the saccade goal. Vision Res, 46(26), 4416-4424. doi: S0042-6989(06)00406-8 [pii]10.1016/j.visres.2006.08.027McFie, J., Piercy, M., & Zangwill, O. L. (1950). Visualspatial agnosia associated with lesions of the right cerebral hemisphere. Brain: A Journal of Neurology.McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology, 34(7), 939-944. McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Jr., Kawas, C. H., . . . Phelps, C. H. (2011). The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 7(3), 263-269. doi: 10.1016/j.jalz.2011.03.005S1552-5260(11)00101-4 [pii]McMonagle, P., Deering, F., Berliner, Y., & Kertesz, A. (2006). The cognitive profile of posterior cortical atrophy. Neurology, 66(3), 331-338. Medendorp, W. P., Goltz, H. C., Crawford, J. D., & Vilis, T. (2005). Integration of target and effector information in human posterior parietal cortex for the planning of action. Journal of Neurophysiology, 93(2), 954-962. doi: DOI 10.1152/jn.00725.2004Mendez, M. F. (2001). Visuospatial deficits with preserved reading ability in a patient with posterior cortical atrophy. Cortex, 37(4), 535-543. Mendez, M. F., & Cherrier, M. M. (1998). The evolution of alexia and simultanagnosia in posterior cortical atrophy. Neuropsychiatry Neuropsychol Behav Neurol, 11(2), 76-82. Mendez, M. F., Ghajarania, M., & Perryman, K. M. (2002). Posterior cortical atrophy: Clinical characteristics and differences compared to Alzheimer's disease. Dementia and Geriatric Cognitive Disorders, 14(1), 33-40. Mendez, M. F., Mendez, M. A., Martin, R., Smyth, K. A., & Whitehouse, P. J. (1990). Complex visual disturbances in Alzheimer's disease. Neurology, 40(3 Part 1), 439-439.Mendez, M. F., Shapira, J. S., & Clark, D. G. (2007). "apperceptive" alexia in posterior cortical atrophy. Cortex, 43(2), 264-270. doi: Doi 10.1016/S0010-9452(08)70481-7Merigan, W. H. (2000). Cortical area V4 is critical for certain texture discriminations, but this effect is not dependent on attention. Vis Neurosci, 17(6), 949-958. doi: S095252380017614X [pii]Merriam, E. P., Genovese, C. R., & Colby, C. L. (2003). Spatial updating in human parietal cortex. Neuron, 39(2), 361-373. doi: Doi 10.1016/S0896-6273(03)00393-3Mesulam, M., Wicklund, A., Johnson, N., Rogalski, E., Leger, G. C., Rademaker, A., . . . Bigio, E. H. (2008). Alzheimer and frontotemporal pathology in subsets of primary progressive aphasia. Ann Neurol, 63(6), 709-719. doi: 10.1002/ana.21388Metzler-Baddeley, C., Baddeley, R. J., Lovell, P. G., Laffan, A., & Jones, R. W. (2010). Visual impairments in dementia with Lewy bodies and posterior cortical atrophy. Neuropsychology, 24(1), 35-48. doi: 10.1037/a00168342010-00119-006 [pii]Michel, F., & Henaff, M. A. (2004). Seeing without the occipito-parietal cortex: Simultagnosia as a shrinkage of the attentional visual field. Behavioural Neurology, 15(1-2), 3-13. Midorikawa, A., Nakamura, K., Nagao, T., & Kawamura, M. (2008). Residual perception of moving objects: Dissociation of moving and static objects in a case or posterior cortical atrophy. European Neurology, 59(3-4), 152-158. doi: Doi 10.1159/000114035Migliaccio, R., Agosta, F., Rascovsky, K., Karydas, A., Bonasera, S., Rabinovici, G. D., . . . Gorno-Tempini, M. L. (2009). Clinical syndromes associated with posterior atrophy Early age at onset AD spectrum. Neurology, 73(19), 1571-1578. doi: Doi 10.1212/Wnl.0b013e3181c0d427Migliaccio, R., Agosta, F., Scola, E., Magnani, G., Cappa, S. F., Pagani, E., . . . Filippi, M. (2012). Ventral and dorsal visual streams in posterior cortical atrophy: A DT MRI study. Neurobiology of Aging, 33(11), 2572-2584. doi: DOI 10.1016/j.neurobiolaging.2011.12.025Migliaccio, R., Agosta, F., Toba, M. N., Samri, D., Corlier, F., de Souza, L. C., . . . Bartolomeo, P. (2012). Brain networks in posterior cortical atrophy: A single case tractography study and literature review. Cortex, 48(10), 1298-1309. doi: DOI 10.1016/j.cortex.2011.10.002Mizuno, M., Sartori, G., Liccione, D., Battelli, L., & Campo, R. (1996). Progressive visual agnosia with posterior cortical atrophy. Clinical Neurology and Neurosurgery, 98(2), 176-178. doi: Doi 10.1016/0303-8467(95)00091-7Molenberghs, P., Mesulam, M. M., Peeters, R., & Vandenberghe, R. R. C. (2007). Remapping attentional priorities: Differential contribution of superior parietal lobule and intraparietal sulcus. Cerebral Cortex, 17(11), 2703-2712. doi: DOI 10.1093/cercor/bhl179Morrison, J. H., Scherr, S. Lewis, D. A., Campbell, M. J., Bloom, F. E., Rogers, J., & Benoit, R. (1986). The laminar and regional distribution of neocortical somatostatin and neuritic plaques: implications for Alzheimer’s disease as a global neocortical disconnection syndrome. The biological substrates of Alzheimer’s disease, 115-131.Motoyoshi, I., & Kingdom, F. A. (2007). Differential roles of contrast polarity reveal two streams of second-order visual processing. Vision Res, 47(15), 2047-2054. doi: S0042-6989(07)00141-1 [pii]10.1016/j.visres.2007.03.015Murtha, S., Chertkow, H., Beauregard, M., & Evans, A. (1999). The neural substrate of picture naming. Journal of Cognitive Neuroscience, 11(4), 399-423.Mycroft, R. H., Behrmann, M., & Kay, J. (2009). Visuoperceptual deficits in letter-by-letter reading? Neuropsychologia, 47(7), 1733-1744. doi: DOI 10.1016/j.neuropsychologia.2009.02.014Nandy, A. S., & Tjan, B. S. (2007). The nature of letter crowding as revealed by first- and second-order classification images. J Vis, 7(2), 5 1-26. doi: 10.1167/7.2.57/2/5 [pii]Nazir, T. A., Jacobs, A. M., & O’Regan, J. K. (1998). Letter legibility and visual word recognition. Memory & cognition, 26(4), 810-821.Nestor, P. J., Caine, D., Fryer, T. D., Clarke, J., & Hodges, J. R. (2003). The topography of metabolic deficits in posterior cortical atrophy (the visual variant of Alzheimer's disease) with FDG-PET. Journal of Neurology Neurosurgery and Psychiatry, 74(11), 1521-1529. doi: DOI 10.1136/jnnp.74.11.1521O'Dowd, B. S., & de Zubicaray, G. I. (2003). Progressive dysgraphia in a case of posterior cortical atrophy. Neurocase, 9(3), 251-260. doi: 10.1076/neur.9.3.251.15561Paap, K. R., Newsome, S. L., McDonald, J. E., & Schvaneveldt, R. W. (1982). An activation–verification model for letter and word recognition: The word-superiority effect. Psychological review, 89(5), 573.Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4(7), 739-744. doi: Doi 10.1038/89532Patterson, K. E. (1978). Phonemic dyslexia: Errors of meaning and the meaning of errors. The Quarterly journal of experimental psychology, 30(4), 587-607.Patterson, K., & Kay, J. (1982). Letter-by-letter reading: psychological descriptions of a neurological syndrome. Q J Exp Psychol A, 34(Pt 3), 411-441. Paxton, J. L., Peavy, G. M., Jenkins, C., Rice, V. A., Heindel, W. C., & Salmon, D. P. (2007). Deterioration of visual-perceptual organization ability in Alzheimer's disease. Cortex, 43(7), 967-975. doi: Doi 10.1016/S0010-9452(08)70694-4Pelak, V. S., Smyth, S. F., Boyer, P. J., & Filley, C. M. (2011). Computerized visual field defects in posterior cortical atrophy. Neurology, 77(24), 2119-2122. doi: Doi 10.1212/Wnl.0b013e31823e9f2aPelli, D. G., Palomares, M., & Majaj, N. J. (2004). Crowding is unlike ordinary masking: Distinguishing feature integration from detection. Journal of Vision, 4(12), 1136-1169. doi: Doi 10.1167/4.12.12Pelli, D. G., Palomares, M., & Majaj, N. J. (2004). Crowding is unlike ordinary masking: distinguishing feature integration from detection. J Vis, 4(12), 1136-1169. doi: 10:1167/4.12.124/12/12 [pii]Pelli, D. G., & Tillman, K. A. (2008). The uncrowded window of object recognition. Nature Neuroscience, 11(10), 1129-1135. Pelli, D. G., Tillman, K. A., Freeman, J., Su, M., Berger, T. D., & Majaj, N. J. (2007). Crowding and eccentricity determine reading rate. J Vis, 7(2), 20 21-36. doi: 10.1167/7.2.207/2/20 [pii]Pernet, C., Valdois, S., Celsis, P., & Demonet, J. F. (2006). Lateral masking, levels of processing and stimulus category: A comparative study between normal and dyslexic readers. Neuropsychologia, 44(12), 2374-2385. doi: DOI 10.1016/j.neuropsychologia.2006.05.003Peters, R., Poulter, R., Warner, J., Beckett, N., Burch, L., & Bulpitt, C. (2008). Smoking, dementia and cognitive decline in the elderly, a systematic review. BMC Geriatr, 8, 36. doi: 10.1186/1471-2318-8-361471-2318-8-36 [pii]Pflugshaupt, T., Gutbrod, K., Wurtz, P., von Wartburg, R., Nyffeler, T., de Haan, B., . . . Mueri, R. M. (2009). About the role of visual field defects in pure alexia. Brain, 132(Pt 7), 1907-1917. doi: 10.1093/brain/awp141awp141 [pii]Pierrot-Deseilligny, C., Gray, F., & Brunet, P. (1986). Infarcts of both inferior parietal lobules with impairment of visually guided eye movements, peripheral visual inattention and optic ataxia. Brain, 109 ( Pt 1), 81-97. Pierrot-Deseilligny, C., Milea, D., & Muri, R. M. (2004). Eye movement control by the cerebral cortex. Current Opinion in Neurology, 17(1), 17-25. doi: Doi 10.1097/00019052-200402000-00005Pinon, M. C., Gattass, R., & Sousa, A. P. (1998). Area V4 in Cebus monkey: extent and visuotopic organization. Cerebral Cortex, 8(8), 685-701. Pisella, L., Sergio, L., Blangero, A., Torchin, H., Vighetto, A., & Rossetti, Y. (2009). Optic ataxia and the function of the dorsal stream: Contributions to perception and action. Neuropsychologia, 47(14), 3033-3044. doi: DOI 10.1016/j.neuropsychologia.2009.06.020Plaut, D. C. (2005). Connectionist approaches to reading. The science of reading: A handbook, 24-38.Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychological review, 103(1), 56.Plaut, D. C., & Shallice, T. (1993). Deep Dyslexia - a Case-Study of Connectionist Neuropsychology. Cognitive Neuropsychology, 10(5), 377-500. doi: Doi 10.1080/02643299308253469Poder, E., & Wagemans, J. (2007). Crowding with conjunctions of simple features. Journal of Vision, 7(2). doi: Artn 23 Doi 10.1167/7.2.23Price, C. J., & Devlin, J. T. (2003). The myth of the visual word form area. Neuroimage, 19(3), 473-481. doi: S1053811903000843 [pii]Price, C. J., & Humphreys, G. W. (1995). Contrasting Effects of Letter-Spacing in Alexia - Further Evidence That Different Strategies Generate Word-Length Effects in Reading. Quarterly Journal of Experimental Psychology Section a-Human Experimental Psychology, 48(3), 573-597. Price, C.J., Winterburn, D., Giraud, A.L., Moore, C.J., Noppeney, U., 2003. Cortical localisation of the visual and auditory word form areas: a reconsideration of the evidence. Brain LangQiao, E., Vinckier, F., Szwed, M., Naccache, L., Valabregue, R., Dehaene, S., & Cohen, L. (2010). Unconsciously deciphering handwriting: Subliminal invariance for handwritten words in the visual word form area. Neuroimage, 49(2), 1786-1799. doi: DOI 10.1016/j.neuroimage.2009.09.034Qiu, C., Kivipelto, M., Aguero-Torres, H., Winblad, B., & Fratiglioni, L. (2004). Risk and protective effects of the APOE gene towards Alzheimer's disease in the Kungsholmen project: variation by age and sex. Journal of Neurology Neurosurgery and Psychiatry, 75(6), 828-833. doi: DOI 10.1136/jnnp.2003.021493Qiu, C., Kivipelto, M., & von Strauss, E. (2009). Epidemiology of Alzheimer's disease: occurrence, determinants, and strategies toward intervention. Dialogues Clin Neurosci, 11(2), 111-128. Qiu, F. T. T., & von der Heydt, R. (2005). Figure and ground in the visual cortex: V2 combines stereoscopic cues with Gestalt rules. Neuron, 47(1), 155-166. doi: DOI 10.1016/j.neuron.2005.05.028Quental, N. B., Brucki, S. M., & Bueno, O. F. (2013). Visuospatial function in early Alzheimer's disease--the use of the Visual Object and Space Perception (VOSP) battery. Plos One, 8(7), e68398. doi: 10.1371/journal.pone.0068398PONE-D-12-31335 [pii]Ray, N. J., Fowler, S., & Stein, J. F. (2005). Yellow filters can improve magnocellular function: motion sensitivity, convergence, accommodation, and reading. Ann N Y Acad Sci, 1039, 283-293. doi: 1039/1/283 [pii]10.1196/annals.1325.027Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychol Bull, 124(3), 372-422. Rayner, K., & Pollatsek, A. The psychology of reading, 1989.Reicher, G. M. (1969). Perceptual recognition as a function of meaninfulness of stimulus material. J Exp Psychol, 81(2), 275-280. Renner, J. A., Burns, J. M., Hou, C. E., McKeel, D. W., Storandt, M., & Morris, J. C. (2004). Progressive posterior cortical dysfunction - A clinicopathologic series. Neurology, 63(7), 1175-1180. Reynolds, J. H., Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19(5), 1736-1753. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature neuroscience, 2(11), 1019-1025.Riddoch, J. (1990). Neglect and the Peripheral Dyslexias. Cognitive Neuropsychology, 7(5-6), 369-389. doi: Doi 10.1080/02643299008253449Ridgway, G. R., Lehmann, M., Barnes, J., Rohrer, J. D., Warren, J. D., Crutch, S. J., & Fox, N. C. (2012). Early-onset Alzheimer disease clinical variants Multivariate analyses of cortical thickness. Neurology, 79(1), 80-84. doi: Doi 10.1212/Wnl.0b013e31825dce28Ridgway, G. R., Omar, R., Ourselin, S., Hill, D. L. G., Warren, J. D., & Fox, N. C. (2009). Issues with threshold masking in voxel-based morphometry of atrophied brains. Neuroimage, 44(1), 99-111. doi: DOI 10.1016/j.neuroimage.2008.08.045Roberts, D. J., Woollams, A. M., Kim, E., Beeson, P. M., Rapcsak, S. Z., & Ralph, M. A. L. (2013). Efficient visual object and word recognition relies on high spatial frequency coding in the left posterior fusiform gyrus: evidence from a case-series of patients with ventral occipito-temporal cortex damage. Cerebral Cortex, 23(11), 2568-2580.Robertson, L. C. (2003). Binding, spatial attention and perceptual awareness. Nature Reviews Neuroscience, 4(2), 93-102. doi: Doi 10.1038/Nrn1030Robinson, G. L., & Foreman, P. J. (1999). Scotopic sensitivity/Irlen syndrome and the use of coloured filters: a long-term placebo-controlled study of reading strategies using analysis of miscue. Percept Mot Skills, 88(1), 35-52.Rogelet, P., Delafosse, A., & Destee, A. (1996). Posterior cortical atrophy: Unusual feature of Alzheimer's disease. Neurocase, 2(6), 495-501. Roorda, A., & Williams, D. R. (1999). The arrangement of the three cone classes in the living human eye. Nature, 397(6719), 520-522. doi: 10.1038/17383Rosenbloom, M. H., Alkalay, A., Agarwal, N., Baker, S. L., O'Neil, J. P., Janabi, M., . . . Rabinovici, G. D. (2011). Distinct clinical and metabolic deficits in PCA and AD are not related to amyloid distribution. Neurology, 76(21), 1789-1796. Rosenbloom, M. H., Alkalay, A., Agarwal, N., Baker, S. L., ONeill, J. P., Janabi, M., . . . Rabinovici, G. D. (2010). Distinct Clinical and Metabolic Deficits in Alzheimer's Disease and Posterior Cortical Atrophy Are Not Related to Amyloid Distribution. Neurology, 74(9), A301-A301. Ross, S. K., Graham, N., StuartGreen, L., Prins, M., Xuereb, J., Patterson, K., & Hodges, J. R. (1996). Progressive biparietal atrophy: An atypical presentation of Alzheimer's disease. Journal of Neurology Neurosurgery and Psychiatry, 61(4), 388-395. Rossetti, Y., Revol, P., McIntosh, R., Pisella, L., Rode, G., Danckert, J., . . . Milner, A. D. (2005). Visually guided reaching: bilateral posterior parietal lesions cause a switch from fast visuomotor to slow cognitive control. Neuropsychologia, 43(2), 162-177. doi: DOI 10.1016/j.neuropsychologia.2004.11.004Rossor, M. N. (1994). Management of Neurological Disorders - Dementia. Journal of Neurology Neurosurgery and Psychiatry, 57(12), 1451-1456. doi: DOI 10.1136/jnnp.57.12.1451Rossor, M. N., Fox, N. C., Mummery, C. J., Schott, J. M., & Warren, J. D. (2010). The diagnosis of young-onset dementia. Lancet Neurology, 9(8), 793-806. doi: 10.1016/S1474-4422(10)70159-9S1474-4422(10)70159-9 [pii]Rovio, S., Kareholt, I., Helkala, E. L., Viitanen, M., Winblad, B., Tuomilehto, J., . . . Kivipelto, M. (2005). Leisure-time physical activity at midlife and the risk of dementia and Alzheimer's disease. Lancet Neurology, 4(11), 705-711. doi: Doi 10.1016/S1474-4422(05)70198-8Rumiati, R. I., & Humphreys, G. W. (1997). Visual object agnosia without alexia or prosopagnosia: Arguments for separate knowledge stores. Visual Cognition, 4(2), 207-217. Russell, C., Malhotra, P., Deidda, C., & Husain, M. (2013). Dynamic attentional modulation of vision across space and time after right hemisphere stroke and in ageing. Cortex, 49(7), 1874-1883. doi: 10.1016/j.cortex.2012.10.005 S0010-9452(12)00307-3 [pii]Russell, C., Malhotra, P., & Husain, M. (2004). Attention modulates the visual field in healthy observers and parietal patients. Neuroreport, 15(14), 2189-2193. Saffran, E. M., & Coslett, H. B. (1996). ''Attentional dyslexia'' in Alzheimer's disease: A case study. Cognitive Neuropsychology, 13(2), 205-228. Saffran, E. M., Fitzpatrick-DeSalme, E. J., & Coslett, H. B. (1990). Visual disturbances in dementia. Modular deficits in Alzheimer-type dementia, 44, 297-327.Sala, S. D., Spinnler, H., & Trivelli, C. (1996). Slowly progressive impairment of spatial exploration and visual perception. Neurocase, 2(4), 299-323.Salmon, D. P., & Bondi, M. W. (2009). Neuropsychological Assessment of Dementia. Annual Review of Psychology, 60, 257-282. doi: DOI 10.1146/annurev.psych.57.102904.190024Sampson, E. L., Warren, J. D., & Rossor, M. N. (2004). Young onset dementia. Postgraduate Medical Journal, 80(941), 125-139. doi: DOI 10.1136/pgmj.2003.011171Sandberg, M. A., & Gaudio, A. R. (2006). Reading speed of patients with advanced retinitis pigmentosa or choroideremia. Retina, 26(1), 80-88. doi: 00006982-200601000-00013 [pii]Scahill, R. I., Schott, J. M., Stevens, J. M., Rossor, M. N., & Fox, N. C. (2002). Mapping the evolution of regional atrophy in Alzheimer's disease: Unbiased analysis of fluid-registered serial MRI. Proceedings of the National Academy of Sciences of the United States of America, 99(7), 4703-4707. doi: DOI 10.1073/pnas.052587399Schattka, K. I., Radach, R., & Huber, W. (2010). Eye movement correlates of acquired central dyslexia. Neuropsychologia, 48(10), 2959-2973. doi: DOI 10.1016/j.neuropsychologia.2010.06.005Scheuner, D., Eckman, C., Jensen, M., Song, X., Citron, M., Suzuki, N., . . . Younkin, S. (1996). Secreted amyloid beta-protein similar to that in the senile plaques of Alzheimer's disease is increased in vivo by the presenilin 1 and 2 and APP mutations linked to familial Alzheimer's disease. Nat Med, 2(8), 864-870. Schiller, P. H. (1992). The ON and OFF channels of the visual system. Trends Neurosci, 15(3), 86-92. doi: 0166-2236(92)90017-3 [pii]Schott, J. M., Ridha, B. H., Crutch, S. J., Healy, D. G., Uphill, J. B., Warrington, E. K., . . . Fox, N. C. (2006). Apolipoprotein E genotype modifies the phenotype of Alzheimer disease. Archives of Neurology, 63(1), 155-156. doi: DOI 10.1001/archneur.63.1.155Schonell, F., & Goodacre, E. (1971). The psychology and teaching of reading. Oliver & Boyd.Seguin, J., Formaglio, M., Perret-Liaudet, A., Quadrio, I., Tholance, Y., Rouaud, O., . . . Krolak-Salmon, P. (2011). CSF biomarkers in posterior cortical atrophy. Neurology, 76(21), 1782-1788. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological review, 96(4), 523.Sekuler, E. B., & Behrmann, M. (1996). Perceptual cues in pure alexia. Cognitive Neuropsychology, 13(7), 941-974. Selkoe, D. J. (2001). Alzheimer's disease: genes, proteins, and therapy. Physiol Rev, 81(2), 741-766. Sereno, M. I., Dale, A. M., Reppas, J. B., Kwong, K. K., Belliveau, J. W., Brady, T. J., . . . Tootell, R. B. H. (1995). Borders of Multiple Visual Areas in Humans Revealed by Functional Magnetic-Resonance-Imaging. Science, 268(5212), 889-893. doi: DOI 10.1126/science.7754376Sereno, M. I., Pitzalis, S., & Martinez, A. (2001). Mapping of contralateral space in retinotopic coordinates by a parietal cortical area in humans. Science, 294(5545), 1350-1354. doi: DOI 10.1126/science.1063695Shakespeare, T. J., Yong, K. X. X., Frost, C., Kim, L. G., Warrington, E. K., & Crutch, S. J. (2013). Scene perception in posterior cortical atrophy: categorization, description and fixation patterns. Frontiers in Human Neuroscience, 7. doi: Artn 621Doi 10.3389/Fnhum.2013.00621Shallice, T., & Coughlan, A. K. (1980). Modality specific word comprehension deficits in deep dyslexia. J Neurol Neurosurg Psychiatry, 43(10), 866-872. Shallice, T., & Evans, M. E. (1978). The involvement of the frontal lobes in cognitive estimation. Cortex, 14(2), 294-303. Sitek, E. J., Narozanska, E., Peplonska, B., Filipek, S., Barczak, A., Styczynska, M., . . . Zekanowski, C. (2013). A Patient with Posterior Cortical Atrophy Possesses a Novel Mutation in the Presenilin 1 Gene. Plos One, 8(4). doi: ARTN e61074DOI 10.1371/journal.pone.0061074Smith, A. T., Singh, K. D., Williams, A. L., & Greenlee, M. W. (2001). Estimating receptive field size from fMRI data in human striate and extrastriate visual cortex. Cerebral Cortex, 11(12), 1182-1190. Snowden, J. S., Stopford, C. L., Julien, C. L., Thompson, J. C., Davidson, Y., Gibbons, L., . . . Mann, D. (2007). Cognitive phenotypes in Alzheimer's disease and genetic risk. Cortex, 43(7), 835-845. Spieler, D. H., & Balota, D. A. (1997). Bringing computational models of word naming down to the item level. Psychological Science, 8(6), 411-416. Spieler, D. H., & Balota, D. A. (2000). Factors influencing word naming in younger and older adults. Psychology and Aging, 15(2), 225-231. doi: Doi 10.1037/0882-7974.15.2.225Spinelli, D., De Luca, M., Judica, A., & Zoccolotti, P. (2002). Crowding effects on word identification in developmental dyslexia. Cortex, 38(2), 179-200. doi: Doi 10.1016/S0010-9452(08)70649-XSpinelli, D., De Luca, M., Judica, A., & Zoccolotti, P. (2002). Crowding effects on word identification in developmental dyslexia. Cortex, 38(2), 179-200. doi: Doi 10.1016/S0010-9452(08)70649-XStark, M. E., Grafman, J., & Fertig, E. (1997). A restricted 'spotlight' of attention in visual object recognition. Neuropsychologia, 35(9), 1233-1249. doi: Doi 10.1016/S0028-3932(97)00049-3Stenbacka, L., & Vanni, S. (2007). fMRI of peripheral visual field representation. Clin Neurophysiol, 118(6), 1303-1314. doi: S1388-2457(07)00047-8 [pii]10.1016/j.clinph.2007.01.023Stein, J. (2003). Visual motion sensitivity and reading. Neuropsychologia, 41(13), 1785-1793. doi: Doi 10.1016/S0028-3932(03)00179-9Stopford, C. L., Snowden, J. S., Thompson, J. C., & Neary, D. (2008). Variability in cognitive presentation of Alzheimer's disease. Cortex, 44(2), 185-195. doi: DOI 10.1016/j.cortex.2005.11.002Strain, E., Patterson, K., & Seidenberg, M. S. (1995). Semantic effects in single-word naming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(5), 1140.Strasburger, H. (2005). Unfocussed spatial attention underlies the crowding effect in indirect form vision. Journal of Vision, 5(11), 1024-1037. doi: Doi 10.1167/5.11.8Strasburger, H., Harvey, L. O., & Rentschler, I. (1991). Contrast Thresholds for Identification of Numeric Characters in Direct and Eccentric View. Perception & Psychophysics, 49(6), 495-508. doi: Doi 10.3758/Bf03212183Szwed, M., Dehaene, S., Kleinschmidt, A., Eger, E., Valabregue, R., Amadon, A., & Cohen, L. (2011). Specialization for written words over objects in the visual cortex. Neuroimage, 56(1), 330-344. doi: DOI 10.1016/j.neuroimage.2011.01.073Tanaka, K. (1997). Mechanisms of visual object recognition: monkey and human studies. Current Opinion in Neurobiology, 7(4), 523-529. Tang-Wai, D., & Mapstone, M. (2006). What are we seeing? Is posterior cortical atrophy just Alzheimer disease? Neurology, 66(3), 300-301. doi: DOI 10.1212/01.wnl.0000202093.81603.d8Tang-Wai, D. F., Graff-Radford, N. R., Boeve, B. F., Dickson, D. W., Parisi, J. E., Crook, R., . . . Petersen, R. C. (2004). Clinical, genetic, and neuropathologic characteristics of posterior cortical atrophy. Neurology, 63(7), 1168-1174. Tang-Wai, D. F., Josephs, K. A., Boeve, B. F., Dickson, D. W., Parisi, J. E., & Petersen, R. C. (2003). Pathologically confirmed corticobasal degeneration presenting with visuospatial dysfunction. Neurology, 61(8), 1134-1135. Taylor, A. M., & Warrington, E. K. (1973). Visual discrimination in patients with localized cerebral lesions. Cortex, 9(1), 82-93. Tenovuo, O., Kemppainen, N., Aalto, S., Nagren, K., & Rinne, J. O. (2008). Posterior Cortical Atrophy: A Rare Form of Dementia with in vivo Evidence of Amyloid-beta Accumulation. Journal of Alzheimers Disease, 15(3), 351-355. Terry, R. D., Masliah, E., Salmon, D. P., Butters, N., Deteresa, R., Hill, R., . . . Katzman, R. (1991). Physical Basis of Cognitive Alterations in Alzheimers-Disease - Synapse Loss Is the Major Correlate of Cognitive Impairment. Annals of Neurology, 30(4), 572-580. doi: DOI 10.1002/ana.410300410Thompson-Schill, S. L., D'Esposito, M., & Kan, I. P. (1999). Effects of repetition and competition on activity in left prefrontal cortex during word generation. Neuron, 23(3), 513-522.Toet, A., & Levi, D. M. (1992). The two-dimensional shape of spatial interaction zones in the parafovea. Vision Res, 32(7), 1349-1357. Townsend, J. T. (1971). Theoretical Analysis of an Alphabetic Confusion Matrix. Perception & Psychophysics, 9(1A), 40-&. doi: Doi 10.3758/Bf03213026Townsend, J. T., Taylor, S. G., & Brown, D. R. (1971). Lateral Masking for Letters with Unlimited Viewing Time. Perception & Psychophysics, 10(5), 375-&. doi: Doi 10.3758/Bf03207464Townsend, J. T., Taylor, S. G., & Brown, D. R. (1971). Lateral Masking for Letters with Unlimited Viewing Time. Perception & Psychophysics, 10(5), 375-&. doi: Doi 10.3758/Bf03207464Tripathy, S. P., & Cavanagh, P. (2002). The extent of crowding in peripheral vision does not scale with target size. Vision Res, 42(20), 2357-2369. doi: S0042698902001979 [pii]Tripathy, S. P., & Levi, D. M. (1994). Long-range dichoptic interactions in the human visual cortex in the region corresponding to the blind spot. Vision Res, 34(9), 1127-1138. doi: 0042-6989(94)90295-X [pii]Tschanz, J. T., Corcoran, C. D., Schwartz, S., Treiber, K., Green, R. C., Norton, M. C., . . . Lyketsos, C. G. (2011). Progression of Cognitive, Functional, and Neuropsychiatric Symptom Domains in a Population Cohort With Alzheimer Dementia: The Cache County Dementia Progression Study. American Journal of Geriatric Psychiatry, 19(6), 532-542. doi: Doi 10.1097/Jgp.0b013e3181faec23van der Heijden, A. H., Malhas, M. S., & van den Roovaart, B. P. (1984). An empirical interletter confusion matrix for continuous-line capitals. Percept Psychophys, 35(1), 85-88. Vandenberghe, R., Dupont, P., DeBruyn, B., Bormans, G., Michiels, J., Mortelmans, L., & Orban, G. A. (1996). The influence of stimulus location on the brain activation pattern in detection and orientation discrimination - A PET study of visual attention. Brain, 119, 1263-1276. doi: DOI 10.1093/brain/119.4.1263Vandenberghe, R., Gitelman, D. R., Parrish, T. B., & Mesulam, M. M. (2001). Functional specificity of superior parietal mediation of spatial shifting. Neuroimage, 14(3), 661-673. doi: DOI 10.1006/nimg.2001.0860Videaud, H., Torny, F., Prado-Jean, A., & Couratier, P. (2009). Use of the Visual Object and Space Perception (VOSP) test battery in two cases of posterior cortical atrophy. Neurocase, 15(1), 32-36. doi: Pii 905972983Doi 10.1080/13554790802570480Vinckier, F., Naccache, L., Papeix, C., Forget, J., Hahn-Barma, V., Dehaene, S., & Cohen, L. (2006). "What" and "where" in word reading: Ventral coding of written words revealed by parietal atrophy. Journal of Cognitive Neuroscience, 18(12), 1998-2012. doi: DOI 10.1162/jocn.2006.18.12.1998von Gunten, A., Bouras, C., Kovari, E., Giannakopoulos, P., & Hof, P. R. (2006). Neural substrates of cognitive and behavioral deficits in atypical Alzheimer's disease. Brain Res Rev, 51(2), 176-211. doi: S0165-0173(05)00169-4 [pii]10.1016/j.brainresrev.2005.11.003Wakai, M., Honda, H., Takahashi, A., Kato, T., Ito, K., & Hamanaka, T. (1994). Unusual Findings on Pet Study of a Patient with Posterior Cortical Atrophy. Acta Neurologica Scandinavica, 89(6), 458-461. Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007). Visual field maps in human cortex. Neuron, 56(2), 366-383. doi: S0896-6273(07)00774-X [pii]10.1016/j.neuron.2007.10.012Warrington, E. K. (1984). Recognition Memory Test: Rmt.(Words). Test Booklet 1. NFER-Nelson Publishing Company.Warrington, E. K. (1985). Agnosia: the impairment of object recognition. Handbook of clinical neurology, 45, 333-349.Warrington, E. K. (1986). Visual deficits associated with occipital lobe lesions in man. Experimental Brain Research Supplementum, 11, 247-261.Warrington, E. K. (1996). The Camden Memory Tests: Manual (Vol. 1). Psychology Press.Warrington .E.K., & James, M. (1967). Disorders of Visual Perception in Patients with Localised Cerebral Lesions. Neuropsychologia, 5(3), 253-&. doi: Doi 10.1016/0028-3932(67)90040-1Warrington .E.K., & James, M. (1967). Disorders of Visual Perception in Patients with Localised Cerebral Lesions. Neuropsychologia, 5(3), 253-&. doi: Doi 10.1016/0028-3932(67)90040-1Warrington, E. K., Cipolotti, L., & McNeil, J. (1993). Attentional dyslexia: a single case study. Neuropsychologia, 31(9), 871-885. doi: 0028-3932(93)90145-P [pii]Warrington, E. K., & James, M. (1988). Visual Apperceptive Agnosia - a Clinico-Anatomical Study of 3 Cases. Cortex, 24(1), 13-32. Warrington, E. K., & James, M. (1991). A New Test of Object Decision - 2d Silhouettes Featuring a Minimal View. Cortex, 27(3), 377-383. Warrington, E. K., & James, M. (1991). VOSP: the visual object and space perception battery, Thames Valley Test Company, Bury St. Edmunds, Suffolk.Warrington, E. K., & Langdon, D. W. (2002). Does the spelling dyslexic read by recognizing orally spelled words? An investigation of a letter-by-letter reader. Neurocase, 8(3), 210-218. Warrington, E. K., McKenna, P., & Orpwood, L. (1998). Single word comprehension: A concrete and abstract word synonym test. Neuropsychological Rehabilitation, 8(2), 143-154. Warrington, E. K., & Shallice, T. (1980). Word-form dyslexia. Brain, 103(1), 99-112. Warrington, E. K., & Taylor, A. M. (1973). The contribution of the right parietal lobe to object recognition. Cortex, 9(2), 152-164. Wassle, H., & Boycott, B. B. (1991). Functional architecture of the mammalian retina. Physiol Rev, 71(2), 447-480. Weekes, B. S. (1997). Differential effects of number of letters on word and nonword naming latency. Quarterly Journal of Experimental Psychology Section a-Human Experimental Psychology, 50(2), 439-456. doi: Doi 10.1080/027249897392170Wheeler, D. D. (1970). Processes in Word Recognition. Cognitive Psychology, 1(1), 59-85. Whitwell, J. L., Jack, C. R., Kantarci, K., Weigand, S. D., Boeve, B. F., Knopman, D. S., . . . Josephs, K. A. (2007). Imaging correlates of posterior cortical atrophy. Neurobiology of Aging, 28(7), 1051-1061. doi: DOI 10.1016/j.neurobiolaging.2006.05.026Wilkinson, F., Wilson, H. R., & Ellemberg, D. (1997). Lateral interactions in peripherally viewed texture arrays. J Opt Soc Am A Opt Image Sci Vis, 14(9), 2057-2068. Willison, J. R., & Warrington, E. K. (1992). Cognitive Retardation in a Patient with Preservation of Psychomotor Speed. Behavioural Neurology, 5(2), 113-116. Wolford, G. (1975). Perturbation model for letter identification. Psychol Rev, 82(3), 184-199. Wolford, G., & Chambers, L. (1984). Contour interaction as a function of retinal eccentricity. Percept Psychophys, 36(5), 457-460. Wolpert, D. M., Goodbody, S. J., & Husain, M. (1998). Maintaining internal representations the role of the human superior parietal lobe. Nature Neuroscience, 1(6), 529-533. doi: Doi 10.1038/2245Woollams, A. M., Ralph, M. A. L., Plaut, D. C., & Patterson, K. (2007). SD-squared: on the association between semantic dementia and surface dyslexia. Psychological review, 114(2), 316.Yeatman, J. D., Rauschecker, A. M., & Wandell, B. A. (2013). Anatomy of the visual word form area: Adjacent cortical circuits and long-range white matter connections. Brain and Language, 125(2), 146-155. doi: DOI 10.1016/j.bandl.2012.04.010Yealland, L. R. (1916). Gunshot Wound involving Visual Centre, with Visual Disorientation. Proceedings of the Royal Society of Medicine, 9(Sect Ophthalmol), 97.Yong, K. X., Warren, J. D., Warrington, E. K., & Crutch, S. J. (2013). Intact reading in patients with profound early visual dysfunction. Cortex. doi: S0010-9452(13)00012-9 [pii]10.1016/j.cortex.2013.01.009Ziegler, J. C., Perry, C., Jacobs, A. M., & Braun, M. (2001). Identical words are read differently in different languages. Psychological Science, 12(5), 379-384.Zhou, H., Friedman, H. S., & von der Heydt, R. (2000). Coding of border ownership in monkey visual cortex. Journal of Neuroscience, 20(17), 6594-6611. doi: 20/17/6594 [pii]Zorzi, M., Barbiero, C., Facoetti, A., Lonciari, I., Carrozzi, M., Montico, M., . . . Ziegler, J. C. (2012). Extra-large letter spacing improves reading in dyslexia. Proceedings of the National Academy of Sciences of the United States of America, 109(28), 11455-11459. doi: DOI 10.1073/pnas.1205566109 ................
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