Department of Mathematics and Statistics | Washington ...



Modeling autosomal dominant Alzheimer’s disease with machine learningPatrick H. Lucketta, Austin McCullougha, Brian A. Gordona, Jeremy Straina, Shaney Floresa, Aylin Dincera, John McCarthya, Todd Kuffnera, Ari Sterna, Sarah B. Bermanb, Jasmeer P. Chhatwalc, Carlos Cruchagaa, Anne M. Fagana, Martin R. Farlowd, Nick C. Foxe, Mathias Juckerf, Johannes Leving,q,w, Colin L. Mastersh, Hiroshi Morii, James M. Noblej, Stephen Sallowayk, Peter R. Schofieldl,x, Adam M. Brickmanm, William S. Brooksl,x, David M. Cashe, Michael Fulhamn, Bernardino Ghettio, Clifford R. Jack, Jrp, Jonathan V?gleinq, William Klunkr, Robert Koeppes, Hwamee Oht, Yi Suu, Michael Weinerv, Qing Wanga, Laura Swishera, Dan Marcusa, Deborah Koudelisa, Nelly Joseph-Mathurina, Lisa Casha, Russ Hornbecka, Chengjie Xionga, Richard J. Perrina, Celeste M. Karcha, Jason Hassenstaba, Eric McDadea, John C. Morrisa, Tammie L.S. Benzingera, Randall J. Batemana, Beau M. Ancesa for the Dominantly Inherited Alzheimer Network (DIAN) Washington University in St. Louis, 660 S Euclid Ave, St. Louis, MO 63110, USAUniversity of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USABrigham and Women's Hospital, Massachusetts General Hospital, 75 Francis St, Boston, MA 02115, USAIndiana University, 107 S Indiana Ave, Bloomington, IN 47405, USADementia Research Centre, UCL Queen Square Institute of Neurology, London UK WC1E 6BT, UK German Center for Neurodegenerative Disease, Otfried-Müller-Stra?e 23, 72076, Tübingen, GermanyLudwig Maximilian University of Munich, Geschwister-Scholl-Platz 1, 80539 Munich, GermanyFlorey Institute, The University of Melbourne, 30 Royal Parade, Parkville VIC 3052, AustraliaOsaka City University, 3 Chome-3-138 Sugimoto, Sumiyoshi Ward, Osaka, 558-8585, JapanTaub Institute for Research on Alzheimer’s Disease and the Aging Brain, G.H. Sergievsky Center, and Department of Neurology, Columbia University Irving Medical Center, 710 W. 168th St, New York, NY 10032Brown University, 69 Brown St Box 1822, Providence, RI 02912, USANeuroscience Research Australia, 139 Barker St, Randwick, Sydney NSW 2031, AustraliaColumbia University, 630 W. 168th St., New York, NY 10032, USAUniversity of Sydney, Camperdown NSW 2006, AustraliaIndiana University, 107 S Indiana Ave, Bloomington, IN 47405, USAMayo Clinic, 200 First St. SW, Rochester, MN 55905, USAGerman Center for Neurodegenerative Diseases, Feodor-Lynen-Strasse 17, 81377 Munich University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USAUniversity of Michigan, 500 S State St, Ann Arbor, MI 48109, USAButler University, 4600 Sunset Ave, Indianapolis, IN 46208, USABanner Alzheimer Institute, 901 E Willetta St 1st Floor, Phoenix, AZ 85006, USAUniversity of California, 9500 Gilman Dr, La Jolla, CA 92093 , USAMunich Cluster for Systems Neurology (SyNergy), Feodor-Lynen-Str. 17, 81377 Munich, GermanyUniversity of New South Wales, Sydney NSW 2052, AustraliaCorresponding author: Patrick Luckett, PhD, Washington University School of Medicine Department of Neurology, Campus Box 8111, 660 S. Euclid Avenue, St. Louis, MO 63110Phone: 314-747-8423 Fax: 314-747-8427Email: luckett.patrick@wustl.eduWord count: Abstract = 150 words, Text = 3500 words, Number of tables = 1, Number of figures = 5, Number of supplementary tables = 1, Number of supplementary figures = 8ABSTRACTINTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer’s disease.METHODS: Longitudinal structural MRI, amyloid PET, and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein e4 (APOE e4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh Compound-B (R2=0.95), fluorodeoxyglucose (R2=0.93), and atrophy (R2=0.95) in mutation carriers compared to non-carriers.DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.Keywords: Autosomal dominant Alzheimer disease (ADAD), Machine learning, Pittsburgh compound B (PiB), Fluorodeoxyglucose (FDG), Magnetic resonance imaging (MRI)INTRODUCTIONAlzheimer’s disease (AD) is the most common form of dementia, accounting for 60%–70% of the 50 million dementia cases worldwide ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1063/1.3590158","ISBN":"9781412928168","ISSN":"00218979","abstract":"WHO fact sheet on dementia providing key facts and information on signs and symptoms, rates, risk factors, social and economic impacts, human rights, WHO response.","author":[{"dropping-particle":"","family":"World Health Organization","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"WHO","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2017"]]},"page":"751-760","title":"Dementia Fact sheet","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"[1]","plainTextFormattedCitation":"[1]","previouslyFormattedCitation":"[1]"},"properties":{"noteIndex":0},"schema":""}[1]. AD leads to slow cognitive decline, behavioral and psychiatric disorders, and impairments in functional status. Pathological features of AD include the accumulation of amyloid-beta (A?) plaques, neurofibrillary tau tangles, and neuronal/synaptic losses that correspond with atrophy and decreased glucose metabolism ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1101/cshperspect.a006189","ISBN":"2157-1422 (Electronic)","ISSN":"21571422","PMID":"22229116","abstract":"The neuropathological hallmarks of Alzheimer disease (AD) include \"positive\" lesions such as amyloid plaques and cerebral amyloid angiopathy, neurofibrillary tangles, and glial responses, and \"negative\" lesions such as neuronal and synaptic loss. Despite their inherently cross-sectional nature, postmortem studies have enabled the staging of the progression of both amyloid and tangle pathologies, and, consequently, the development of diagnostic criteria that are now used worldwide. In addition, clinicopathological correlation studies have been crucial to generate hypotheses about the pathophysiology of the disease, by establishing that there is a continuum between \"normal\" aging and AD dementia, and that the amyloid plaque build-up occurs primarily before the onset of cognitive deficits, while neurofibrillary tangles, neuron loss, and particularly synaptic loss, parallel the progression of cognitive decline. Importantly, these cross-sectional neuropathological data have been largely validated by longitudinal in vivo studies using modern imaging biomarkers such as amyloid PET and volumetric MRI.","author":[{"dropping-particle":"","family":"Serrano-Pozo","given":"Alberto","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frosch","given":"Matthew P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masliah","given":"Eliezer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hyman","given":"Bradley T.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cold Spring Harbor Perspectives in Medicine","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2011"]]},"page":"6189","title":"Neuropathological alterations in Alzheimer disease","type":"article-journal","volume":"1"},"uris":[""]}],"mendeley":{"formattedCitation":"[2]","plainTextFormattedCitation":"[2]","previouslyFormattedCitation":"[2]"},"properties":{"noteIndex":0},"schema":""}[2]. The most common form of AD occurs in older age and is known as late-onset Alzheimer’s disease (LOAD). Autosomal dominant Alzheimer’s disease (ADAD) accounts for less than 1% of all AD cases and is caused by pathogenic mutations in amyloid precursor protein (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes that lead to early increases in Aβ deposition in the brain, which, in turn, is hypothesized to initiate a cascade that causes cognitive decline ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/alzrt59","ISSN":"17589193","abstract":"Autosomal-dominant Alzheimer's disease has provided significant understanding of the pathophysiology of Alzheimer's disease. The present review summarizes clinical, pathological, imaging, biochemical, and molecular studies of autosomal-dominant Alzheimer's disease, highlighting the similarities and differences between the dominantly inherited form of Alzheimer's disease and the more common sporadic form of Alzheimer's disease. Current developments in autosomal-dominant Alzheimer's disease are presented, including the international Dominantly Inherited Alzheimer Network and this network's initiative for clinical trials. Clinical trials in autosomal-dominant Alzheimer's disease may test the amyloid hypothesis, determine the timing of treatment, and lead the way to Alzheimer's disease prevention. ? 2011 BioMed Central Ltd.","author":[{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Strooper","given":"Bart","non-dropping-particle":"De","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lemere","given":"Cynthia A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Windisch","given":"Manfred","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Alzheimer's Research and Therapy","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2011"]]},"page":"1-13","title":"Autosomal-dominant Alzheimer's disease: A review and proposal for the prevention of Alzheimer's disease","type":"article-journal","volume":"3"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.3389/fneur.2015.00142","ISSN":"16642295","abstract":"Our understanding of the pathogenesis of Alzheimer disease (AD) has been greatly influenced by investigation of rare families with autosomal dominant mutations that cause early onset AD. Mutations in the genes coding for amyloid precursor protein (APP), presenilin 1 (PSEN-1), and presenilin 2 (PSEN-2) cause over-production of the amyloid-β peptide (Aβ) leading to early deposition of Aβ in the brain, which in turn is hypothesized to initiate a cascade of processes, resulting in neuronal death, cognitive decline, and eventual dementia. Studies of cerebrospinal fluid (CSF) from individuals with the common form of AD, late-onset AD (LOAD), have revealed that low CSF Aβ42 and high CSF tau are associated with AD brain pathology. Herein, we review the literature on CSF biomarkers in autosomal dominant AD (ADAD), which has contributed to a detailed road map of AD pathogenesis, especially during the preclinical period, prior to the appearance of any cognitive symptoms. Current drug trials are also taking advantage of the unique characteristics of ADAD and utilizing CSF biomarkers to accelerate development of effective therapies for AD.","author":[{"dropping-particle":"","family":"Schindler","given":"Suzanne Elizabeth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Frontiers in Neurology","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2015"]]},"page":"142","title":"Autosomal dominant Alzheimer disease: A unique resource to study CSF biomarker changes in preclinical AD","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"[3,4]","plainTextFormattedCitation":"[3,4]","previouslyFormattedCitation":"[3,4]"},"properties":{"noteIndex":0},"schema":""}[3,4]. The age of onset of cognitive impairment in ADAD mutation carriers (MC) is earlier than LOAD and remains fairly consistent within a family, allowing for calculation of the estimated age of symptom onset (EAO) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1212/WNL.0000000000000596","ISSN":"1526-632X","PMID":"24928124","abstract":"OBJECTIVE To identify factors influencing age at symptom onset and disease course in autosomal dominant Alzheimer disease (ADAD), and develop evidence-based criteria for predicting symptom onset in ADAD. METHODS We have collected individual-level data on ages at symptom onset and death from 387 ADAD pedigrees, compiled from 137 peer-reviewed publications, the Dominantly Inherited Alzheimer Network (DIAN) database, and 2 large kindreds of Colombian (PSEN1 E280A) and Volga German (PSEN2 N141I) ancestry. Our combined dataset includes 3,275 individuals, of whom 1,307 were affected by ADAD with known age at symptom onset. We assessed the relative contributions of several factors in influencing age at onset, including parental age at onset, age at onset by mutation type and family, and APOE genotype and sex. We additionally performed survival analysis using data on symptom onset collected from 183 ADAD mutation carriers followed longitudinally in the DIAN Study. RESULTS We report summary statistics on age at onset and disease course for 174 ADAD mutations, and discover strong and highly significant (p < 10(-16), r2 > 0.38) correlations between individual age at symptom onset and predicted values based on parental age at onset and mean ages at onset by mutation type and family, which persist after controlling for APOE genotype and sex. CONCLUSIONS Significant proportions of the observed variance in age at symptom onset in ADAD can be explained by family history and mutation type, providing empirical support for use of these data to estimate onset in clinical research.","author":[{"dropping-particle":"","family":"Ryman","given":"Davis C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Acosta-Baena","given":"Natalia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bird","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Danek","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frommelt","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Langbaum","given":"Jessica B S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lopera","given":"Francisco","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moreno","given":"Sonia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reiman","given":"Eric M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Steve","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tariot","given":"Pierre N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurology","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2014"]]},"page":"253-260","title":"Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis.","type":"article-journal","volume":"83"},"uris":[""]}],"mendeley":{"formattedCitation":"[5]","plainTextFormattedCitation":"[5]","previouslyFormattedCitation":"[5]"},"properties":{"noteIndex":0},"schema":""}[5].Multiple neuroimaging methods have been used to evaluate in vivo changes in the brain due to AD. [11C]Pittsburgh Compound-B (PiB) has high affinity for Aβ plaques, with distributions similar to those seen at autopsy ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1097/RLU.0000000000000547","ISSN":"15360229","abstract":"OBJECTIVES The aim of this article was to review the current role of brain PET in the diagnosis of Alzheimer dementia. The characteristic patterns of glucose metabolism on brain FDG-PET can help in differentiating Alzheimer's disease from other causes of dementia such as frontotemporal dementia and dementia of Lewy body. Amyloid brain PET may exclude significant amyloid deposition and thus Alzheimer's disease in appropriate clinical setting. CONCLUSIONS FDG-PET and amyloid PET imaging are valuable in the assessment of patients with Alzheimer's disease.","author":[{"dropping-particle":"","family":"Marcus","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mena","given":"Esther","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Subramaniam","given":"Rathan M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Clinical Nuclear Medicine","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2014"]]},"page":"413","title":"Brain PET in the diagnosis of Alzheimer's disease","type":"article-journal","volume":"39"},"uris":[""]}],"mendeley":{"formattedCitation":"[6]","plainTextFormattedCitation":"[6]","previouslyFormattedCitation":"[6]"},"properties":{"noteIndex":0},"schema":""}[6]. PiB PET has been employed in ADAD to identify amyloid deposition, with amyloid deposition identified more than 20 years prior to EAO in MC ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1056/NEJMoa1202753","ISSN":"1533-4406","PMID":"22784036","abstract":"BACKGROUND The order and magnitude of pathologic processes in Alzheimer's disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer's disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant's age at baseline assessment and the parent's age at the onset of symptoms of Alzheimer's disease to calculate the estimated years from expected symptom onset (age of the participant minus parent's age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS Concentrations of amyloid-beta (Aβ)(42) in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS We found that autosomal dominant Alzheimer's disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer's disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer's disease. (Funded by the National Institute on Aging and others; DIAN number, NCT00869817.).","author":[{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Xianyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Santacruz","given":"Anna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oliver","given":"Angela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moulder","given":"Krista","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"page":"795-804","title":"Clinical and biomarker changes in dominantly inherited Alzheimer's disease.","type":"article-journal","volume":"367"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1016/S1474-4422(15)00135-0","ISSN":"14744465","abstract":"Background: The biomarker model of Alzheimer's disease postulates a dynamic sequence of amyloidosis, neurodegeneration, and cognitive decline as an individual progresses from preclinical Alzheimer's disease to dementia. Despite supportive evidence from cross-sectional studies, verification with long-term within-individual data is needed. Methods: For this prospective cohort study, carriers of autosomal dominant Alzheimer's disease mutations (aged ≥21 years) were recruited from across the USA through referrals by physicians or from affected families. People with mutations in PSEN1, PSEN2, or APP were assessed at the University of Pittsburgh Alzheimer's Disease Research Center every 1-2 years, between March 23, 2003, and Aug 1, 2014. We measured global cerebral amyloid β (Aβ) load using 11C-Pittsburgh Compound-B PET, posterior cortical metabolism with 18F-fluorodeoxyglucose PET, hippocampal volume (age and sex corrected) with T1-weighted MRI, verbal memory with the ten-item Consortium to Establish a Registry for Alzheimer's Disease Word List Learning Delayed Recall Test, and general cognition with the Mini Mental State Examination. We estimated overall biomarker trajectories across estimated years from symptom onset using linear mixed models, and compared these estimates with cross-sectional data from cognitively normal control individuals (age 65-89 years) who were negative for amyloidosis, hypometabolism, and hippocampal atrophy. In the mutation carriers who had the longest follow-up, we examined the within-individual progression of amyloidosis, metabolism, hippocampal volume, and cognition to identify progressive within-individual changes (a significant change was defined as an increase or decrease of more than two Z scores standardised to controls). Findings: 16 people with mutations in PSEN1, PSEN2, or APP, aged 28-56 years, completed between two and eight assessments (a total of 83 assessments) over 2-11 years. Significant differences in mutation carriers compared with controls (p<0·01) were detected in the following order: increased amyloidosis (7·5 years before expected onset), decreased metabolism (at time of expected onset), decreased hippocampal volume and verbal memory (7·5 years after expected onset), and decreased general cognition (10 years after expected onset). Among the seven participants with longest follow-up (seven or eight assessments spanning 6-11 years), three individuals had active amyloidosis without progressive neurodegeneration…","author":[{"dropping-particle":"","family":"Yau","given":"Wai Ying Wendy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tudorascu","given":"Dana L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ikonomovic","given":"Snezana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"James","given":"Jeffrey A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Minhas","given":"Davneet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mowrey","given":"Wenzhu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sheu","given":"Lei K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Snitz","given":"Beth E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weissfeld","given":"Lisa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gianaros","given":"Peter J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aizenstein","given":"Howard J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Julie C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mathis","given":"Chester A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lopez","given":"Oscar L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet Neurology","id":"ITEM-2","issue":"8","issued":{"date-parts":[["2015"]]},"page":"804-813","title":"Longitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer's disease: A prospective cohort study","type":"article-journal","volume":"14"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1212/WNL.0000000000006277","ISSN":"1526632X","abstract":"OBJECTIVE: To assess the onset, sequence, and rate of progression of comprehensive biomarker and clinical measures across the spectrum of Alzheimer disease (AD) using the Dominantly Inherited Alzheimer Network (DIAN) study and compare these to cross-sectional estimates. METHODS: We conducted longitudinal clinical, cognitive, CSF, and neuroimaging assessments (mean of 2.7 [±1.1] visits) in 217 DIAN participants. Linear mixed effects models were used to assess changes in each measure relative to individuals' estimated years to symptom onset and to compare mutation carriers and noncarriers. RESULTS: Longitudinal β-amyloid measures changed first (starting 25 years before estimated symptom onset), followed by declines in measures of cortical metabolism (approximately 7-10 years later), then cognition and hippocampal atrophy (approximately 20 years later). There were significant differences in the estimates of CSF p-tau181 and tau, with elevations from cross-sectional estimates preceding longitudinal estimates by over 10 years; further, longitudinal estimates identified a significant decline in CSF p-tau181 near symptom onset as opposed to continued elevations. CONCLUSION: These longitudinal estimates clarify the sequence and temporal dynamics of presymptomatic pathologic changes in autosomal dominant AD, information critical to a better understanding of the disease. The pattern of biomarker changes identified here also suggests that once β-amyloidosis begins, additional pathologies may begin to develop less than 10 years later, but more than 15 years before symptom onset, an important consideration for interventions meant to alter the disease course.","author":[{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Guoqiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hassenstab","given":"Jason","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paumier","given":"Katrina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Allegri","given":"Ricardo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berman","given":"Sarah B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Noble","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farlow","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chhatwal","given":"Jasmeer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graff-Radford","given":"Neill R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levin","given":"Johannes","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jucker","given":"Mathias","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurology","id":"ITEM-3","issue":"14","issued":{"date-parts":[["2018"]]},"page":"1295-1306","title":"Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease","type":"article-journal","volume":"91"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1016/S1474-4422(18)30028-0","ISSN":"14744465","abstract":"Background: Models of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal. Methods: Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at (number NCT00869817) Findings: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning). Interpretation: Mutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologie…","author":[{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler 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J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hassenstab","given":"Jason","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jack","given":"Clifford R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hornbeck","given":"Russ C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paumier","given":"Katrina L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ances","given":"Beau M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berman","given":"Sarah 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M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiner","given":"Michael M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raichle","given":"Marcus E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet Neurology","id":"ITEM-4","issue":"3","issued":{"date-parts":[["2018"]]},"page":"241-250","title":"Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"[7–10]","plainTextFormattedCitation":"[7–10]","previouslyFormattedCitation":"[7–10]"},"properties":{"noteIndex":0},"schema":""}[7–10]. Abbreviations. Aβ: Amyloid beta, ADAD: autosomal dominant Alzheimer disease, ANN: artificial neural networks, DIAN: Dominantly Inherited Alzheimer Network, DIAN-TU: Dominantly Inherited Alzheimer Network Trials Unit, EAO: expected age of symptom onset, EYO: estimated years to symptomatic onset, FDG: [18F]Fluorodeoxyglucose, MC: mutation carrier, ML: machine learning, NC: non-carrier, PiB: [11C]Pittsburgh Compound-B, RMSE: root mean squared error, ROIs: regions of interest, SUVRs: standardized uptake value ratiosStudies have also shown increases in PiB retention in MC are associated with a worsening cognitive performance, a decrease in glucose metabolism, and a decrease in hippocampal volume ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1056/NEJMoa1202753","ISSN":"1533-4406","PMID":"22784036","abstract":"BACKGROUND The order and magnitude of pathologic processes in Alzheimer's disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer's disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant's age at baseline assessment and the parent's age at the onset of symptoms of Alzheimer's disease to calculate the estimated years from expected symptom onset (age of the participant minus parent's age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS Concentrations of amyloid-beta (Aβ)(42) in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS We found that autosomal dominant Alzheimer's disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer's disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer's disease. (Funded by the National Institute on Aging and others; DIAN number, NCT00869817.).","author":[{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Xianyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Santacruz","given":"Anna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oliver","given":"Angela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moulder","given":"Krista","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"page":"795-804","title":"Clinical and biomarker changes in dominantly inherited Alzheimer's disease.","type":"article-journal","volume":"367"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1093/brain/awy050","ISSN":"14602156","abstract":"? The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@. See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article. Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (～ 24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great po…","author":[{"dropping-particle":"","family":"Oxtoby","given":"Neil P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Young","given":"Alexandra L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cash","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schott","given":"Jonathan M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alexander","given":"Daniel C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Brain","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2018"]]},"page":"1529-1544","title":"Data-driven models of dominantly-inherited Alzheimer's disease progression","type":"article-journal","volume":"141"},"uris":[""]}],"mendeley":{"formattedCitation":"[7,11]","plainTextFormattedCitation":"[7,11]","previouslyFormattedCitation":"[7,11]"},"properties":{"noteIndex":0},"schema":""}[7,11]. [18F]Fluorodeoxyglucose (FDG) uptake reflects glucose metabolism and has shown promise in discriminating symptomatic MCs from cognitively normal, mutation-negative non-carriers (NC) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1097/RLU.0000000000000547","ISSN":"15360229","abstract":"OBJECTIVES The aim of this article was to review the current role of brain PET in the diagnosis of Alzheimer dementia. The characteristic patterns of glucose metabolism on brain FDG-PET can help in differentiating Alzheimer's disease from other causes of dementia such as frontotemporal dementia and dementia of Lewy body. Amyloid brain PET may exclude significant amyloid deposition and thus Alzheimer's disease in appropriate clinical setting. CONCLUSIONS FDG-PET and amyloid PET imaging are valuable in the assessment of patients with Alzheimer's disease.","author":[{"dropping-particle":"","family":"Marcus","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mena","given":"Esther","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Subramaniam","given":"Rathan M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Clinical Nuclear Medicine","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2014"]]},"page":"413","title":"Brain PET in the diagnosis of Alzheimer's disease","type":"article-journal","volume":"39"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1212/WNL.0000000000006277","ISSN":"1526632X","abstract":"OBJECTIVE: To assess the onset, sequence, and rate of progression of comprehensive biomarker and clinical measures across the spectrum of Alzheimer disease (AD) using the Dominantly Inherited Alzheimer Network (DIAN) study and compare these to cross-sectional estimates. METHODS: We conducted longitudinal clinical, cognitive, CSF, and neuroimaging assessments (mean of 2.7 [±1.1] visits) in 217 DIAN participants. Linear mixed effects models were used to assess changes in each measure relative to individuals' estimated years to symptom onset and to compare mutation carriers and noncarriers. RESULTS: Longitudinal β-amyloid measures changed first (starting 25 years before estimated symptom onset), followed by declines in measures of cortical metabolism (approximately 7-10 years later), then cognition and hippocampal atrophy (approximately 20 years later). There were significant differences in the estimates of CSF p-tau181 and tau, with elevations from cross-sectional estimates preceding longitudinal estimates by over 10 years; further, longitudinal estimates identified a significant decline in CSF p-tau181 near symptom onset as opposed to continued elevations. CONCLUSION: These longitudinal estimates clarify the sequence and temporal dynamics of presymptomatic pathologic changes in autosomal dominant AD, information critical to a better understanding of the disease. The pattern of biomarker changes identified here also suggests that once β-amyloidosis begins, additional pathologies may begin to develop less than 10 years later, but more than 15 years before symptom onset, an important consideration for interventions meant to alter the disease course.","author":[{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Guoqiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hassenstab","given":"Jason","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paumier","given":"Katrina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Allegri","given":"Ricardo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berman","given":"Sarah B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Noble","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farlow","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chhatwal","given":"Jasmeer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graff-Radford","given":"Neill R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levin","given":"Johannes","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jucker","given":"Mathias","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurology","id":"ITEM-2","issue":"14","issued":{"date-parts":[["2018"]]},"page":"1295-1306","title":"Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease","type":"article-journal","volume":"91"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1016/S1474-4422(18)30028-0","ISSN":"14744465","abstract":"Background: Models of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal. Methods: Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at (number NCT00869817) Findings: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning). Interpretation: Mutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologie…","author":[{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler 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from families with autosomal dominant Alzheimer's disease: a longitudinal study","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"[6,9,10]","plainTextFormattedCitation":"[6,9,10]","previouslyFormattedCitation":"[6,9,10]"},"properties":{"noteIndex":0},"schema":""}[6,9,10]. In ADAD, studies have shown FDG uptake in MCs is decreased in the precuneus and is inversely correlated with PiB binding. Marked decreases in glucose metabolism occur approximately 5–10 years before EAO in MCs ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.nicl.2017.12.003","ISSN":"22131582","abstract":"Autosomal dominant Alzheimer's disease (ADAD) is a small subset of Alzheimer's disease that is genetically determined with 100% penetrance. It provides a valuable window into studying the course of pathologic processes that leads to dementia. Arterial spin labeling (ASL) MRI is a potential AD imaging marker that non-invasively measures cerebral perfusion. In this study, we investigated the relationship of cerebral blood flow measured by pseudo-continuous ASL (pCASL) MRI with measures of cerebral metabolism (FDG PET) and amyloid deposition (Pittsburgh Compound B (PiB) PET). Thirty-one participants at risk for ADAD (age 39 ± 13 years, 19 females) were recruited into this study, and 21 of them received both MRI and FDG and PiB PET scans. Considerable variability was observed in regional correlations between ASL-CBF and FDG across subjects. Both regional hypo-perfusion and hypo-metabolism were associated with amyloid deposition. Cross-sectional analyses of each biomarker as a function of the estimated years to expected dementia diagnosis indicated an inverse relationship of both perfusion and glucose metabolism with amyloid deposition during AD development. These findings indicate that neurovascular dysfunction is associated with amyloid pathology, and also indicate that ASL CBF may serve as a sensitive early biomarker for AD. The direct comparison among the three biomarkers provides complementary information for understanding the pathophysiological process of AD.","author":[{"dropping-particle":"","family":"Yan","given":"Lirong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Collin Y.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wong","given":"Koon Pong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Sung Cheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mack","given":"Wendy J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jann","given":"Kay","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coppola","given":"Giovanni","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Danny J.J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage: Clinical","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2018"]]},"page":"751-760","title":"Regional association of pCASL-MRI with FDG-PET and PiB-PET in people at risk for autosomal dominant Alzheimer's disease","type":"article-journal","volume":"17"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1016/S1474-4422(18)30028-0","ISSN":"14744465","abstract":"Background: Models of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal. Methods: Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at (number NCT00869817) Findings: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning). Interpretation: Mutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologie…","author":[{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler 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B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brickman","given":"Adam M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cash","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chhatwal","given":"Jasmeer P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Correia","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"F?rster","given":"Stefan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graff-Radford","given":"Neill 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M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiner","given":"Michael M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raichle","given":"Marcus E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet Neurology","id":"ITEM-2","issue":"3","issued":{"date-parts":[["2018"]]},"page":"241-250","title":"Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"[10,12]","plainTextFormattedCitation":"[10,12]","previouslyFormattedCitation":"[10,12]"},"properties":{"noteIndex":0},"schema":""}[10,12].Structural MRI provides a method to evaluate regional volumetric changes in neurodegeneration that occur with disease progression ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.nicl.2017.08.011","ISSN":"22131582","PMID":"28856092","abstract":"Background Understanding the variation in uptake between different amyloid PET tracers is important to appropriately interpret data using different amyloid tracers. Therefore, we compared the uptake differences in [18F]Flutemetamol (FMT) and [11C]PiB (PiB) PET in the same people. Methods Structural MRI, FMT PET and PiB PET were each performed in 30 young cognitively normal (yCN), 31 elderly cognitively normal (eCN) and 21 Alzheimer's disease dementia (AD) participants. PiB and FMT images for each participant were compared quantitatively using voxel- and region-based analyses. Region of interest (ROI) analyses included comparisons of grey matter (GM) regions as well as white matter (WM) regions. Regional comparisons of each tracer between different groups and comparisons of the two modalities within the different groups were performed. To compare mean SUVr between modalities, and between diagnostic groups, we used paired t-tests and Student's t-test, respectively. We also compared the ability of the two tracers to discriminate between diagnostic groups using AUROC estimates. The effect of using different normalization regions on SUVr values was also evaluated. Results Both FMT and PiB showed greater uptake throughout GM structures in AD vs. eCN or yCN. In all dual-modality group comparisons (FMT vs. PiB in yCN, eCN, and AD), greater WM uptake was seen with FMT vs. PiB. In yCN and eCN greater diffuse GM uptake was seen with FMT vs. PiB. When comparing yCN to eCN within each tracer, greater WM uptake was seen in eCN vs yCN. Conclusions Flutemetamol and PiB show similar topographical GM uptake in AD and CN participants and the tracers show comparable group discrimination. Greater WM accumulation with FMT suggests that quantitative differences vs. PiB will be apparent when using WM or GM as a reference region. Both imaging tracers demonstrate increased WM uptake in older people. These findings suggest that using different amyloid tracers or different methods of analyses in serial brain imaging in an individual may result in artifactual amyloid change measurements. Clinical use of several amyloid tracers in the same patient will have challenges that need to be carefully considered.","author":[{"dropping-particle":"","family":"Lowe","given":"Val J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lundt","given":"Emily","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Knopman","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Senjem","given":"Matthew L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gunter","given":"Jeffrey L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schwarz","given":"Christopher G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kemp","given":"Bradley J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jack","given":"Clifford R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Petersen","given":"Ronald C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage: Clinical","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2017"]]},"page":"295-302","title":"Comparison of [18F]Flutemetamol and [11C]Pittsburgh Compound-B in cognitively normal young, cognitively normal elderly, and Alzheimer's disease dementia individuals","type":"article-journal","volume":"16"},"uris":[""]}],"mendeley":{"formattedCitation":"[13]","plainTextFormattedCitation":"[13]","previouslyFormattedCitation":"[13]"},"properties":{"noteIndex":0},"schema":""}[13]. MRI can reveal regional brain atrophy, which is a characteristic feature of neurodegeneration due to synaptic losses ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1101/cshperspect.a006213","ISSN":"21571422","abstract":"Imaging has played a variety of roles in the study of Alzheimer disease (AD) over the past four decades. Initially, computed tomography (CT) and then magnetic resonance imaging (MRI) were used diagnostically to rule out other causes of dementia. More recently, a variety of imaging modalities including structural and functional MRI and positron emission tomography (PET) studies of cerebral metabolism with fluoro-deoxy-d-glucose (FDG) and amyloid tracers such as Pittsburgh Compound-B (PiB) have shown characteristic changes in the brains of patients with AD, and in prodromal and even presymptomatic states that can help rule-in the AD pathophysiological process. No one imaging modality can serve all purposes as each have unique strengths and weaknesses. These modalities and their particular utilities are discussed in this article. The challenge for the future will be to combine imaging biomarkers to most efficiently facilitate diagnosis, disease staging, and, most importantly, development of effective disease-modifying therapies.","author":[{"dropping-particle":"","family":"Johnson","given":"Keith A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cold Spring Harbor Perspectives in Medicine","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2012"]]},"page":"6213","title":"Brain imaging in Alzheimer disease","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"[14]","plainTextFormattedCitation":"[14]","previouslyFormattedCitation":"[14]"},"properties":{"noteIndex":0},"schema":""}[14]. ADAD is characterized by progressive atrophy that manifests as changes initially in the temporal lobes and subcortical regions with eventual spread to other regions. Observed changes in atrophy are related to the spread of neurofibrillary tangles in AD ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1101/cshperspect.a006213","ISSN":"21571422","abstract":"Imaging has played a variety of roles in the study of Alzheimer disease (AD) over the past four decades. Initially, computed tomography (CT) and then magnetic resonance imaging (MRI) were used diagnostically to rule out other causes of dementia. More recently, a variety of imaging modalities including structural and functional MRI and positron emission tomography (PET) studies of cerebral metabolism with fluoro-deoxy-d-glucose (FDG) and amyloid tracers such as Pittsburgh Compound-B (PiB) have shown characteristic changes in the brains of patients with AD, and in prodromal and even presymptomatic states that can help rule-in the AD pathophysiological process. No one imaging modality can serve all purposes as each have unique strengths and weaknesses. These modalities and their particular utilities are discussed in this article. The challenge for the future will be to combine imaging biomarkers to most efficiently facilitate diagnosis, disease staging, and, most importantly, development of effective disease-modifying therapies.","author":[{"dropping-particle":"","family":"Johnson","given":"Keith A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cold Spring Harbor Perspectives in Medicine","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2012"]]},"page":"6213","title":"Brain imaging in Alzheimer disease","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"[14]","plainTextFormattedCitation":"[14]","previouslyFormattedCitation":"[14]"},"properties":{"noteIndex":0},"schema":""}[14]. Machine learning (ML) is a branch of artificial intelligence that can learn to extract patterns from existing data to predict future events ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1148/radiol.2018171820","ISSN":"0033-8419","PMID":"29944078","abstract":"Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.","author":[{"dropping-particle":"","family":"Choy","given":"Garry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Khalilzadeh","given":"Omid","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Michalski","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Do","given":"Synho","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Samir","given":"Anthony E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pianykh","given":"Oleg S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Geis","given":"J. Raymond","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V.","family":"Pandharipande","given":"Pari","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brink","given":"James A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dreyer","given":"Keith J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Radiology","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2018"]]},"page":"318-328","title":"Current Applications and Future Impact of Machine Learning in Radiology","type":"article-journal","volume":"288"},"uris":[""]}],"mendeley":{"formattedCitation":"[15]","plainTextFormattedCitation":"[15]","previouslyFormattedCitation":"[15]"},"properties":{"noteIndex":0},"schema":""}[15]. Advances in ML offer promise for a number of applications, including medical imaging and predictive analytics ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1148/rg.2017160130","ISBN":"2356-6140","ISSN":"0271-5333","PMID":"28212054","abstract":"Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algo- rithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best com- bination of these image features for classifying the image or com- puting some metric for the given image region. There are several methods that can be used, each with different strengths and weak- nesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm ex- ist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learn- ing has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works.","author":[{"dropping-particle":"","family":"Erickson","given":"Bradley J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Korfiatis","given":"Panagiotis","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Akkus","given":"Zeynettin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kline","given":"Timothy L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"RadioGraphics","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017"]]},"page":"505-515","title":"Machine Learning for Medical Imaging","type":"article-journal","volume":"37"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1148/radiol.2018171820","ISSN":"0033-8419","PMID":"29944078","abstract":"Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.","author":[{"dropping-particle":"","family":"Choy","given":"Garry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Khalilzadeh","given":"Omid","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Michalski","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Do","given":"Synho","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Samir","given":"Anthony E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pianykh","given":"Oleg S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Geis","given":"J. Raymond","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V.","family":"Pandharipande","given":"Pari","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brink","given":"James A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dreyer","given":"Keith J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Radiology","id":"ITEM-2","issue":"2","issued":{"date-parts":[["2018"]]},"page":"318-328","title":"Current Applications and Future Impact of Machine Learning in Radiology","type":"article-journal","volume":"288"},"uris":[""]}],"mendeley":{"formattedCitation":"[15,16]","plainTextFormattedCitation":"[15,16]","previouslyFormattedCitation":"[15,16]"},"properties":{"noteIndex":0},"schema":""}[15,16]. Compared to traditional statistics that provide primarily group-level results, ML algorithms can predict clinical outcomes at the individual level and could enable personalized treatments that provide targeted care for patients ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2215-0366(15)00549-0","ISBN":"2215-0374","ISSN":"22150374","PMID":"26772057","abstract":"Big data analytics are gaining traction in psychiatric research and might provide predictive models for both clinical practice and public health systems. Big data is a broad term used to denote volumes of large and complex measurements. Machine learning, also known as pattern recognition, represents a range of techniques used to analyze big data by identifying patterns of interaction among variables. Compared with traditional statistical methods that provide primarily average group-level results, machine learning algorithms allow predictions and stratification of clinical outcomes at the level of an individual subject. This article discusses big data analytics and machine learning. The authors highlight key 2015 achievements in the field of big data analytics regarding clinical outcomes in psychiatry. (PsycINFO Database Record (c) 2016 APA, all rights reserved)","author":[{"dropping-particle":"","family":"Passos","given":"Ives Cavalcante","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mwangi","given":"Benson","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kapczinski","given":"Flávio","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet Psychiatry","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2016"]]},"page":"13-15","title":"Big data analytics and machine learning: 2015 and beyond","type":"article-journal","volume":"3"},"uris":[""]}],"mendeley":{"formattedCitation":"[17]","plainTextFormattedCitation":"[17]","previouslyFormattedCitation":"[17]"},"properties":{"noteIndex":0},"schema":""}[17]. Although a number of studies have applied ML to neuroimaging measures to study LOAD ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1148/radiol.2018180958","ISBN":"2228-5504 (Electronic)\\r1735-9066 (Linking)","ISSN":"1527-1315","PMID":"30398430","abstract":"Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods Prospective 18F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. ? RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.","author":[{"dropping-particle":"","family":"Ding","given":"Yiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sohn","given":"Jae Ho","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kawczynski","given":"Michael G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Trivedi","given":"Hari","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harnish","given":"Roy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jenkins","given":"Nathaniel W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lituiev","given":"Dmytro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Copeland","given":"Timothy P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aboian","given":"Mariam S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mari Aparici","given":"Carina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Behr","given":"Spencer C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flavell","given":"Robert R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Shih-Ying","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zalocusky","given":"Kelly A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nardo","given":"Lorenzo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seo","given":"Youngho","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hawkins","given":"Randall A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hernandez Pampaloni","given":"Miguel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hadley","given":"Dexter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Franc","given":"Benjamin L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Radiology","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2018"]]},"page":"456-464","title":"A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.","type":"article-journal","volume":"290"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1016/j.neuroimage.2014.10.002","ISSN":"10959572","PMID":"25312773","abstract":"Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.","author":[{"dropping-particle":"","family":"Moradi","given":"Elaheh","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pepe","given":"Antonietta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gaser","given":"Christian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huttunen","given":"Heikki","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tohka","given":"Jussi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2015"]]},"page":"398-412","title":"Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects","type":"article-journal","volume":"104"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1007/s11682-015-9448-7","ISBN":"1168201594","ISSN":"19317565","PMID":"26363784","abstract":"The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.","author":[{"dropping-particle":"","family":"Khazaee","given":"Ali","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ebrahimzadeh","given":"Ata","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Babajani-Feremi","given":"Abbas","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Brain Imaging and Behavior","id":"ITEM-3","issue":"3","issued":{"date-parts":[["2016"]]},"page":"799-817","title":"Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease","type":"article-journal","volume":"10"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1148/radiol.2016152703","ISBN":"0033-8419","ISSN":"0033-8419","PMID":"27383395","abstract":"Automated classification of perfusion maps enabled us to distinguish patients with various stages of Alzheimer disease with high accuracy.","author":[{"dropping-particle":"","family":"Collij","given":"Lyduine E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Heeman","given":"Fiona","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kuijer","given":"Joost P. A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ossenkoppele","given":"Rik","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benedictus","given":"Marije R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"M?ller","given":"Christiane","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verfaillie","given":"Sander C. J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sanz-Arigita","given":"Ernesto J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"van","family":"Berckel","given":"Bart N. M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"van der","family":"Flier","given":"Wiesje M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scheltens","given":"Philip","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barkhof","given":"Frederik","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wink","given":"Alle Meije","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Radiology","id":"ITEM-4","issue":"3","issued":{"date-parts":[["2016"]]},"page":"865-875","title":"Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease","type":"article-journal","volume":"281"},"uris":[""]},{"id":"ITEM-5","itemData":{"DOI":"10.3389/fnhum.2017.00380","ISSN":"1662-5161","abstract":"Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data related to 58 AD, 108 mild cognitive impairment (MCI) and 120 healthy elderly (HE) subjects from the Australian imaging, biomarkers and lifestyle (AIBL) dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL) technique, yielding an average of >95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with t-contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (para)hippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by r2 = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with r2 = 0.62 (p < 0.01). Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD.","author":[{"dropping-particle":"","family":"Youssofzadeh","given":"Vahab","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McGuinness","given":"Bernadette","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maguire","given":"Liam","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wong-Lin","given":"KongFatt","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Frontiers in Human Neuroscience","id":"ITEM-5","issue":"1","issued":{"date-parts":[["2017"]]},"page":"380","title":"Multi-kernel learning with dartel improves combined MRI-PET classification of Alzheimer’s disease in AIBL data: Group and individual analyses","type":"article-journal","volume":"11"},"uris":[""]}],"mendeley":{"formattedCitation":"[18–22]","plainTextFormattedCitation":"[18–22]","previouslyFormattedCitation":"[18–22]"},"properties":{"noteIndex":0},"schema":""}[18–22], few studies to date have applied these techniques to ADAD. Because time until conversion to symptomatic impairment can be estimated with EAO, ADAD provides a unique opportunity for ML to model the progression of the disease and provide decision support to evaluate therapies currently being investigated in the Dominantly Inherited Alzheimer Network (DIAN) Trials Unit (DIAN-TU).In this longitudinal study, we used artificial neural networks (ANNs) to evaluate progression to cognitive impairment using multimodal neuroimaging biomarkers. Specifically, within a cohort of MCs (n = 131) and NCs (n = 74), we used ANNs to investigate: (1) changes in Aβ deposition (using PiB), (2) changes in glucose metabolism (using FDG), and (3) brain atrophy (using structural MRI) as a function of aging in relation to EAO. Further, we utilized feature selection to identify regions that were the strongest discriminators of mutation status for each modality. We then performed Monte Carlo simulations to identify cutoffs for the identified regions. This data-driven approach provides an opportunity to discover novel mechanisms and disease trajectories specific for ADAD. METHODS2.1 ParticipantsOne hundred thirty-one MCs with mutations in PSEN1, PSEN2, or APP and 74 healthy, mutation-negative NCs were recruited from sites participating in the DIAN study. Participants from the 12th data freeze with genetic, clinical, and longitudinal neuroimaging data that passed quality control procedures were included. The Washington University Institutional Review Board provided supervisory review and human subjects’ approval. Participants provided written, informed consent or assent with proxy consent. All study procedures were approved by the Washington University Human Research Protection Office and the institutional review boards of the participating sites. 2.2 Clinical ClassificationThe CDR? Dementia Staging Instrument was used to assess dementia status at each clinical assessment ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1212/wnl.43.11.2412-a","ISSN":"0028-3878","abstract":"The Washington Univfersity Clinical Dementia Rating (CDR) is increasingly used in longitudinal studies and clinical trials for staging the severity of Alzheimer's disease (AD). The CDR is derived from aemi-structured interview with the patient and an appropriate informant and rates impairment in each of six cognitive categories (Memory, Orientation, Judgment and Problem Solving, Community affairs, Home and Hobbies, and Personal Care) on a five-pont scale. A new version of the Clinical Dementia Rating (CDR) more appropriately uses information regarding performance of financial transactions for rating Judgment and Problem Solving rather than Community Affairs. The new version is presented here for interested readers, along with improved clinical scoring rules for the global CDR.","author":[{"dropping-particle":"","family":"Morris","given":"J. C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurology","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2012"]]},"page":"1588-1592","title":"The Clinical Dementia Rating (CDR): Current version and scoring rules","type":"article-journal","volume":"41"},"uris":[""]}],"mendeley":{"formattedCitation":"[23]","plainTextFormattedCitation":"[23]","previouslyFormattedCitation":"[23]"},"properties":{"noteIndex":0},"schema":""}[23]. A participant’s EAO was calculated at each visit on the basis of the participant’s current age relative to the family mutation–specific expected age at onset of dementia symptoms ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1212/WNL.0000000000000596","ISSN":"1526-632X","PMID":"24928124","abstract":"OBJECTIVE To identify factors influencing age at symptom onset and disease course in autosomal dominant Alzheimer disease (ADAD), and develop evidence-based criteria for predicting symptom onset in ADAD. METHODS We have collected individual-level data on ages at symptom onset and death from 387 ADAD pedigrees, compiled from 137 peer-reviewed publications, the Dominantly Inherited Alzheimer Network (DIAN) database, and 2 large kindreds of Colombian (PSEN1 E280A) and Volga German (PSEN2 N141I) ancestry. Our combined dataset includes 3,275 individuals, of whom 1,307 were affected by ADAD with known age at symptom onset. We assessed the relative contributions of several factors in influencing age at onset, including parental age at onset, age at onset by mutation type and family, and APOE genotype and sex. We additionally performed survival analysis using data on symptom onset collected from 183 ADAD mutation carriers followed longitudinally in the DIAN Study. RESULTS We report summary statistics on age at onset and disease course for 174 ADAD mutations, and discover strong and highly significant (p < 10(-16), r2 > 0.38) correlations between individual age at symptom onset and predicted values based on parental age at onset and mean ages at onset by mutation type and family, which persist after controlling for APOE genotype and sex. CONCLUSIONS Significant proportions of the observed variance in age at symptom onset in ADAD can be explained by family history and mutation type, providing empirical support for use of these data to estimate onset in clinical research.","author":[{"dropping-particle":"","family":"Ryman","given":"Davis C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Acosta-Baena","given":"Natalia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bird","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Danek","given":"Adrian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frommelt","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Langbaum","given":"Jessica B S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lopera","given":"Francisco","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moreno","given":"Sonia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reiman","given":"Eric M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Steve","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tariot","given":"Pierre N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurology","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2014"]]},"page":"253-260","title":"Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis.","type":"article-journal","volume":"83"},"uris":[""]}],"mendeley":{"formattedCitation":"[5]","plainTextFormattedCitation":"[5]","previouslyFormattedCitation":"[5]"},"properties":{"noteIndex":0},"schema":""}[5]. Parental age at first progressive cognitive decline was used if the mutation-specific EAO was unknown. EAO was calculated identically for both MCs and NCs. All clinical evaluators were blinded to the mutation status of participants. The presence/absence and type of mutation were determined using polymerase chain reaction amplification followed by Sanger sequencing ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1056/NEJMoa1202753","ISSN":"1533-4406","PMID":"22784036","abstract":"BACKGROUND The order and magnitude of pathologic processes in Alzheimer's disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer's disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant's age at baseline assessment and the parent's age at the onset of symptoms of Alzheimer's disease to calculate the estimated years from expected symptom onset (age of the participant minus parent's age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS Concentrations of amyloid-beta (Aβ)(42) in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS We found that autosomal dominant Alzheimer's disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer's disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer's disease. (Funded by the National Institute on Aging and others; DIAN number, NCT00869817.).","author":[{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Xianyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Santacruz","given":"Anna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oliver","given":"Angela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moulder","given":"Krista","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"page":"795-804","title":"Clinical and biomarker changes in dominantly inherited Alzheimer's disease.","type":"article-journal","volume":"367"},"uris":[""]}],"mendeley":{"formattedCitation":"[7]","plainTextFormattedCitation":"[7]","previouslyFormattedCitation":"[7]"},"properties":{"noteIndex":0},"schema":""}[7].2.3 MRI Acquisition and ProcessingMRI was performed using the Alzheimer’s Disease Neuroimaging Initiative protocol (ADNI) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.jalz.2010.03.004","ISSN":"15525260","abstract":"Functions of the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) core fall into three categories: (1) those of the central MRI core laboratory at Mayo Clinic, Rochester, Minnesota, needed to generate high quality MRI data in all subjects at each time point; (2) those of the funded ADNI MRI core imaging analysis groups responsible for analyzing the MRI data; and (3) the joint function of the entire MRI core in designing and problem solving MR image acquisition, pre-processing, and analyses methods. The primary objective of ADNI was and continues to be improving methods for clinical trials in Alzheimer's disease. Our approach to the present (\"ADNI- GO\") and future (\"ADNI-2,\" if funded) MRI protocol will be to maintain MRI methodological consistency in the previously enrolled \"ADNI-1\" subjects who are followed up longitudinally in ADNI-GO and ADNI-2. We will modernize and expand the MRI protocol for all newly enrolled ADNI-GO and ADNI-2 subjects. All newly enrolled subjects will be scanned at 3T with a core set of three sequence types: 3D T1-weighted volume, FLAIR, and a long TE gradient echo volumetric acquisition for micro hemorrhage detection. In addition to this core ADNI-GO and ADNI-2 protocol, we will perform vendor-specific pilot sub-studies of arterial spin-labeling perfusion, resting state functional connectivity, and diffusion tensor imaging. One of these sequences will be added to the core protocol on systems from each MRI vendor. These experimental sub-studies are designed to demonstrate the feasibility of acquiring useful data in a multicenter (but single vendor) setting for these three emerging MRI applications. ? 2010 The Alzheimer's Association. All rights reserved.","author":[{"dropping-particle":"","family":"Jack","given":"Clifford R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bernstein","given":"Matt A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Borowski","given":"Bret J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gunter","given":"Jeffrey L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thompson","given":"Paul M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schuff","given":"Norbert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Krueger","given":"Gunnar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Killiany","given":"Ronald J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Decarli","given":"Charles S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dale","given":"Anders M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carmichael","given":"Owen W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tosun","given":"Duygu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiner","given":"Michael W.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Alzheimer's and Dementia","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2010"]]},"page":"212-220","title":"Update on the Magnetic Resonance Imaging core of the Alzheimer's Disease Neuroimaging Initiative","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"[24]","plainTextFormattedCitation":"[24]","previouslyFormattedCitation":"[24]"},"properties":{"noteIndex":0},"schema":""}[24]. Sites used a 3T scanner that passed regular quality control assessments. The ADNI Imaging Core screened images for compliance. T1 weighted images at 1.1 x 1.1 x 1.2 mm voxel resolution were acquired for participants. FreeSurfer 5.3 ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.neuroimage.2012.01.021","ISSN":"10538119","abstract":"FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. ? 2012 Elsevier Inc.","author":[{"dropping-particle":"","family":"Fischl","given":"Bruce","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2012"]]},"page":"774-781","title":"FreeSurfer","type":"article-journal","volume":"62"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1073/pnas.200033797","ISSN":"0027-8424","PMID":"10984517","abstract":"Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.","author":[{"dropping-particle":"","family":"Fischl","given":"B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dale","given":"A M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of the National Academy of Sciences of the United States of America","id":"ITEM-2","issue":"20","issued":{"date-parts":[["2000"]]},"page":"11050-11055","title":"Measuring the thickness of the human cerebral cortex from magnetic resonance images.","type":"article-journal","volume":"97"},"uris":[""]}],"mendeley":{"formattedCitation":"[25,26]","plainTextFormattedCitation":"[25,26]","previouslyFormattedCitation":"[25,26]"},"properties":{"noteIndex":0},"schema":""}[25,26] was used to perform volumetric segmentation, cortical surface reconstruction, and to define cortical and subcortical regions of interest (ROIs). Segmentations were inspected and edited as needed by members of the DIAN Imaging Core. A regression approach was used to correct subcortical volumes for intracranial volumes. Volumetric measures were averaged across hemispheres. FreeSurfer-defined cortical and subcortical ROIs (44 total) were used for regional processing of PET data. The FreeSurfer-defined ROIs were derived from the Deskian/Killiany atlas ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.neuroimage.2006.01.021","ISSN":"10538119","PMID":"16530430","abstract":"In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1?mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment. ? 2006 Elsevier Inc. All rights reserved.","author":[{"dropping-particle":"","family":"Desikan","given":"Rahul S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ségonne","given":"Florent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fischl","given":"Bruce","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Quinn","given":"Brian T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dickerson","given":"Bradford C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blacker","given":"Deborah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckner","given":"Randy L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dale","given":"Anders M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maguire","given":"R. Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hyman","given":"Bradley T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Albert","given":"Marilyn S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Killiany","given":"Ronald J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage","id":"ITEM-1","issued":{"date-parts":[["2006"]]},"title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"[27]","plainTextFormattedCitation":"[27]","previouslyFormattedCitation":"[27]"},"properties":{"noteIndex":0},"schema":""}[27] for segmentation. These are standard regions used for volumetric analyses.2.4 PET Acquisition and ProcessingAmyloid PET was performed using a bolus injection of PiB. Data from the 40–70-minute post-injection timeframe were converted to regional standardized uptake value ratios (SUVRs) relative to the cerebellar gray matter using FreeSurfer-derived ROIs (PET Unified Pipeline) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pone.0073377","ISSN":"19326203","abstract":"In vivo quantification of β-amyloid deposition using positron emission tomography is emerging as an important procedure for the early diagnosis of the Alzheimer's disease and is likely to play an important role in upcoming clinical trials of disease modifying agents. However, many groups use manually defined regions, which are non-standard across imaging centers. Analyses often are limited to a handful of regions because of the labor-intensive nature of manual region drawing. In this study, we developed an automatic image quantification protocol based on FreeSurfer, an automated whole brain segmentation tool, for quantitative analysis of amyloid images. Standard manual tracing and FreeSurfer-based analyses were performed in 77 participants including 67 cognitively normal individuals and 10 individuals with early Alzheimer's disease. The manual and FreeSurfer approaches yielded nearly identical estimates of amyloid burden (intraclass correlation = 0.98) as assessed by the mean cortical binding potential. An MRI test-retest study demonstrated excellent reliability of FreeSurfer based regional amyloid burden measurements. The FreeSurfer-based analysis also revealed that the majority of cerebral cortical regions accumulate amyloid in parallel, with slope of accumulation being the primary difference between regions.","author":[{"dropping-particle":"","family":"Su","given":"Yi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"D'Angelo","given":"Gina M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vlassenko","given":"Andrei G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Gongfu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Snyder","given":"Abraham Z.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Christensen","given":"Jon J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vora","given":"Shivangi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mintun","given":"Mark A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS ONE","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2013"]]},"page":"73377","title":"Quantitative analysis of PiB-PET with FreeSurfer ROIs","type":"article-journal","volume":"8"},"uris":[""]}],"mendeley":{"formattedCitation":"[28]","plainTextFormattedCitation":"[28]","previouslyFormattedCitation":"[28]"},"properties":{"noteIndex":0},"schema":""}[28]. Glucose metabolism imaging was performed with a single bolus injection of FDG. A 30-minute dynamic acquisition beginning 30 minutes post-injection was acquired. The last 20 minutes of each FDG scan were converted to SUVRs using the cerebellar gray matter as a reference region. All PET data were partial volume corrected using a regional spread function technique ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.neuroimage.2014.11.058","ISSN":"10959572","abstract":"Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.","author":[{"dropping-particle":"","family":"Su","given":"Yi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Snyder","given":"Abraham Z.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raichle","given":"Marcus E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ances","given":"Beau M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aldea","given":"Patricia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cash","given":"Lisa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Christensen","given":"Jon J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Friedrichsen","given":"Karl","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hornbeck","given":"Russ C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farrar","given":"Angela M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Owen","given":"Christopher J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brickman","given":"Adam M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Julie C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thompson","given":"Paul M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernadino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saykin","given":"Andrew J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Keith A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2015"]]},"page":"55-64","title":"Partial volume correction in quantitative amyloid imaging","type":"article-journal","volume":"107"},"uris":[""]},{"id":"ITEM-2","itemData":{"ISSN":"0161-5505","PMID":"9591599","abstract":"UNLABELLED The accuracy of PET for measuring regional radiotracer concentrations in the human brain is limited by the finite resolution capability of the scanner and the resulting partial volume effects (PVEs). We designed a new algorithm to correct for PVEs by characterizing the geometric interaction between the PET system and the brain activity distribution. METHODS The partial volume correction (PVC) algorithm uses high-resolution volumetric MR images correlated with the PET volume. We used a PET simulator to calculate recovery and cross-contamination factors of identified tissue components in the brain model. These geometry-dependent transfer coefficients form a matrix representing the fraction of true activity from each distinct brain region observed in any given set of regions of interest. This matrix can be inverted to correct for PVEs, independent of the tracer concentrations in each tissue component. A sphere phantom was used to validate the simulated point-spread function of the PET scanner. Accuracy and precision of the PVC method were assessed using a human basal ganglia phantom. A constant contrast experiment was performed to explore the recovery capability and statistic error propagation of PVC in various noise conditions. In addition, a dual-isotope experiment was used to evaluate the ability of the PVC algorithm to recover activity concentrations in small structures surrounded by background activity with a different radioactive half-life. This models the time-variable contrast between regions that is often seen in neuroreceptor studies. RESULTS Data from the three-dimensional brain phantom demonstrated a full recovery capability of PVC with less than 10% root mean-square error in terms of absolute values, which decreased to less than 2% when results from four PET slices were averaged. Inaccuracy in the estimation of 18F tracer half-life in the presence of 11C background activity was in the range of 25%-50% before PVC and 0%-6% after PVC, for resolution varying from 6 to 14 mm FWHM. In terms of noise propagation, the degradation of the coefficient of variation after PVC was found to be easily predictable and typically on the order of 25%. CONCLUSION The PVC algorithm allows the correction for PVEs simultaneously in all identified brain regions, independent of tracer levels.","author":[{"dropping-particle":"","family":"Rousset","given":"O G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ma","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Evans","given":"A C","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of nuclear medicine : official publication, Society of Nuclear Medicine","id":"ITEM-2","issue":"5","issued":{"date-parts":[["1998"]]},"page":"904-911","title":"Correction for partial volume effects in PET: principle and validation.","type":"article-journal","volume":"39"},"uris":[""]}],"mendeley":{"formattedCitation":"[29,30]","plainTextFormattedCitation":"[29,30]","previouslyFormattedCitation":"[29,30]"},"properties":{"noteIndex":0},"schema":""}[29,30]. PET images were aligned to the T1 image processed using FreeSurfer. PET scanner–specific filters were applied to account for differences in spatial resolution and to achieve a common resolution (8 mm) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.neuroimage.2009.01.057","ISSN":"10538119","abstract":"This work is part of the multi-center Alzheimer's Disease Neuroimaging Initiative (ADNI), a large multi-site study of dementia, including patients having mild cognitive impairment (MCI), probable Alzheimer's disease (AD), as well as healthy elderly controls. A major portion of ADNI involves the use of [18F]-fluorodeoxyglucose (FDG) with positron emission tomography (PET). The objective of this paper is the reduction of inter-scanner differences in the FDG-PET scans obtained from the 50 participating PET centers having fifteen different scanner models. In spite of a standardized imaging protocol, systematic inter-scanner variability in PET images from various sites is observed primarily due to differences in scanner resolution, reconstruction techniques, and different implementations of scatter and attenuation corrections. Two correction steps were developed by comparison of 3-D Hoffman brain phantom scans with the 'gold standard' digital 3-D Hoffman brain phantom: i) high frequency correction; where a smoothing kernel for each scanner model was estimated to smooth all images to a common resolution and ii) low frequency correction; where smooth affine correction factors were obtained to reduce the attenuation and scatter correction errors. For the phantom data, the high frequency correction reduced the variability by 20%-50% and the low frequency correction further reduced the differences by another 20%-25%. Correction factors obtained from phantom studies were applied to 95 scans from normal control subjects obtained from the participating sites. The high frequency correction reduced differences similar to the phantom studies. However, the low frequency correction did not further reduce differences; hence further refinement of the procedure is necessary. ? 2009 Elsevier Inc. All rights reserved.","author":[{"dropping-particle":"","family":"Joshi","given":"Aniket","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koeppe","given":"Robert A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fessler","given":"Jeffrey A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"NeuroImage","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2009"]]},"page":"154-159","title":"Reducing between scanner differences in multi-center PET studies","type":"article-journal","volume":"46"},"uris":[""]}],"mendeley":{"formattedCitation":"[31]","plainTextFormattedCitation":"[31]","previouslyFormattedCitation":"[31]"},"properties":{"noteIndex":0},"schema":""}[31]. The DIAN imaging core performed quality control checks on the PET Unified Pipeline processing.2.5 Machine Learning and Statistical AnalysesML analyses were performed in MATLAB R2018b. Deep feedforward ANNs were trained for each of the neuroimaging modalities. Feedforward ANNs map an input to an output by composing sets of smaller functions laid out as a directed acyclic graph ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nmeth.3707","ISBN":"978-0262035613","ISSN":"0028-0836","PMID":"26017442","abstract":"Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.","author":[{"dropping-particle":"","family":"Goodfellow","given":"Ian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bengio","given":"Yoshua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Courville","given":"Aaron","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"publisher":"MIT Press","title":"Deep Learning","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"[32]","plainTextFormattedCitation":"[32]","previouslyFormattedCitation":"[32]"},"properties":{"noteIndex":0},"schema":""}[32]. The feasibility of these networks is based on the Universal Approximation Theorem, which states a neural network with a single hidden layer contains a finite set of artificial neurons that approximate continuous functions on subsets of Rn ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/0893-6080(91)90009-T","ISBN":"0893-6080","ISSN":"08936080","PMID":"25246403","abstract":"We show that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to Lp(μ) performance criteria, for arbitrary finite input environment measures μ, provided only that sufficiently many hidden units are available. If the activation function is continuous, bounded and nonconstant, then continuous mappings can be learned uniformly over compact input sets. We also give very general conditions ensuring that networks with sufficiently smooth activation functions are capable of arbitrarily accurate approximation to a function and its derivatives. ? 1991.","author":[{"dropping-particle":"","family":"Hornik","given":"Kurt","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neural Networks","id":"ITEM-1","issue":"2","issued":{"date-parts":[["1991"]]},"page":"251-257","title":"Approximation capabilities of multilayer feedforward networks","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"[33]","plainTextFormattedCitation":"[33]","previouslyFormattedCitation":"[33]"},"properties":{"noteIndex":0},"schema":""}[33]. Our ANNs contained 4 hidden layers with 10 artificial neurons in each layer. The network architecture was decided based on design methodologies ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/1-84628-303-5","ISBN":"9780971732100","PMID":"673255","abstract":"Introductory textbook on neural networks that uses MATLAB as a simulator and has a nice annotated bibliography for every chapter.","author":[{"dropping-particle":"","family":"Hagan","given":"M T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Demuth","given":"H B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beale","given":"M H","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Boston Massachusetts PWS","id":"ITEM-1","issued":{"date-parts":[["1995"]]},"title":"Neural Network Design","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"ISBN":"9781604390216","abstract":"Programming Neural Networks in Java will show the intermediate to advanced Java programmer how to create neural networks. This book attempts to teach neural network programming through two mechanisms. First the reader is shown how to create a reusable neural network package that could be used in any Java program. Second, this reusable neural network package is applied to several real world problems that are commonly faced by IS programmers. This book covers such topics as Kohonen neural networks, multi layer neural networks, training, back propagation, and many other topics. Chapter 1: Introduction to Neural Networks (Wednesday, November 16, 2005) Computers can perform many operations considerably faster than a human being. Yet there are many tasks where the computer falls considerably short of its human counterpart. There are numerous examples of this. Given two pictures a preschool child could easily tell the difference between a cat and a dog. Yet this same simple problem would confound today's computers.","author":[{"dropping-particle":"","family":"Heaton","given":"Jeff","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Java Developers Journal","id":"ITEM-2","issued":{"date-parts":[["2002"]]},"title":"Programming Neural Networks in Java","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"[34,35]","plainTextFormattedCitation":"[34,35]","previouslyFormattedCitation":"[34,35]"},"properties":{"noteIndex":0},"schema":""}[34,35], incremental pruning, and cross-validation. Further details on model design and validation can be found in supplementary material (Methods- Machine Learning Model Design). An ANN was trained to output all ROIs for each modality. Input to the models included age, sex, APOE ?4 status, mutation status, the amount of time in the future to predict, and the given imaging variables (MRI or SUVR) for 44 FreeSurfer ROIs. A complete list of the ROIs can be found in Supplementary Table 1. The output of each model corresponded to the ROI values at a time point in the future. Rates of change were calculated by subtracting scans at time point N by the scan at time point N-1. Rates were then divided by the number of months between the scans to obtain a normalized rate of change. The mean time between scans was 2.6 years (±1.4). If a participant had more than 2 scanning sessions, all possible combinations were evaluated. Using the first time point, data were projected into the future by iteratively adding the normalized rate of change, and these data were used for training. For each point, the rate of change was used to project the data ± 3 years from the current age. Data were projected into the future and the past to avoid biasing the model to later phases of the disease. We chose this window based on previous work ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S1474-4422(18)30028-0","ISSN":"14744465","abstract":"Background: Models of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal. Methods: Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at (number NCT00869817) Findings: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning). Interpretation: Mutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologie…","author":[{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler 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B.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brickman","given":"Adam M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cash","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chhatwal","given":"Jasmeer P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Correia","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"F?rster","given":"Stefan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graff-Radford","given":"Neill 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from families with autosomal dominant Alzheimer's disease: a longitudinal study","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"[10]","plainTextFormattedCitation":"[10]","previouslyFormattedCitation":"[10]"},"properties":{"noteIndex":0},"schema":""}[10], which showed the biomarkers’ rate of change is not constant along the disease continuum. Predictive features of mutation status were ranked according to importance using a Relief algorithm ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S0031-3203(01)00046-2","ISBN":"1-55860-247-X","ISSN":"00313203","PMID":"21600290","abstract":"For real-world concept learning problems, feature selection is important to speed up learning and to improve concept quality. We review and analyze past approaches to feature selection and note their strengths and weaknesses. We then introduce and theoretically examine a new algorithm Relief which selects relevant features using a statistical method. Relief does not depend on heuristics, is accurate even if features interact, and is noise-tolerant. It requires only linear time in the number of given features and the number of training instances, regardless of the target concept complexity. The algorithm also has certain limitations such as non- optimal feature set size. Ways to overcome the limitations are suggested. We also report the test results of comparison between Relief and other feature selection algorithms. The empirical results support the theoretical analysis, suggesting a practical approach to feature selection","author":[{"dropping-particle":"","family":"Kira","given":"Kenji","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rendell","given":"Larry A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of the ninth international workshop on Machine learning","id":"ITEM-1","issued":{"date-parts":[["1992"]]},"page":"249-256","title":"A practical approach to feature selection","type":"paper-conference"},"uris":[""]}],"mendeley":{"formattedCitation":"[36]","plainTextFormattedCitation":"[36]","previouslyFormattedCitation":"[36]"},"properties":{"noteIndex":0},"schema":""}[36]. Relief algorithms detect conditional dependencies between attributes using a nearest neighbor approach, with features ranked by estimating how well their values distinguish between proximal comparisons. Further, cutoff points for PiB, FDG, and brain volumetrics were identified based on the likelihood of the values generated by Monte Carlo model simulations. The simulations generated an equal number (by mutation status) of random sample points from the multivariate distribution defined by the mean and covariance matrix of the data given a specific mutation status, age, and EAO range ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/9780471722069","ISBN":"9780471722069","abstract":"Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications.","author":[{"dropping-particle":"","family":"Kotz","given":"Samuel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Balakrishnan","given":"N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Norman L.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Continuous Multivariate Distributions, Models and Applications: Second Edition","id":"ITEM-1","issued":{"date-parts":[["2005"]]},"title":"Continuous Multivariate Distributions, Models and Applications: Second Edition","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"[37]","plainTextFormattedCitation":"[37]","previouslyFormattedCitation":"[37]"},"properties":{"noteIndex":0},"schema":""}[37].We also trained a linear regression model to compare the results to our ANN. This comparative analysis was performed due to recent research suggesting that, in some cases, linear models can outperform nonlinear models ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1101/757054","abstract":"In recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes. We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw. In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In fact, linear models kept improving as the sample size approached ~10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility. Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any examined scale.","author":[{"dropping-particle":"","family":"Schulz","given":"Marc-Andre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yeo","given":"B T Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vogelstein","given":"Joshua T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mourao-","given":"Janaina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kather","given":"Jakob N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kording","given":"Konrad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Richards","given":"Blake","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bzdok","given":"Danilo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mourao-Miranada","given":"Janaina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kather","given":"Jakob N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kording","given":"Konrad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Richards","given":"Blake","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bzdok","given":"Danilo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"bioRxiv","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"[38]","plainTextFormattedCitation":"[38]","previouslyFormattedCitation":"[38]"},"properties":{"noteIndex":0},"schema":""}[38]. When training the regression model, all methods previously described for training the ANN were applied. Each biomarker was modeled separately, and the models were trained using 5-fold cross-validation. Cross-validation was performed at the participant level, and all results reported were derived by combining the test data results from each of the 5 folds of cross-validation. In addition, the input to the regression model was the same as the ANN, but the only output considered was the precuneus. We chose the precuneus as it is highly predictive and heavily involved in disease progression in ADAD ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S1474-4422(18)30028-0","ISSN":"14744465","abstract":"Background: Models of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal. Methods: Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at (number NCT00869817) Findings: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning). Interpretation: Mutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologie…","author":[{"dropping-particle":"","family":"Gordon","given":"Brian A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler 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from families with autosomal dominant Alzheimer's disease: a longitudinal study","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"[10]","plainTextFormattedCitation":"[10]","previouslyFormattedCitation":"[10]"},"properties":{"noteIndex":0},"schema":""}[10], making it optimal for comparison. Further, multivariate linear regression was performed which output all brain regions in the same manner as the ANN. The regression model utilized ordinary multivariate normal maximum likelihood estimation with the full variance-covariance matrix and constant, linear, and interaction terms. We also performed the zero rule algorithm on the data to compare baseline predictability using the mean of the output values observed in the training data compared to the testing data.RESULTS3.1 Demographics of the CohortDetailed demographics are presented in Table 1. Participants were matched for age, sex, and education.Table 1. Demographics of participantsMutation carriers (MC)Mutation-negative non-carriers (NC)p ValuesN13174Age (years) ± SD39.2 ± 10.639.3 ± 10.2.95Sex (% Male)40%35%.72Education (years) ± SD14.3 ± 2.715.1 ± 2.6.06APOE ?4 (% carriers)39%36%.81EAO (years) ± SD46.3 ± 6.848.1 ± 5.7.90Abbreviations: SD, standard deviation; APOE ?4, apolipoprotein ?4; EAO, estimated age of symptom onset.3.2 PiBThe Relief algorithm identified the nucleus accumbens, caudate, precuneus, anterior cingulate, pallidum, putamen, and middle frontal regions as strong predictors of mutation status. The ANN was able to predict the future PiB values with an average R2 value of 0.95 and RMSE of 0.2. Figure 1 depicts results for the 4 best-predicted ROIs. The algorithm was able to accurately estimate the values in both MCs and NCs, with the NCs having lower SUVRs compared to the MCs. Supplementary Figure 1 shows the model predictions for MCs based on distance from EAO for PiB. Two relatively distinct clouds were seen for PiB, with lower SUVRs seen at greater distances from EAO, while MCs closer to EAO had elevated PiB SUVRs.Figure 1. Results of Pittsburgh Compound-B (PiB) predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red). Correlation and RMSE of predicted versus actual values. The ANN was able to predict future PiB values with an average R2 of 0.95 and RMSE of 0.2 in both MCs and NCs.3.3 FDGThe strongest predictors of mutation status with respect to metabolism were the pericalcarine, caudate, precuneus, fusiform, anterior cingulate, insula, and transverse temporal regions. The ANN was able to predict future FDG values with an R2 value of 0.93 and RMSE of 0.02 in both groups. Figure 2 depicts results for the 4 best-predicted ROIs. The algorithm showed a trend of MCs having lower future FDG values than NCs. Supplementary Figure 2 shows the model predictions for MCs based on distance from EAO for FDG. Two clouds are seen for FDG, with higher SUVRs seen at greater distances from EAO, while MCs closer to EAO had lower FDG SUVRs.Figure 2. Results of fluorodeoxyglucose (FDG) predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red) in select ROIs. Correlation and root mean squared error (RMSE) of predicted versus actual values. The ANN was able to predict future FDG values with an average R2 of 0.93 and RMSE of 0.02 in MCs and NCs, with MCs showing trends of lower predicted FDG values than NCs3.4 VolumeThe strongest predictors of mutation status with respect to brain atrophy were seen in the nucleus accumbens, pericalcarine, caudate, precuneus, anterior cingulate, insula, entorhinal cortex, pallidum, and transverse temporal regions. The ANN was able to predict changes in brain volumes with an average R2 value of 0.95. Figure 3 depicts results for the 4 best-predicted regions. The algorithm showed a general trend of MCs having more brain atrophy than NCs. Supplementary Figure 3 shows the model predictions for MCs as a function of distance from EAO for brain volumes. Figure 3. Results of brain volumetric predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red). Correlation and root mean squared error (RMSE) of predicted versus actual values. The ANN was able to predict changes in brain volumes with an average R2 value of 0.95 and showed a general trend of MCs having more brain atrophy than NCs.Figure 4. (Top left) Simulated biomarker evolution for total mean cortical and subcortical Pittsburgh Compound-B (PiB), total mean cortical and subcortical fluorodeoxyglucose (FDG), and total gray matter volume (scaled to a common interval) derived from the artificial neural network (ANN) in mutation carriers (MC). Shaded region indicates model variability, with EAO marked by perpendicular line. (Top right) Simulated biomarker evolution for total mean cortical and subcortical PiB, total mean cortical and subcortical FDG, and total gray matter volume (scaled to a common interval) derived from the ANN in mutation non-carriers (NC). (Bottom left) Normalized biomarker rate of change for mean PiB, mean FDG, and total gray matter volume (scaled to a common interval) fit to a polynomial curve showing 95% confidence interval. (Bottom right) Mean absolute error of predicted (normalized) biomarker values given the amount of time in the future to predict, fit with a 2-degree polynomial curve projected into the future. Errors increased linearly with an increase in the amount of time in the future to predict.3.5 SimulationsUsing the trained models, amyloid accumulation, changes in metabolism, and brain atrophy were simulated for MCs and NCs (Figure 4, top). Consistent with previous work, the models showed that in the MC group, the earliest changes are in amyloid deposition, which follows a sigmoidal trajectory and continues to accumulate past EAO. A biphasic response was seen for metabolism, with changes occurring earlier than expected, and progressive decline was observed in atrophy throughout the course of the disease, with the greatest changes occurring just prior to EAO. The NC groups showed little change over time for all modalities.We fitted a polynomial curve to the normalized rates of change for each of the neuroimaging biomarkers (Figure 4, bottom left). Consistent with the models, amyloid showed an inverted U shape, with increases occurring early in the disease, and subsequently followed by a gradual decline in rate of PiB accumulation. FDG showed a slight increase in the early stages, followed by a gradual decrease when the distance from EAO approached 0. Finally, brain volumetrics showed a gradual increase in the rate of decline throughout progression to EAO.Figure 4 (bottom right) shows the normalized models errors based on years to predict (e.g., the error for a participant’s PET/MRI values predicted 1 year in the future versus the error for predicting 5 years in the future). A 2 degree polynomial curve was fit to the error data, which showed a predominantly linear increase with increasing number of years to predict. The fit lines were projected into the future for up to 40 years. The plot shows that the model maintains a mean absolute error less than 0.1 up to 10 years in the future. The individual biomarkers showed similar trends, only at different scales. Supplementary Figures 4–6 display the results of the Monte Carlo model simulations for each of the highly predictive regions for each modality. Larger values on the y-axis represent a greater likelihood of producing a given value. For PiB, clear cut-points were observed between MCs and NCs with nearly 100% specificity. Cut-points were 1.17 for the nucleus accumbens, 1.3 for the caudate, 1.4 for the precuneus, and 1.2 SUVR for total cortical mean. For FDG, the cut-points were less defined for some regions. Cut-points for the anterior cingulate, caudate, precuneus, and total cortical mean ranged from 1.4–1.825 SUVR. The model simulations indicate MCs had a trend for decreased FDG in each of these ROIs, as well as a biphasic response in the caudate and anterior cingulate. For brain volumes, MCs had greater atrophy than NCs. Cutoffs were identified for the nucleus accumbens (550 mm3), caudate (3300 mm3), precuneus (8500 mm3), and total gray matter (575,000 mm3).3.6 Alternative Analysis MethodsSupplementary Figure 8 displays the error histograms [probabilities of errors (actual-predicted)] for the ANN versus the regression model for PiB in the precuneus. Although both models performed very well, the performance obtained through regression was lower than that obtained through the ANN. The ANN’s error probability distribution was highly clustered around 0 (RMSE = 0.17), whereas the regression model showed greater dispersion (RMSE = 0.28), indicating a greater likelihood of making a larger error compared to the ANN. Similar results were seen using FDG and volumetric data. Whole brain average RMSE for the ANN, multivariate linear regression, and zero rule algorithm are listed in the bottom of supplementary table 1. DISCUSSIONOur models yielded high accuracy in predicting amyloid accumulation, changes in metabolism, and brain atrophy in ADAD. The Relief algorithm identified both subcortical (caudate) and cortical (precuneus and anterior cingulate) ROIs as the strongest predictors of mutation status. Figure 5 displays the strongest predictors for each modality. For amyloid PET, which is believed to reflect the earliest changes in ADAD, changes were primarily seen within subcortical (pallidum, nucleus accumbens, caudate, putamen, and entorhinal) compared to cortical regions (middle frontal, anterior cingulate, and precuneus). For changes in metabolism measured by FDG, which reflect changes later in the disease process compared to amyloid, more cortical (insula, fusiform, middle frontal, precuneus, anterior cingulate, pericalcarine, and transverse temporal) rather than subcortical (caudate) regions were involved. Figure 5. Strongest predictors of mutation carrier (MC) status for autosomal dominant Alzheimer’s disease (ADAD) as identified by Relief algorithms. The strongest predictors across all modalities were the precuneus, caudate, and anterior cingulate. Changes in amyloid PET (PiB, blue circle) were primarily seen within subcortical regions. Changes in metabolism (FDG, orange circle) showed more cortical involvement. Volumetric changes (Volume, green circle) showed both cortical and subcortical involvement.For changes that occur late in the disease process due to atrophy, both cortical (precuneus, anterior cingulate, pericalcarine, transverse temporal) and subcortical (caudate, pallidum, nucleus accumbens, entorhinal, thalamus) regions were affected. This suggests that the disease may start within subcortical areas and quickly involve additional subcortical and cortical regions. Overall, these analyses point to multiple hubs being affected early in the disease process, followed by spread to other brain regions (Supplementary Figure 7). Supplementary Table 1 lists the RMSE for the individual ROIs for each of the 3 biomarkers, as well as the mean overall RMSE of the models compared to the zero rule algorithm and multivariate linear regression.In the amyloid analysis, the model achieved 0.95 R2 and 0.2 RMSE (see Figure 1). The model showed PiB uptake was greater in MCs compared to NCs for most regions. Our results also confirm that the presence of amyloid alone is insufficient for conversion to symptomatic AD. The simulated trajectory for mean cortical amyloid accumulation (see Figure 4, top left) showed deposition started to occur approximately 15–20 years before EAO. These results are consistent with other studies that focused on global and regional amyloid deposition ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1056/NEJMoa1202753","ISSN":"1533-4406","PMID":"22784036","abstract":"BACKGROUND The order and magnitude of pathologic processes in Alzheimer's disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer's disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant's age at baseline assessment and the parent's age at the onset of symptoms of Alzheimer's disease to calculate the estimated years from expected symptom onset (age of the participant minus parent's age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS Concentrations of amyloid-beta (Aβ)(42) in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS We found that autosomal dominant Alzheimer's disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer's disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer's disease. (Funded by the National Institute on Aging and others; DIAN number, NCT00869817.).","author":[{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Xianyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Santacruz","given":"Anna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oliver","given":"Angela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moulder","given":"Krista","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"page":"795-804","title":"Clinical and biomarker changes in dominantly inherited Alzheimer's disease.","type":"article-journal","volume":"367"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1093/brain/awy050","ISSN":"14602156","abstract":"? The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@. See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article. Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (～ 24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great po…","author":[{"dropping-particle":"","family":"Oxtoby","given":"Neil P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Young","given":"Alexandra L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cash","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L.S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bateman","given":"Randall J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schott","given":"Jonathan M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alexander","given":"Daniel C.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Brain","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2018"]]},"page":"1529-1544","title":"Data-driven models of dominantly-inherited Alzheimer's disease progression","type":"article-journal","volume":"141"},"uris":[""]}],"mendeley":{"formattedCitation":"[7,11]","plainTextFormattedCitation":"[7,11]","previouslyFormattedCitation":"[7,11]"},"properties":{"noteIndex":0},"schema":""}[7,11]. Our model indicates a sigmoidal trajectory of accumulation for amyloid, with a slow increase 20–30 years from EAO, an abrupt increase 0–15 years from EAO, and slowing to an eventual decline after EAO. This is consistent with what has been hypothesized to occur in LOAD ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S1474-4422(09)70299-6","ISSN":"14744422","abstract":"Currently available evidence strongly supports the position that the initiating event in Alzheimer's disease (AD) is related to abnormal processing of β-amyloid (Aβ) peptide, ultimately leading to formation of Aβ plaques in the brain. This process occurs while individuals are still cognitively normal. Biomarkers of brain β-amyloidosis are reductions in CSF Aβ42 and increased amyloid PET tracer retention. After a lag period, which varies from patient to patient, neuronal dysfunction and neurodegeneration become the dominant pathological processes. Biomarkers of neuronal injury and neurodegeneration are increased CSF tau and structural MRI measures of cerebral atrophy. Neurodegeneration is accompanied by synaptic dysfunction, which is indicated by decreased fluorodeoxyglucose uptake on PET. We propose a model that relates disease stage to AD biomarkers in which Aβ biomarkers become abnormal first, before neurodegenerative biomarkers and cognitive symptoms, and neurodegenerative biomarkers become abnormal later, and correlate with clinical symptom severity. ? 2010 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Jack","given":"Clifford R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Knopman","given":"David S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jagust","given":"William J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shaw","given":"Leslie M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiner","given":"Michael W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Petersen","given":"Ronald C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Trojanowski","given":"John Q.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet Neurology","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2010"]]},"page":"119-128","title":"Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade","type":"article-journal","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"[39]","plainTextFormattedCitation":"[39]","previouslyFormattedCitation":"[39]"},"properties":{"noteIndex":0},"schema":""}[39]. As a point of reference, we calculated the normalized rates of change for all mutation-positive participants (see Figure 4, bottom left). The normalized rate of amyloid deposition shows a consistent increase from roughly 10 years prior to EAO followed by slowing in the rate of accumulation after EAO. Only after EAO does the rate of accumulation diminish, which is consistent with the sigmoidal model trajectory. With regard to metabolism, our model yielded 0.93 R2 and 0.02 RMSE. Although the MCs had greater decreases in FDG for most brain regions, the separation between the 2 groups was not as well defined compared to PiB. This is likely because the rate and amount of change are less extreme compared to amyloid (see Figure 4, top left). Our model indicates metabolism did not decrease below a baseline until 10 years before symptom onset and continued to decline after EAO. These results are consistent with the normalized rate of change (see Figure 4, bottom left). The rate of metabolism did not decline below baseline until 10 years prior to EAO, followed by a steady decline. An uptick in metabolic activity was observed in the early stages of amyloid accumulation and did not begin to decrease until amyloid significantly increased. This was observed in the simulated trajectory and the normalized rate of change. Similar results were observed within the precuneus in a cross-sectional analysis ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1056/NEJMoa1202753","ISSN":"1533-4406","PMID":"22784036","abstract":"BACKGROUND The order and magnitude of pathologic processes in Alzheimer's disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer's disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant's age at baseline assessment and the parent's age at the onset of symptoms of Alzheimer's disease to calculate the estimated years from expected symptom onset (age of the participant minus parent's age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS Concentrations of amyloid-beta (Aβ)(42) in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS We found that autosomal dominant Alzheimer's disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer's disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer's disease. (Funded by the National Institute on Aging and others; DIAN number, NCT00869817.).","author":[{"dropping-particle":"","family":"Bateman","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiong","given":"Chengjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Benzinger","given":"Tammie L S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagan","given":"Anne M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goate","given":"Alison","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fox","given":"Nick C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marcus","given":"Daniel S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cairns","given":"Nigel J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Xianyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Blazey","given":"Tyler M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Santacruz","given":"Anna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buckles","given":"Virginia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oliver","given":"Angela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moulder","given":"Krista","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aisen","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghetti","given":"Bernardino","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klunk","given":"William E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McDade","given":"Eric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martins","given":"Ralph N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masters","given":"Colin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mayeux","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ringman","given":"John M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rossor","given":"Martin N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schofield","given":"Peter R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sperling","given":"Reisa A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Salloway","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"John C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dominantly Inherited Alzheimer Network","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"page":"795-804","title":"Clinical and biomarker changes in dominantly inherited Alzheimer's disease.","type":"article-journal","volume":"367"},"uris":[""]}],"mendeley":{"formattedCitation":"[7]","plainTextFormattedCitation":"[7]","previouslyFormattedCitation":"[7]"},"properties":{"noteIndex":0},"schema":""}[7]. Rate of change analysis revealed this primarily occurs in the basal ganglia. Because the basal ganglia show the least toxic response to amyloid deposition ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1523/JNEUROSCI.0730-07.2007","ISSN":"02706474","abstract":"The amyloid cascade hypothesis suggests that the aggregation and deposition of amyloid-βprotein is an initiating event in Alzheimer's disease (AD). Using amyloid imaging technology, such as the positron emission tomography (PET) agent Pittsburgh compound-B (PiB), it is possible to explore the natural history of preclinical amyloid deposition in people at high risk for AD. With this goal in mind, asymptomatic (n = 5) and symptomatic (n = 5) carriers of presenilin-1 (PS1) mutations (C410Y or A426P) that lead to early-onset AD and noncarrier controls from both kindreds (n = 2) were studied with PiB-PET imaging and compared with sporadic AD subjects (n = 12) and controls from the general population (n = 18). We found intense and focal PiB retention in the striatum of all 10 PS1 mutation carriers studied (ages 35-49 years). In most PS1 mutation carriers, there also were increases in PiB retention compared with controls in cortical brain areas, but these increases were not as great as those observed in sporadic AD subjects. The two PS1 mutation carriers with a clinical diagnosis of early-onset AD did not show the typical regional pattern of PiB retention observed in sporadic AD. Postmortem evaluation of tissue from two parents of PS1C410Y subjects in this study confirmed extensive striatal amyloid deposition, along with typical cortical deposition. The early, focal striatal amyloid deposition observed in these PS1 mutation carriers is often is not associated with clinical symptoms. Copyright ? 2007 Society for Neuroscience.","author":[{"dropping-particle":"","family":"Klunk","given":"William E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Julie C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mathis","given":"Chester A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tsopelas","given":"Nicholas D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lopresti","given":"Brian J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ziolko","given":"Scott K.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bi","given":"Wenzhu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hoge","given":"Jessica A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cohen","given":"Ann D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ikonomovic","given":"Milos D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saxton","given":"Judith A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Snitz","given":"Beth E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pollen","given":"Daniel A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Moonis","given":"Majaz","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lippa","given":"Carol F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Swearer","given":"Joan M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Keith A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rentz","given":"Dorene M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fischman","given":"Alan J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aizenstein","given":"Howard J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"DeKosky","given":"Steven T.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Neuroscience","id":"ITEM-1","issued":{"date-parts":[["2007"]]},"title":"Amyloid deposition begins in the striatum of presenilin-1 mutation carriers from two unrelated pedigrees","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1007/BF00294646","ISSN":"00016322","abstract":"The nature of senile plaques (SP) in the striatum in 14 cases of Alzheimer's disease (AD) was investigated with the modified Bielschowsky stain and immunohistochemistry using antibodies to a β amyloid synthetic peptide, ubiquitin, tau protein, and paired helical filaments (PHF). Striatal SP, composed of β amyloid deposits with or without neuritic elements, were demonstrated in all AD cases examined. Compact and perivascular amyloid deposits were concentrated in the ventral striatum, including the nucleus accumbens. Many diffuse amyloid deposis in the ventral striatum contained ubiquitin-positive granular elements, presumably representing dystrophic neurites, whereas most of those in the dorsal striatum did not have such elements. On the other hand, most compact amyloid deposits in both ventral and dorsal striatum had ubiquitin immunoreactivity. Dystrophic neurites with tau or PHF immunoreactivity were detected particularly around compact amyloid deposits. Our results indicate that the ventral striatum, which is closely affiliated with the limbic system, is frequently affected by amyloid deposits with dystrophic neurites, and suggest that the ventral striatum is particularly vulnerable to AD. Furthermore, our results suggest that amyloid deposits, especially compact deposits, may induce dystrophic neurites. ? 1990 Springer-Verlag.","author":[{"dropping-particle":"","family":"Suenaga","given":"T.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hirano","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Llena","given":"J. F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yen","given":"S. H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dickson","given":"D. W.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Acta Neuropathologica","id":"ITEM-2","issued":{"date-parts":[["1990"]]},"title":"Modified Bielschowsky stain and immunohistochemical studies on striatal plaques in Alzheimer's disease","type":"article-journal"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1111/j.1365-2990.1997.tb01302.x","ISSN":"03051846","abstract":"The distribution of amyloid β peptide (Aβ) was quantified in the corpus striatum and pallidum of 10 patients with Alzheimer's disease (AD) and three patients with both Alzheimer's disease and Parkinson's disease (AD-PD). Aβ occurred almost exclusively in plaques that did not have neurites or amyloid cores. Caudate, accumbens nuclei and rostral putamen contained more of the diffuse plaques than did caudal putamen. No diffuse plaques were found in the neighbouring globus pallidus. This distribution of Aβ deposition may reflect the distribution of diseased synaptic cortical afferents rather than a putative vascular source of Aβ.","author":[{"dropping-particle":"","family":"Brilliant","given":"M. J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elble","given":"R. J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghobrial","given":"M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Struble","given":"R. G.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neuropathology and Applied Neurobiology","id":"ITEM-3","issued":{"date-parts":[["1997"]]},"title":"The distribution of amyloid β protein deposition in the corpus striatum of patients with Alzheimer's disease","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"[40–42]","plainTextFormattedCitation":"[40–42]","previouslyFormattedCitation":"[40–42]"},"properties":{"noteIndex":0},"schema":""}[40–42], these transient increases may be prominent because these neurons mount a compensatory response preceding significant amyloid accumulation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/bies.201500004","ISSN":"15211878","abstract":"Traditionally, the impairment of cognitive functions in Alzheime{\\'r}s disease (AD) is thought to result from a reduction in neuronal and synaptic activities, and ultimately cell death. Here, we review recent in vivo evidence from mouse models and human patients indicating that, particularly in early stages of AD, neuronal circuits are hyperactive instead of hypoactive. Functional analyses at many levels, from single neurons to neuronal populations to large-scale networks, with a variety of electrophysiological and imaging techniques have revealed two forms of AD-related hyperactivity and provided first insights into the synaptic mechanisms. The unexpected finding that hyperactivity is an early neuronal dysfunction represents a major conceptual shift in our understanding of AD that may have important implications for the development of therapeutic approaches.","author":[{"dropping-particle":"","family":"Busche","given":"Marc Aurel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Konnerth","given":"Arthur","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"BioEssays","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2015"]]},"page":"624-632","title":"Neuronal hyperactivity - A key defect in Alzheimer's disease?","type":"article-journal","volume":"37"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1001/archneurol.2009.15","ISSN":"00039942","abstract":"Alzheimer disease (AD) is associated with cognitive decline and increased incidence of seizures. Seizure activity in AD has been widely interpreted as a secondary process resulting from advanced stages of neurodegeneration, perhaps in combination with other age-related factors. However, recent findings in animal models of AD have challenged this notion, raising the possibility that aberrant excitatory neuronal activity represents a primary upstream mechanism that may contribute to cognitive deficits in these models. The following observations suggest that such activity may play a similar role in humans with AD: (1) patients with sporadic AD have an increased incidence of seizures that appears to be independent of disease stage and highest in cases with early onset; (2) seizures are part of the natural history of many pedigrees with autosomal dominant early-onset AD, including those with mutations in presenilin-1, presenilin-2, or the amyloid precursor protein, or with duplications of wild-type amyloid precursor protein; (3) inheritance of the major known genetic risk factor for AD, apolipoprotein E4, is associated with subclinical epileptiform activity in carriers without dementia; and (4) some cases of episodic amnestic wandering and disorientation in AD are associated with epileptiform activity and can be prevented with antiepileptic drugs. Here we review recent experimental data demonstrating that high levels of β-amyloid in the brain can cause epileptiform activity and cognitive deficits in transgenic mouse models of AD. We conclude that β-amyloid peptides may contribute to cognitive decline in AD by eliciting similar aberrant neuronal activity in humans and discuss potential clinical and therapeutic implications of this hypothesis.","author":[{"dropping-particle":"","family":"Palop","given":"Jorge J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mucke","given":"Lennart","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Archives of Neurology","id":"ITEM-2","issue":"4","issued":{"date-parts":[["2009"]]},"page":"435-440","title":"Epilepsy and cognitive impairments in alzheimer disease","type":"article-journal","volume":"66"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1098/rstb.2015.0429","ISSN":"14712970","abstract":"One contribution of 15 to a Theo Murphy meeting issue 'Evolution brings Ca 2? and ATP together to control life and death'. An essential feature of Alzheimer's disease (AD) is the accumulation of amy-loid-b (Ab) peptides in the brain, many years to decades before the onset of overt cognitive symptoms. We suggest that during this very extended early phase of the disease, soluble Ab oligomers and amyloid plaques alter the function of local neuronal circuits and large-scale networks by disrupting the balance of synaptic excitation and inhibition (E/I balance) in the brain. The analysis of mouse models of AD revealed that an Ab-induced change of the E/I balance caused hyperactivity in cortical and hippocampal neurons, a breakdown of slow-wave oscillations, as well as network hypersynchrony. Remarkably, hyperactivity of hippocampal neurons precedes amyloid plaque formation, suggesting that hyperactivity is one of the earliest dys-functions in the pathophysiological cascade initiated by abnormal Ab accumulation. Therapeutics that correct the E/I balance in early AD may pre-vent neuronal dysfunction, widespread cell loss and cognitive impairments associated with later stages of the disease. This article is part of the themed issue 'Evolution brings Ca 2? and ATP together to control life and death'.","author":[{"dropping-particle":"","family":"Busche","given":"Marc Aurel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Konnerth","given":"Arthur","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Philosophical Transactions of the Royal Society B: Biological Sciences","id":"ITEM-3","issue":"1700","issued":{"date-parts":[["2016"]]},"page":"20150429","title":"Impairments of neural circuit function in Alzheimer’s disease","type":"article-journal","volume":"371"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1016/j.neurobiolaging.2014.08.014","ISSN":"15581497","abstract":"Neuronal activity directly promotes the production and secretion of amyloid β (Aβ). Interestingly, neuronal hyperactivity can be observed in presymptomatic stages of both sporadic and familial Alzheimer's disease (AD) and in several AD mouse models. In this review, we will highlight the recent evidence for neuronal hyperactivity before or during the onset of cognitive defects in mild cognitive impairment. Furthermore, we review specific molecular mechanisms through which neuronal hyperactivity affects Aβ production and degradation. With these data, we will provide more insight into the 2-faced nature of neuronal hyperactivity: does enhanced neuronal activity during the presymptomatic stages of AD provide protection against the earliest disease processes or is it a pathogenic contributor to AD?","author":[{"dropping-particle":"","family":"Stargardt","given":"Anita","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Swaab","given":"Dick F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bossers","given":"Koen","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurobiology of Aging","id":"ITEM-4","issue":"1","issued":{"date-parts":[["2015"]]},"page":"1-11","title":"The storm before the quiet: Neuronal hyperactivity and Aβ in the presymptomatic stages of Alzheimer's disease","type":"article-journal","volume":"36"},"uris":[""]},{"id":"ITEM-5","itemData":{"DOI":"10.1523/JNEUROSCI.5845-11.2012","ISSN":"02706474","PMID":"22457485","abstract":"Brain region-specific deposition of extracellular amyloid plaques principally composed of aggregated amyloid-β (Aβ) peptide is a pathological signature of Alzheimer's disease (AD). Recent human neuroimaging data suggest that resting-state functional connectivity strength is reduced in patients with AD, cognitively normal elderly harboring elevated amyloid burden, and in advanced aging. Interestingly, there exists a striking spatial correlation between functional connectivity strength in cognitively normal adults and the location of Aβ plaque deposition in AD. However, technical limitations have heretofore precluded examination of the relationship between functional connectivity, Aβ deposition, and normal aging in mouse models. Using a novel functional connectivity optical intrinsic signal (fcOIS) imaging technique, we demonstrate that Aβ deposition is associated with significantly reduced bilateral functional connectivity in multiple brain regions of older APP/PS1 transgenic mice. The amount of Aβ deposition in each brain region was associated with the degree of local, age-related bilateral functional connectivity decline. Normal aging was associated with reduced bilateral functional connectivity specifically in retrosplenial cortex. Furthermore, we found that the magnitude of regional bilateral functional correlation in young APP/PS1 mice beforeAβ plaque formation was proportional to the amount of region-specific plaque deposition seen later in older APP/PS1 mice. Together, these findings suggest that Aβ deposition and normal aging are associated with region-specific disruption of functional connectivity and that the magnitude of local bilateral functional connectivity predicts regional vulnerability to subsequentAβ deposition in mouse brain. ? 2012 the authors.","author":[{"dropping-particle":"","family":"Bero","given":"Adam W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bauer","given":"Adam Q.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stewart","given":"Floy R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"White","given":"Brian R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cirrito","given":"John R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raichle","given":"Marcus E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Culver","given":"Joseph P.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Holtzman","given":"David M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Neuroscience","id":"ITEM-5","issue":"13","issued":{"date-parts":[["2012"]]},"page":"4334-4340","title":"Bidirectional relationship between functional connectivity and amyloid-β deposition in mouse brain","type":"article-journal","volume":"32"},"uris":[""]}],"mendeley":{"formattedCitation":"[43–47]","plainTextFormattedCitation":"[43–47]","previouslyFormattedCitation":"[43–47]"},"properties":{"noteIndex":0},"schema":""}[43–47]. However, at a point, the brain is no longer able to buffer changes when amyloid deposition becomes significant.Our model showed total gray matter volume slightly declined during the early stages of ADAD, followed by a dramatic decrease 5–10 years prior to EAO. The decrease in volume occurred when metabolism was decreased and amyloid had accumulated. Volumetrics continued to decline even after EAO. The model was able to predict volumes with an R2 of 0.95. These findings have clinical importance for the care of people with ADAD, as well as implications for the utility of ML in developing diagnostic and predictive tests. This is true for both the clinical setting, as well as prevention trials. Accurately identifying participants whose progression patterns differ from model predictions could allow for more personalized medicine and decision support in evaluating the effects of specific therapies in clinical trials. As we have shown, while both linear regression and our ANN performed well in predicting disease progression, the ANN had a lower error rate. The utilization of more accurate models could lead to better decision-making and improved efficiency of research and clinical trials. Precision medicine through machine learning could offer specific treatments tailored to a patient based on the disease subtype and response to a treatment. This is especially relevant for diseases such as ADAD and LOAD, which show chronic progression over long periods of time, as well as variability in terms of symptoms, risk factors, and progression. Because it is not possible to know beforehand what model is most appropriate or will yield the “best” results, it is important to analyze multiple models, linear and nonlinear, to provide optimal care for patients.Future work will involve further training, validation, and testing of the proposed models. Specifically, conducting blinded out of sample testing on newly acquired data to ensure issues such as overfitting do not occur. Further, alternative network models will be considered. As more longitudinal time points are acquired for participants, time series specific networks, such as long short term memory networks may be more appropriate. Lastly, alternative forms of feature selection should be considered to investigate the relationships between biomarkers and brain regions. 4.1. ConclusionTo provide targeted treatment to persons with ADAD, novel methods are needed to model disease trajectories. We have shown ANNs can accurately forecast amyloid accumulation, changes in glucose metabolism, and brain atrophy. Using feature extraction methods, we identified the strongest predictors of mutation status over 44 brain regions. Our results show a sigmoidal progression of amyloid accumulation, a biphasic response to glucose metabolism, and a gradual increase in brain atrophy in MCs compared to NCs. Our models indicate disease progression is primarily in subcortical regions, followed by cortical involvement within anterior and posterior portions of the brain.ACKNOWLEDGEMENTS, FUNDING, AND DISCLOSURES5.1. AcknowledgementsWe would like to acknowledge the participants and their families, without whom these studies would not be possible. We additionally thank all of the participating researchers and coordinators in the DIAN () who support the studies. DIAN identifier: NCT00869817.5.2. FundingThis research was funded by the National Institutes of Health (NIH) [grant numbers K01AG053474, R01AG052550, UFAG 032438, UL1TR000448, P30NS098577, R01EB009352, P50AG05131, U01AG042791, U01AG042791-S1 (FNIH and Accelerating Medicines Partnership), R1AG046179]; the German Center for Neurodegenerative Diseases (DZNE); the National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Centre; and the Medical Research Council Dementias Platform UK [grant numbers MR/L023784/1, MR/009076/1, BrightFocus Foundation A2018817F, Alzheimer Association International Research Grant Program #AARFD-20-681815, NSF DMS 156243, and an anonymous organization. We acknowledge the support of Fred Simmons and Olga Mohan, the Barnes-Jewish Hospital Foundation, the Charles F. and Joanne Knight Alzheimer Research Initiative, the Hope Center for Neurological Disorders, the Mallinckrodt Institute of Radiology, the Paula and Rodger O. Riney fund, and the Daniel J. Brennan fund. Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially funded by the NIH [grant numbers 1S10RR022984–01A1, 1S10OD018091–01].5.3. Competing InterestsThe authors declare no conflicts of interest. Anne Fagan received research funding from the National Institute on Aging of the National Institutes of Health, Biogen, Centene, Fujirebio, and Roche Diagnostics. She is a member of the scientific advisory boards for Roche Diagnostics, Genentech, and AbbVie and also consults for Araclon/Grifols, DiademRes, DiamiR, and Otsuka. Carlos Cruchaga receives research support from Biogen, EISAI, Alector, and Parabon. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Dr. Cruchaga is also a member of the advisory board of ADx Healthcare, Halia Therapeutics, and Vivid Genomics. Jasmeer P. Chhatwal served on the medical advisory board for Otsuka Pharmaceuticals. Johannes Levin reports speaker’s fees from Bayer Vital, speaker’s fees from Willi Gross Foundation, consulting fees from Axon Neuroscience, consulting fees from Ionis Pharmaceuticals, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers, compensation for work as part-time CMO from MODAG GmbH, and non-financial support from AbbVie outside the submitted work. John Morris is funded by NIH grants [numbers P50AG005681, P01AG003991, P01AG026276, UF1AG032438]. Dr. Jack serves on an independent data monitoring board for Roche and has served as a speaker for Eisai, but he receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer Disease Research Professorship of the Mayo Clinic. Eric McDade is involved in a clinical trial on AV-1451 sponsored by Avid and serves on a data safety monitoring committee for Eli-Lilly. 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Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2010;9:119–28. (09)70299-6.[40]Klunk WE, Price JC, Mathis CA, Tsopelas ND, Lopresti BJ, Ziolko SK, et al. Amyloid deposition begins in the striatum of presenilin-1 mutation carriers from two unrelated pedigrees. J Neurosci 2007. .[41]Suenaga T, Hirano A, Llena JF, Yen SH, Dickson DW. Modified Bielschowsky stain and immunohistochemical studies on striatal plaques in Alzheimer’s disease. Acta Neuropathol 1990. .[42]Brilliant MJ, Elble RJ, Ghobrial M, Struble RG. The distribution of amyloid β protein deposition in the corpus striatum of patients with Alzheimer’s disease. Neuropathol Appl Neurobiol 1997. .[43]Busche MA, Konnerth A. Neuronal hyperactivity - A key defect in Alzheimer’s disease? BioEssays 2015;37:624–32. .[44]Palop JJ, Mucke L. Epilepsy and cognitive impairments in alzheimer disease. Arch Neurol 2009;66:435–40. .[45]Busche MA, Konnerth A. Impairments of neural circuit function in Alzheimer’s disease. Philos Trans R Soc B Biol Sci 2016;371:20150429. .[46]Stargardt A, Swaab DF, Bossers K. The storm before the quiet: Neuronal hyperactivity and Aβ in the presymptomatic stages of Alzheimer’s disease. Neurobiol Aging 2015;36:1–11. .[47]Bero AW, Bauer AQ, Stewart FR, White BR, Cirrito JR, Raichle ME, et al. Bidirectional relationship between functional connectivity and amyloid-β deposition in mouse brain. J Neurosci 2012;32:4334–40. . FIGURE LEGENDSFigure 1. Results of Pittsburgh Compound-B (PiB) predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red). Correlation and RMSE of predicted versus actual values. The ANN was able to predict future PiB values with an average R2 of 0.95 and RMSE of 0.2 in both MCs and NCs.Figure 2. Results of fluorodeoxyglucose (FDG) predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red) in select ROIs. Correlation and root mean squared error (RMSE) of predicted versus actual values. The ANN was able to predict future FDG values with an average R2 of 0.93 and RMSE of 0.02 in MCs and NCs, with MCs showing trends of lower predicted FDG values than NCs.Figure 3. Results of brain volumetric predictions for mutation carriers (MC) (blue) and non-carriers (NC) (red). Correlation and root mean squared error (RMSE) of predicted versus actual values. The ANN was able to predict changes in brain volumes with an average R2 value of 0.95 and showed a general trend of MCs having more brain atrophy than NCs.Figure 4. (Top left) Simulated biomarker evolution for total mean cortical and subcortical Pittsburgh Compound-B (PiB), total mean cortical and subcortical fluorodeoxyglucose (FDG), and total gray matter volume (scaled to a common interval) derived from the artificial neural network (ANN) in mutation carriers (MC). Shaded region indicates model variability, with EAO marked by perpendicular line. (Top right) Simulated biomarker evolution for total mean cortical and subcortical PiB, total mean cortical and subcortical FDG, and total gray matter volume (scaled to a common interval) derived from the ANN in mutation non-carriers (NC). (Bottom left) Normalized biomarker rate of change for mean PiB, mean FDG, and total gray matter volume (scaled to a common interval) fit to a polynomial curve showing 95% confidence interval. (Bottom right) Mean absolute error of predicted (normalized) biomarker values given the amount of time in the future to predict, fit with a 2-degree polynomial curve projected into the future. Errors increased linearly with an increase in the amount of time in the future to predict.Figure 5. Strongest predictors of mutation carrier (MC) status for autosomal dominant Alzheimer’s disease (ADAD) as identified by Relief algorithms. The strongest predictors across all modalities were the precuneus, caudate, and anterior cingulate. Changes in amyloid PET (PiB, blue circle) were primarily seen within subcortical regions. Changes in metabolism (FDG, orange circle) showed more cortical involvement. Volumetric changes (Volume, green circle) showed both cortical and subcortical involvement.AppendixConsortia: Dominantly Inherited Alzheimer NetworkRicardo Allegri, Randall J. Bateman, Jacob Bechara, Tammie L.S. Benzinger, Sarah Berman, Courtney Bodge, Susan Brandon, William Brooks, Jill Buck, Virginia Buckles, Sochenda Chea, Jasmeer Chhatwal, Patricio Chrem, Helena Chui, Jake Cinco, Clifford R Jack Jr, Carlos Cruchaga, Tamara Donahue, Jane Douglas, Noelia Edigo, Nilufer Erekin-Taner, Anne Fagan, Martin R. Farlow, Nick C. Fox, Colleen Fitzpatrick, Gigi Flynn,Erin Franklin, Hisako Fujii, Douglas Galasko, Cortaiga Gant, Samantha Gardener, Bernardino Ghetti, Alison Goate, Jill Goldman, Brian Gordon, Neill Graff-Radford, Julia Gray, Alexander Groves, Jason Hassenstab, Laura Hoechst-Swisher, David Holtzman, Russ Hornbeck, Siri Houeland DiBari, Takeshi Ikeuchi, Snezana Ikonomovic, Gina Jerome, Mathias Jucker, Celeste Karch, Kensaku Kasuga, Takeshi Kawarabayashi, William Klunk, Robert Koeppe, Elke Kuder-Buletta, Christoph Laske, Jae-Hong Lee, Johannes Levin, Ralph Martins, Neal Scott Mason, Ralph Martins, Colin L. Masters, Denise Maue-Dreyfus, Eric McDade, Hiroshi Mori, John C. Morris, Akem Nagamatsu, Katie Neimeyer, James M.Noble, Joanne Norton, Richard J. Perrin, Marc Raichle, Alan Renton, John Ringman, Jee Hoon Roh, Stephen Salloway, Peter R. Schofield, Hiroyuki Shimada, Wendy Sigurdson, Hamid Sohrabi, Paige Sparks, Kazushi Suzuki, Kevin Taddei, Peter Wang, Chengjie Xiong, Xiong Xu ................
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