University of Edinburgh



Title:Cognitive function, disease burden and the structural connectome in systemic lupus erythematosusAuthors:Stewart J. Wiseman1, Mark E. Bastin1, E. Nicole Amft2, Jill F.F. Belch3, Stuart H. Ralston4, Joanna M. Wardlaw1. Affiliation:1 Centre for Clinical Brain Sciences, University of Edinburgh, UK2 Department of Rheumatology, Western General Hospital, Edinburgh, UK3 Division of Cardiovascular and Diabetes Medicine, University of Dundee, UK4 Centre for Genomic and Experimental Medicine, University of Edinburgh, UKCorrespondence:Mark E. Bastin, Centre for Clinical Brain Sciences, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU. E-mail: Mark.Bastin@ed.ac.ukTel: 0131 537 2660Fax: 0131 242 6210Running head: Cognition and brain network connections in lupusKey words: Connectome, SLE, cognition, MRIABSTRACTObjective: To investigate brain structural connectivity in relation to cognitive abilities and systemic damage in systemic lupus erythematosus (SLE).Methods: Structural and diffusion magnetic resonance imaging (MRI) data were acquired from 47 patients with SLE. Brains were segmented into 85 cortical and subcortical regions and combined with whole brain tractography to generate structural connectomes using graph theory. Global cognitive abilities were assessed using a composite variable g, derived from the first principal component of three common clinical screening tests of neurological function. SLE damage (LD) was measured using a composite of a validated SLE damage score and disease duration. Relationships between network connectivity metrics, cognitive ability and systemic damage were investigated. Hub nodes were identified. Multiple linear regression, adjusting for covariates, was employed to model the outcomes g and LD as a function of network metrics. Results: The network measures of density (standardised ? = 0.266, P = 0.025) and strength (standardised ? = 0.317, P = 0.022) were independently related to cognitive abilities. Strength (standardised ? = -0.330, P = 0.048), mean shortest path length (standardised ? = 0.401, P = 0.020), global efficiency (standardised ? = -0.355, P = 0.041) and clustering coefficient (standardised ? = -0.378, P = 0.030) were independently related to systemic damage. Network metrics were not related to current disease activity. Conclusion: Better cognitive abilities and more SLE damage are related to brain topological network properties in this sample of SLE patients, even those without neuropsychiatric involvement and after correcting for important covariates. These data show that connectomics might be useful for understanding and monitoring cognitive function and white matter damage in SLE.INTRODUCTIONMild cognitive impairments are common in systemic lupus erythematosus (SLE). The neural substrates are unknown which makes alleviating symptoms challenging. Understanding how brain structure correlates with the systemic damage caused since SLE diagnosis and its impact on cognitive abilities could help unravel an underlying mechanism and lead to better therapies. Damage to the physical brain white matter communication infrastructure could disrupt the coherence of structural networks resulting in impairments. Advanced brain imaging techniques could help identify asymptomatic brain damage associated with this disease.ConnectomicsADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1137/S003614450342480", "ISBN" : "00361445", "ISSN" : "12948322", "PMID" : "24174898", "abstract" : "An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a \"rich club,\" centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. 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Finally, we describe a Matlab toolbox () accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. ?? 2009 Elsevier Inc.", "author" : [ { "dropping-particle" : "", "family" : "Rubinov", "given" : "Mikail", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sporns", "given" : "Olaf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "NeuroImage", "id" : "ITEM-2", "issue" : "3", "issued" : { "date-parts" : [ [ "2010" ] ] }, "page" : "1059-1069", "publisher" : "Elsevier Inc.", "title" : "Complex network measures of brain connectivity: Uses and interpretations", "type" : "article-journal", "volume" : "52" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^1,2", "plainTextFormattedCitation" : "1,2", "previouslyFormattedCitation" : "^1,2" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }1,2 uses graph theoryADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1038/nrn2575", "ISBN" : "1471-0048 (Electronic)", "ISSN" : "1471-0048", "PMID" : "19190637", "abstract" : "Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. 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Brain imaging featuresADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/S1474-4422(13)70124-8", "ISSN" : "1474-4465", "PMID" : "23867200", "abstract" : "Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. 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Associations between cognitive abilities and biomarkers of brain microstructural integrity derived from diffusion magnetic resonance imaging (dMRI) were also found, but did not survive adjustment for covariates (age, disease duration, steroid use and an estimate of prior cognitive ability)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Wiseman SJ, Bastin ME, Hamilton IF, Hunt D, Ritchie SJ, Amft EN, Thomson S, Belch JFF, Ralston SH", "given" : "Wardlaw JM.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Lupus", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016" ] ] }, "page" : "0-0", "title" : "Fatigue and cognitive function in systemic lupus erythematosus: associations with white matter microstructural damage. A diffusion tensor MRI study and meta-analysis.", "type" : "article-journal", "volume" : "0" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁸", "plainTextFormattedCitation" : "8", "previouslyFormattedCitation" : "⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }8. Here, the relationship between cognitive abilities, systemic damage caused by SLE and structural network metrics is investigated. We include both SLE and neuropsychiatric (NPSLE) patients, and not just NPSLE patients, as many SLE patients also complain of symptoms that could relate to early brain changes. An estimate of prior cognitive abilities and other covariates such as patients that were older, had greater volumes of cerebral disease on brain imaging and antiphospholipid status are adjusted for. 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This novel work is the first to use graph theory to model the brain’s structural connectivity in relation to cognition in SLE.METHODSSubjectsConsecutive patients seen by a consultant rheumatologist (E.N.A.) at a specialist SLE clinic between April and December 2014 were invited to join the study. From the 51 subjects that participated, 47 had available connectome and cognitive data for the present analysis. 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The South East Scotland Research Ethics Committee gave study approval (01, 14/SS/0003), and all participants gave written consent. Cognitive assessmentsFor pragmatism, current cognitive function was assessed with validated screening tools rather than a full neuropsychological battery, including the Montreal Cognitive Assessment (MoCA),ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Narseddine", "given" : "ZS", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Phillips", "given" : "NA", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bedirian", "given" : "V", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Charbonneau", "given" : "Simon", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Whitehead", "given" : "Victor", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Collin", "given" : "Isabelle", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Cummings", "given" : "JL", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chertkow", "given" : "H", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of the American Geriatrics Society", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2005" ] ] }, "page" : "695-9", "title" : "The Montreal Cognitive Assessment , MoCA : A Brief Screening Tool for Mild Cognitive Impairment", "type" : "article-journal", "volume" : "53" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹¹", "plainTextFormattedCitation" : "11", "previouslyFormattedCitation" : "¹¹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }11 Addenbrooke’s Cognitive Examination – Revised (ACER)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1159/000351671", "ISSN" : "1421-9824", "PMID" : "23949210", "abstract" : "BACKGROUND/AIMS: The aims of this study were to validate the newly developed version of the Addenbrooke's Cognitive Examination (ACE-III) against standardised neuropsychological tests and its predecessor (ACE-R) in early dementia.\n\nMETHODS: A total of 61 patients with dementia (frontotemporal dementia, FTD, n = 33, and Alzheimer's disease, AD, n = 28) and 25 controls were included in the study.\n\nRESULTS: ACE-III cognitive domains correlated significantly with standardised neuropsychological tests used in the assessment of attention, language, verbal memory and visuospatial function. The ACE-III also compared very favourably with its predecessor, the ACE-R, with similar levels of sensitivity and specificity.\n\nCONCLUSION: The results of this study provide objective validation of the ACE-III as a screening tool for cognitive deficits in FTD and AD.", "author" : [ { "dropping-particle" : "", "family" : "Hsieh", "given" : "Sharpley", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Schubert", "given" : "Samantha", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hoon", "given" : "Christopher", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mioshi", "given" : "Eneida", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hodges", "given" : "John R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Dementia and geriatric cognitive disorders", "id" : "ITEM-1", "issue" : "3-4", "issued" : { "date-parts" : [ [ "2013", "1" ] ] }, "page" : "242-50", "title" : "Validation of the Addenbrooke's Cognitive Examination III in frontotemporal dementia and Alzheimer's disease.", "type" : "article-journal", "volume" : "36" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹²", "plainTextFormattedCitation" : "12", "previouslyFormattedCitation" : "¹²" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }12 and Mini Mental State Examination (MMSE)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Folstein", "given" : "MF", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Folstein", "given" : "SE", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McHugh", "given" : "PR", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "J. Psychiat. Res.", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1975" ] ] }, "page" : "189-98", "title" : "Mini-mental state. A practical method for grading the cognitive state of patients for the clinician.", "type" : "article-journal", "volume" : "12" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹³", "plainTextFormattedCitation" : "13", "previouslyFormattedCitation" : "¹³" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }13. The National Adult Reading Test (NART)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Nelson", "given" : "HR", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Willison", "given" : "JR", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "NFER_Nelson Publishing", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1982" ] ] }, "title" : "National Adult Reading Test (NART):Test Manual.", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹⁴", "plainTextFormattedCitation" : "14", "previouslyFormattedCitation" : "¹⁴" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }14 was used to adjust for premorbid intelligence. The NART is a validatedADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "McGurn", "given" : "B", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Starr", "given" : "J M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Topfer", "given" : "J A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pattie", "given" : "A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Whiteman", "given" : "M C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lemmon", "given" : "H A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Whalley", "given" : "L J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Deary", "given" : "I J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Neurology", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2004" ] ] }, "page" : "1184-87", "title" : "Pronunciation of irregular words is preserved in dementia , validating premorbid IQ estimation", "type" : "article-journal", "volume" : "62" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹⁵", "plainTextFormattedCitation" : "15", "previouslyFormattedCitation" : "¹⁵" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }15 estimate of premorbid intelligence as it appears broadly resilient to age-related cognitive decline. Disease activityCurrent SLE disease activity was assessed using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Gladman", "given" : "DD", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ibanez", "given" : "D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Urowitz", "given" : "MB", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "J Rheumatol", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2002" ] ] }, "page" : "288-91", "title" : "Systemic lupus erythematosus disease activity index 2000.", "type" : "article-journal", "volume" : "29" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹⁶", "plainTextFormattedCitation" : "16", "previouslyFormattedCitation" : "¹⁶" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }16 and British Isles Lupus Assessment Group 2004 (BILAG)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1093/rheumatology/keh624", "ISSN" : "1462-0324", "PMID" : "15814577", "abstract" : "OBJECTIVE: To devise a more discriminating version of the British Isles Lupus Assessment Group (BILAG) disease activity index and to show that it is reliable.\n\nMETHODS: A nominal consensus approach was undertaken by members of BILAG to update and improve the BILAG lupus disease activity index. The index has been revised following intense consultations over a 1-yr period. It has been assessed in two real-patient exercises. These involved patients with diverse clinical features of SLE, including gastrointestinal, hepatic and ophthalmic problems, which the earlier versions of the index did not fully take into account. Reliability in terms of the ability to differentiate patients was assessed by calculating intraclass correlation coefficients. The level of agreement between physicians was determined by calculating the ratio of estimates of the standard error (SE) attributable to the physicians to the SE attributable to the patients.\n\nRESULTS: Good reliability and high levels of physician agreement were observed in one or both exercises in the constitutional, mucocutaneous, neurological, cardiorespiratory, renal, ophthalmic and haematological systems. In contrast, the musculoskeletal system did not score as well, although providing more clear-cut glossary definitions should greatly improve the situation.\n\nCONCLUSIONS: Some significant changes in the BILAG disease activity index to assess patients with SLE are proposed. The process of demonstrating validity and reliability has started with these two exercises assessing real patients. Further validation studies are under way. BILAG 2004 is likely to be valuable in clinical trials assessing new therapies for the treatment of SLE, as it provides a more comprehensive system-based disease activity measure than has been available previously.", "author" : [ { "dropping-particle" : "", "family" : "Isenberg", "given" : "D a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rahman", "given" : "a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Allen", "given" : "E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Farewell", "given" : "V", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Akil", "given" : "M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bruce", "given" : "I N", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "D'Cruz", "given" : "D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Griffiths", "given" : "B", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Khamashta", "given" : "M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Maddison", "given" : "P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "McHugh", "given" : "N", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Snaith", "given" : "M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Teh", "given" : "L S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Yee", "given" : "C S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zoma", "given" : "a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gordon", "given" : "C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Rheumatology (Oxford, England)", "id" : "ITEM-1", "issue" : "7", "issued" : { "date-parts" : [ [ "2005", "7" ] ] }, "page" : "902-6", "title" : "BILAG 2004. Development and initial validation of an updated version of the British Isles Lupus Assessment Group's disease activity index for patients with systemic lupus erythematosus.", "type" : "article-journal", "volume" : "44" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "¹⁷", "plainTextFormattedCitation" : "17", "previouslyFormattedCitation" : "¹⁷" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }17 tools. Accumulated permanent damage from SLE was assessed with the Systemic Lupus International Collaborating Clinics (SLICC)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Gladman", "given" : "Dafna", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ginzler", "given" : "Ellen", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Goldsmith", "given" : "Charles", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fortin", "given" : "Paul", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Liang", "given" : "Matthew", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Urowitz", "given" : "Murray", "non-dropping-particle" : 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Antiphospholipid statusA definite diagnosis of antiphospholipid syndrome (APS) was made with consideration to the international consensus statementADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1111/j.1538-7836.2006.01753.x", "ISBN" : "1538-7933 (Print)\\r1538-7836 (Linking)", "ISSN" : "15387933", "PMID" : "16420554", "abstract" : "New clinical, laboratory and experimental insights, since the 1999 publication of the Sapporo preliminary classification criteria for antiphospholipid syndrome (APS), had been addressed at a workshop in Sydney, Australia, before the Eleventh International Congress on antiphospholipid antibodies. In this document, we appraise the existing evidence on clinical and laboratory features of APS addressed during the forum. Based on this, we propose amendments to the Sapporo criteria. 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Blood markers of lupus anticoagulant and anticardiolipin antibodies (isotypes IgG and IgM) were collected as part of the study; historical blood results were also reviewed.MRI acquisitionAll MRI data were acquired using a GE Signa Horizon HDxt 1.5 T scanner (General Electric, Milwaukee, WI, USA) using a self-shielding gradient set with maximum gradient strength of 33 mT m-1 and an 8-channel phased-array head coil. The scan protocol included axial T2-, gradient-recalled echo-, fluid-attenuated inversion recovery-, sagittal T2- and high-resolution coronal 3D T1-weighted volume sequences, and a whole brain dMRI acquisition. The dMRI protocol consisted of three T2-weighted and 32 diffusion-weighted (b=1000 s mm-2) axial single-shot spin-echo echo-planar (EP) imaging volumes (field of view 240 240 mm, matrix 128 128, TR 13.75 s and TE 78.4 ms). Each volume comprised 56 contiguous 2.5 mm thick axial slices with 1.875 mm in-plane resolution. Detailed scanning parameters have been published previoulsyADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Wiseman SJ, Bastin ME, Hamilton IF, Hunt D, Ritchie SJ, Amft EN, Thomson S, Belch JFF, Ralston SH", "given" : "Wardlaw JM.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Lupus", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016" ] ] }, "page" : "0-0", "title" : "Fatigue and cognitive function in systemic lupus erythematosus: associations with white matter microstructural damage. 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Mean shortest path length is inversely related to the other connectivity metrics.Image review, visual rating and quantitative analysisAll MRI scans were reviewed by a consultant neuroradiologist (J.M.W.) blind to all other data. Imaging features of SVD were defined per STRIVE guidelines.ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/S1474-4422(13)70124-8", "ISSN" : "1474-4465", "PMID" : "23867200", "abstract" : "Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. 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With a 1.5-MR unit we studied 12 Alzheimer patients, four subjects suffering from multiinfarct dementia and nine age-matched controls. Punctate or early confluent high-signal abnormalities in the deep white matter, noted in 60% of both Alzheimer patients and controls, were unrelated to the presence of hypertension or other vascular risk factors. A significant number of Alzheimer patients exhibited a more extensive smooth \"halo\" of periventricular hyperintensity when compared with controls (p = .024). Widespread deep white-matter hyperintensity (two patients) and extensive, irregular periventricular hyperintensity (three patients) were seen in multiinfarct dementia. Areas of high signal intensity affecting hippocampal and sylvian cortex were also present in five Alzheimer and two multiinfarct dementia patients, but absent in controls. Discrete, small foci of deep white-matter hyperintensity are not characteristic of Alzheimer's disease nor do they appear to imply a vascular cause for the dementing illness. The frequently observed \"halo\" of periventricular hyperintensity in Alzheimer's disease may be of diagnostic importance. 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Intracranial (ICV), CSF, brain tissue (BTV) and WMH volumes were measured using Analyse 11.0 () and in-house software ‘MCMxxxVI’, see . These methods were developed locally and have been validatedADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1111/j.1747-4949.2011.00683.x", "ISSN" : "1747-4949", "PMID" : "22111801", "abstract" : "As the population of the world ages, age-related cognitive decline is becoming an ever-increasing problem. However, the changes in brain structure that accompany normal aging, and the role they play in cognitive decline, remain to be fully elucidated.", "author" : [ { "dropping-particle" : "", "family" : "Wardlaw", "given" : "Joanna M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bastin", "given" : "Mark E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vald\u00e9s Hern\u00e1ndez", "given" : "Maria C", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Maniega", "given" : "Susana Mu\u00f1oz", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Royle", "given" : "Natalie a", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Morris", "given" : "Zoe", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Clayden", "given" : "Jonathan D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sandeman", "given" : "Elaine M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Eadie", "given" : "Elizabeth", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Murray", "given" : "Catherine", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Starr", "given" : "John M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Deary", "given" : "Ian J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "International journal of stroke : official journal of the International Stroke Society", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2011", "12" ] ] }, "page" : "547-59", "title" : "Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol.", "type" : "article-journal", "volume" : "6" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1002/brb3.415", "ISSN" : "21623279", "author" : [ { "dropping-particle" : "", "family" : "Vald\u00e9s Hern\u00e1ndez", "given" : "Maria del C.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Armitage", "given" : "Paul A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thrippleton", "given" : "Michael J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chappell", "given" : "Francesca", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sandeman", "given" : "Elaine", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mu\u00f1oz Maniega", "given" : "Susana", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shuler", "given" : "Kirsten", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wardlaw", "given" : "Joanna M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Brain and Behavior", "id" : "ITEM-2", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "n/a-n/a", "title" : "Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke", "type" : "article-journal", "volume" : "415" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^22,23", "plainTextFormattedCitation" : "22,23", "previouslyFormattedCitation" : "^22,23" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }22,23. All segmented volumes were visually inspected for accuracy and to avoid erroneous classification. We corrected for head size by dividing the quantitative WMH volume by the ICV. Statistical analysisData distributions were checked graphically for normality. Pearson’s correlation coefficient was used to assess the relationship between network connectivity measures and other variables. Principal components analysis was used to create two composite variables: cognitive ability (g) and SLE systemic damage (LD) (where g was derived from three cognitive test scores (MoCA, ACER and MMSE) and the first component explained 70% of variance; and LD was derived from the SLICC damage index plus disease duration and the first component explained 78% of variance). The connectivity measures were scaled (mean = 0, standard deviation = 1) and then used as explanatory variables in models using multiple linear regression with g and LD as outcomes of interest, controlling for age, disease duration, WMH volume, steroids, antiphospholipid status and NART. All analyses were conducted in R v3.3.0 ()ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "R Core Team", "given" : "", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2013" ] ] }, "page" : "R Foundation for Statistical Computing, Vienna, Au", "title" : "R: A language and environment for statistical computing.", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁴", "plainTextFormattedCitation" : "24", "previouslyFormattedCitation" : "²⁴" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }24. Where there were multiple correlational comparisons, a threshold of P < 0.01 was used to denote significance (rather than adjustment for multiple testing which is often too conservative); importantly the P value is secondary to our primary interest being the magnitude of parameter estimatesADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "0000000000000", "author" : [ { "dropping-particle" : "", "family" : "Gardner", "given" : "Martin J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Altman", "given" : "Douglas G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "BMJ", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "1986" ] ] }, "page" : "746-750", "title" : "Statistics in Medicine Confidence intervals rather than P values : estimation rather than hypothesis testing", "type" : "article-journal", "volume" : "292" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1136/annrheumdis-2014-206186", "ISSN" : ", 1468-2060", "PMID" : "25261576", "abstract" : "From 2006 to 2014, I have carried out approximately 200 statistical reviews of manuscripts for ARD. My most frequent review comments concern the following:\\n1. Report how missing data were handled.\\n2. Limit the number of covariates in regression analyses.\\n3. Do not use stepwise selection of covariates.\\n4. Use analysis of covariance (ANCOVA) to adjust for baseline values in randomised controlled trials.\\n5. Do not use ANCOVA to adjust for baseline values in observational studies.\\n6. Dichotomising a continuous variable: a bad idea.\\n7. Student's t test is better than non-parametric tests.\\n8. Do not use Yates\u2019 continuity correction.\\n9. Mean (SD) is also relevant for non-normally distributed data.\\n10. Report estimate, CI and (possibly) p value\u2014in that order of importance.\\n11. Post hoc power calculations\u2014do not do it.\\n12. Do not test for baseline imbalances in a randomised controlled trial.\\n13. Report actual p values with 2 digits, maximum 3 decimals.\\n14. Format for reporting CIs.", "author" : [ { "dropping-particle" : "", "family" : "Lydersen", "given" : "Stian", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Annals of the Rheumatic Diseases", "id" : "ITEM-2", "issue" : "2", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "323-325", "title" : "Statistical review: frequently given comments", "type" : "article-journal", "volume" : "74" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "^25,26", "plainTextFormattedCitation" : "25,26", "previouslyFormattedCitation" : "^25,26" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }25,26, which include 95% confidence intervals. RESULTSSubjectsForty-seven subjects of mean age 48.5 (SD 13.7, range 20 to 76) years had connectome data (Table 1). Less than one-fifth (17%) were hypertensive, none had diabetes, 12.7% were current smokers, and one subject had a history of stroke. One patient had incomplete cognitive data, two did not complete the NART test and three (6%) were being monitored for active NPSLE. Four patients were left-handed.Antiphospholipid statusSeven subjects (14.9%) had a definitive diagnosis of APS, and in each case there were neurological and/or thrombus involvement (stroke, transient isch?mic attack, deep vein thrombosis, primary emboli and severe migraine). Additionally, several other patients without a diagnosis of APS had one or more positive screens for lupus anticoagulant and raised anticardiolipin antibodies, and within these subjects further evidence of neurological involvement (aquaporin 4 antibodies, neurolupus, migraines, epilepsy, anxiety, depression and memory loss) was observed.Structural network connectivity and other variablesThe network metrics are highly correlated among each other (r values 0.54 to 0.99). Table 2 shows associations between network metrics and other variables measured in this patient group. Mean shortest path length displayed relationships inverse to the other network metrics, as expected.Four of the network metrics (mean shortest path length (r = 0.32), global efficiency (r = -0.31), clustering coefficient (r = -0.33) and mean edge weight (r = -0.34)) were correlated with age. All network metrics were inversely associated with disease duration (r values -0.31 to -0.39; mean shortest path length was positively correlated (r = 0.39)). All network metrics (bar network density) were inversely related to WMH volume (r values -0.41 to -0.54; mean shortest path length was positively correlated (r = 0.51)). All network metrics (bar mean edge weight, although even here the correlation coefficient was 0.28) were associated with g. All network metrics (bar density) were associated with SLICC. The two disease activity measures, SLEDAI and BILAG, were not related to network measures.Cognitive ability, SLE systemic damage and network measures globallyThe NART score correlated strongly with g (r = 0.69, P < 0.0001). The network measures density (standardised ? = 0.266, P = 0.025) and strength (standardised ? = 0.317, P = 0.022) were independently related to g in adjusted analyses (Table 3). All network connectivity measures were significantly associated with LD in unadjusted analyses. Strength (standardised ? = -0.330, P = 0.048), mean shortest path length (standardised ? = 0.401, P = 0.020), global efficiency (standardised ? = -0.355, P = 0.041) and clustering coefficient (standardised ? = -0.378, P = 0.030) maintained independent relationships in adjusted analyses (Table 3). Network hubs, cognitive ability, SLE damage and network measures locallyA total of 17 nodes were identified as network hubs (Figure 1). The nodes, as measured by nodal strength, which correlated most strongly with g included the right caudate (r = 0.55), left precentral (r = 0.50), left rostral middlefrontal (r = 0.41), and right lingual (r = 0.41) regions, although none of these were hub nodes (Figure 1). Some nodes had inverse relationships, including the right hippocampus (r = -0.32).As indicated in Figure 1, the general finding was for weaker correlations between nodal strength and LD compared with g. The nodes with the strongest relationships between nodal strength and LD included right superior parietal (r = -0.38), right caudate (r = -0.37), right rostral middlefrontal (r = -0.36), right pericalcarine (r = -0.36), right superior temporal (r = -0.32) right lateral occipital (r = -0.31), and left pericalcarine (r = -0.31) regions. A predilection for the right-side is noted.DISCUSSIONCognitive abilities (g) were related to brain network topology such that poorer levels of segregation (indicated by clustering coefficient) as a marker for sub-network modularity, and integration (indicated by path length) as a marker for the connectedness of the brain, were associated with worse overall contemporaneous cognitive performance. Prior cognitive abilities, age and WMH volume are known to co-associate with current cognitive abilities yet the network measures remained independently related to current cognitive abilities in adjusted analyses that also corrected for antiphospholipid status. The network metrics did not associate with an estimate of prior cognitive ability. In a recent study of 80 patients with schizophreniaADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1017/S1355617715000867", "ISBN" : "1355617715", "ISSN" : "1355-6177", "PMID" : "26888620", "abstract" : "Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants ( N =116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity\u2014connections among high degree \u201crich club\u201d nodes, \u201cfeeder\u201d connections to these rich club nodes, and \u201clocal\u201d connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample ( N =101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. ( JINS , 2016, 22 , 240\u2013249)", "author" : [ { "dropping-particle" : "", "family" : "Yeo", "given" : "Ronald A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ryman", "given" : "Sephira G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Heuvel", "given" : "Martijn P.", "non-dropping-particle" : "van den", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Reus", "given" : "Marcel A.", "non-dropping-particle" : "de", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Jung", "given" : "Rex E.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pommy", "given" : "Jessica", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mayer", "given" : "Andrew R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ehrlich", "given" : "Stefan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Schulz", "given" : "S. 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In our prior analysisADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Wiseman SJ, Bastin ME, Hamilton IF, Hunt D, Ritchie SJ, Amft EN, Thomson S, Belch JFF, Ralston SH", "given" : "Wardlaw JM.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Lupus", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016" ] ] }, "page" : "0-0", "title" : "Fatigue and cognitive function in systemic lupus erythematosus: associations with white matter microstructural damage. A diffusion tensor MRI study and meta-analysis.", "type" : "article-journal", "volume" : "0" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁸", "plainTextFormattedCitation" : "8", "previouslyFormattedCitation" : "⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }8 of the same cohort with quantitative tractography, better cognitive function was associated with lower levels of mean diffusivity as a biomarker for structurally intact white matter, but the relationship was confounded by age and an estimate of prior cognitive ability. Here, the relationship withstood adjustment, suggesting network measures could explain more variance in cognitive abilities than dMRI biomarkers measured in principle fibre tracts alone. Lawrence et al.ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "abstract" : "Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies.", "author" : [ { "dropping-particle" : "", "family" : "Lawrence", "given" : "Andrew J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chung", "given" : "Ai Wern", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Morris", "given" : "Robin G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Markus", "given" : "Hugh S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Barrick", "given" : "Thomas R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issue" : "0", "issued" : { "date-parts" : [ [ "2014" ] ] }, "page" : "1-24", "title" : "Structural network efficiency is associated with cognitive impairment in small vessel disease", "type" : "article-journal", "volume" : "44" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁸", "plainTextFormattedCitation" : "28", "previouslyFormattedCitation" : "²⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }28 similarly found associations with cognition were stronger for network measures than for other conventional dMRI metrics. Recently, an association between global network efficiency and cognitive performance in 436 patients (mean age 65.2 years SD 8.8) with clinically evident SVD was reportedADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1002/hbm.23032", "ISBN" : "1097-0193 (Electronic)\\r1065-9471 (Linking)", "ISSN" : "10970193", "PMID" : "26466741", "abstract" : "Cerebral small vessel disease (SVD), including white matter hyperintensities (WMH), lacunes and microbleeds, and brain atrophy, are related to cognitive impairment. However, these magnetic resonance imaging (MRI) markers for SVD do not account for all the clinical variances observed in subjects with SVD. Here, we investigated the relation between conventional MRI markers for SVD, network efficiency and cognitive performance in 436 nondemented elderly with cerebral SVD. We computed a weighted structural connectivity network from the diffusion tensor imaging and deterministic streamlining. We found that SVD-severity (indicated by higher WMH load, number of lacunes and microbleeds, and lower total brain volume) was related to networks with lower density, connection strengths, and network efficiency, and to lower scores on cognitive performance. In multiple regressions models, network efficiency remained significantly associated with cognitive index and psychomotor speed, independent of MRI markers for SVD and mediated the associations between these markers and cognition. This study provides evidence that network (in)efficiency might drive the association between SVD and cognitive performance. This hightlights the importance of network analysis in our understanding of SVD-related cognitive impairment in addition to conventional MRI markers for SVD and might provide an useful tool as disease marker. Hum Brain Mapp, 2015. \u00a9 2015 Wiley Periodicals, Inc.", "author" : [ { "dropping-particle" : "", "family" : "Tuladhar", "given" : "Anil M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Dijk", "given" : "Ewoud", "non-dropping-particle" : "van", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zwiers", "given" : "Marcel P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Norden", "given" : "Anouk G W", "non-dropping-particle" : "van", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Laat", "given" : "Karlijn F.", "non-dropping-particle" : "de", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shumskaya", "given" : "Elena", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Norris", "given" : "David G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Leeuw", "given" : "Frank Erik", "non-dropping-particle" : "de", "parse-names" : false, "suffix" : "" } ], "container-title" : "Human Brain Mapping", "id" : "ITEM-1", "issue" : "October 2015", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "300-310", "title" : "Structural network connectivity and cognition in cerebral small vessel disease", "type" : "article-journal", "volume" : "310" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁹", "plainTextFormattedCitation" : "29", "previouslyFormattedCitation" : "²⁹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }29. A greater volume of WMH, number of lacunes and microbleeds correlated with reduced network density, strength, and global and local efficiency (correlation coefficients ranging from -0.19 to -0.62). Moreover, path analysis showed that network (in)efficiency might drive the association between SVD and cognitive ability. Another studyADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "abstract" : "Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies.", "author" : [ { "dropping-particle" : "", "family" : "Lawrence", "given" : "Andrew J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chung", "given" : "Ai Wern", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Morris", "given" : "Robin G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Markus", "given" : "Hugh S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Barrick", "given" : "Thomas R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issue" : "0", "issued" : { "date-parts" : [ [ "2014" ] ] }, "page" : "1-24", "title" : "Structural network efficiency is associated with cognitive impairment in small vessel disease", "type" : "article-journal", "volume" : "44" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁸", "plainTextFormattedCitation" : "28", "previouslyFormattedCitation" : "²⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }28 found that 115 patients of mean age 70.2 years (SD 9.7) with symptomatic SVD had reduced network efficiency versus age-matched healthy controls, and that global network efficiency related to worse performance on tests of processing speed, executive functioning, and gait velocity but not memory.The network metrics, bar density, showed an inverse association with WMH volume. Prior studies of older subjects with established clinically-evident SVD (N=436; age ~65 years)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1002/hbm.23032", "ISBN" : "1097-0193 (Electronic)\\r1065-9471 (Linking)", "ISSN" : "10970193", "PMID" : "26466741", "abstract" : "Cerebral small vessel disease (SVD), including white matter hyperintensities (WMH), lacunes and microbleeds, and brain atrophy, are related to cognitive impairment. However, these magnetic resonance imaging (MRI) markers for SVD do not account for all the clinical variances observed in subjects with SVD. Here, we investigated the relation between conventional MRI markers for SVD, network efficiency and cognitive performance in 436 nondemented elderly with cerebral SVD. We computed a weighted structural connectivity network from the diffusion tensor imaging and deterministic streamlining. We found that SVD-severity (indicated by higher WMH load, number of lacunes and microbleeds, and lower total brain volume) was related to networks with lower density, connection strengths, and network efficiency, and to lower scores on cognitive performance. In multiple regressions models, network efficiency remained significantly associated with cognitive index and psychomotor speed, independent of MRI markers for SVD and mediated the associations between these markers and cognition. This study provides evidence that network (in)efficiency might drive the association between SVD and cognitive performance. This hightlights the importance of network analysis in our understanding of SVD-related cognitive impairment in addition to conventional MRI markers for SVD and might provide an useful tool as disease marker. Hum Brain Mapp, 2015. \u00a9 2015 Wiley Periodicals, Inc.", "author" : [ { "dropping-particle" : "", "family" : "Tuladhar", "given" : "Anil M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Dijk", "given" : "Ewoud", "non-dropping-particle" : "van", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zwiers", "given" : "Marcel P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Norden", "given" : "Anouk G W", "non-dropping-particle" : "van", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Laat", "given" : "Karlijn F.", "non-dropping-particle" : "de", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shumskaya", "given" : "Elena", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Norris", "given" : "David G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Leeuw", "given" : "Frank Erik", "non-dropping-particle" : "de", "parse-names" : false, "suffix" : "" } ], "container-title" : "Human Brain Mapping", "id" : "ITEM-1", "issue" : "October 2015", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "300-310", "title" : "Structural network connectivity and cognition in cerebral small vessel disease", "type" : "article-journal", "volume" : "310" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁹", "plainTextFormattedCitation" : "29", "previouslyFormattedCitation" : "²⁹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }29, lacunar stroke (N=115; age ~70 years)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "abstract" : "Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies.", "author" : [ { "dropping-particle" : "", "family" : "Lawrence", "given" : "Andrew J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chung", "given" : "Ai Wern", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Morris", "given" : "Robin G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Markus", "given" : "Hugh S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Barrick", "given" : "Thomas R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issue" : "0", "issued" : { "date-parts" : [ [ "2014" ] ] }, "page" : "1-24", "title" : "Structural network efficiency is associated with cognitive impairment in small vessel disease", "type" : "article-journal", "volume" : "44" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "²⁸", "plainTextFormattedCitation" : "28", "previouslyFormattedCitation" : "²⁸" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }28 and cerebral amyloid angiopathy (N=38; age ~69 years)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1093/brain/awu316", "ISBN" : "0006-8950", "ISSN" : "1460-2156", "PMID" : "25367025", "abstract" : "Cerebral amyloid angiopathy is a common form of small-vessel disease and an important risk factor for cognitive impairment. The mechanisms linking small-vessel disease to cognitive impairment are not well understood. We hypothesized that in patients with cerebral amyloid angiopathy, multiple small spatially distributed lesions affect cognition through disruption of brain con-nectivity. We therefore compared the structural brain network in patients with cerebral amyloid angiopathy to healthy control subjects and examined the relationship between markers of cerebral amyloid angiopathy-related brain injury, network efficiency, and potential clinical consequences. Structural brain networks were reconstructed from diffusion-weighted magnetic resonance imaging in 38 non-demented patients with probable cerebral amyloid angiopathy (69 AE 10 years) and 29 similar aged control participants. The efficiency of the brain network was characterized using graph theory and brain amyloid deposition was quanti-fied by Pittsburgh compound B retention on positron emission tomography imaging. Global efficiency of the brain network was reduced in patients compared to controls (0.187 AE 0.018 and 0.201 AE 0.015, respectively, P 5 0.001). Network disturbances were most pronounced in the occipital, parietal, and posterior temporal lobes. Among patients, lower global network efficiency was related to higher cortical amyloid load (r = \u00c00.52; P = 0.004), and to magnetic resonance imaging markers of small-vessel disease including increased white matter hyperintensity volume (P 5 0.001), lower total brain volume (P = 0.02), and number of micro-bleeds (trend P = 0.06). Lower global network efficiency was also related to worse performance on tests of processing speed (r = 0.58, P 5 0.001), executive functioning (r = 0.54, P = 0.001), gait velocity (r = 0.41, P = 0.02), but not memory. Correlations with cognition were independent of age, sex, education level, and other magnetic resonance imaging markers of small-vessel disease. These findings suggest that reduced structural brain network efficiency might mediate the relationship between advanced cerebral amyloid angiopathy and neurologic dysfunction and that such large-scale brain network measures may represent useful outcome markers for tracking disease progression.", "author" : [ { "dropping-particle" : "", "family" : "Reijmer", "given" : "Yael D", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fotiadis", "given" : "Panagiotis", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Martinez-Ramirez", "given" : "Sergi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Salat", "given" : "David H", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Schultz", "given" : "Aaron", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shoamanesh", "given" : "Ashkan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ayres", "given" : "Alison M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vashkevich", "given" : "Anastasia", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rosas", "given" : "Diana", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Schwab", "given" : "Kristin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Leemans", "given" : "Alexander", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Biessels", "given" : "Geert-Jan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rosand", "given" : "Jonathan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Johnson", "given" : "Keith A", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Viswanathan", "given" : "Anand", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gurol", "given" : "M Edip", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Greenberg", "given" : "Steven M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kistler", "given" : "J P", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Brain", "id" : "ITEM-1", "issue" : "179", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "179-188", "title" : "Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy", "type" : "article-journal", "volume" : "138" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³⁰", "plainTextFormattedCitation" : "30", "previouslyFormattedCitation" : "³⁰" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }30 also found this association. Yet the present cohort are two decades younger and have a lower burden of visible SVD than those with established clinical SVDADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Wiseman SJ, Hamilton IF, Jardin C, Barclay G, Hunt D, Ritchie S, Amft N, Ralston SH, Thomson S, Belch JFF", "given" : "Wardlaw JM.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Stroke", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016" ] ] }, "title" : "Small vessel disease in systemic lupus erythematosus.", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "⁵", "plainTextFormattedCitation" : "5", "previouslyFormattedCitation" : "⁵" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }5, and so the relationship with network structure is noteworthy and could mean SVD-induced damage to the network is an early feature of SLE that accumulates to impact cognition, even in those without neuropsychiatric involvement. Network density is the fraction of present connections to possible connections. It is unclear why density has a weaker and non-significant relationship to WMH volume in our data, although connection weights are excluded from the calculation of density meaning the topology is represented without ‘adjustment’ for water molecule anisotropy which broadly represents the integrity of the connections rather than the number of connections per se.The systemic damage caused since SLE diagnosis (LD) was strongly inversely associated with network metrics in adjusted analyses, which included correcting for the most powerful predictor of damage – age. The SLICC damage index and disease duration also related to LD when analysed separately (results from fully-adjusted linear models not shown). However, there was no relationship between structural network connectivity and current disease activity (SLEDAI or BILAG), a finding which contrasts with an fMRI studyADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1371/journal.pone.0074530", "ISSN" : "19326203", "PMID" : "24069318", "abstract" : "Cerebral involvement is common in patients with systemic Lupus erythematosus (SLE) and is characterized by multiple clinical presentations, including cognitive disorders, headaches, and syncope. Several neuroimaging studies have demonstrated cerebral dysfunction during different tasks among SLE patients; however, there have been few studies designed to characterize network alterations or to identify clinical markers capable of reflecting the cerebral involvement in SLE patients. This study was designed to characterize the profile of the cerebral activation area and the functional connectivity of cognitive function in SLE patients by using a task-based and a resting state functional magnetic resonance imaging (fMRI) technique, and to determine whether or not any clinical biomarkers could serve as an indicator of cerebral involvement in this disease. The well-established cognitive function test (Paced Visual Serial Adding Test [PVSAT]) was used. Thirty SLE patients without neuropsychiatric symptoms and 25 age- and gender-matched healthy controls were examined using PVSAT task-based and resting state fMRI. Outside the scanner, the performance of patients and the healthy controls was similar. In the PVSAT task-based fMRI, patients presented significantly expanded areas of activation, and the activated areas exhibited significantly higher functional connectivity strength in patients in the resting state. A positive correlation existed between individual connectivity strength and disease activity scoring. No correlation with cerebral involvement existed for serum markers, such as C3, C4, and anti-dsDNA. Thus, our findings may shed new light on the pathologic mechanism underlying neuropsychiatric SLE, and suggests that disease activity may be a potential effective biomarker reflecting cerebral involvement in SLE.", "author" : [ { "dropping-particle" : "", "family" : "Hou", "given" : "Jingming", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lin", "given" : "Yun", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhang", "given" : "Wei", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Song", "given" : "Lingheng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wu", "given" : "Wenjing", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wang", "given" : "Jian", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhou", "given" : "Daiquan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zou", "given" : "Qinghua", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fang", "given" : "Yongfei", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "He", "given" : "Mei", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Li", "given" : "Haitao", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "PLoS ONE", "id" : "ITEM-1", "issue" : "9", "issued" : { "date-parts" : [ [ "2013" ] ] }, "page" : "1-9", "title" : "Abnormalities of Frontal-Parietal Resting-State Functional Connectivity Are Related to Disease Activity in Patients with Systemic Lupus Erythematosus", "type" : "article-journal", "volume" : "8" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³¹", "plainTextFormattedCitation" : "31", "previouslyFormattedCitation" : "³¹" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }31 that found functional network connectivity was strongly correlated to SLEDAI score in 30 patients. The lack of association between disease activity and network measures, but association with permanent damage, could reflect the temporal relationship with inflammatory flares (as captured in the activity tools) which do not immediately translate into network damage but instead accumulate longitudinally. Studies that combine structural and functional network connectivity in SLE over time would be informative.In the current work, we also ranked nodes based on connectivity to the rest of the network and designated the top 20% by connectivity as network hubs. Network hubs were broadly similar to those identified in SLE patients by Xu et alADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Xu", "given" : "X", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hui", "given" : "E.S.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mok", "given" : "MY", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Jian", "given" : "J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lau", "given" : "WCS", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mak", "given" : "HKF", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Am J Neuroradiol", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016" ] ] }, "title" : "Structural brain network reorganization in patients with neuropsychiatric systemic lupus erythematosus", "type" : "article-journal" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³²", "plainTextFormattedCitation" : "32", "previouslyFormattedCitation" : "³²" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }32. The relationship between nodes and cognitive ability, and separately nodes and SLE systemic damage, did not have predilection for hub nodes but instead appeared distributed across the network. As with other connectome studies, the spatial scale of tractography and connectomics is several orders of magnitude larger than the underlying architecture of interest, namely axons (MRI voxels are roughly 1 or 2 mm3 versus microns for axon dimensions), such that the metrics here are only estimates of the ‘true’ neural pathwaysADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1227/NEU.0b013e318258e9ff", "ISBN" : "1524-4040 (Electronic)\\r0148-396X (Linking)", "ISSN" : "0148396X", "PMID" : "22705717", "abstract" : "Knowledge of the properties of white matter fiber tracts isa crucial and necessary step toward a precise understanding of the functional architecture of the living human brain. Previously, this knowledge was severely limited, as it was difficult to visualize these structures or measure their functions in vivo. The HCP has recently generated considerable interest because of its potential to explore connectivity and its relationship with genetics and behavior. For neuroscientists and the lay public alike, the ability to assess, measure, and explore this wealth of layered information concerning how the brain is wired is a much sought after prize.The navigation of the human connectome and the discovery of how it is affected through genetics, and in a range of neurological and psychiatric diseases, have far reaching implications. From a range of ongoing connectomics related activities, the systematic characterization of brain connectedness and the resulting functional aspects of such connectivity will not only realize the work of Ram\u00f3n y Cajal and others, but will also greatly expand our understanding of the brain, the mind, and what it is to be truly human. The similarities and differences that mark normal diversity will help us to understand variation among people and set the stage to chart genetic influences on typical brain development and decline during aging. What is more, an understanding of how brains might become disordered will shed light on autism, schizophrenia, Alzheimer\u2019s, and other diseases that exact a tremendous and terrible social and economic toll.", "author" : [ { "dropping-particle" : "", "family" : "Toga", "given" : "Arthur W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Clark", "given" : "Kristi A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thompson", "given" : "Paul M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shattuck", "given" : "David W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Horn", "given" : "John Darrell", "non-dropping-particle" : "Van", "parse-names" : false, "suffix" : "" } ], "container-title" : "Neurosurgery", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2012" ] ] }, "page" : "1-5", "title" : "Mapping the human connectome", "type" : "article-journal", "volume" : "71" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³³", "plainTextFormattedCitation" : "33", "previouslyFormattedCitation" : "³³" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }33. Additionally, the number and choice of nodes needs to be considered carefully as this can affect the connectivity outputADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.neuroimage.2009.12.027", "ISBN" : "1095-9572", "ISSN" : "10538119", "PMID" : "20035887", "abstract" : "Whole-brain anatomical connectivity in living humans can be modeled as a network with diffusion-MRI and tractography. Network nodes are associated with distinct grey-matter regions, while white-matter fiber bundles serve as interconnecting network links. However, the lack of a gold standard for regional parcellation in brain MRI makes the definition of nodes arbitrary, meaning that network nodes are defined using templates employing either random or anatomical parcellation criteria. Consequently, the number of nodes included in networks studied by different authors has varied considerably, from less than 100 up to more than 104. Here, we systematically and quantitatively assess the behavior, structure and topological attributes of whole-brain anatomical networks over a wide range of nodal scales, a variety of grey-matter parcellations as well as different diffusion-MRI acquisition protocols. We show that simple binary decisions about network organization, such as whether small-worldness or scale-freeness is evident, are unaffected by spatial scale, and that the estimates of various organizational parameters (e.g. small-worldness, clustering, path length, and efficiency) are consistent across different parcellation scales at the same resolution (i.e. the same number of nodes). However, these parameters vary considerably as a function of spatial scale; for example small-worldness exhibited a difference of 95% between the widely-used automated anatomical labeling (AAL) template (??? 100 nodes) and a 4000-node random parcellation (??AAL = 1.9 vs. ??4000 = 53.6 ?? 2.2). These findings indicate that any comparison of network parameters across studies must be made with reference to the spatial scale of the nodal parcellation. ?? 2009 Elsevier Inc. All rights reserved.", "author" : [ { "dropping-particle" : "", "family" : "Zalesky", "given" : "Andrew", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fornito", "given" : "Alex", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Harding", "given" : "Ian H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Cocchi", "given" : "Luca", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Y??cel", "given" : "Murat", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Pantelis", "given" : "Christos", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bullmore", "given" : "Edward T.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "NeuroImage", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2010" ] ] }, "page" : "970-983", "publisher" : "Elsevier Inc.", "title" : "Whole-brain anatomical networks: Does the choice of nodes matter?", "type" : "article-journal", "volume" : "50" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³⁴", "plainTextFormattedCitation" : "34", "previouslyFormattedCitation" : "³⁴" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }34 and there is no universally accepted cortical parcellation schemeADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.jneumeth.2010.01.014", "ISBN" : "1872-678X (Electronic)\\n0165-0270 (Linking)", "ISSN" : "01650270", "PMID" : "20096730", "abstract" : "MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches. \u00a9 2010 Elsevier B.V.", "author" : [ { "dropping-particle" : "", "family" : "Hagmann", "given" : "Patric", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Cammoun", "given" : "Leila", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gigandet", "given" : "Xavier", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gerhard", "given" : "Stephan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ellen Grant", "given" : "P.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wedeen", "given" : "Van", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Meuli", "given" : "Reto", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Thiran", "given" : "Jean Philippe", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Honey", "given" : "Christopher J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sporns", "given" : "Olaf", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Neuroscience Methods", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2010" ] ] }, "page" : "34-45", "publisher" : "Elsevier B.V.", "title" : "MR connectomics: Principles and challenges", "type" : "article-journal", "volume" : "194" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "³⁵", "plainTextFormattedCitation" : "35", "previouslyFormattedCitation" : "³⁵" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }35. We are unable to comment on how connectivity might change over time, nor comment on specific domains of cognitive ability such as memory and processing speed. We acknowledge that the cognitive tools used are not as sensitive as a full psychometric battery in detecting cognitive impairments, but they are routinely used as clinical screening tools and were chosen for pragmatism to be delivered within 20 mins. We did not have access to data on dose of currently prescribed steroids, nor estimates of cumulative dosages or treatment duration so cannot comment on how these might affect the connectome metrics. Finally, the lack of a control group is a limitation here, and could be addressed in future studies.The current study, the first to analyse brain structural networks and cognitive abilities in SLE, has shown that network metrics relate to disease duration, SLE-induced damage, WMH volume as a marker of SVD and cognitive abilities in this sample of patients. Patterns of connections derived from the science of connectomics could be used to assess and monitor the brain’s involvement in SLE, including treatment response. Further worthwhile research should assess the connectome-cognition relationship in SLE longitudinally.Acknowledgements:This study was funded by Lupus UK and the University of Edinburgh. We acknowledge support from the Scottish Lupus Exchange registry. Conflicts of Interest / Disclosures:The authors declare no conflict of interest.References 1. Sporns O. Structure and function of complex brain networks. Dialogues Clin Neurosci 2013;15:247–62. doi:10.1137/S003614450342480.2. Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 2010;523:1059–69. doi:10.1016/j.neuroimage.2009.10.003.3. Bullmore ET, Sporns. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186–98. doi:10.1038/nrn2575.4. Sporns O. 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J Neurosci Methods 2010;194:34–45. doi:10.1016/j.jneumeth.2010.01.014.Table 1: Subject characteristics.Demographics N47 Female (%)43/47 (91.5%) Age, years (SD; range) Disease duration, months (Q1 to Q3)48.5 (13.7; 20 to 76)49 (24 to 118) Steroids (currently prescribed)17/47 (36%) Diagnosed neuropsychiatric SLE3/47 (6%)Vascular risk factors Hypertension (%)8/47 (17%) Average systolic blood pressure, mmHg (SD)126 (19.6) Average diastolic blood pressure, mmHg (SD)75 (13.7) Diabetes (%)0 (0%) Current smoker (%)6/47 (12.7%) Total cholesterol, mmol/L (SD)5/47 (0.98) BMI, kg / m2 (SD)28.9 (6.6) History of stroke (%)1/47 (2.1%)Antiphospholipid status Diagnosed APS (%)7/47 (14.9%) Ever positive lupus anticoagulant screen (%)11/47 (23.4%) Anticardiolipin IgG (Q1 to Q3)2.95 (1.97 to 5.32). Reference 0 to 13.3 Anticardiolipin IgM (Q1 to Q3)1.65 (1.12 to 3.22). Reference 0 to 9.8Rheumatology scores SLICC (Q1 to Q3)0 (0 to 1) SLEDAI (Q1 to Q3)2 (0 to 4) BILAG (Q1 to Q3)1 (1 to 9)Cognitive ability MoCA (Q1 to Q3) (n = 46)26.5 (25 to 28). Max 30; normal ≥ 26 ACER (Q1 to Q3) (n = 46)92.0 (88.2 to 94). Max 100; normal ≥ 88 MMSE (Q1 to Q3) (n = 46)28.5 (27 to 30). Max 30; normal ≥ 27 NART (Q1 to Q3) (n = 45)34.0 (27 to 38) Fatigue, anxiety and depression FSS (SD)5.0 (1.7). Reference 2.3 (0.7); p < 0.0001 Anxiety (Q1 to Q3)6.0 (3 to 12). Depression (Q1 to Q3)8.0 (6 to 12). Brain imaging Brain tissue volume, ml (SD)1171 (113) WMH volume, ml (Q1 to Q3)0.8 (0.4 to 1.9) Total SVD score (Q1 to Q3)1 (1 to 1). Possible range 0 to 4Network connectivity measuresSLE Density (SD)30.94 (1.05) Strength (SD)10.75 (0.63) Mean shortest path length (SD)4.10 (0.17) Global efficiency (SD)0.28 (0.01) Clustering coefficient (SD)0.28 (0.01) Mean edge weight (SD)0.41 (0.02)Values are mean (standard deviation), median (Q1 to Q3), or number (%). ACER = Addenbrooke’s Cognitive Examination – Revised, APS = antiphospholipid syndrome, BILAG = British Isle Lupus Assessment Group, BMI = body mass index, FSS = Fatigue Severity Scale, MoCA = Montreal Cognitive Assessment, MMSE = Mini Mental State Examination, NART = National Adult Reading Test, SLEDAI = Systemic Lupus Erythematosus Disease Activity Index, SLICC = Systemic Lupus International Collaborating Clinincs, SVD = small vessel disease, WMH = white matter hyperintensities. Table 2: Relationship between network connectivity and other variables in SLE (N=47).r 95%CIr 95%CIAgeWMH volumeDensity-0.12-0.39 to 0.17-0.12-0.40 to 0.17Strength-0.28-0.52 to 0.00-0.41-0.62 to -0.14 *Mean shortest path 0.32 0.03 to 0.55 0.51 0.26 to 0.69 *Global efficiency-0.31-0.55 to -0.03-0.50-0.69 to -0.25 *Clustering coefficient-0.33-0.56 to -0.04-0.52-0.70 to -0.27 *Mean edge weight-0.34-0.57 to -0.06-0.54-0.72 to -0.30 *g-0.11-0.39 to 0.18 0.00-0.29 to 0.29LD 0.37 0.09 to 0.59 * 0.11-0.18 to 0.38SLICCDisease durationDensity-0.23-0.48 to 0.06-0.35-0.58 to -0.07Strength-0.34-0.57 to -0.06-0.39-0.61 to -0.12 *Mean shortest path 0.39 0.11 to 0.61 * 0.39 0.12 to 0.61 *Global efficiency-0.35-0.58 to -0.07-0.36-0.59 to -0.08 *Clustering coeficient-0.38-0.60 to -0.10 *-0.37-0.59 to -0.09 *Mean edge weight-0.33-0.56 to -0.05-0.31-0.55 to -0.03g-0.34-0.57 to -0.06-0.37-0.60 to -0.09 *LD 0.88 0.80 to 0.93 * 0.88 0.80 to 0.93 *NARTgDensity 0.27-0.02 to 0.52 0.48 0.22 to 0.67 *Strength 0.22-0.08 to 0.48 0.45 0.18 to 0.65 *Mean shortest path-0.14-0.42 to 0.15-0.34-0.57 to -0.06 Global efficiency 0.15-0.15 to 0.42 0.35 0.06 to 0.58Clustering coeficient 0.14-0.16 to 0.41 0.32 0.04 to 0.56Mean edge weight 0.09-0.20 to 0.38 0.28-0.01 to 0.52g 0.69 0.50 to 0.82 * ------LD-0.21-0.48 to 0.08-0.40-0.62 to -0.13 *SLEDAIBILAGDensity-0.13-0.40 to 0.16-0.12-0.39 to 0.17Strength-0.07-0.35 to 0.22-0.06-0.34 to 0.22Mean shortest path 0.04-0.25 to 0.32 0.01-0.29 to 0.28Global efficiency-0.02-0.30 to 0.27-0.01-0.28 to 0.29Clustering coeficient-0.02-0.31 to 0.26-0.03-0.32 to 0.25Mean edge weight 0.01-0.27 to 0.30 0.02-0.26 to 0.31g-0.05-0.33 to 0.24-0.07-0.36 to 0.22LD-0.03-0.32 to 0.25 0.00-0.28 to 0.28Data are Pearson’s correlation coefficients (r). g and LD are composites of three measures of cognitive ability (MoCA + ACER + MMSE) and two measures of SLE damage (disease duration + SLICC), respectively (proportion of shared variance = 70% and 78% respectively). Bold indicates 95%CI does not pass through zero (i.e.P<0.05), however, owing to the large number of comparisons, a threshold of P<0.01 (denoted by *) is also highlighted to support the effect size estimate. ACER = Addenbrooke’s Cognitive Examination – Revised, BILAG = British Isle Lupus Assessment Group, MoCA = Montreal Cognitive Assessment, MMSE = Mini Mental State Examination, NART = National Adult Reading Test, SLEDAI = Systemic Lupus Erythematosus Disease Activity Index, SLICC = Systemic Lupus International Collaborating Clinincs, WMH = white matter hyperintensities. Table 3: Multiple linear regression showing relationship between cognitive abilities (g), SLE systemic damage (LD) and brain network connectivity in SLE (N=47).? SE ? P value? SE ? P valueUnadjustedAdjusted*Relationship to cognitive abilities (g):Density 0.4900.1360.001 0.2660.1140.025Strength 0.4740.1410.001 0.3170.1330.022Mean shortest path-0.3550.1460.019-0.2420.1460.106Global efficiency 0.3640.1470.017 0.2490.1430.090Clustering coefficient 0.3330.1470.028 0.2070.1460.164Mean edge weight 0.2850.1480.062 0.1910.1450.196Relationship to SLE damage (LD):Density-0.3260.1410.025-0.2100.1450.155Strength-0.4140.1360.004-0.3300.1620.048Mean shortest path 0.4440.1330.001 0.4010.1650.020Global efficiency-0.4060.1360.004-0.3550.1680.041Clustering coefficient-0.4230.1350.003-0.3780.1670.030Mean edge weight-0.3650.1390.011-0.3060.1730.084? = standardised beta. * Adjusted for age, WMH volume, steroids, NART, diagnosed APS and ever positive lupus anti-cogaulant. g is a composite (MoCA + ACER + MMSE). LD is a composite (disease duration + SLICC). The relationship with g was also adjusted for disease duration. ACER = Addenbrooke’s Cognitive Examination – Revised, APS = antiphospholipid syndrome, MoCA = Montreal Cognitive Assessment, MMSE = Mini Mental State Examination, NART = National Adult Reading Test, SLICC = Systemic Lupus International Collaborating Clinincs, WMH = white matter hyperintensities. Note: The individual network connectivity measures as predictor variables are not modelled together in one large model due to multicolinearity between connectivity variables, instead each individual row is a separate regression model.Figure 1: The relationship between nodal strength and the composite score for cognitive ability (g) for left and right side of the brain; there is a tendency for positive associations, such that greater cognitive ability correlates with higher nodal strength. Significant correlations are indicated, while blank entries represent non-significant r values. Also shown, the relationship between nodal strength and lupus damage (LD); here there is a tendency for negative associations, such that greater lupus damage correlates with worse connectivity. Graphic is ordered top to bottom by ‘nodal connectivity’ derived from betweeness centrality and degree, with the top 20% of nodes (first 17 nodes listed) designated as network hubs. Deep grey matter structures are also noted. ................
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