Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach

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dc.contributor.author Cui, Y
dc.contributor.author Wen, W
dc.contributor.author Lipnicki, DM
dc.contributor.author Beg, MF
dc.contributor.author Jin, JS
dc.contributor.author Luo, S
dc.contributor.author Zhu, W
dc.contributor.author Kochan, NA
dc.contributor.author Reppermund, S
dc.contributor.author Zhuang, L
dc.contributor.author Raamana, PR
dc.contributor.author Liu, T
dc.contributor.author Trollor, JN
dc.contributor.author Wang, L
dc.contributor.author Brodaty, H
dc.contributor.author Sachdev, PS
dc.date.accessioned 2014-04-03T01:06:09Z
dc.date.issued 2012-01-16
dc.identifier.citation NeuroImage, 2012, 59 (2), pp. 1209 - 1217
dc.identifier.citation NeuroImage, 2012, 59 (2), pp. 1209 - 1217
dc.identifier.issn 1053-8119
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/22355
dc.description.abstract Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a sample of community-dwelling adults aged 70-90. years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS. +. LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09%; sensitivity, 51.96%; specificity, 78.40%; and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of individuals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults. © 2011 Elsevier Inc.
dc.description.abstract Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a sample of community-dwelling adults aged 70-90. years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS. +. LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09%; sensitivity, 51.96%; specificity, 78.40%; and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of individuals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults. © 2011 Elsevier Inc.
dc.language eng
dc.language eng
dc.relation.isbasedon 10.1016/j.neuroimage.2011.08.013
dc.title Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach
dc.type Journal Article
dc.description.version Published
dc.parent NeuroImage
dc.parent NeuroImage
dc.journal.volume 2
dc.journal.volume 59
dc.journal.number 2 en_US
dc.publocation San Diego en_US
dc.identifier.startpage 1209 en_US
dc.identifier.endpage 1217 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 0903 Biomedical Engineering
dc.personcode 118004
dc.percentage 100 en_US
dc.classification.name Biomedical Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Amnestic MCI
dc.description.keywords DTI
dc.description.keywords DTI
dc.description.keywords Early diagnosis
dc.description.keywords Early diagnosis
dc.description.keywords MRI
dc.description.keywords MRI
dc.description.keywords Pattern recognition
dc.description.keywords Subcortical brain structures
dc.description.keywords Subcortical brain structures
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true


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