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

Cui, Y; Wen, W; Lipnicki, DM; Beg, MF; Jin, JS; Luo, S; Zhu, W; Kochan, NA; Reppermund, S; Zhuang, L; Raamana, PR; Liu, T; Trollor, JN; Wang, L; Brodaty, H; Sachdev, PS

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

Cui, Y Wen, W Lipnicki, DM Beg, MF Jin, JS Luo, S Zhu, W Kochan, NA Reppermund, S Zhuang, L Raamana, PR Liu, T

Trollor, JN Wang, L Brodaty, H Sachdev, PS

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Publication Type:: Journal Article
Citation:: NeuroImage, 2012, 59 (2), pp. 1209 - 1217
Issue Date:: 2012-01-16

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Field	Value	Language
dc.contributor.author	Cui, Y	en_US
dc.contributor.author	Wen, W	en_US
dc.contributor.author	Lipnicki, DM	en_US
dc.contributor.author	Beg, MF	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Zhu, W	en_US
dc.contributor.author	Kochan, NA	en_US
dc.contributor.author	Reppermund, S	en_US
dc.contributor.author	Zhuang, L	en_US
dc.contributor.author	Raamana, PR	en_US
dc.contributor.author	Liu, T https://orcid.org/0000-0002-7783-3073	en_US
dc.contributor.author	Trollor, JN	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Brodaty, H	en_US
dc.contributor.author	Sachdev, PS	en_US
dc.date.available	2011-08-09	en_US
dc.date.issued	2012-01-16	en_US
dc.identifier.citation	NeuroImage, 2012, 59 (2), pp. 1209 - 1217	en_US
dc.identifier.issn	1053-8119	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.	en_US
dc.relation.ispartof	NeuroImage	en_US
dc.relation.isbasedon	10.1016/j.neuroimage.2011.08.013	en_US
dc.subject.classification	Neurology & Neurosurgery	en_US
dc.subject.mesh	Brain	en_US
dc.subject.mesh	Nerve Fibers, Myelinated	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Amnesia	en_US
dc.subject.mesh	Atrophy	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Image Enhancement	en_US
dc.subject.mesh	Geriatric Assessment	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Cognition Disorders	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Aged, 80 and over	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Diffusion Tensor Imaging	en_US
dc.title	Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	59	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	11 Medical and Health Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

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.

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http://hdl.handle.net/10453/22355