Classifying MCI subtypes in community-dwelling elderly using cross-sectional and longitudinal MRI-based biomarkers

Guan, H; Liu, T; Jiang, J; Tao, D; Zhang, J; Niu, H; Zhu, W; Wang, Y; Cheng, J; Kochan, NA; Brodaty, H; Sachdev, P; Wen, W

Classifying MCI subtypes in community-dwelling elderly using cross-sectional and longitudinal MRI-based biomarkers

Guan, H Liu, T Jiang, J Tao, D

Zhang, J Niu, H Zhu, W Wang, Y Cheng, J Kochan, NA Brodaty, H Sachdev, P Wen, W

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Publication Type:: Journal Article
Citation:: Frontiers in Aging Neuroscience, 2017, 9 (SEP)
Issue Date:: 2017-09-26

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Field	Value	Language
dc.contributor.author	Guan, H	en_US
dc.contributor.author	Liu, T	en_US
dc.contributor.author	Jiang, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Niu, H	en_US
dc.contributor.author	Zhu, W	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Cheng, J	en_US
dc.contributor.author	Kochan, NA	en_US
dc.contributor.author	Brodaty, H	en_US
dc.contributor.author	Sachdev, P	en_US
dc.contributor.author	Wen, W	en_US
dc.date.available	2017-09-12	en_US
dc.date.issued	2017-09-26	en_US
dc.identifier.citation	Frontiers in Aging Neuroscience, 2017, 9 (SEP)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123756
dc.description.abstract	© 2017 Guan, Liu, Jiang, Tao, Zhang, Niu, Zhu, Wang, Cheng, Kochan, Brodaty, Sachdev and Wen. Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73-85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.	en_US
dc.relation.ispartof	Frontiers in Aging Neuroscience	en_US
dc.relation.isbasedon	10.3389/fnagi.2017.00309	en_US
dc.title	Classifying MCI subtypes in community-dwelling elderly using cross-sectional and longitudinal MRI-based biomarkers	en_US
dc.type	Journal Article
utslib.citation.volume	SEP	en_US
utslib.citation.volume	9	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0601 Biochemistry and Cell Biology	en_US
utslib.for	1109 Neurosciences	en_US
utslib.for	1702 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	open_access
pubs.issue	SEP	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2017 Guan, Liu, Jiang, Tao, Zhang, Niu, Zhu, Wang, Cheng, Kochan, Brodaty, Sachdev and Wen. Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73-85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.

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http://hdl.handle.net/10453/123756