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Front Aging Neurosci. 2017 Sep 26;9:309. doi: 10.3389/fnagi.2017.00309. eCollection 2017.

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers.

Guan H1, Liu T1,2,3, Jiang J4,5, Tao D6,7, Zhang J1,2, Niu H1,3, Zhu W4,8, Wang Y8, Cheng J9, Kochan NA4,5, Brodaty H4,10, Sachdev P4,5, Wen W4,5.

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School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China.
Beijing Advanced Innovation Center for Biomedical Engineering, Beijing, China.
Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia.
UBTech Sydney Artificial Intelligence Institute, Faculty of Engineering and Information Technologies, University of Sydney, Darlington, NSW, Australia.
The School of Information Technologies, Faculty of Engineering and Information Technologies, University of Sydney, Darlington, NSW, Australia.
Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
NIBIB, NICHD, National Institutes of Health, Bethesda, MD, United States.
Dementia Collaborative Research Centre, University of New South Wales, Sydney, NSW, Australia.


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.


MRI; biomarker; early diagnosis; feature selection; longitudinal data; machine learning; mild cognitive impairment

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