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- Title
Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers.
- Authors
Hao Guan; Tao Liu; Jiyang Jiang; Dacheng Tao; Jicong Zhang; Haijun Niu; Wanlin Zhu; Yilong Wang; Jian Cheng; Kochan, Nicole A.; Brodaty, Henry; Sachdev, Perminder; Wei Wen
- Abstract
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.
- Subjects
MILD cognitive impairment; COGNITION disorders; OLDER people; MAGNETIC resonance imaging; DIAGNOSTIC imaging; MACHINE learning; DEMENTIA
- Publication
Frontiers in Aging Neuroscience, 2017, p1
- ISSN
1663-4365
- Publication type
Article
- DOI
10.3389/fnagi.2017.00309