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

• Published in: Frontiers in aging neuroscience Vol. 9; p. 309
• Main Authors: Guan, Hao, Liu, Tao, Jiang, Jiyang, Tao, Dacheng, Zhang, Jicong, Niu, Haijun, Zhu, Wanlin, Wang, Yilong, Cheng, Jian, Kochan, Nicole A., Brodaty, Henry, Sachdev, Perminder, Wen, Wei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.09.2017; Frontiers; Frontiers Media S.A
• Subjects: Alzheimer's disease; Amygdala; Artificial intelligence; Atrophy; biomarker; Biomarkers; Brain research; Classification; Cognition & reasoning; Cognitive ability; Computer Science; Cortex; Dementia; early diagnosis; Engineering; Etiology; feature selection; Geriatrics; longitudinal data; Machine learning; Magnetic resonance imaging; Medical Imaging; Medical research; Memory; mild cognitive impairment; MRI; Neuroscience; NMR; Nuclear magnetic resonance; Australia; Beijing China; China; early diagnosis; MRI; mild cognitive impairment; biomarker; machine learning; feature selection; longitudinal data
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2017.00309

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.