Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis

• Published in: Frontiers in aging neuroscience Vol. 10; p. 417
• Main Authors: Qiao, Jianping, Lv, Yingru, Cao, Chongfeng, Wang, Zhishun, Li, Anning
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 17.12.2018; Frontiers Media S.A
• Subjects: Alzheimer's disease; Automation; Biomarkers; Brain; Brain mapping; brain network; Causality; Classification; Cognitive ability; Deep learning; Electroencephalography; Functional magnetic resonance imaging; granger causality; independent component analysis; Learning algorithms; Medical imaging; Mental disorders; Methods; Neural networks; Neurodegenerative diseases; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Older people; Pattern recognition; China; deep learning; independent component analysis; granger causality; Alzheimer’s disease; brain network
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2018.00417

Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer's disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.