Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis - Qiao, Jianping; Lv, Yingru; Cao, Chongfeng; Wang, Zhishun; Li, Anning (Frontiers in Aging Neuroscience, 2018)
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 extracted features by seed-based correlation which not only needed prior information but also ignored the relationship between voxels of the resting state functional magnetic resonance imaging (fMRI). 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 component analysis (3LHPM-ICA) approach was proposed firstly in order to address the issues in spatial individual independent component analysis including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of independent components (ICs) of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity features. Secondly, 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 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. The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the powerful method of distinguishing AD clinical data from normal controls, 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.