Decoding brain functional connectivity implicated in AD and MCI

Abstract

Deep neural networks have been demonstrated to extract high level features from neuroimaging data when classifying brain states. Identifying salient features characterizing brain states further refines the focus of clinicians and allows design of better diagnostic systems. We demonstrate this while performing classification of resting-state functional magnetic resonance imaging (fMRI) scans of patients suffering from Alzheimers Disease (AD) and Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI). We use a 5-layer feed-forward deep neural network (DNN) to derive relevance scores of input features and show that an empirically selected subset of features improves accuracy scores for patient classification. The common distinctive salient brain regions were in the uncus and medial temporal lobe which closely correspond with previous studies. The proposed methods have cross-modal applications with several neuropsychiatric disorders.