Spatial Similarity-Aware Learning and Fused Deep Polynomial Network for Detection of Obsessive-Compulsive Disorder

Abstract

Hereditary mental illness (e.g., obsessive-compulsive disorder (OCD)) shall reduce the quality of daily life of patients. To detect OCD objectively, sparse learning is an effective method for constructing a brain functional connectivity network (BFCN) since it can remove redundant information in the data and retain valuable biological characteristics. However, the spatial relationship between adjacent or bilaterally symmetric brain regions in each subject is ignored by most existing methods. To address this limitation, a spatial similarity aware learning is proposed in this work to construct BFCNs. Specifically, a smoothing regularization term is devised to constrain the model via embracing the spatial relationship between brain regions. To further learn the informative feature and reduce feature dimension of a BFCN, we leverage a new fused deep polynomial network (FDPN) framework via stacking a multi-layer deep polynomial network (DPN) model, where a weighting scheme is used to fuse features from different output layers. FDPN can learn the high-level discriminative features of BFCN to reduce the feature dimensionality. By fusing the traditional machine learning and deep learning strategies, our proposed method can achieve promising performance to distinguish OCD and unaffected first-degree relatives (UFDRs) using the imaging data collected in the local hospital. The experimental results demonstrate that our method outperforms the state-of-the-art competing methods.