Abstract
OBJECT: Obsessive-compulsive disorder (OCD) is a mental disease in which people experience uncontrollable and repetitive thoughts or behaviors. Clinical diagnosis of OCD is achieved by using neuropsychological assessment metrics, which are often subjectively affected by psychologists and patients. In this study, we propose a classification model for OCD diagnosis using functional MR images. METHODS: Using functional connectivity (FC) matrices calculated from brain region of interest (ROI) pairs, a novel Riemann Kernel principal component analysis (PCA) model is employed for feature extraction, which preserves the topological information in the FC matrices. Hierarchical features are then fed into an ensemble classifier based on the XGBoost algorithm. Finally, decisive features extracted during classification are used to investigate the brain FC variations between patients with OCD and healthy controls. RESULTS: The proposed algorithm yielded a classification accuracy of 91.8%. Additionally, the well-known cortico-striatal-thalamic-cortical (CSTC) circuit and cerebellum were found as highly related regions with OCD. To further analyze the cerebellar-related function in OCD, we demarcated cerebellum into three subregions according to their anatomical and functional property. Using these three functional cerebellum regions as seeds for brain connectivity computation, statistical results showed that patients with OCD have decreased posterior cerebellar connections. CONCLUSIONS: This study provides a new and efficient method to characterize patients with OCD using resting-state functional MRI. We also provide a new perspective to analyze disease-related features. Despite of CSTC circuit, our model-driven feature analysis reported cerebellum as an OCD-related region. This paper may provide novel insight to the understanding of genetic etiology of OCD.