Diagnosis of OCD using functional connectome and Riemann kernel PCA.

Obsessive-compulsive disorder (OCD) is a mental disorder characterized by repeated thoughts or behaviors, which is also associated with anxiety and tics. Clinically, the diagnosis of OCD mainly depends on subjects symptoms and psychological rating scales. In this study, we proposed an imaging based diagnosis method using functional MRI to classify OCD patients and healthy controls, with a novel log Euclidean based kernel Principal Component Analysis (PCA) as feature extractor. In particular, functional connectivity (FC) matrix was computed for each subject as the FC correlations of each pair of brain regions of interest. To better reduce feature dimension and extract the most discriminative features, we propose to use log Euclidean geodesic distance as the distance of two matrices and apply a Gaussian kernel PCA to FC matrix for feature extraction, given the graph Laplacian matrix of a FC matrix is symmetric positive define (SPD) matrix and the set of SPD matrix forms a Riemannian manifold. We further employed gradient boosted decision trees (XGBoost) to classify the features extracted from log Euclidean based kernel PCA to diagnosis patient groups. Results show that the classification accuracy reaches 91.8% with 90.7% sensitivity and 92.6% specificity, which outperforms current start-of-the-art imaging based diagnosis methods such as 85% in an EEG study. Next, by evaluating the feature importance in the classifier, we found that most contributed connections are cerebellum related, such as cerebellar vermis. These findings may help the understanding of pathology of OCD and provide a surrogate means for clinical diagnosis.