GROUP-WISE SPARSE REPRESENTATION OF RESTING-STATE FMRI DATA FOR BETTER UNDERSTANDING OF SCHIZOPHRENIA

Resting-state functional magnetic resonance imaging (rsfMRI) has become a powerful technique for analyzing cognitive function and its disruption in mental diseases, including schizophrenia. It has played an important role in analyzing and diagnosing mental diseases. In this paper, we present a novel machine learning approach called group-wise sparse representation of rs-fMRI signals to find differences between schizophrenia patients and healthy controls. Firstly, we extract the fMRI signals from all subjects that are registered into the MNI atlas space in the pre-processing step to build a large input signal matrix. Secondly, we use online dictionary learning and sparse coding methods to derive the coefficient matrix. Thirdly, we use two-sample t-test to analyze the regions of increased and decreased activity in schizophrenia patients compared to healthy controls. Finally, by using the AAL atlas the distributions of the detected regions are obtained. We test our approach on the COBRE dataset and the experimental results show that the schizophrenia patients have increased activities mainly at the fronto-parietal network. All other networks excluding the fronto-parietal network show decreased phenomenon. These results provide novel insights into better understanding of schizophrenia.