Incorporating white matter functional connectivity in schizophrenia detection via Three-Way Cross-Correlation...

In recent years, an increasing body of research supports the "disconnection" hypothesis of schizophrenia, which posits that many of the clinical symptoms of the disorder are strongly associated with abnormal functional connectivity between brain regions, particularly those involving white matter (WM) dysfunction. However, most current schizophrenia detection methods mainly focus on signals or functional connectivity features in gray matter (GM) regions, overlooking functional associations involving WM regions and pathways. To address this gap, we propose a novel feature construction method, called Three-Way Cross-Correlation Coefficient (TW3C), incorporating WM functional connectivity for schizophrenia detection based on resting-state functional magnetic resonance imaging data. This method quantifies joint GM-WM-GM correlation patterns by calculating the cross-correlation of blood oxygen level-dependent (BOLD) signals between paired GM nodes and WM bundles. Experiments on two publicly available datasets, COBRE and UCLA, demonstrate that this method achieves mean cross-validated classification accuracies of 68.85% and 74.36%, respectively. Further analysis identified the bilateral superior longitudinal fasciculus, left cingulum, left posterior limb of the internal capsule, and right anterior limb of the internal capsule as key WM pathways associated with schizophrenia. Twenty-five highly discriminative GM-WM-GM tuples were identified across the two datasets, with recurrent involvement of the thalamus, cerebellum, sensorimotor, and limbic regions. Most tuples showed reduced TW3C values in schizophrenia, indicating altered joint correlation patterns involving GM and WM regions. Our findings indicate that incorporating WM functional signals provides a complementary perspective for schizophrenia classification and may facilitate further investigation of functional abnormalities involving GM and WM regions.