Exploring resting-state network dysconnectivity in schizophrenia with single-subject ICA

Objective: Resting-state networks (RSNs) consist of coherent spontaneous activity patterns that support a wide range of sensorimotor and higher-order cognitive functions. In schizophrenia (SZ), RSN alterations reflect disruptions in the brain's functional architecture. Given the heterogeneity of SZ, accurate spatial mapping of RSNs at the individual level is crucial for characterizing altered brain connectivity in a more personalized manner. To achieve this, we used single-subject independent component analysis (ICA) to extract RSNs at the individual level, preserving unique functional patterns and accounting for variability among SZ patients. Methods: We analyzed a resting-state functional magnetic resonance imaging dataset from 74 SZ patients and 74 matched healthy controls (HCs) obtained from the publicly available COINS database. Using single-subject ICA, we extracted 14 distinct RSNs associated with sensory, motor, and higher-order cognitive functions. Voxel-wise statistical comparisons were performed to identify spatial differences between the groups. Results: The SZ group exhibited widespread RSN alterations in regions associated with visual, motor, and cognitive processing. Significant spatial differences were observed within each network, with the most extensive changes occurring in the somatomotor network and three cognitive networks: the cingulo-insular, medial prefrontal, and left frontoparietal networks. Within the default mode network, differences between SZ patients and HC were observed exclusively in visual areas. Conclusions: Single-subject ICA provides a valuable approach for investigating RSN alterations in SZ and enables a detailed, individualized characterization of functional connectivity disruptions. The extensive connectivity alterations in visual, motor, and cognitive networks highlight the complex interplay among these systems in SZ.