Shared and Distinct Dysfunction of Dynamic Connectivity Networks across Schizophrenia, Bipolar Disorder and Major Depression Disorder

Converging evidence indicates that each nominally distinct psychiatric disease entity have both broadly shared and distinct risk genes, clinical symptoms, and brain structural and functional disruption. Many studies used the static (time-averaged) functional connectivity to examine the similarities and differences in functional brain networks in psychiatric disorders. However, little is known about the similarities and differences in dynamic functional connectivity (FC) networks across multiple major psychiatric disorders. A total of 655 participants (125 with schizophrenia (SZ), 121 with bipolar disorder (BD), 192 with major depressive disorder (MDD), and 217 demographically matched healthy controls (HC) completed resting-state functional magnetic resonance imaging at a single site. We used sliding-window approach to construct dynamic FC networks, and used k-means clustering to obtain four dissociated networks by different states. We identified shared and distinct dysconnectivity across these psychiatric disorders compared with HC in each state. We found dysconnectivity in psychosis were state-specific, rather than a time-invariant global abnormality. SZ, BD and MDD shared decreased intra-network FC (especially frontoparietal control network (FPN)), whereas increased inter-network FC (especially between visual network (VN) and both default mode network (DMN) and FPN). Almost all dysconnectivity in BD and MDD were included in those in SZ, with SZ had distinct dysconnectivity that preferentially involving inter-network dysconnectivity between high-level cognitive networks. Many dysconnectivity were not detected by static FC. These findings shed new light on the current transdiagnostic knowledge, and advocate future researches use dynamic methods to study psychiatric disorders, besides use static methods.