Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia

Introduction functional impairments in schizophrenia may arise from disruptions in large-scale brain networks. Emerging evidence highlights the cerebellum's role in cognitive and affective regulations, yet its directional influence remains poorly understood. This study examines effective connectivity (EC) within cortico-striato-cerebellar networks in schizophrenia and healthy adults. Methods resting-state functional magnetic resonance imaging data from the Centers of Biomedical Research Excellence (COBRE), including people with schizophrenia and healthy controls (n = 134), were used to analyze intrinsic activity and effective connectivity. Cerebellar clusters showing reduced amplitude of low frequency fluctuations (ALFF) and fractional ALFF in schizophrenia were mapped to the Buckner 17-network atlas to define regions of interest (ROIs). Along with prefrontal and striatal ROIs defined a priori, these served as nodes in Group Iterative Multiple Model Estimation (GIMME). In addition to the best-fitting model of the full sample, schizophrenia participants were stratified by symptomology according to the positive and negative syndrome scale. Results four cerebellar voxel clusters in the posterolateral and anteromedial regions showed lower ALFF/fALFF in schizophrenia compared to controls. GIMME revealed distinct intra-cerebellar and cerebello-prefrontal EC patterns in schizophrenia that were absent in controls, including novel directed paths involving the cerebellar control network representation, consistent with its emergence as a central hub. These patterns persisted across symptom-defined subgroups, suggesting core network reorganization. Conclusions findings support the cerebellum's involvement in disrupted network dynamics in schizophrenia, particularly its directional influence over prefrontal targets. EC analyses uncovered cerebellar reorganization that may underlie affective deficits in schizophrenia, offering novel targets for circuit-level interventions.