Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia

有效连接性揭示精神分裂症中不同的脑-小脑网络组织

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Abstract

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 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 model that best fits the full sample, schizophrenia participants were stratified by symptomology according to the positive and negative syndrome scale. RESULTS: Four clusters of cerebellar voxels showed lower ALFF/fALFF in schizophrenia compared to controls. GIMME revealed distinct intra-cerebellar and cerebello-prefrontal effective connectivity patterns in schizophrenia paths that were absent in controls. Cerebellar 13, associated with the control network, emerged as a central hub in schizophrenia. 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 in its directional influence over prefrontal targets. Effective connectivity analyses uncovered cerebellar reorganization that may underlie cognitive and affective deficits in schizophrenia, offering novel targets for circuit-level interventions.

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