A frame network study of first-episode schizophrenia, ultra-high risk, and healthy populations.

Abstract

Schizophrenia is a complex neuropsychiatric disorder, and the abnormalities in brain networks during its early stages remain incompletely understood. Previously, we identified a stable high-intensity functional network, termed the "Frame Network," in healthy individuals and observed its aberrations in schizophrenia patients. This study aimed to utilize this network to explore disconnection abnormalities in early-stage schizophrenia. This study compared drug-naïve first-episode schizophrenia patients (FES, n = 83), ultra-high risk of schizophrenia (UHR, n = 65), and matched healthy controls (HC, n = 67). Frame networks were analyzed across groups, and differences were assessed using networks from healthy people (HP) derived from stable connections in two public datasets. Network-Based Statistics (NBS)-predict identified connections for a disease classification model. FES patients were divided into two subtypes, and connections related to negative symptoms were identified using Connectome-based Predictive Modeling (CPM). UHR and FES patients showed increasing abnormalities in frame connections compared to controls. HP and FES frame networks effectively differentiated groups. Connections crucial for classification were found in the prefrontal motor cortex. Patients divided into two subtypes showed distinct pathological presentations. Frame networks predicted negative symptoms effectively. Variations in regions such as the visual and prefrontal cortex were observed based on symptom severity, indicating diverse underlying connection differences in the clinical heterogeneity of schizophrenia. Our findings indicate that Frame Network abnormalities likely play a significant role in early-stage pathological processes of schizophrenia and show promise as biomarkers for disease classification and symptom prognosis.