￼The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using sam- ples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time win- dows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘carto- graphic profile’ of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differ- ences between CHR and HC groups. Within the CHR group, using integrated FC net- works, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of bio- markers of psychosis risk.
cartographic profile, clinical high-risk for psychosis, network analysis, network based statistics, network integration, task fMRI
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