Background: Psychosis and depression patients exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC), allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders.
Methods: In the present study, we report the analysis of dFC in a large patient sample including 127 clinical high-risk patients (CHR), 142 recent-onset psychosis (ROP) patients, 134 recent-onset depression (ROD) patients, and 256 healthy controls (HC). A sliding window-based technique was used to calculate the time-dependent FC in resting-state MRI data, followed by clustering to reveal recurrent FC states in each diagnostic group.
Results: We identified five unique FC states, which could be identified in all groups with high consistency (rmean = 0.889, sd = 0.116). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly-connected FC state in ROD patients (p < 0.0005) compared to most other groups, and a common increase in the lifetime of a FC state characterised by high sensorimotor and cingulo-opercular connectivities in all patient groups compared to the HC group (p < 0.0002). Canonical correlation analysis revealed a mode which exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < 0.0029), which was associated with positive psychosis symptom severity and several dFC parameters.
Conclusions: Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression, psychosis and clinical risk states.
Copyright © 2024. Published by Elsevier Inc.