Edge-Centric Analysis of Time-Varying Functional Connectivity in Schizophrenia Using the COBRE Dataset

Aims Differences in static and dynamic resting-state functional connectivity have been identified patients with schizophrenia, individuals at high risk of psychosis those psychotic-like experiences. Analysis is important to understand the temporal fluctuations connectivity. Studies conducted using methods such as sliding-window technique co-activation patterns (CAPs). In this study edge-centric analysis has used examine differences time-varying schizophrenia compared healthy controls. This method overcomes some limitations other it higher resolution unwraps data without applying additional modelling or requiring user-defined specification parameters. Methods We analysed fMRI from 67 81 controls Center for Biomedical Research Excellence (COBRE) dataset. The edge-time series these subjects was calculated by omitting averaging step when calculating correlation between time each node. effectively unwrapped correlations produced a measure cofluctuation timeframe. edge can be aggregated into single whole brain co-fluctuation root sum square (RSS). frequency amplitude peaks activity seen during troughs. Results results showed that mean peak lower (t-stat= -3.13, p = 0.0021). Patients also had significantly less frequent -2.80, 0.0058).The pattern activation more homogenous control schizophrenia. networks were activated than peaks, troughs transitionary points. Conclusion suggests cofluctuations are amplitude. keeping previous studies which spend states large-scale Further looking larger transdiagnostic samples antipsychotic naïve will build on results.