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