On co-activation pattern analysis and non-stationarity of resting brain activity

Abstract The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity. Highlights CAP was conducted for real fMRI data and a stationary null model. The results of CAP analysis were similar for the real and simulated data. Similar results for real and simulated data in both ROI- and voxel-based analyses. Results of CAP analysis may not reflect non-stationarity of resting brain activity.