The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks - Guan, Sihai; Jiang, Runzhou; Bian, Haikuo; Yuan, Jiajin; Xu, Peng; Meng, Chun; Biswal, Bharat (Frontiers in Neuroscience, 2020)
The linearity and stationarity of fMRI time series need be understood due to their important roles in the choice of approaches for brain network analysis. In this paper, we investigated the stationarity and linearity of resting-state fMRI (rs-fMRI) time series data from the Midnight Scan Club datasets. The degree of stationarity (DS) and degree of nonlinearity (DN) were respectively estimated for the time series of all gray matter voxels. The similarity and difference between the DS and DN were assessed in terms of voxels, and intrinsic brain networks including the visual network, somatomotor network, dorsal attention network, ventral attention, limbic network, frontoparietal network, and default mode network. The test-retest scans were utilized to quantify the reliability of DS and DN. We found that DS and DN maps had overlapping spatial distribution. Meanwhile, the probability density estimate function of DS had a long tail and that of DN had a more normal distribution. Specifically, stronger DS was present in the somatomotor, limbic and ventral attention networks compared to other networks, and stronger DN was found in the somatomotor, visual, limbic, ventral attention, and default mode networks. The percentage of overlapping voxels between DS and DN demonstrated a decreasing trend from default mode, ventral attention, somatomotor, frontoparietal, dorsal attention, visual, and limbic networks. Furthermore, the ICC values of DS were higher than those of DN. Our results suggest that different functional networks have distinct nonstationarity and nonlinear properties owing to the complexity of rs-fMRI time series. Thus, caution should be taken when analyzing fMRI data (both resting state and task activation) using simplified models.