The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks
Document Type
Article
Publication Date
6-11-2020
Abstract
The linearity and stationarity of fMRI time series need to be understood due to their important roles in the choice of approach 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 the degree of non-linearity (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 network, 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 in different networks demonstrated a decreasing trend in the order default mode, ventral attention, somatomotor, frontoparietal, dorsal attention, visual, and limbic. Furthermore, the ICC values of DS were higher than those of DN. Our results suggest that different functional networks have distinct properties of non-stationarity and non-linearity 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.
Identifier
85087025640 (Scopus)
Publication Title
Frontiers in Neuroscience
External Full Text Location
https://doi.org/10.3389/fnins.2020.00493
e-ISSN
1662453X
ISSN
16624548
Volume
14
Grant
61871420
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Guan, Sihai; Jiang, Runzhou; Bian, Haikuo; Yuan, Jiajin; Xu, Peng; Meng, Chun; and Biswal, Bharat, "The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks" (2020). Faculty Publications. 5226.
https://digitalcommons.njit.edu/fac_pubs/5226
