The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks.

Jiajin Yuan,Sihai Guan,Haikuo Bian,Peng Xu,Runzhou Jiang,Bharat Biswal,Chun Meng

doi:10.3389/fnins.2020.00493

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.

Highlights

Functional magnetic resonance imaging has become an important method for investigating system-level brain activity (Biswal et al, 1995, 2010; He, 2013; Gordon et al, 2017; Gratton et al, 2018)
As displayed by three slice maps in terms of voxels across nine subjects and their 10 test-retest sessions, we found that the resting-state brain had varied degree of stationarity (DS) and degree of non-linearity (DN) values in different regions (Figures 2A–E)
We investigated the degree of stationarity (DS) and the degree of non-linearity (DN) of resting-state Functional magnetic resonance imaging (fMRI) (rs-fMRI) time series of all gray matter voxels and intrinsic brain networks from the Midnight Scan Club datasets

Summary

Introduction

Functional magnetic resonance imaging (fMRI) has become an important method for investigating system-level brain activity (Biswal et al, 1995, 2010; He, 2013; Gordon et al, 2017; Gratton et al, 2018). Stationarity, Non-stationarity and Non-linearity of Resting-State Brain in general, implies that the statistic or model parameter of interest does not change over time (Smith et al, 2012, 2013; Liu and Duyn, 2013; Allen et al, 2014; Shine et al, 2016; Suk et al, 2016; Yaesoubi et al, 2018). Since resting-state fMRI (rs-fMRI) is a powerful tool for studying human functional brain networks, it is necessary to understand stationarity in the rs-fMRI time series. Ou et al, used a Bayesian connectivity change point model to statistically investigate rsfMRI signals and found that it could differentiate the temporal dynamics of functional interactions between children with attention deficit hyperactivity disorder and matched controls (Ou et al, 2014). Bullmore et al, provided a review of wavelet methods used for the analysis of potentially nonstationary fMRI time-series signals (Bullmore et al, 2004)

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Conclusion