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

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 defaultmode 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.

Structural Features of the Human Connectome That Facilitate the Switching of Brain Dynamics via Noradrenergic Neuromodulation

“…Neuroimaging recording techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow the characterization of functional connectivity (FC) of the brain, from which the functional integration and segregation can be quantified using network theory tools (Bullmore and Sporns, 2009 ; González et al, 2016 ). The observed patterns of FC reflect the diversity of neuronal dynamics that emerge, among others, from the nonlinear dynamics of brain regions interconnected through structural connectivity (SC) (Deco and Jirsa, 2012 ; Lord et al, 2017 ; Guan et al, 2020 ). FC continuously evolves even in resting conditions (Allen et al, 2014 ; Hansen et al, 2015 ; Cabral et al, 2017 ), moreover, it changes across several tasks, highlighting the flexible nature of brain dynamics (Cohen and D'Esposito, 2016 ; Shine et al, 2016 , 2019 ; Wang et al, 2021 ).…”

Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

“…In spite of this structural connectivity (SC) remaining fixed over short timescales, different patterns of functional connectivity (FC) can be observed during the execution of particular behavioral tasks [ 2 ]. Moreover, functional Magnetic Resonance Imaging (fMRI) neuroimaging studies show that during a resting state the FC is not static, but rather evolves over the recording time [ 8 – 10 ], highlighting the non-linear and non-stationary properties of the FC [ 11 ]. In a similar way, the integration and segregation of brain activity are not static over time [ 3 , 12 ].…”

The backbone network of dynamic functional connectivity

“…An explanation for these results can be the fact that in WBN the global and spatial information of the network are considered in latent parameters of each node because of their dependency on the parameters of every other node in the network, which can result in a more stable null model. Another reason can be the fact that the resting-state time courses of different regions can demonstrate variations in statistical properties ( Guan et al, 2020 ; Gultepe & He, 2013 ). Moreover, stationary linear Gaussian (SLG) models might lack the ability to explain more complex aspects of fMRI dynamics.…”