Abstract

Despite converging evidence on the involvement of large-scale distributed brain networks in response to stress, the effect of stress on the components of these networks is less clear. Although some studies identify higher regional activities in response to stress, others observe an opposite effect in the similar regions. Studies based on synchronized activities and coactivation of these components also yield similar differing results. However, these differences are not necessarily contradictory once we observe the effect of stress on these functional networks in terms of the change in information processing capacity of their components. In the present study, we investigate the utility of such a shift in the analysis of the effect of stress on distributed cortical regions through quantification of the flow of information among them. For this purpose, we use the self-assessed responses of 216 individuals to stress-related questionnaires and systematically select 20 of them whose responses showed significantly higher and lower susceptibility to stress. We then use these 20 individuals’ resting-state multi-channel electroencephalography (EEG) recordings (both Eyes-Closed (EC) and Eyes-Open (EO) settings) and compute the distributed flow of information among their cortical regions using transfer entropy (TE). The contribution of the present study is three-fold. First, it identifies that the stress-susceptibility is characterized by the change in flow of information in fronto-parietal brain network. Second, it shows that these regions are distributed bi-hemispherically and are sufficient to significantly differentiate between the individuals with high versus low stress-susceptibility. Third, it verifies that the high stress-susceptibility is markedly associated with a higher parietal-to-frontal flow of information. These results provide further evidence for the viewpoint in which the brain’s modulation of information is not necessarily accompanied by the change in its regional activity. They further construe the effect of stress in terms of a disturbance that disrupts the flow of information among the brain’s distributed cortical regions. These observations, in turn, suggest that some of the differences in the previous findings perhaps reflect different aspects of impaired distributed brain information processing in response to stress. From a broader perspective, these results posit the use of TE as a potential diagnostic/prognostic tool in identification of the effect of stress on distributed brain networks that are involved in stress-response.

Highlights

  • Stress is a catalyst for many emotional disorders [1] that affect over 400 million individuals worldwide [2]

  • In the case of EO, we found 21 EEG channels that survived these thresholding steps and that were common between all participants in HIGH stress-susceptible group (AF3, AF4, C1, C2, C6, CP3, CP4, CP5, F1, F2, F6, FC5, FC6, FT7, FT8, Fp2, Fz, O1, P1, P6, PO4)

  • We investigated the potential effect of stress on cortical flow of information using transfer entropy (TE) [46,47,48]

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Summary

Introduction

Stress is a catalyst for many emotional disorders [1] that affect over 400 million individuals worldwide [2]. Viard et al [27], Zhang et al [28], and van Oort et al [29] reported their reduced connectivity These findings on the change in brain (de)activation in response to stress are not necessarily contradictory once one interprets the effect of stress on the brain in terms of its information processing capacity. It identifies that the stress-susceptibility is characterized by the change in flow of information in fronto-parietal brain network It verifies that these distributed fronto-parietal regions that expand bi-hemispherically are sufficient to significantly differentiate between the HIGH and LOW stress-susceptible groups. The present results provide further evidence for the viewpoint in which the brain’s modulation of information is not necessarily accompanied by the change in its regional activity [30] They further construe the effect of stress in terms of a disturbance that disrupts the flow of information among the brain’s distributed cortical regions. This observation becomes more intriguing considering the recent surge in application of machine learning and statistical frameworks to decoding of the brain activity [57,58,59,60]

Methods
Participants
Acquisition
Preprocessing
EEG Channels Inclusion
An Overview of the Participants’ Selection and EEG Inclusion Process
Analysis
Total TEs
Distributed TEs
GLM Analysis of the Channels with Significantly Different Total TEs
Discussion
Limitations and Future
Full Text
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