Neural Indications of Stress-Induced Mental Overload

Overview

The aim of the current research is to characterize the complex interaction between stress and cognitive workload. Furthermore, the investigators aim to create a functional magnetic resonance imaging (fMRI) inspired Electroencephalogram (EEG) brain-based bio-marker for cognitive load under stress. Secondary project 1 aim: The aim of this study is to characterize the link between sensorimotor network (SMN) within and between functional connectivity following the stress response and its association with physiological indices and self-report measures. Secondary project 2 aim: To elucidate temporal alterations of topological patterns (i.e., integration and segregation), the investigators seek to examine resting state fMRI data before and after a cognitive load task and an acute stress induction.

Research in the past years on stress and its influence on cognitive workload suggests that their relationship is not simply linear. On one hand, stress disrupts the processes of attention, memory, and complicated decision-making. While on the other hand, stress response allows the individual to recognize threats quickly, react accordingly, return the body to homeostasis, and prepare the organism for future challenges. However, it is still unclear why different individuals deal with cognitive workload under stress differently, and which brain mechanisms are underlying these processes. In this current research the use of non-invasive imaging techniques, such as EEG and fMRI, in addition to physiological measurements, such as heart rate, skin conductance, and eye movements, will allow an objective characterization of the individual's response to cognitive workload under stress. Secondary Project 1: Psychological stress has an immense influence on mental and physical homeostasis. Stress reactivity and recovery involve distributed neural activation, but it is unclear which neural mechanism underlies mental and physical associations of stress adversities. One candidate for such a connection is the sensorimotor network (SMN); comprised of S1, M1, the posterior insula, and the ventral posterior thalamus. The most recognized role of the somato-sensorimotor network is the processing of bodily sensory inputs, represented in the sensory homunculus. Despite the clear involvement of body reaction to stress, evidence regarding the involvement of the sensorimotor network in the modulation of the mental stress response is currently lacking. Previous studies found decreased or increased resting state-FC (rsFC) between the Posterior PCC (PCC); a major node in DMN, and two major nodes in the SMN, the posterior insula and thalamus, respectively (Vaisvaser et al., 2013). Additionally, a recent study (Zhang et al., 2020) that applied graph analysis, a method to depict segregation and integration typology of brain networks, found that under lab-induced stress, the SMN exhibited higher between-networks FC, the DMN exhibited enhanced within-FC, and the CEN exhibited decreased within-FC. Moreover, the SMN was found to have a high connection ratio within its own network nodes. These findings demonstrate an enhanced tendency of the SMN to communicate with other functional networks under acute stress. These findings could be framed as a change in network typology under conditions of high demands; assuming higher between networks FC in contrast to states of low demand (Shine, 2019). Nevertheless, there is limited evidence about the change of resting-state functional brain networks following a stressful event. Such an approach will help portray the neural mechanism of reactivity and possibly recovery from stress; a major source of inter-individual differences. We aim to uncover the involvement of the sensorimotor network in response to acute stress and its association with other functional neural networks, physiological stress response, and self-report characteristics. Secondary Project 2: Functional connectivity changes due to a stressogenic experience were thoroughly researched. For the most part, studies have only assessed functional connectivity using the conventional static connectivity approach; thus, neglecting temporal alterations of topological patterns (i.e., integration and segregation) that remained unclear.