Neural Indications of Stress-Induced Mental Overload

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Neural Indications of Stress-Induced Mental Overload

Conditions
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Healthy, Stress, Psychological

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Healthy subjects - assessed via the health questionnaire attached as an addition to the protocol Without any known neurological disease Normal or corrected vision All subjects must apply the standard criteria for inclusion and exclusion for a medical MRI scan, according to the MRI safety screening questionnaire of the Wohl MRI institute of the Tel-Aviv Sourasky Medical Center. Exclusion Criteria: Neurological injury or disease Claustrophobia Unremoved metals (according to the MRI safety screening questionnaire)

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 55 Years

Sexes Eligible for Study
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Male

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| cognitive load with/ without stress induction<br>Participants will engage in demanding cognitive load computerized tasks (such as N-back and Stroop tasks). They will engage in these tasks once in a non-stressful (neutral condition), and once in a stressful condition. Psychological stress will be induced by the following methods: Limiting time for task completion Providing negative feedback on participants' performance in relation to others Presentation of sudden, loud sounds during task | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in behavioral and fMRI signal (BOLD) | We anticipate to see changes in relevant brain networks via fMRI (measuring BOLD signal), and performance in a computerized task. | 1 day | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in heart rate and HRV | We expect stress induction to influence heart rate measures | 1 day | 
| Changes in electrodermal activity | We expect both stress and cognitive load to influence the nor-adrenergic system. We expect to measure these effects via electrodermal activity. | 1 day | 
| Changes in Pupil Dilation | We expect both stress and load to influence the nor-adrenergic system. We expect to measure these effects via pupil dilation. | 1 day | 
| Change in within functional connectivity of the sensorimotor network (bold fMRI signal) | Hypothesis: The sensorimotor network (SMN) will show higher within-network cohesion during rest post vs. pre high-stress in comparison to low stress (control) sessions. | day 1 | 
| Change in functional connectivity metrics between sensorimotor network and other resting-state networks (default, salience, central executive) (bold fMRI signal) | Hypothesis: The sensorimotor network (SMN) will show higher between-network cohesion with other rs-networks (salience, default, central executive) during rest post vs rest pre, in high stress vs. low stress (control). | day 1 | 
| 3. Associations between within and between SMN cohesion metrics (outcomes 1-2) with physiological measures and self-report indices (bold fMRI signal, heart rate recordings, self-report questionnaires) | Hypothesis: The sensorimotor network within-network cohesion and between-network cohesion changes (see outcomes 1-2) will be associated with physiological indices (heart rate, heart rate variability) and individual self-report measures (BDI, STAI-T, LSAS, NEO-FFI, subjective stressfulness, unpleasantness and cognitive load reports during scans). | day 1 | 
| Transformers Framework for fMRI analysis | Data-driven approach: The investigators intend to employ TFF (a Transformers Framework for fMRI analysis) on the dataset. Our main objective is to show through a cross-validated training process the ability of TFF to predict stress induction in resting state fMRI scans of individual subjects as a binary classification task where scans that were recorded post stress induction are treated as stressful and scans that were recorded pre-stress induction are treated as non-stressful. Outcome measure/ analysis: We will then use ETFF, which is a complementary explainability pipeline that can be assembled on top of TFF, to further examine the model's decision-making process, and try to characterize the spatial-temporal BOLD signal patterns leading to its decision. | day 1 | 
| Temporal alterations of topological patterns (integration and segregation) | Topological patterns will be assessed with a data-driven method combines graph theory measures and computational tools. This method separates the brain activity into two distinct whole-brain functional states: If stress-induced topological changes will be detected, an additional corroboration step will be executed by applying a machine-learning model for the classification of two brain types-a stressed brain and a non-stressed brain using the previously assessed topological patterns. | day 1 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Stress response, Cognitive Load