HCC: Small: Investigating the temporal dynamics of resilience during human-computer interaction: an EEG-fNIRS study

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Many people spend part of the day in stressful conditions, which are known in general to be a major contributor to burnout, diminished productivity, and numerous other health risks, whereas some people (including for example air traffic controllers, soldiers, and first responders) work in high stress environments seemingly with no ill effects. Why certain individuals perform better under high stress than others with similar training remains unexplored. Resilience, the ability to maintain performance during stressful events, is a crucial attribute of healthy individuals who work in such environments. Because resilience has important implications for human well-being and task performance, there is a need to identify healthy individuals who are vulnerable to stress-related performance decline, preferably prior to actual performance under stress, to facilitate delivery of focused intervention that might help prevent the development of clinical stress disorders that pose a tremendous burden both for the individual and for society in general. This project will fundamentally advance our understanding of stress resilience by addressing the limitations of existing stress resilience measures and identifying potential biomarkers of stress resilience. Project outcomes will contribute to the assessment of the capabilities of healthy individuals who must maintain performance in the face of escalating cognitive demands such as emergency room personnel and first responders, prediction of their expected variation over time, and applicant suitability of recruitment for occupations within challenging operating environments.The objective of this research is to identify neural signatures, and to develop predictive models of task performance using neurophysiological features of dynamic resilience. This work will utilize hybrid EEG-fNIRS and community detection techniques to track brain organization during human-computer interaction under stress. Combining both EEG and fNIRS has great potential to provide deeper insight into stress resilience than either modality could alone. The dynamic functional connectivity approach employed in this study will likely provide insight into neural processes underlying resilience, while the dynamic network measures identified may serve as potential biomarkers for stress resilience. This project will not only enhance scientific knowledge on the relationship between temporal dynamics of resilience and neural adaptations but will also provide novel dynamic resilience biomarkers that may be used to determine the limits of human performance in high-stakes occupational settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.