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Abstract
Using data from a longitudinal viral challenge study, we find that the post-exposure viral shedding and symptom severity are associated with a novel measure of pre-exposure cognitive performance variability (CPV), defined before viral exposure occurs. Each individual’s CPV score is computed from data collected from a repeated NeuroCognitive Performance Test (NCPT) over a 3 day pre-exposure period. Of the 18 NCPT measures reported by the tests, 6 contribute materially to the CPV score, prospectively differentiating the high from the low shedders. Among these 6 are the 4 clinical measures digSym-time, digSym-correct, trail-time, and reaction-time, commonly used for assessing cognitive executive functioning. CPV is found to be correlated with stress and also with several genes previously reported to be associated with cognitive development and dysfunction. A perturbation study over the number and timing of NCPT sessions indicates that as few as 5 sessions is sufficient to maintain high association between the CPV score and viral shedding, as long as the timing of these sessions is balanced over the three pre-exposure days. Our results suggest that variations in cognitive function are closely related to immunity and susceptibility to severe infection. Further studying these relationships may help us better understand the links between neurocognitive and neuroimmune systems which is timely in this COVID-19 pandemic era.
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Details
1 University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, USA (GRID:grid.214458.e) (ISNI:0000000086837370)
2 Duke University School of Medicine, Departments of Psychiatry and Medicine, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961)
3 Duke University Medical Center, Duke Center for Applied Genomics and Precision Medicine, Durham, USA (GRID:grid.189509.c) (ISNI:0000000100241216)
4 University of Virginia School of Medicine, Department of Pediatrics, Charlottesville, USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X)
5 National Institutes of Health, All of Us Research Program, Bethesda, USA (GRID:grid.94365.3d) (ISNI:0000 0001 2297 5165)
6 University of Michigan, Department of Electrical Engineering and Computer Science, Department of Biomedical Engineering, and Department of Statistics, Ann Arbor, USA (GRID:grid.214458.e) (ISNI:0000000086837370)