Brain Functional Connectivity Scans, Acquired Years Before the Pandemic, Predict COVID-19 Infections in Older Adults: Data From 3,662 Participants

Abdallah, C.

Background: Our behavioral traits, and subsequent actions, could affect the risk of exposure to the coronavirus disease of 2019 (COVID-19). The current study aimed to determine whether unique brain endophenotypes predict the COVID-19 infection risk. Methods: This research was conducted using the UK Biobank Resource. Functional magnetic resonance imaging scans acquired before the COVID-19 pandemic in a cohort of general population older adults (n=3,662) were used to compute the whole-brain functional connectomes. A network-informed machine learning approach was used to identify connectome and nodal fingerprints that predicted positive COVID-19 status during the pandemic up to February 4th, 2021. Results: Brain scans, acquired an average of 3 years before COVID-19 testing, significantly predicted the infection results. The predictive models successfully identified 6 fingerprints that were associated with COVID-19 positive, compared to negative status (all p values < 0.005). Overall, lower integration across the brain modules and increased segregation, as reflected by internal within module connectivity, were associated with higher infection rates. More specifically, COVID-19 infections were predicted by 1) reduced connectivity between the central executive and ventral salience, as well as between the dorsal salience and default mode networks; 2) increased internal connectivity within the default mode, ventral salience, subcortical and sensorimotor networks; and 3) increased connectivity between the ventral salience, subcortical and sensorimotor networks. Conclusion: Individuals are at increased risk of COVID-19 infections if their brain connectome is consistent with reduced connectivity in the top-down attention and executive networks, along with increased internal connectivity in the introspective and instinctive networks.