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Altered resting state EEG microstate dynamics in acute-phase pediatric mild traumatic brain injury.

Sattari, S.; Damji, S.; McLeod, J.; Mirian, M. M.; Wu, L.; Virji-Babul, N.

2024-10-27 neurology
10.1101/2024.10.26.24316185 medRxiv
Show abstract

ObjectiveSport-related concussion presents significant diagnostic and monitoring challenges, especially in youth populations. This study investigates the potential of EEG microstate analysis as a tool for assessing acute-phase brain activity changes in adolescent male athletes following a concussion. We analyzed resting-state EEG data from 32 participants in a between-subjects design, comparing participants with acute concussion (within two weeks of injury) to an age- and sex-matched sample with no reported history of concussion. MethodologyWe applied a modified k-means clustering algorithm to group resting-state EEG topographical maps into seven clusters, with each cluster represented by one of the canonical microstate classes (A-G). Average duration, occurrence rate, and time coverage for each microstate were extracted. ResultsStatistically significant differences in mean duration, occurrence rate, and time coverage of microstates B and E were observed. Specifically, the mean duration, occurrence and time coverage of microstate E showed a significant decrease in the concussed cohort in comparision to the controls (p < 0.001). In addition, the mean duration, occurrence rate and time coverage was higher in the concussed cohort in comparision with the healthy cohort (p = 0.003). A significant negative linear relationship was found between microstate E and symptom severity (p = 0.006, F = 15.72). DiscussionThese results suggest that mild traumatic brain injury may disrupt the dynamic interaction of large-scale brain networks, hinting at potential biomarkers of injury. This study may help to inform future work on objective, brain-based tools for diagnosis and recovery assessment in concussed adolescents. Further research in larger, more diverse populations is necessary to validate these potential biomarkers.

Published in Scientific Reports (predicted rank #7) · training set

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