Concussion is a global health concern; however, the neurophysiological underpinnings remain poorly understood, with a lack of clinically relevant, objective brain-based approaches. We investigated the potential of EEG microstate analysis to characterize alterations in brain activity post-concussion. We applied a modified k-means clustering algorithm to multi-channel resting-state EEG data, comparing participants within 2 weeks post-concussion to age- and sex-matched healthy controls. EEG topographical maps were classified into seven clusters, with each cluster represented by canonical microstates (A–G). Average duration, occurrence rate, and time coverage for each microstate were extracted and compared between groups. Multiple correlation analyses were performed between microstate measures and symptom severity within the concussed group. The concussed group showed a statistically significant reduction in the duration of microstate E and the duration, occurrence, and time coverage of microstate G compared to controls. The occurrence rate and time coverage of microstate C were significantly higher in the concussed group. There was a positive correlation between symptom severity and the duration and occurrence of microstate E; however, this did not reach statistical significance. Together, these findings indicate that acute concussion disrupts the dynamic interactions of large-scale brain networks and suggest that EEG microstate analysis applied to resting-state data may provide a potential biomarker of injury.
Sattari et al. (Mon,) studied this question.