Abstract
A growing body of research suggests that impacts to the head, including sub-concussive impacts, carry risks for long-term detrimental effects on cognition and brain health. Despite the potential for negative health consequences associated with sub-concussive impacts, there is currently no reliable and objective method used in clinical practice to assess whether a particular sub-concussive impact affected the brain. In this preliminary study, we developed a machine-learning classifier to detect changes in brain electrophysiological activity following sub-concussive impacts that occur during soccer ball heading. We recorded EEG from soccer players before and after they repeatedly headed a soccer ball, and trained classifiers to distinguish between an individual's EEG patterns before and after these sub-concussive impacts. The classifiers were able to identify post-impact EEG recordings with significantly higher accuracy than would be expected by chance, both 1 h and 24 h after the impacts occurred. After controlling for electrophysiological changes attributed to exercise, changes to brain activity attributable to soccer heading were detectable at 24 h post-heading, but not at 1-h post-heading. The observed time-course of EEG changes mirrors a similar pattern seen in traumatic brain injury, in which an inflammatory cascade is manifest 24 to 48-h post-injury; we suggest that EEG changes following sub-concussive impacts may stem from inflammation or some other physiological process that unfolds on a similar timescale. These results are an important step toward developing an EEG-based tool that can assess whether electrophysiological consequences are present following sub-concussive head impacts.
Authors
Brookshire, G., Pennati, A., Yoder, K. J., Tweardy, M., Quirk, C., Perkins, M., Gerrol, S., Raethel, S., Nikjou, D., Nikolova, S., Leonard, M., Crepeau, A., Dodick, D. W., Schwedt, T. J., & Lucero, C.
https://doi.org/10.3389/fneur.2025.1500796