| Description: |
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. |