Reduced Cortical Excitability is Associated with Cognitive Symptoms in Concussed Adolescent Football Players
Bibliographic record
Abstract
Background: American tackle football is associated with high rates of concussion, leading to neurophysiological disturbances and debilitating clinical symptoms. Previous investigations of the neurophysiological effects of concussion have largely ignored aperiodic neurophysiological activity, which is a marker of cortical excitability. Purpose: We examined whether concussion during a season of high school football is related to changes in aperiodic and periodic neurophysiological activity and whether any such changes are associated with clinical outcomes. Materials and Methods: Pre- and post-season resting-state magnetoencephalography (MEG) data were collected from 91 high school football players over as many as four seasons of play, for a total of 278 data collections. During these seasons of football play, a cohort of 10 individuals were diagnosed with concussion. MEG data were source-imaged, frequency-transformed and parameterized, and linear mixed models were used to examine effects of concussion on pre-to-post-season changes in neurophysiological activity. Scores on the Post-Concussive Symptom Inventory were correlated with pre-to-post-season neurophysiological changes to determine their clinical relevance. Results: Concussion was associated with increased aperiodic exponents in superior frontal cortices, indicating a relative reduction in cortical excitability. This slowing of aperiodic neurophysiology mediated concussion effects on raw delta and gamma power and was associated with worse cognitive concerns across participants. Pre-to-post-season changes in aperiodic-corrected alpha and theta rhythmic activity were also decreased in posterior cortices in concussed players. Conclusion: These findings indicate that concussion alters both the excitability and rhythmic signaling of the cortex, with differing spatial topographies and implications for clinical symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".