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Record W7117370860 · doi:10.1016/j.isci.2025.114575

Reduced cortical excitability is associated with head impact severity and cognitive symptoms in adolescent football players

2025· article· en· W7117370860 on OpenAlexafffund
Kevin Yu, Alex I. Wiesman, Elizabeth M. Davenport, Laura A. Flashman, Jillian E. Urban, Srikantan S. Nagarajan, Kiran Solingpuram Sai, Joel D Stitzel, Joseph A. Maldjian, Christopher T. Whitlow

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcGill UniversitySimon Fraser UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchWake Forest School of MedicineNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMagnetoencephalographyNeurophysiologyConcussionCognitionFootballFootball playersElectroencephalographyPoison controlCollege football

Abstract

fetched live from OpenAlex

American tackle football is associated with high rates of concussion, leading to neurophysiological disturbances and debilitating clinical symptoms, but previous investigations have largely ignored aperiodic neurophysiological activity. In this study, we use pre- and post-season resting-state magnetoencephalography (MEG) to examine whether concussion during a season of football is related to changes in neurophysiology and whether any such changes are associated with clinical outcomes. Concussion was associated with increased aperiodic exponents in superior frontal cortices, indicating a relative reduction in cortical excitability, which accounted for effects on raw delta and gamma power and was associated with worse cognitive concerns across participants. Subtler effects of non-concussive head-impact exposure on neurophysiology were similar to those of concussion. These findings indicate that concussion alters the excitability of the cortex, similar to much more subtle effects of non-concussive exposure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.392
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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