Effects of Repeated Concussions and Sex on Early Processing of Emotional Facial Expressions as Revealed by Electrophysiology
Bibliographic record
Abstract
OBJECTIVES: Concussions affect the processing of emotional stimuli. This study aimed to investigate how sex interacts with concussion effects on early event-related brain potentials (ERP) measures (P1, N1) of emotional facial expressions (EFE) processing in asymptomatic, multi-concussion athletes during an EFE identification task. METHODS: Forty control athletes (20 females and 20 males) and 43 multi-concussed athletes (22 females and 21 males), recruited more than 3 months after their last concussion, were tested. Participants completed the Beck Depression Inventory II, the Beck Anxiety Inventory, the Post-Concussion Symptom Scale, and an Emotional Facial Expression Identification Task. Pictures of male and female faces expressing neutral, angry, and happy emotions were randomly presented and the emotion depicted had to be identified as fast as possible during EEG acquisition. RESULTS: Relative to controls, concussed athletes of both sex exhibited a significant suppression of P1 amplitude recorded from the dominant right hemisphere while performing the emotional face expression identification task. The present study also highlighted a sex-specific suppression of the N1 component amplitude after concussion which affected male athletes. CONCLUSIONS: These findings suggest that repeated concussions alter the typical pattern of right-hemisphere response dominance to EFE in early stages of EFE processing and that the neurophysiological mechanisms underlying the processing of emotional stimuli are distinctively affected across sex. (JINS, 2018, 24, 673-683).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".