Resting-state causal brain connectivity in youth female athletes suggest sex-related differences following subacute concussion
Bibliographic record
Abstract
Abstract Objective Youth male athletes show changes in resting-state causal brain connectivity following subacute concussion; however, little is known about how concussion alters causal brain connectivity in female youth. In this study, we compared resting-state causal brain connectivity in healthy and subconcussed females. Materials and methods Data from 11 concussed and 15 healthy control female athletes were included in this study. Five minutes of resting state eyes-closed EEG data were collected from all participants. SCAT5 data were also collected from all concussed participants. Causal connectivity was calculated from EEG source data. Network topology was evaluated using the degree assortativity coefficient, a summary statistic describing network structure of information flow between source locations. Results We observed three main results: 1) a qualitative difference in the spatial pattern of the most active connections, marked by posterior connectivity shifting in the concussed group, 2) an increase in the magnitude of connectivity in the concussed group, and 3) no significant difference in degree assortativity between the concussed and control groups. Conclusion Causal connectivity changes following concussion in females do not follow the same trends reported in males. These findings suggest a potential sex difference in injury response and may have implications for recovery.
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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) | 0.004 | 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".