Scale‐free functional brain dynamics during recovery from sport‐related concussion
Bibliographic record
Abstract
Abstract Studies using blood‐oxygenation‐level‐dependent functional magnetic resonance imaging (BOLD fMRI) have characterized how the resting brain is affected by concussion. The literature to date, however, has largely focused on measuring changes in the spatial organization of functional brain networks. In the present study, changes in the temporal dynamics of BOLD signals are examined throughout concussion recovery using scaling (or fractal) analysis. Imaging data were collected for 228 university‐level athletes, 61 with concussion and 167 athletic controls. Concussed athletes were scanned at the acute phase of injury (1–7 days postinjury), the subacute phase (8–14 days postinjury), medical clearance to return to sport (RTS), 1 month post‐RTS and 1 year post‐RTS. The wavelet leader multifractal approach was used to assess scaling ( c1) and multifractal ( c2) behavior. Significant longitudinal changes were identified for c1, which was lowest at acute injury, became significantly elevated at RTS, and returned near control levels by 1 year post‐RTS. No longitudinal changes were identified for c2. Secondary analyses showed that clinical measures of acute symptom severity and time to RTP were related to longitudinal changes in c1. Athletes with both higher symptoms and prolonged recovery had elevated c1 values at RTS, while athletes with higher symptoms but rapid recovery had reduced c1 at acute injury. This study provides the first evidence for long‐term recovery of BOLD scale‐free brain dynamics after a concussion.
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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 | 0.000 | 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) | 0.001 | 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".