Post-Concussion Changes in the Functional Brain Connectome Relative to Pre-Injury Baseline: A Prospective Observational Study
Bibliographic record
Abstract
There is growing concern about the long-term health consequences of concussion, stemming from its high incidence and evidence of post-injury sequelae. This has raised critical questions about clinical assessments of concussion recovery, and whether brain function has fully recovered at medical clearance. The major knowledge gap is only partly addressed by conventional cross-sectional neuroimaging studies, due to a lack of pre-injury baseline imaging. To address this gap, 187 university-level athletes had resting-state functional magnetic resonance imaging collected at pre-season baseline. Of this cohort, 25 were later concussed, with imaging at early symptomatic injury (SYM), medical clearance to return to play (RTP), and 1–3 months post-RTP (POST). An additional 27 uninjured athletes were reimaged as controls. Brain maps were parcellated, and functional connectivity was measured between regions. Mixed models assessed connectivity change at each post-concussion session, along with the moderating effect of time to medical clearance. Concussed athletes had a significantly altered connectome, with predominantly reduced frontotemporal connectivity. Effects were most extensive at SYM, with diminishing but significant effects at RTP and POST, all of which exceeded uninjured control variability (all z ≤ −3.71, p ≤ 0.001). For participants with a longer time to medical clearance, more extensive connectivity decreases were also seen at all post-concussion imaging sessions. These findings provide direct evidence that functional brain recovery lags beyond medical clearance, with more pronounced effects among individuals who have prolonged clinical recovery. Such prospective analyses provide a unique window into biological recovery processes, with major implications for the clinical management of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".