Neurometabolites and sport-related concussion: From acute injury to one year after medical clearance
Bibliographic record
Abstract
Sport-related concussion is associated with acute disturbances in neurometabolic function, with effects that may last weeks to months after injury. However, is presently unknown whether these disturbances resolve at medical clearance to return to play (RTP) or continue to evolve over longer time intervals. Moreover, little is known about how these neurometabolic changes correlate with other measures of brain physiology. In this study, these gaps were addressed by evaluating ninety-nine (99) university-level athletes, including 33 with sport-related concussion and 66 without recent injury, using multi-parameter magnetic resonance imaging (MRI), which included single-voxel spectroscopy (SVS), diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). The concussed athletes were scanned at the acute phase of injury (27/33 imaged), medical clearance to RTP (25/33 imaged), one month post-RTP (25/33 imaged) and one year post-RTP (13/33 imaged). We measured longitudinal changes in N-acetyl aspartate (NAA) and myo-inositol (Ins), over the course of concussion recovery. Concussed athletes showed no significant abnormalities or longitudinal change in NAA values, whereas Ins was significantly elevated at RTP and one month later. Interestingly, Ins response was attenuated by a prior history of concussion. Subsequent analyses identified significant associations between Ins values, DTI measures of white matter microstructure and fMRI measures of functional connectivity. These associations varied over the course of concussion recovery, suggesting that elevated Ins values at RTP and beyond reflect distinct changes in brain physiology, compared to acute injury. These findings provide novel information about neurometabolic recovery after a sport-related concussion, with evidence of disturbances that persist beyond medical clearance to RTP.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".