Neurometabolic Changes in the Acute Phase after Sports Concussions Correlate with Symptom Severity
Bibliographic record
Abstract
Sports concussion is a major problem that affects thousands of people in North America every year. Despite negative neuroimaging findings, many athletes display neurophysiological alterations and post-concussion symptoms such as headaches and sensitivity to light and noise. It is suspected that neurometabolic changes may underlie these changes. In this study we investigated the effects of sports concussion on brain metabolism using (1)H-MR spectroscopy by comparing a group of 12 non-concussed athletes with a group of 12 concussed athletes of the same age (mean 22.5 years) and education (mean 16 years). All athletes were scanned 1-6 days post-concussion in a 3T Siemens MRI, and were administered a symptom scale to evaluate post-concussion symptomatology. Participants also completed a neuropsychological test battery to assess verbal memory, visual memory, information processing speed, and reaction time, and no group differences were detected relative to controls. Concussed athletes showed a higher number of symptoms than non-concussed athletes, and they also showed a significant decrease in glutamate in the primary motor cortex (M1), as well as significant decreases in N-acetylaspartate in the prefrontal and primary motor cortices. No changes were observed in the hippocampus. Furthermore, the metabolic changes in M1 correlated with self-reported symptom severity despite equivalent neuropsychological performance. These results confirm cortical neurometabolic changes in the acute post-concussion phase, and demonstrate for the first time a correlation between subjective self-reported symptoms and objective physical changes that may be related to increased vulnerability of the concussed brain.
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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 | 0.001 | 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".