Magnetic Resonance Spectroscopy of Traumatic Brain Injury and Subconcussive Hits: A Systematic Review and Meta–Analysis
Bibliographic record
Abstract
Magnetic resonance spectroscopy (MRS) is a non-invasive technique used to study metabolites in the brain. MRS findings in traumatic brain injury (TBI) and subconcussive hit literature have been mixed. The most common observation is a decrease in N -acetyl-aspartate (NAA), traditionally considered a marker of neuronal integrity. Other metabolites, however, such as creatine (Cr), choline (Cho), glutamate+glutamine (Glx) and myo -inositol (mI) have shown inconsistent changes in these populations. The objective of this systematic review and meta-analysis was to synthesize MRS literature in brain injury and explore factors (biological factors such as brain region, injury severity, time since injury, demographics and technical methodological factors such as field strength, acquisition parameters, analysis approach) that may contribute to differential findings. One hundred and thirty-eight studies met inclusion criteria for the systematic review and of those, 62 NAA, 24 Cr, 49 Cho, 18 Glx, and 21 mI studies met inclusion criteria for meta-analysis. A random effects model was used for meta-analyses with brain region as a subgroup for each of the five metabolites studied. Meta-regression was used to examine the influence of potential moderators including injury severity, time since injury, age, sex, tissue composition, and methodological factors. In this analysis of 1428 unique brain-injured subjects and 1132 controls, the corpus callosum was identified as a brain region highly susceptible to metabolite alteration. NAA was consistently decreased in TBI of all severities, but not in subconcussive hits. Cho and mI were found to be increased in moderate-to-severe TBI but not in mild TBI. Glx and Cr were largely unaffected, but did show alterations in certain conditions.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".