Glymphatic Dysfunction Reflects Post‐Concussion Symptoms: Changes Within 1 Month and After 3 Months
Bibliographic record
Abstract
OBJECTIVE: Mild traumatic brain injury (mTBI) may alter glymphatic function; however, its progression and variability remain obscure. This study examined glymphatic function following mTBI within 1 month and after 3 months post-injury to determine whether variations in glymphatic function are associated with post-traumatic symptom severity. METHODS: Glymphatic function was estimated using diffusion tensor image analysis along the perivascular space (DTI-ALPS). This index was measured in 39 individuals with mTBI (47.21 ± 14.88 years) at initial and follow-up assessments, and in 35 age-matched controls (44.62 ± 13.12 years), using manually defined regions of interest at the lateral ventricle level. A linear mixed-effects (LME) model was used to compare ALPS indices among groups. Additional LME analyses evaluated continuous associations between the ALPS index and symptom severity, as assessed by the Rivermead Post-Concussion Symptoms Questionnaire (RPCSQ). Based on ALPS changes, patients were classified into increasing and decreasing subgroups, and comparative analyses of RPCSQ trajectories were conducted. RESULTS: At baseline, the index did not differ between patients with mTBI and controls; at follow-up, it was significantly lower in the mTBI group. Longitudinal ALPS changes were significantly associated with RPCSQ scores, whereas baseline ALPS showed only a marginal association with initial symptom severity. Individuals in the decreasing ALPS group demonstrated more severe overall symptoms and a slower rate of symptom resolution. INTERPRETATION: Glymphatic dysfunction, as represented by the ALPS index, may be associated with persistent post-traumatic symptoms. A time-dependent approach incorporating individual recovery trajectories may be essential when assessing glymphatic biomarkers in mTBI.
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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".