Grey Matter Volume and Fractional Anisotropy as Correlates of Cognitive Improvement in Traumatic Brain Injury Over a 6-Month Period
Bibliographic record
Abstract
Abstract Objective In this study we explored how neuroimaging and blood biomarkers relate to cognitive recovery in traumatic brain injury (TBI) patients. Methods Sixteen participants with moderate to severe traumatic brain injury (TBI) were enrolled, with blood samples, MRI, and diffusion tensor imaging (DTI) collected at enrollment and six months. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Disability Rating Scale, and Montreal Cognitive Assessment (MoCA) were also administered at both time points to evaluate neuropsychological and functional outcomes. Results Fractional anisotropy (FA) in the genu (r s = 0.937, p = 0.002) and splenium of the corpus callosum (r s = 0.955, p < 0.001) was strongly correlated with changes in RBANS - Attention scores. Fornix FA was correlated with changes in RBANS - Total (r s = 0.928, p = 0.008), and left tapetum FA was correlated with changes in RBANS - Visuospatial scores (r s = 0.964, p < 0.001). Right temporal fusiform cortex grey matter (GM) volume was correlated with changes in RBANS - Attention scores (r s = 0.975, p = 0.005). Blood biomarkers did not show significance. Conclusion Imaging markers like FA and GM volume appear to help predict cognitive recovery in TBI, supporting the potential use of neuroimaging to guide rehabilitation strategies.
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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 | 0.002 |
| 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".