White Matter Integrity and Its Relationship to Cognitive-Motor Integration in Females with and without Post-Concussion Syndrome
Bibliographic record
Abstract
Fifteen percent of individuals who sustain a concussion go on to develop post-concussion syndrome (PCS). These persistent symptoms are believed to be attributed to damage to white matter tracts and impaired neurotransmission. Specifically, declines in white matter integrity after concussion have been found along the long-coursing axons underlying the frontoparietal network. This network is essential for the performance of visuomotor transformation tasks requiring cognitive-motor integration (CMI). We have previously observed deficits in performance on CMI-based tasks in those who have a history of concussion, but were asymptomatic. The aim of this study was to investigate performance on a CMI task, as well as white matter integrity differences along frontoparietal-cerebellar white matter tracts, in those with PCS compared to healthy controls. We hypothesized an association between the behavioral and brain structural measures. Twenty-six female participants (13 with PCS for ≥6 months and 13 healthy controls) completed four computer-based visuomotor CMI tasks. In addition, diffusion tensor images (DTIs) were acquired. No statistically significant differences were found in CMI performance between groups (p > 0.05). Further, there were no statistically significant differences between groups on any DTI metrics (p > 0.05). However, examination of the data collapsed across participants revealed significant associations between performance on a CMI task and white matter integrity. Further investigation into additional causes of symptoms in those with PCS (including psychological and cervicogenic factors) will strengthen our understanding of this diverse group. Nonetheless, this study demonstrates that white matter integrity is related to levels of performance in tasks that require rule-based movement control.
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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 | 0.001 |
| 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) | 0.002 | 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".