White matter integrity and its relationship to cognitive-motor integration in females with post-concussion syndrome
Bibliographic record
Abstract
Objective Cognitive-motor integration (CMI) is required in sport when performing movements where a rule is used to align the required motor output and the guiding visual information. Previous research has shown CMI declines in young athletes with a concussion history, deemed recovered at the time of evaluation. The purpose of this study was to characterise differences in symptoms, CMI, and white matter integrity (fractional anisotropy, FA) in those with post-concussion syndrome (PCS) and healthy controls. We hypothesised that those with PCS would have decreased FA and CMI performance, with increased symptom scores. Participants Twelve females were included; 6 with PCS for 6 months or more, and 6 age-matched healthy controls with no concussion history. Methods Participants were administered the SCAT3, four visuomotor CMI tasks, and diffusion weighted images were acquired. Participants displaced a cursor from a central target to peripheral targets by sliding their finger on a horizontally placed tablet either directly to the viewed target or with decoupled eye-hand coordination (targets viewed on a vertical monitor, 180° feedback rotation, or both). Results We observed worse symptom scores and impaired performance in CMI tasks, as well as decreased mean FA in bilateral corticospinal tracts beneath the premotor and primary motor cortices and in the white matter underlying the right superior parietal lobule, in those with PCS compared to healthy controls. Conclusions CMI decline may be related to decreased FA within the frontal-parietal-subcortical network. Measuring CMI, a skill crucial to athletes, provides an effective behavioural means for detection of brain alterations associated with concussion. Competing interests None.
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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) | 0.002 | 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".