A comparison of cognitive-motor integration performance and resting state functional brain network connectivity in female athletes suggests intact motor network and visuomotor skill in those with a concussion history
Bibliographic record
Abstract
Abstract Structural neural changes following concussion are often not captured by standard imaging techniques. However, there is growing evidence that damage to white matter tracts and change in functional network connectivity may be observed following concussive injury. We investigated behavioural performance on a cognitive-motor integration (CMI) task in conjunction with alterations in resting state functional connectivity (rs-FC) in brain networks in a population of 30 female varsity athletes, with 16 having a previous history of concussion. Behavioural performance on accuracy, timing, and trajectory measures of a CMI task were assessed between the concussion history group and the control group. Rs-FC within the nodes of the Default Mode Network (DMN), Dorsal Attention Network (DAN), the Frontoparietal Network, and the Anterior Cerebellar Lobule Network, was assessed against performance scores on accuracy, timing, and trajectory measures. Main findings indicate no difference in behavioural performance between those with concussion history and those without, in contrast to previous findings in a group of primarily male varsity athletes. In addition, no difference in rs-FC was noted in correlation with behavioural performance scores on either accuracy, timing, or trajectory. These findings may suggest sex-related differences in performance on a CMI task, and a resiliency in both functional network connectivity and visuomotor skilled performance in varsity female athletes.
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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) | 0.004 | 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".