The Effects of Mild Traumatic Brain Injury on Cognitive-Motor Integration for Skilled Performance
Bibliographic record
Abstract
Adults exposed to blast and blunt impact often experience mild traumatic brain injury, affecting neural functions related to sensory, cognitive, and motor function. In this perspective article, we will review the effects of impact and blast exposure on functional performance that requires the integration of these sensory, cognitive, and motor control systems. We describe cognitive-motor integration and how it relates to successfully navigating skilled activities crucial for work, duty, sport, and even daily life. We review our research on the behavioral effects of traumatic impact and blast exposure on cognitive-motor integration in both younger and older adults, and the neural networks that are involved in these types of skills. Overall, we have observed impairments in rule-based skilled performance as a function of both physical impact and blast exposure. The extent of these impairments depended on the age at injury and the sex of the individual. It appears, however, that cognitive-motor integration deficits can be mitigated by the level of skill expertise of the affected individual, suggesting that such experience imparts resiliency in the brain networks that underly the control of complex visuomotor performance. Finally, we discuss the next steps needed to comprehensively understand the impact of trauma and blast exposure on functional movement control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".