Exploring the Functional and Structural Brain Alterations following Youth Concussion Utilizing a Dual-task Paradigm
Bibliographic record
Abstract
There are many benefits to sports participation for youth, despite the increased risk of sustaining a concussion. However, return-to-play protocols often fail to capture underlying disruptions in brain function or structure due to their limited ecological validity. Assessments often rely on single dimensional tasks and rarely integrate cognitive-motor components critical in sports. The overall objective of my dissertation was to investigate brain function and structure in youth with concussion using dual task paradigms. Using functional magnetic resonance imaging, the first study evaluated the chronic impact of concussion on grey matter microstructure and the relationship between changes in structure and dual-task performance. Cortical thinning was evident in brain regions critical to task coordination and was associated with poor task performance during dual-task. Switching over to functional near infrared spectroscopy, the second study investigated brain activation patterns during a cognitive-motor task, where youth with concussion exhibited altered activity in fronto-parietal brain regions, which was particularly prominent during the dual-task despite unimpaired task performance. Following two participants with concussion from study two, the third part of this thesis was a longitudinal case study that evaluated whether altered brain activation patterns persisted over the recovery trajectory. In line with study two, the dual-task elicited perturbed activity in more brain regions than the single tasks and these perturbations lingered at 3 months post-injury, despite symptom resolution. The findings suggest that the concussed brain can adapt to maintain dual task performance in the face of increased neural demand. However, interpreting our findings according to the capacity sharing model, there is a likely a limit to this compensation; it is likely that post-symptom resolution, some youth recovering from concussion may not be able to allocate sufficient neural resources to meet the high demands of sport and thereby risk subsequent injury. Taken together, this thesis provides support for a holistic approach to exploring concussion management that integrates brain function measurement during more sport-relevant tasks with the aim of minimizing subsequent concussions and ensuring youth continue to experience the benefits of sports participation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".