P.026 Structural deficits with preserved kinematic performance after sport-related concussion
Bibliographic record
Abstract
Background: Identifying white matter abnormalities after acute concussion is challenging due to variable microstructural changes and individual imaging limitations. Combining diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) improves sensitivity to alterations. This study integrates neuroimaging and behavioural assessments to improve detection and characterization of abnormalities for clinical management. Methods: We recruited 12 recently concussed athletes (21 ± 2.1 years, 7 ± 4.6 days post-injury; 9 completed behavioural testing) and 24 controls. All participants underwent DTI and NODDI to assess white matter integrity. Kinematic performance was evaluated using the Kinarm exoskeleton robot’s Reverse Visually Guided Reaching (RVGR) task. Group differences in imaging and kinematic metrics were analyzed using permutation-based and parametric tests, controlling for age and sex. Results: Concussed athletes had elevated fractional anisotropy, reduced mean and radial diffusivity, and lower isotropic volume fraction in affected tracts. However, no group differences emerged in RVGR parameters, indicating intact sensorimotor function despite imaging abnormalities. Conclusions: Our findings reveal that acute concussion leads to measurable microstructural changes without corresponding functional deficits on a cognitive inhibition task. These findings highlight the clinical utility of neuroimaging for early and precise diagnosis, emphasizing its sensitivity over behavioural measures to detect subtle impairment for acute concussion management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".