A pilot study of the impact of an exercise intervention on brain structure, cognition, and psychosocial symptoms in individuals with relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Despite pharmacological treatment, many individuals with multiple sclerosis (MS) continue to experience symptoms and medication side effects. Exercise holds promise for MS, but changes in brain structure following exercise have not been thoroughly investigated, and important cognitive and psychosocial variables are rarely primary outcomes. The aim of this pilot study was to investigate whether a 12-week exercise intervention would improve white matter integrity in the brain, or cognition, symptoms of fatigue, and depressed mood for individuals with relapsing-remitting MS (RRMS). METHOD: Thirteen participants completed 12 weeks of speeded walking. Baseline and post-intervention testing included 3T diffusion tensor imaging (DTI) to assess white matter and neuropsychological testing to assess cognition, fatigue, and mood. Image pre-processing and analyses were performed in functional magnetic resonance imaging of the Brain Software Library. RESULTS: Post-intervention, there were no significant changes in white matter compared to baseline. Post-intervention, individuals with RRMS performed significantly better on the Symbol Digit Modalities Test (SDMT), reported fewer perceived memory problems, and endorsed less fatigue. Performance was not significantly different on Trails or Digit Span, and there were no significant changes in reports of mood. CONCLUSION: Although 12 weeks of speeded walking did not improve white matter integrity, exercise may hold promise for managing some symptoms of RRMS in the context of this study population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".