Effects of rhythmic-cued gait training on gait-like task related brain activation in people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Walking impairment is one of the most debilitating symptoms of multiple sclerosis (MS). A better understanding of brain mechanisms underlying successful gait training could help to improve development of targeted therapy. We therefore investigated changes in brain activation associated with improvements in walking function after rhythmic-cued gait training. METHODS: Thirty-one people with MS (pwMS; median EDSS = 2.5, range:2.0-5.0) and 17 age- and sex-matched healthy controls (HC) completed behavioural and MRI assessments at baseline and post-intervention (four weeks after baseline). All included pwMS received a four-week actual and/or imagined gait training with rhythmic-auditory cueing, while HC received no intervention. All participants performed a bipedal ankle plantar- and dorsiflexion and a corresponding motor-imagery task during fMRI. PwMS displaying a > 5 % walking distance increase in the 2-Minute Walk Test (2MWT) from baseline to post-intervention were defined as responders. RESULTS: Responders did not differ from non-responders in terms of demographics, clinical variables, and walking function at baseline. Responders, non-responders, and HC showed similar movement-related brain activation at baseline. At post-intervention, responders showed decreased brain activation within the premotor cortex, precuneus, and middle frontal gyrus during the movement task. Stronger decreases within these areas were associated with higher walking function improvements in all pwMS after controlling for potential confounders. No association was observed between walking function and motor imagery-related brain activation changes. CONCLUSION: Improved walking function after rhythmic-cued gait training was associated with reduced brain activation in motor planning and attention areas. This suggests a more efficient recruitment of areas subserving motor function after successful training.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".