Multiparametric MRI Assessment of Morpho-Functional Muscle Changes Following a 6-Month FES-Cycling Training Program: Pilot Study in People With a Complete Spinal Cord Injury
Bibliographic record
Abstract
Background: Spinal cord injuries (SCIs) cause debilitating secondary conditions such as severe muscle deterioration, cardiovascular, and metabolic dysfunctions, significantly impacting patients' quality of life. Functional electrical stimulation (FES) combined with cycling exercise (FES-cycling) has shown promise in improving muscle function and health in individuals with SCI. Objective: This pilot study aimed to investigate the potential role of multiparametric magnetic resonance imaging (MRI) to assess muscle health during and after an FES-cycling rehabilitation program. Methods: Four male participants with chronic SCI underwent a 6-month FES-cycling training program, consisting of two 30-minute sessions per week. MRI scans were performed at baseline (T0), after 3 months (T1), at the end of the training (T2), and 1-month posttraining (T3). The MRI protocol included T1-weighted imaging for volume quantification, Dixon imaging for fat fraction, multi-echo spin echo for T2 relaxation times, and diffusion tensor imaging to assess diffusion parameters. Results: Muscle hypertrophy was observed, with an average increase in muscle volume of 22.3% at T1 and 36.7% at T2 compared with baseline. One month posttraining, muscle volume remained 23.2% higher than baseline. Fat fraction decreased from 11.1% at T0 to 9.1% at T2, with a rebound to 10.9% at T3. T2 relaxation times showed a reduction even though this was not consistent among participants. Diffusion tensor imaging parameters revealed subtle changes in muscle tissue microstructure, with a decrease in fractional anisotropy mainly associated to an increase of radial diffusivity. Conclusions: Although preliminary, this study provides evidence that 6 months of low-intensity FES-bike training can increase muscle volume and decrease fat infiltration in individuals with SCI. The study demonstrates that the use of a multiparametric MRI provides comprehensive insights into both macroscopic and microscopic changes within muscle tissues, supporting its integration into clinical practice for assessing the efficacy of rehabilitation interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".