Post-Stroke Rehabilitation: Neurophysiology Processes of Bilateral Movement Training and Interlimb Coupling—A Systematic Review
Bibliographic record
Abstract
Background: Bilateral movement training (BMT) and interlimb coupling have emerged as promising neurophysiologically-based rehabilitation approaches for stroke survivors. However, the underlying mechanisms and optimal implementation strategies remain incompletely understood. This systematic review explored the neurophysiological principles underlying BMT and interlimb coupling interventions that led to positive clinical post-stroke rehabilitation outcomes, focusing on identifying the most effective bilateral and interlimb movement strategies. Methods: A 10-year literature search (2014–2024) following PRISMA guidelines was conducted across PubMed, Cochrane, and Google Scholar databases using keywords including stroke rehabilitation, bilateral movement training, cross-education, interlimb coupling, and interlimb transfer. Studies were included if they involved human subjects, clinical trials, stroke survivors, and described bilateral training protocols. Data extraction focused on neurophysiological mechanisms, intervention characteristics, and clinical outcomes. Quality assessment was performed using validated methodological appraisal tools, including the Newcastle-Ottawa Scale and Cochrane RoB 2.0. Results: Of 199 initially identified studies, 28 met inclusion criteria for detailed analysis. BMT demonstrated effectiveness in enhancing motor recovery by engaging neurophysiological mechanisms, including central pattern generators, interhemispheric coupling, and cortical disinhibition. High-intensity BMT provided significant gains for individuals with moderate to severe impairments, while low-intensity training benefited early recovery stages. Interventions incorporating task-specific exercises, robotic assistance, sensory enhancement, and virtual reality showed particular promise for addressing motor recovery complexities. However, significant research gaps were identified, including limited understanding of individualized responses to BMT, insufficient research on combined upper and lower limb training, and minimal integration of advanced technologies. Conclusions: BMT and interlimb coupling play critical roles in post-stroke rehabilitation by facilitating neural plasticity and interlimb coordination. Integrating robotic assistance, sensory enhancement, and virtual reality with BMT offers a robust framework for maximizing rehabilitation outcomes. Future research should prioritize longitudinal studies, personalized rehabilitation approaches, technology integration, and stratified interventions tailored to individual needs to optimize neuroplasticity and enhance quality of life for stroke survivors.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".