Neuromuscular electrical stimulation‐promoted plasticity of the human brain
Bibliographic record
Abstract
The application of neuromuscular electrical stimulation (NMES) to paretic limbs has demonstrated utility for motor rehabilitation following brain injury. When NMES is delivered to a mixed peripheral nerve, typically both efferent and afferent fibres are recruited. Muscle contractions brought about by the excitation of motor neurons are often used to compensate for disability by assisting actions such as the formation of hand aperture, or by preventing others including foot drop. In this context, exogenous stimulation provides a direct substitute for endogenous neural drive. The goal of the present narrative review is to describe the means through which NMES may also promote sustained adaptations within central motor pathways, leading ultimately to increases in (intrinsic) functional capacity. There is an obvious practical motivation, in that detailed knowledge concerning the mechanisms of adaptation has the potential to inform neurorehabilitation practice. In addition, responses to NMES provide a means of studying CNS plasticity at a systems level in humans. We summarize the fundamental aspects of NMES, focusing on the forms that are employed most commonly in clinical and experimental practice. Specific attention is devoted to adjuvant techniques that further promote adaptive responses to NMES thereby offering the prospect of increased therapeutic potential. The emergent theme is that an association with centrally initiated neural activity, whether this is generated in the context of NMES triggered by efferent drive or via indirect methods such as mental imagery, may in some circumstances promote the physiological changes that can be induced through peripheral electrical stimulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".