Does Functional Electrical Stimulation for Foot Drop Strengthen Corticospinal Connections?
Bibliographic record
Abstract
BACKGROUND: Long-term use of a foot-drop stimulator applying functional electrical stimulation (FES) to the common peroneal nerve improves walking performance even when the stimulator is off. This "therapeutic" effect might result from neuroplastic changes. OBJECTIVE: To determine the effect of long-term use of a foot-drop stimulator on residual corticospinal connections in people with central nervous system disorders. METHODS: Ten people with nonprogressive disorders (eg, stroke) and 26 with progressive disorders (eg, multiple sclerosis) used a foot-drop stimulator for 3 to 12 months while walking in the community. Walking performance and electrophysiological variables were measured before and after FES use. From the surface electromyogram of the tibialis anterior muscle, we measured the following: (1) motor-evoked potential (MEP) from transcranial magnetic stimulation over the motor cortex, (2) maximum voluntary contraction (MVC), and (3) maximum motor wave (M(max)) from stimulating the common peroneal nerve. RESULTS: After using FES, MEP and MVC increased significantly by comparable amounts, 50% and 48%, respectively, in the nonprogressive group and 27% and 17% in the progressive group; the changes were positively correlated (R(2) = .35; P < .001). Walking speed increased with the stimulator off (therapeutic effect) by 24% (P = .008) and 7% (P = .014) in the nonprogressive and progressive groups, respectively. The changes in M(max) were small and not correlated with changes in MEP. CONCLUSIONS: The large increases in MVC and MEP suggest that regular use of a foot-drop stimulator strengthens activation of motor cortical areas and their residual descending connections, which may explain the therapeutic effect on walking speed.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".