Facilitation of Corticospinal Connections in Able-bodied People and People With Central Nervous System Disorders Using Eight Interventions
Bibliographic record
Abstract
BACKGROUND: Voluntary contractions (VOL), functional electrical stimulation (FES), and transcranial magnetic stimulation (TMS) can facilitate corticospinal connections. OBJECTIVE: To find the best methods for increasing corticospinal excitability by testing eight combinations: (1) VOL, (2) FES, (3) FES + VOL, (4) TMS, (5) TMS + VOL, (6) paired associative stimulation (PAS) consisting of FES + TMS, (7) PAS + VOL, and (8) double-pulse TMS + VOL. METHODS: Interventions were applied for 3 × 10 minutes in 15 able-bodied subjects, 14 subjects with stable central nervous system lesions (e.g., chronic stroke, and incomplete spinal cord injury) and 16 subjects with progressive central nervous system conditions (e.g., secondary progressive multiple sclerosis). Motor-evoked potentials (MEP), M-waves, and H-reflexes were monitored over a 1-hour period. RESULTS: Three interventions (PAS, PAS + VOL, and double-pulse TMS + VOL) caused 15% to 20% increases (P < 0.05) in the MEP at a stimulus level that initially produced a half-maximal response (MEP(half)) during a contraction. Interventions were less effective in both clinical groups than in the able-bodied group. Interventions with VOL were more effective in increasing the MEP(half) than those without (P = 0.022). When more modalities were combined, the MEP increases were larger (P = 0.022). CONCLUSIONS: (1) Short-term application of FES, TMS, and VOL can facilitate corticospinal pathways, particularly when methods are combined. (2) The effects may depend on the total activation of neural pathways, which is reduced in central nervous system disorders.
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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 | 0.001 |
| 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) | 0.002 | 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".