Abstract
Restoring dexterous hand function after neurological injury requires precise control over distinct muscles with different neuromuscular architectures. The extrinsic and intrinsic muscles of the human hand differ in anatomy, motor unit structure, and neural input; however, their relative efficiency in converting evoked reflex activity into functional muscle forces, essential for the development of neural stimulation strategies, remains unclear. H-reflex-based stimulation preferentially recruits small and fatigue-resistant motor units through spinal pathways, offering advantages over direct M-wave activation for fine control of finger forces and sustained muscle activation. Here, we quantified reflex-mediated force transmission in these muscle groups using transcutaneous nerve stimulation, high-density electromyography, and finger-specific force measurements in 12 neurologically intact adults. We found that intrinsic muscles produced significantly greater normalized H-reflex-evoked force than extrinsic muscles, a muscle effect consistent across all fingers. This difference was strongly predicted by spinal excitability, as indexed by the ratio between the maximum amplitudes of H-reflex and M-wave, which emerged as a key mechanistic determinant of reflex force efficiency. Notably, greater force selectivity, as measured by a lower finger coactivation index, was associated with enhanced reflex output, suggesting that improved spatial targeting amplifies functional benefits. Higher trial-to-trial variability in intrinsic muscles likely reflects greater cortical modulation, suggesting the need for adaptive stimulation strategies. These results reveal fundamental differences in reflex transmission efficiency between intrinsic and extrinsic hand muscles, providing physiological evidence for optimizing fatigue-resistant neural stimulation protocols in assistive and rehabilitation technologies.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 607-616 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Volume | 34 |
| DOIs | |
| State | Published - 2026 |
All Science Journal Classification (ASJC) codes
- Internal Medicine
- General Neuroscience
- Biomedical Engineering
- Rehabilitation
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