Motor unit firing rate modulation is more impaired during flexion synergy-driven contractions of the biceps brachii in chronic stroke
Bibliographic record
Abstract
Following a hemiparetic stroke, individuals exhibit altered motor unit firing patterns during voluntary muscle contractions, including impairments in firing rate modulation and recruitment. These individuals also exhibit abnormal muscle coactivation through multi-joint synergies (e.g., flexion synergy). Here, we investigate whether motor unit firing activity during flexion synergy-driven contractions of the paretic biceps brachii differs from that of voluntary contractions and use these differences to predict changes in descending motor commands. To accomplish this, we characterized motor unit firing patterns of the biceps brachii in individuals with chronic hemiparetic stroke during voluntary isometric elbow flexion contractions in the paretic and non-paretic limbs, as well as during contractions driven by voluntary effort and by flexion synergy expression in the paretic limb. We observed significant reductions in motor unit firing rate modulation from the non-paretic to paretic limb (non-paretic - paretic: 0.14 pps/%MVT, 95% CI: [0.09 0.19]) that were further reduced during synergy-driven contractions (voluntary paretic - synergy driven: 0.19 pps/%MVT, 95% CI: [0.14 0.25]). Moreover, using recently developed metrics, we evaluated how a stroke-induced reliance on indirect motor pathways alters the inputs that motor units receive and revealed progressive increases in neuromodulatory and inhibitory drive to the motor pool in the paretic limb, with the changes greatest during synergy-driven contractions. These findings suggest that an interplay between heightened neuromodulatory drive and alterations in inhibitory command structure may account for the observed motor unit impairments, further illuminating underlying neural mechanisms involved in the flexion synergy and its impact on motor unit firing patterns post-stroke.
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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 | 0.001 | 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) | 0.001 | 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".