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Record W4387696495 · doi:10.1101/2023.10.16.562612

Human motor unit discharge patterns reveal differences in neuromodulatory and inhibitory drive to motoneurons across contraction levels

2023· preprint· en· W4387696495 on OpenAlexaff
Jakob Škarabot, James A. Beauchamp, Gregory E. P. Pearcey

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInhibitory postsynaptic potentialContraction (grammar)Motor unitNeuroscienceBiologyEndocrinology

Abstract

fetched live from OpenAlex

ABSTRACT All motor commands converge onto motor units (MUs), which transduce the signals into mechanical actions of muscle fibres. This process is highly non-linear due to combinations of ionotropic (excitatory/inhibitory) and metabotropic (neuromodulatory) inputs. Neuromodulatory inputs facilitate dendritic persistent inward currents, which introduce non-linearities in MU discharge patterns and provide insights into the structure of motor commands. Here, we investigated the relative contribution of neuromodulation and the pattern of inhibition to modulate human MU discharge patterns with contraction forces up to 70% maximum. Leveraging MU discharge patterns identified from three human muscles (tibialis anterior – TA, and vastus lateralis and medialis), we show that with increased contraction force, the onset-offset discharge rate hysteresis (ΔF) increased whilst ascending MU discharge patterns become more linear, with lower slopes. In a follow-up experiment, we demonstrated that the observations of increased ΔF and more linear ascending MU discharge patterns with greater contraction force are maintained even when accounting for contraction duration and rate of force increase. We then reverse-engineered TA MU discharge patterns using highly realistic in silico motoneuron pools to substantiate the inferred physiological mechanisms from human recordings. We demonstrate a sharply restricted solution space, whereby the contraction force-induced changes in experimentally obtained MU discharge patterns can only be recreated with increased neuromodulation and a more reciprocal (i.e. push-pull) inhibitory pattern. In summary, our experimental and computational data suggest that neuromodulation and inhibitory patterns are uniquely shaped to generate discharge patterns that support force increases across a large proportion of the motor pool’s recruitment range. Significance statement How the structure of motor commands is modified to scale motor output is largely speculative despite its critical role in the neural control of movement. Here, we demonstrate that human motor unit discharge patterns become more linear and exhibit greater discharge rate hysteresis with greater contraction force. These experimentally observed patterns can only be replicated in silico with biophysical models of spinal motoneurons by increasing neuromodulation and shifting inhibitory commands to be more reciprocal to excitation (i.e., push-pull excitation-inhibition synaptic control). Collectively, these results suggest that the structure of motor commands is uniquely orchestrated to support increases in contraction force.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.245
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

Explore more

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