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Dynamic contraction dependence on the instantaneous motor unit firing rates

2020· article· en· W3017015591 on OpenAlexaff
Eric A. Kirk, Charles L. Rice

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Power Systems and Control
Canadian institutionsWestern University
Fundersnot available
KeywordsIsometric exerciseMotor unitElectromyographyMotor unit recruitmentIsotonicTorqueMuscle contractionContraction (grammar)MathematicsAnatomyPhysicsPhysical medicine and rehabilitationMedicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

The effect of voluntary muscle contraction is to regulate position change about a joint involving recruitment and rate coding of motor units (MU) to produce torque results in the shortening velocity component. Unlike isometric (static) muscle contractions, isotonic (dynamic) muscle contractions can involve feedback to slow the shortening velocity as the joint reaches the terminal position. During dynamic conditions of increasing velocity targets, evidence indicates that the neuromuscular initiation of movement is greater than at isometric conditions, possibly being enhanced by the motor neuron secondary firing rate range. Our objective was to further characterize voluntary neuromuscular control at the level of instantaneous motor unit action potential firings during dynamic shortening muscle contractions. During isometric and isotonic contractions, intramuscular fine‐wire electromyography (EMG) was used to record MU potentials from the anconeus and triceps brachii muscles in healthy younger men and women (range 20–35 years, n= 15). Isotonic contractions were normalized to the maximal isotonic velocity and were loaded at 20% of the maximal isometric voluntary contraction torque. For analysis, the EMG was digitally high‐pass filtered (1 kHz, 3 rd order Butterworth) and MU action potential waveforms were determined and sorted by template matching (Spike2 Wavemark, version 7) with manual inspection. Torque, velocity and position were also measured and at each individual firing rate time‐point and these vectors were aligned. From this, 627 MU trains (mean±standard deviation 42±19, n= 15) during the isotonic contractions in the anconeus muscle and 226 MU trains (38±14, n= 6) in the long head of the triceps brachii muscle were measured. To explore a more comprehensive model related to firing rates, the torque, velocity and position were modelled by linear regression with the residuals of MU train number, participant, testing session, sex, muscle and target velocity removed. From the full model when comparing slope coefficients, there was no significant interactions between torque (β= 48.1x) and velocity (β= −28.5x) on firing rate dependence (p= 0.2). Position (β= 0.4x) had a smaller coefficient on firing rates as compared to torque and velocity and a significant interaction effect with torque (p< 0.05), but not velocity (p= 0.4). These results indicate that a model of instantaneous firing rate dependence on torque, velocity and position requires a re‐examination, especially because the interaction of torque and velocity were significantly correlated (r= 0.5, p< 0.05). Shortening velocity of muscles causing elbow joint extension must be first initiated by a rate of torque development, further supporting that a different conceptual approach may be necessary to understand MU output during dynamic contractions. It may be important in this task to model firing rates as the independent variable (time vector) that will affect succeeding dependent changes of torque, velocity and position. Support or Funding Information Supported by NSERC.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0030.001

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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 designBench or experimental
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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