MétaCan
Menu
← Back to cohort
Record W4405637468 · doi:10.1113/jp287448

Role of forelimb morphology in muscle sensorimotor functions during locomotion in the cat

2024· article· en· W4405637468 on OpenAlexafffund
Seyed Mohammadali Rahmati, Alexander N. Klishko, Ramaldo S. Martin, Nate E. Bunderson, Jeswin A. Meslie, T. Richard Nichols, Ilya A. Rybak, Alain Frigon, Thomas J. Burkholder, Boris I. Prilutsky

Bibliographic record

VenueThe Journal of Physiology · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité de Sherbrooke
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeHamilton Health SciencesNational Institutes of HealthNational Science Foundation
KeywordsForelimbBicepsAnatomyFascicleBrachioradialisSomatosensory systemHindlimbBiologySensory systemNeuroscience

Abstract

fetched live from OpenAlex

Previous studies established strong links between morphological characteristics of mammalian hindlimb muscles and their sensorimotor functions during locomotion. Less is known about the role of forelimb morphology in motor outputs and generation of sensory signals. Here, we measured morphological characteristics of 46 forelimb muscles from six cats. These characteristics included muscle attachments, physiological cross-sectional area (PCSA) and fascicle length. We also recorded full-body mechanics and EMG activity of forelimb muscles during level overground and treadmill locomotion in seven and 16 adult cats of either sex, respectively. We computed forelimb muscle forces along with force- and length-dependent sensory signals mapped onto corresponding cervical spinal segments. We found that patterns of computed muscle forces and afferent activities were strongly affected by the muscle's moment arm, PCSA and fascicle length. Morphology of the shoulder muscles suggests distinct roles of the forelimbs in lateral force production and movements. Patterns of length-dependent sensory activity of muscles with long fibres (brachioradialis, extensor carpi radialis) closely matched patterns of overall forelimb length, whereas the activity pattern of biceps brachii length afferents matched forelimb orientation. We conclude that cat forelimb muscle morphology contributes substantially to locomotor function, particularly to control lateral stability and turning, rather than propulsion. KEY POINTS: Little is known about the role of forelimb muscle morphology in producing motor outputs and generating somatosensory signals. This information is needed to understand the contributions of forelimbs in locomotor control. We measured morphological characteristics of 46 muscles from cat forelimbs, recorded cat walking mechanics and electromyographic activity, and computed patterns of moment arms, length, velocity, activation, and force of forelimb muscles, as well as length- and force-dependent afferent activity during walking. We demonstrated that moment arms, physiological cross-sectional area and fascicle length of forelimb muscles contribute substantially to muscle force production and proprioceptive activity, to the regulation of locomotor cycle phase transitions and to control of lateral stability. The obtained information can guide the development of biologically accurate neuromechanical models of quadrupedal locomotion for exploring and testing novel methods of treatments of central nervous system pathologies by modulating activities in neural pathways controlling forelimbs/arms.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.026
GPT teacher head0.336
Teacher spread0.310 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physiology→Same topicSpinal Cord Injury Research→French-language works237,207→