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Record W3187005872 · doi:10.1093/cercor/bhab167

Microstimulation of the Premotor Cortex of the Cat Produces Phase-Dependent Changes in Locomotor Activity

2021· article· en· W3187005872 on OpenAlexafffund
Nicolas Fortier-Lebel, Toshi Nakajima, Nabiha Yahiaoui, Trevor Drew

Bibliographic record

VenueCerebral Cortex · 2021
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsMicrostimulationPremotor cortexNeuroscienceCortex (anatomy)Motor cortexPsychologyAnatomyBiologyDorsumStimulation

Abstract

fetched live from OpenAlex

To determine the functional organization of premotor areas in the cat pericruciate cortex we applied intracortical microstimulation (ICMS) within multiple cytoarchitectonically identified subregions of areas 4 and 6 in the awake cat, both at rest and during treadmill walking. ICMS in most premotor areas evoked clear twitch responses in the limbs and/or head at rest. During locomotion, these same areas produced phase-dependent modifications of muscle activity. ICMS in the primary motor cortex (area 4γ) produced large phase-dependent responses, mostly restricted to the contralateral forelimb or hindlimb. Stimulation in premotor areas also produced phase-dependent responses that, in some cases, were as large as those evoked from area 4γ. However, responses from premotor areas had more widespread effects on multiple limbs, including the ipsilateral limbs, than did stimulation in 4γ. During locomotion, responses in both forelimb and hindlimb muscles were evoked from cytoarchitectonic areas 4γ, 4δ, 6aα, and 6aγ. However, the prevalence of effects in a given limb varied from one area to another. The results suggest that premotor areas may contribute to the production, modification, and coordination of activity in the limbs during locomotion and may be particularly pertinent during modifications of gait.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.352
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.273
Teacher spread0.243 · 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 teacher head, 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".

Quick stats

Citations16
Published2021
Admission routes2
Has abstractyes

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