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Record W2294126936 · doi:10.1093/cercor/bhv324

Different Patterns of Cortical Inputs to Subregions of the Primary Motor Cortex Hand Representation in<i>Cebus apella</i>

2016· article· en· W2294126936 on OpenAlexafffund
Melvin K. Dea, Adjia Hamadjida, Guillaume Elgbeili, Stephan Quessy, Numa Dancause

Bibliographic record

VenueCerebral Cortex · 2016
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsDouglas CollegeUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPremotor cortexNeurosciencePrimary motor cortexCortex (anatomy)Motor cortexRepresentation (politics)Posterior parietal cortexSupplementary motor areaMotor areaDorsumAnatomyBiologyPsychologyFunctional magnetic resonance imagingStimulation

Abstract

fetched live from OpenAlex

The primary motor cortex (M1) plays an essential role in the control of hand movements in primates and is part of a complex cortical sensorimotor network involving multiple premotor and parietal areas. In a previous study in squirrel monkeys, we found that the ventral premotor cortex (PMv) projected mainly to 3 regions within the M1 forearm representation [rostro-medial (RM), rostro-lateral (RL), and caudo-lateral (CL)] with very few caudo-medial (CM) projections. These results suggest that projections from premotor areas to M1 are not uniform, but rather segregated into subregions. The goal of the present work was to study how inputs from diverse areas of the ipsilateral cortical network are organized within the M1 hand representation. In Cebus apella, different retrograde neuroanatomical tracers were injected in 4 subregions of the hand area of M1 (RM, RL, CM, and CL). We found a different pattern of input to each subregion of M1. RM receives inputs predominantly from dorsal premotor cortex, RL from PMv, CM from area 5, and CL from area 2. These results support that the M1 hand representation is composed of several subregions, each part of a unique cortical network.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.324

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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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

Citations55
Published2016
Admission routes2
Has abstractyes

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