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Record W4223643938 · doi:10.1101/2022.04.01.486703

Functional coupling of the mesencephalic locomotor region and V2a reticulospinal neurons driving forward locomotion

2022· preprint· en· W4223643938 on OpenAlexafffund
Martin Carbó-Tano, Mathilde Lapoix, Xinyu Jia, François Auclair, Réjean Dubuc, Claire Wyart

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersCanadian Institutes of Health ResearchNational Institutes of HealthCampus FranceFondation pour la Recherche MédicaleAgence Nationale de la RechercheGreat Lakes Fishery CommissionNatural Sciences and Engineering Research Council of CanadaEuropean CommissionNew York Stem Cell Foundation
KeywordsBrainstemNeuroscienceLocus coeruleusMedullaGlutamatergicBiologyMidbrainDorsumZebrafishAnatomyCentral nervous systemGlutamate receptorGene

Abstract

fetched live from OpenAlex

Abstract Locomotion in vertebrates relies on high brain centers converging onto the mesencephalic locomotor region (MLR). How the MLR recruits brainstem reticulospinal neurons (RSNs) to initiate locomotion is incompletely understood due to the challenge of recording these cells in vivo. To tackle this question, we leveraged the transparency and genetic accessibility of larval zebrafish. In this model organism, we uncovered the locus of the MLR as a small region dorsal to the locus coeruleus containing glutamatergic and cholinergic neurons. MLR stimulations reliably elicited forward bouts of controlled duration and speed. We find that the MLR elicits forward locomotion by recruiting V2a RSNs in the pontine and retropontine regions, and gradually in the medulla. Remarkably, recruited V2a RSNs in the medulla act as maintain cells encoding speed of forward locomotion. Altogether, our study reveals that the MLR recruits genetically-identified reticulospinal neurons in the medulla to control the kinematics of exploration.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.558
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.002
Research integrity0.0000.001
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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.

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

Citations8
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicZebrafish Biomedical Research ApplicationsFrench-language works237,207