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Record W4406405048 · doi:10.1016/j.celrep.2024.115212

Widespread innervation of motoneurons by spinal V3 neurons globally amplifies locomotor output in mice

2025· article· en· W4406405048 on OpenAlexafffund
Han Zhang, Dylan Deska‐Gauthier, Colin Mackay, Krishnapriya Hari, Ana M. Lucas‐Osma, Joanna Borowska-Fielding, Reese L. Letawsky, Vladimir Rančić, Turgay Akay, Keith K. Fenrich, David J. Bennett, Ying Zhang

Bibliographic record

VenueCell Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsDalhousie UniversityWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health ResearchNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaNational Institute of Mental Health and NeurosciencesCraig H. Neilsen Foundation
KeywordsNeuroscienceBiology

Abstract

fetched live from OpenAlex

While considerable progress has been made in understanding the neuronal circuits that underlie the patterning of locomotor behaviors, less is known about the circuits that amplify motoneuron output to adjust muscle force. Here, we demonstrate that propriospinal V3 neurons (Sim1 + ) account for ∼20% of excitatory input to motoneurons across hindlimb muscles. V3 neurons also form extensive connections among themselves and with other excitatory premotor neurons, such as V2a neurons. Optical activation of V3 neurons in a single segment rapidly amplifies locomotor-related motoneuron output at all lumbar segments in in vitro spinal cord and the awake adult mouse. Despite similar innervation from V3 neurons to flexor and extensor motoneuron pools, V3 neurons preferentially activate extensor muscles. Genetically or optogenetically silencing V3 neurons leads to slower and weaker mice with a reduced ability to adjust extensor muscle force. Thus, V3 neurons serve as global command neurons that amplify locomotion intensity.

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)0.001
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 score0.151
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.010
GPT teacher head0.281
Teacher spread0.271 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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