MétaCan
Menu
Back to cohort
Record W4394820217 · doi:10.3791/66584

Recording Forelimb Muscle Activity in Head-Fixed Mice with Chronically Implanted EMG Electrodes

2024· article· en· W4394820217 on OpenAlexaff
Amy C. Kristl, Turgay Akay, Andrew Miri

Bibliographic record

VenueJournal of Visualized Experiments · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsDalhousie University
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institutes of HealthChicago Community Trust
KeywordsForelimbBiomedical engineeringElectrophysiologyElectromyographyNeuroscienceFixation (population genetics)ElectrodeComputer scienceMaterials scienceAnatomyBiologyMedicineChemistry

Abstract

fetched live from OpenAlex

Powerful genetic and molecular tools available in mouse systems neuroscience research have enabled researchers to interrogate motor system function with unprecedented precision in head-fixed mice performing a variety of tasks. The small size of the mouse makes the measurement of motor output difficult, as the traditional method of electromyographic (EMG) recording of muscle activity was designed for larger animals like cats and primates. Pending commercially available EMG electrodes for mice, the current gold-standard method for recording muscle activity in mice is to make electrode sets in-house. This article describes a refinement of established procedures for hand fabrication of an electrode set, implantation of electrodes in the same surgery as headplate implantation, fixation of a connector on the headplate, and post-operative recovery care. Following recovery, millisecond-resolution EMG recordings can be obtained during head-fixed behavior for several weeks without noticeable changes in signal quality. These recordings enable precise measurement of forelimb muscle activity alongside in vivo neural recording and/or perturbation to probe mechanisms of motor control in mice.

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 score0.005
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.417
Teacher spread0.370 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Visualized ExperimentsSame topicNeuroscience and Neural EngineeringFrench-language works237,207