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Record W4389947649 · doi:10.7554/elife.88551.3

Myomatrix arrays for high-definition muscle recording

2023· article· en· W4389947649 on OpenAlexafffund
Bryce Chung, Muneeb Zia, Kyle Thomas, Jonathan A. Michaels, Amanda Jacob, Andrea Pack, Matthew J. Williams, Kailash Nagapudi, Lay Heng Teng, Eduardo Arrambide, Logan Ouellette, Nicole Oey, Rhuna Gibbs, Philip Anschutz, Jiaao Lu, Yu Wu, Mehrdad Kashefi, Tomomichi Oya, Rhonda Kersten, Alice C. Mosberger, Sean O’Connell, Runming Wang, Hugo Gravato Marques, Ana Rita Mendes, Constanze Lenschow, Gayathri Kondakath, Jeong Jun Kim, William Olson, Kiara N. Quinn, Pierce Perkins, Graziana Gatto, Ayesha Thanawalla, Susan Coltman, Taegyo Kim, Trevor Smith, Benjamin I. Binder‐Markey, Martin Zaback, Christopher K. Thompson, Simon F. Giszter, Abigail L. Person, Martyn Goulding, Eiman Azim, Nitish V. Thakor, Daniel H. O’Connor, Barry A. Trimmer, Susana Q. Lima, Megan R. Carey, Chethan Pandarinath, Rui M Costa, J. Andrew Pruszynski, Muhannad S. Bakir, Samuel J. Sober

Bibliographic record

VenueeLife · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsWestern University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthEuropean Regional Development FundDefense Advanced Research Projects AgencyHalle Institute for Global Research, Emory UniversityNational Institutes of HealthVector InstituteCanada First Research Excellence FundCanada Research ChairsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMcKnight FoundationBanting Research FoundationSalk Institute for Biological StudiesJohns Hopkins UniversityNovo Nordisk FondenCanadian Institutes of Health ResearchAzrieli FoundationSimons FoundationDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan Foundation
KeywordsNeuroscienceMotor unitMotor controlMotor neuronMotor systemBiologyComputer scienceMotor unit recruitmentNervous systemElectromyography

Abstract

fetched live from OpenAlex

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system’s actual motor output – the activation of muscle fibers by motor neurons – typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices (‘Myomatrix arrays’) that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a ‘motor unit,’ during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system’s motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

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 score0.011
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.090
GPT teacher head0.289
Teacher spread0.199 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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