Myomatrix arrays for high-definition muscle recording.

• Type of Material: Article
• Authors: Bryce Chung, Emory University; Muneeb Zia, Georgia Institute of Technology, Atlanta; Kyle Thomas, Emory University and Georgia Tech; Jonathan A Michaels, Western University; Amanda Jacob, Emory University; Andrea Pack, Emory University; Matthew Williams, Emory University and Georgia Tech; Kailash Nagapudi, Emory University; Lay Heng Teng, Emory University; Eduardo Arrambide, Emory University; Logan Ouellette, Emory University; Nicole Oey, Emory University; Rhuna Gibbs, Emory University; Philip Anschutz, Georgia Tech; Jiaao Lu, Georgia Tech; Yu Wu, Georgia Institute of Technology, Atlanta; Mehrdad Kashefi, Western University; Tomomichi Oya, Western University; Rhonda Kersten, Western University; Alice Mosberger, Columbia University; Sean O'Connell, Emory University and Georgia Tech; Runming Wang, Columbia University; Hugo Marques, Champalimaud Neuroscience Programme, Champalimaud Foundation; Ana Rita Mendes, Champalimaud Neuroscience Programme, Champalimaud Foundation; Costanze Lenschow, Champalimaud Neuroscience Programme, Champalimaud Foundation; Gayathri Kondakath, Tufts University; Jeong Jun Kim, Johns Hopkins School of Medicine; William Olson, Johns Hopkins School of Medicine; Kiara Quinn, Johns Hopkins School of Medicine; Pierce Perkins, Johns Hopkins School of Medicine; Graziana Gatto, Salk Institute for Biological Studies; Ayesha Thanawalla, Salk Institute for Biological Studies; Susan Coltman, University of Colorado, Aurora; Taegyo Kim, Drexel University; Trevor Smith, Drexel University; Ben Binder-Markey, Drexel University; Martin Zaback, Temple University; Christopher K Thompson, Temple University; Simon Giszter, Drexel University; Abigail Person, University of Colorado, Aurora; Martyn Goulding, Salk Institute for Biological Studies; Eiman Azim, Salk Institute for Biological Studies; Nitish Thakor, Johns Hopkins School of Medicine; Daniel O'Connor, Johns Hopkins School of Medicine; Barry Trimmer, Tufts University; Susana Q Lima, Champalimaud Neuroscience Programme, Champalimaud Foundation; Megan Carey, Champalimaud Neuroscience Programme, Champalimaud Foundation; Chethan Pandarinath, Emory University; Rui M Costa, Columbia University; Andrew J Pruszynski, Western University; Muhannad Bakir, Georgia Institute of Technology, Atlanta; Samuel Sober, Emory University
• Language: English
• Date: 2023-02-22
• Publisher: bioRxiv
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
• License: Creative Commons Attribution-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1101/2023.02.21.529200
• Title of Journal or Parent Work: bioRxiv
• Grant/Funding Information: See publication for f
• Keywords: Myomatrix arrays

Abstract

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 cellular 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 in identifying pathologies of the motor system.