Literature DB >> 24995476

A freely-moving monkey treadmill model.

Justin D Foster1, Paul Nuyujukian, Oren Freifeld, Hua Gao, Ross Walker, Stephen I Ryu, Teresa H Meng, Boris Murmann, Michael J Black, Krishna V Shenoy.   

Abstract

OBJECTIVE: Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. APPROACH: We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. MAIN
RESULTS: Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. SIGNIFICANCE: Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

Entities:  

Mesh:

Year:  2014        PMID: 24995476     DOI: 10.1088/1741-2560/11/4/046020

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  21 in total

1.  An Inductively-Powered Wireless Neural Recording and Stimulation System for Freely-Behaving Animals.

Authors:  Byunghun Lee; Yaoyao Jia; S Abdollah Mirbozorgi; Mark Connolly; Xingyuan Tong; Zhaoping Zeng; Babak Mahmoudi; Maysam Ghovanloo
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2019-01-07       Impact factor: 3.833

2.  Premotor Cortex Provides a Substrate for the Temporal Transformation of Information During the Planning of Gait Modifications.

Authors:  Toshi Nakajima; Nicolas Fortier-Lebel; Trevor Drew
Journal:  Cereb Cortex       Date:  2019-12-17       Impact factor: 5.357

3.  Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex.

Authors:  Michael Berger; Naubahar Shahryar Agha; Alexander Gail
Journal:  Elife       Date:  2020-05-04       Impact factor: 8.140

4.  Engagement of the Rat Hindlimb Motor Cortex across Natural Locomotor Behaviors.

Authors:  Jack DiGiovanna; Nadia Dominici; Lucia Friedli; Jacopo Rigosa; Simone Duis; Julie Kreider; Janine Beauparlant; Rubia van den Brand; Marco Schieppati; Silvestro Micera; Grégoire Courtine
Journal:  J Neurosci       Date:  2016-10-05       Impact factor: 6.167

5.  Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity.

Authors:  Shreya Saxena; Abigail A Russo; John Cunningham; Mark M Churchland
Journal:  Elife       Date:  2022-05-27       Impact factor: 8.713

6.  Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response.

Authors:  Abigail A Russo; Sean R Bittner; Sean M Perkins; Jeffrey S Seely; Brian M London; Antonio H Lara; Andrew Miri; Najja J Marshall; Adam Kohn; Thomas M Jessell; Laurence F Abbott; John P Cunningham; Mark M Churchland
Journal:  Neuron       Date:  2018-02-01       Impact factor: 17.173

7.  Power-saving design opportunities for wireless intracortical brain-computer interfaces.

Authors:  Nir Even-Chen; Dante G Muratore; Sergey D Stavisky; Leigh R Hochberg; Jaimie M Henderson; Boris Murmann; Krishna V Shenoy
Journal:  Nat Biomed Eng       Date:  2020-08-03       Impact factor: 25.671

Review 8.  Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

Authors:  Kian Ann Ng; Elliot Greenwald; Yong Ping Xu; Nitish V Thakor
Journal:  Med Biol Eng Comput       Date:  2016-01-22       Impact factor: 2.602

9.  A wireless transmission neural interface system for unconstrained non-human primates.

Authors:  Jose A Fernandez-Leon; Arun Parajuli; Robert Franklin; Michael Sorenson; Daniel J Felleman; Bryan J Hansen; Ming Hu; Valentin Dragoi
Journal:  J Neural Eng       Date:  2015-08-13       Impact factor: 5.379

10.  Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics.

Authors:  Marco Capogrosso; Fabien B Wagner; Jerome Gandar; Eduardo Martin Moraud; Nikolaus Wenger; Tomislav Milekovic; Polina Shkorbatova; Natalia Pavlova; Pavel Musienko; Erwan Bezard; Jocelyne Bloch; Grégoire Courtine
Journal:  Nat Protoc       Date:  2018-09       Impact factor: 13.491

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