A freely-moving monkey treadmill model

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

doi:10.1088/1741-2560/11/4/046020

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.

Highlights

Animal models are central to investigating how the brain senses the ever-changing world and responds with a large repertoire of possible behaviors
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
A highly productive head-fixed model, and posture-fixed model as well, is the reaching monkey model where monkeys perform 2D or 3D arm movements and reaching behavior is captured by tracking the endpoint of the arm by target location (e.g., [5]), with a manipulandum (e.g., [6]), or with an infra-red camera tracking system (e.g., [7, 8]), by tracking markers on the whole arm [9], or by direct measurement of the arm position while the arm is in a two degree-of-freedom exoskeleton using joint-angle encoders [10, 11]

Summary

Introduction

Animal models are central to investigating how the brain senses the ever-changing world and responds with a large repertoire of possible behaviors. A highly productive head-fixed model, and posture-fixed model as well, is the reaching monkey model where monkeys perform 2D or 3D arm movements and reaching behavior is captured by tracking the endpoint of the arm by target location (e.g., [5]), with a manipulandum (e.g., [6]), or with an infra-red camera tracking system (e.g., [7, 8]), by tracking markers on the whole arm [9], or by direct measurement of the arm position while the arm is in a two degree-of-freedom exoskeleton using joint-angle encoders [10, 11] These methods limit movements to the region of space where the arm can move but without changing posture and to the ranges of the behavioral sensors. These methods are sufficient for studying 2D and 3D movements such as point-topoint reaching within the aforementioned restricted workspace

Methods

Results

Conclusion