Research Papers

An Engineering Model to Test for Sensory Reweighting: Nonhuman Primates Serve as a Model for Human Postural Control and Vestibular Dysfunction

[+] Author and Article Information
Lara A. Thompson

Biomedical Engineering Program,
School of Engineering and Applied Sciences,
Department of Mechanical Engineering,
University of the District of Columbia,
4200 Connecticut Avenue NW,
Washington, DC 20008;
Harvard-MIT Division of Health
Sciences and Technology,
Massachusetts Institute of Technology,
77 Massachusetts Avenue,
Cambridge, MA 02138
e-mail: lthomps@alum.mit.edu

Csilla Haburcakova

Jenks Vestibular Physiology Laboratory,
Massachusetts Eye and Ear Infirmary,
Boston, MA 02139
e-mail: Csilla_Haburcakova@meei.harvard.edu

Adam D. Goodworth

Department of Rehabilitation Sciences,
University of Hartford,
West Hartford, CT 06117
e-mail: goodworth@hartford.edu

Richard F. Lewis

Departments of Otology and
Laryngology and Neurology,
Harvard Medical School,
Boston, MA 02139;
Jenks Vestibular Physiology Laboratory,
Massachusetts Eye and Ear Infirmary,
Boston, MA 02139
e-mail: richard_lewis@meei.harvard.edu

1Corresponding author.

Manuscript received December 21, 2016; final manuscript received September 14, 2017; published online October 31, 2017. Editor: Beth A. Winkelstein.

J Biomech Eng 140(1), 011008 (Oct 31, 2017) (12 pages) Paper No: BIO-16-1532; doi: 10.1115/1.4038157 History: Received December 21, 2016; Revised September 14, 2017

Quantitative animal models are critically needed to provide proof of concept for the investigation of rehabilitative balance therapies (e.g., invasive vestibular prostheses) and treatment response prior to, or in conjunction with, human clinical trials. This paper describes a novel approach to modeling the nonhuman primate postural control system. Our observation that rhesus macaques and humans have even remotely similar postural control motivates the further application of the rhesus macaque as a model for studying the effects of vestibular dysfunction, as well as vestibular prosthesis-assisted states, on human postural control. Previously, system identification methodologies and models were only used to describe human posture. However, here we utilized pseudorandom, roll-tilt balance platform stimuli to perturb the posture of a rhesus monkey in normal and mild vestibular (equilibrium) loss states. The relationship between rhesus monkey trunk sway and platform roll-tilt was determined via stimulus–response curves and transfer function results. A feedback controller model was then used to explore sensory reweighting (i.e., changes in sensory reliance), which prevented the animal from falling off of the tilting platform. Conclusions involving sensory reweighting in the nonhuman primate for a normal sensory state and a state of mild vestibular loss led to meaningful insights. This first-phase effort to model the balance control system in nonhuman primates is essential for future investigations toward the effects of invasive rehabilitative (balance) technologies on postural control in primates, and ultimately, humans.

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Fig. 1

Schematic of PRTS generation and application

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Fig. 2

Modified sensory integration feedback controller model for the rhesus monkey hindtrunk

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Fig. 3

(a) RMS roll of hindtrunk and (b) foretrunk as a function of stimulus amplitude with standard error bars. Black-dotted lines represent foretrunk or hindtrunk RMS roll value for stationary platform (i.e., quiet-standing). Normal data: 18 cycles per amplitude; mBVH: 23 cycles per amplitude.

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Fig. 4

Foretrunk transfer function gain, phase, and coherence for normal ((a), (c), (e)) and mBVH ((b), (d), (f)) sensory states. Bars shown represent standard error. Normal data: 18 cycles per amplitude; mBVH: 23 cycles per amplitude.

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Fig. 5

Hindtrunk transfer function gain, phase, and coherence for normal ((a), (c), (e)) and mBVH ((b), (d), (f)) sensory states. Bars shown represent standard error. Normal data: 18 cycles per amplitude; mBVH: 23 cycles per amplitude.

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Fig. 6

Model (gray) and measured (black) hindtrunk transfer functions with standard error bars for the normal sensory state ((a)–(c)) mBVH sensory state ((d)–(f))

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Fig. 7

Nonsimultaneous model parameter estimates ((a)–(e)) and NMSE (f) as a function of stimulus amplitude for the normal (black squares) and mBVH (gray circles) sensory states

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Fig. 8

NMSEsim for the normal state as a function of constrained model variation



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