Implementation of a Central Sensorimotor Integration Test for Characterization of Human Balance Control During Stance

Abstract

Balance during stance is regulated by active control mechanisms that continuously estimate body motion, via a "sensory integration" mechanism, and generate corrective actions, via a "sensory-to-motor transformation" mechanism. The balance control system can be modeled as a closed-loop feedback control system for which appropriate system identification methods are available to separately quantify the sensory integration and sensory-to-motor components of the system. A detailed, functionally meaningful characterization of balance control mechanisms has potential to improve clinical assessment and to provide useful tools for answering clinical research questions. However, many researchers and clinicians do not have the background to develop systems and methods appropriate for performing identification of balance control mechanisms. The purpose of this report is to provide detailed information on how to perform what we refer to as "central sensorimotor integration" (CSMI) tests on a commercially available balance test device (SMART EquiTest CRS, Natus Medical Inc, Seattle WA) and then to appropriately analyze and interpret results obtained from these tests. We describe methods to (1) generate pseudorandom stimuli that apply cyclically-repeated rotations of the stance surface and/or visual surround (2) measure and calibrate center-of-mass (CoM) body sway, (3) calculate frequency response functions (FRFs) that quantify the dynamic characteristics of stimulus-evoked CoM sway, (4) estimate balance control parameters that quantify sensory integration by measuring the relative contribution of different sensory systems to balance control (i.e., sensory weights), and (5) estimate balance control parameters that quantify sensory-to-motor transformation properties (i.e., feedback time delay and neural controller stiffness and damping parameters). Additionally, we present CSMI test results from 40 subjects (age range 21-59 years) with normal sensory function, 2 subjects with results illustrating deviations from normal balance function, and we summarize results from previous studies in subjects with vestibular deficits. A bootstrap analysis was used to characterize confidence limits on parameters from CSMI tests and to determine how test duration affected the confidence with which parameters can be measured. Finally, example results are presented that illustrate how various sensory and central balance deficits are revealed by CSMI testing.

Keywords: balance; balance control; orientation; sensorimotor; sensory integration; stance; system identification.

Figures

Figure 1

Balance control model block diagram. Visual, vestibular, and proprioception systems provide accurate measures of body orientation relative to the visual scene, earth vertical, and the stance surface, respectively. A weighted combination of these sensory sources provides an internal orientation estimate. This orientation estimate is supplemented with information regarding the mathematical integral of overall corrective ankle torque, Tc, via a positive “Torque Feedback” loop. The signs on the summations within the “Sensory Integration” subsystem indicate whether the sensory information provides negative feedback control (from visual, vestibular, and proprioceptive systems) or positive feedback control (from torque sensors). The sensory information is used to generate time-delayed corrective torque via a “Neural Controller” and corrective torque from the neural controller is supplemented by torque due to passive muscle/tendon mechanics. The overall corrective ankle torque causes a single-segment inverted pendulum body to change orientation. Laplace transform representations of the dynamic properties of various components are shown.

Figure 2

Calibration example. The test subject is instructed to sway slowly forward and backward while recording anterior-posterior (AP) center-of-pressure (CoP) displacement, and body displacements at hip, xh(t), and shoulder, xs(t), levels. A least squared error fit of a linear combination of hip and shoulder displacements to CoP provides coefficients for use in measuring center-of-mass (CoM) displacement on subsequent tests.

Figure 3

Example single-subject data and data analysis of center-of-mass (CoM) sway evoked by a surface-tilt stimulus with 2° peak-to-peak amplitude during eyes closed stance. Twelve cycles of the surface-tilt stimulus and the corresponding evoked CoM sway are shown (A). Averaging across the last 11 cycles of the stimulus and sway response reveal the close correspondence between stimulus and response (B). A frequency response function represented by gain and phase functions [mean values ±95% confidence limits; see (30)] characterize the balance control dynamics of this individual on this particular test with a coherence function providing information on signal-to-noise quality (C). The solid lines through the frequency response data are based on model parameter estimates. Model-predicted CoM sway based on identified parameters is shown in (A,B) with a variance accounted for (VAF) measure showing that the model accounts for most of the experimental stimulus evoked sway. Comparison of the actual and ideal stimulus (ideal is offset from the actual for comparison) in (B) demonstrates the accurate delivery of the desired stimulus.

Figure 4

Results from a bootstrap analysis used to investigate how test duration affects the distribution of the sensory weight parameter in the eight test conditions. The five vertical bars for each test condition represent the 90th percentile (thick green bar) and 95th percentile (thin blue bar) range of the parameter when results were derived from tests that included either 3, 6, 9, 11, 15, and 20 stimulus-response cycles (arranged left to right for each condition). Mean (red o) and median (black +) values of each distribution are shown.

Figure 5

Mean coherence and identified parameter values for the 8 test conditions from 40 subjects whose stimulus-response behavior was modeled using a proportional-derivative neural controller plus torque feedback are shown in (A). Subject data points from 2 and 4° stimulus-amplitude tests for the 4 test types are connected by lines with black lines and points indicating smaller parameter values on the 4° tests compared to the 2° tests and red lines indicating larger parameter values on the 4° tests. Boxplots next to the individual points show median values (center horizontal line), lower and upper 25th and 75th percentile values (lower and upper edges of the box), approximate 95% confidence limits on the median values (notches on the box), error bars (spanning smallest to largest individual values that are not considered to be outliers), and outlying data points (+'s). (B) shows values of the normalized integration control factor derived from model fits using a proportional-integral-derivative neural controller. Parameters from the single subject whose model fit for test condition five was not compatible with stability are not included.

Figure 6

Correlation of neural controller and torque feedback parameters with mgh (body mass excluding feet x gravity constant × center of mass height above ankle joints). Individual values are shown for 40 subjects from the surface-tilt, eyes closed, 2° tests. In (A), the Kp and Kd parameters are from the model with proportional-derivative control plus torque feedback and Ki is from the model with proportional-integral-derivative control. (B) shows a negative correlation between the torque feedback parameter Kt and mgh.

Figure 7

Correlation and Bland-Altman plots comparing model fit mean square error (MSE) and parameters from the model with PD (proportional-derivative control) plus torque feedback with parameters from a PID (proportional-integral-derivative) model. Comparisons are shown for results from 40 subjects on the surface-tilt, eyes closed, 2° amplitude test.

Figure 8

Results associated with phaseless lowpass filtering of center-of-pressure (CoP) to estimate center-of-mass (CoM) displacement. (A) Example traces of CoP and CoM displacement measures from EquiTest recordings, sway rod estimates, and phaseless lowpass filtering. (B) Distribution of cutoff frequencies that minimized errors between CoM derived from phaseless lowpass filtering and sway rod estimates. (C) Comparison of sensory weights based on sway rod and lowpass filtered CoP measures of CoM from 40 subjects on the 4 test conditions with 2° amplitudes. Gray dashed line shows 1:1 slope. Significance values on all comparisons in (C) are p < 0.001. (D) Bland Altman plots comparing the sensory weight measures shown in (C).

Figure 9

Example results from previous studies showing sensory weight measures in subjects with bilateral vestibular loss (A) and well compensated unilateral vestibular loss (B) in comparison to subjects with normal vestibular function. Bilateral vestibular loss results from Peterka (6) and unilateral loss results from Peterka (11).

Figure 10

Example results from a subject with chronic balance complaints following mild traumatic brain injury (mTBI) in comparison to a control subject. Model fits (solid lines) to the frequency response function data in (A) identified equal sensory weight measures of Wprop = 0.42 for both subjects. The divergence between the mTBI and control results are accounted for by the mTBI subject having a lower value of the neural controller proportional gain parameter, Kp, and lower value of the torque feedback parameter, Kt. The functional consequences of these differences are that the surface-tilt stimulus evoked much larger sway in the mTBI subject (B).

Figure 11

Example results from a subject with no known physiological deficits but who showed large oscillatory sway patterns (A). The frequency response function data and parameters identified from the model fit (B) are compatible with normal balance control suggesting the possibility that the subject was purposely trying to disrupt testing.