The Sensorimotor Locus of Balance Control in Elderly Gait

Overview

The aging population is at an exceptionally high risk of debilitating falls, contributing significantly to reduced independence and quality of life. It remains extremely challenging to screen for falls risk, and programs designed to mitigate falls risk have only modestly influenced the sizeable portion of the aging population experiencing one or more falls annually. Balance control in standing and walking depends on integrating reliable sensory feedback and on planning and executing appropriate motor responses. Walking balance control is especially dynamic, requiring active and coordinated adjustments in posture (i.e., trunk stabilization) and foot placement from step to step. Accordingly, using a custom, immersive virtual environment, the investigators have shown that sensory (i.e., optical flow) perturbations, especially when applied during walking, elicit strong and persistent motor responses to preserve balance. Exciting pilot data suggest that these motor responses are remarkably more prevalent in old age, presumably governed by an increased reliance on vision for balance control. Additional pilot data suggest that prolonged exposure to these perturbations may effectively condition successful balance control strategies. Founded on these recent discoveries, and leveraging the increase reliance on vision for balance control in old age, the investigators stand at the forefront of a potentially transformative new approach for more effectively identifying and mitigating age-related falls risk. The investigator's overarching hypothesis is that optical flow perturbations, particularly when applied during walking, can effectively identify balance deficits due to aging and falls history and can subsequently condition the neuromechanics of successful balance control via training.

Specific Aim 1. Investigate sensory, motor, and cognitive-motor mechanisms governing susceptibility to optical flow perturbations. Aging increases the reliance on vision for balance control. However, central and peripheral mechanisms underlying aging and falls history effects on the susceptibility to optical flow perturbations are unclear. Hypothesis 1: Entrainment to optical flow perturbations will correlate most strongly with visual dependence and decreased somatosensory function, alluding to an age-associated process of multi-sensory reweighting. Methods: Multivariate models will quantify the extent to which strategically-selected sensory (i.e., visual dependence via rod/frame test, somatosensory function), motor (i.e., rate of torque development, timed sit-to-stand) and cognitive-motor (i.e., interference) mechanisms underlie inter-individual differences in susceptibility to perturbations. Specific Aim 2. Estimate the efficacy of prolonged optical flow perturbations to condition the neuromechanics of walking balance control in older adult fallers. Pilot data from young adults suggests that prolonged exposure to optical flow perturbations may condition reactive strategies used to successfully control walking balance. The investigator's premise is that dynamic perturbation training can improve resilience to unexpected balance disturbances. Here, the investigators conduct a preliminary test of the effects of training with optical flow perturbations on walking balance in older adult fallers. Hypothesis 2: (a) Older adults with a history of falls will adapt to prolonged exposure to perturbations, conditioning their step to step adjustments in walking balance control, and (b) improving their response to unexpected balance challenges following training. Methods: In two 20 min sessions, on different days in a randomized cross-over design, older adults with a history of falls will walk with ("treatment" session) and without ("control" session) prolonged exposure to optical flow perturbations. The investigators will assess time-dependent changes in the neuromechanics of walking balance during training and after-effects via gait variability, dynamic stability, and performance on a series of real-world like targeting and obstacle avoidance tasks.