Adaptation to continuous perturbation of balance: progressive reduction of postural muscle activity with invariant or increasing oscillations of the center of mass depending on perturbation frequency and vision conditions.

We investigated the adaptation of balancing behavior during a continuous, predictable perturbation of stance consisting of 3-min backward and forward horizontal sinusoidal oscillations of the support base. Two visual conditions (eyes-open, EO; eyes-closed, EC) and two oscillation frequencies (LF, 0.2 Hz; HF, 0.6 Hz) were used. Center of Mass (CoM) and Center of Pressure (CoP) oscillations and EMG of Soleus (Sol) and Tibialis Anterior (TA) were recorded. The time course of each variable was estimated through an exponential model. An adaptation index allowed comparison of the degree of adaptation of different variables. Muscle activity pattern was initially prominent under the more challenging conditions (HF, EC and EO; LF, EC) and diminished progressively to reach a steady state. At HF, the behavior of CoM and CoP was almost invariant. The time-constant of EMG adaptation was shorter for TA than for Sol. With EC, the adaptation index showed a larger decay in the TA than Sol activity at the end of the balancing trial, pointing to a different role of the two muscles in the adaptation process. At LF, CoM and CoP oscillations increased during the balancing trial to match the platform translations. This occurred regardless of the different EMG patterns under EO and EC. Contrary to CoM and CoP, the adaptation of the muscle activities had a similar time-course at both HF and LF, in spite of the two frequencies implying a different number of oscillation cycles. During adaptation, under critical balancing conditions (HF), postural muscle activity is tuned to that sufficient for keeping CoM within narrow limits. On the contrary, at LF, when vision permits, a similar decreasing pattern of muscle activity parallels a progressive increase in CoM oscillation amplitude, and the adaptive balancing behavior shifts from the initially reactive behavior to one of passive riding the platform. Adaptive balance control would rely on on-line computation of risk of falling and sensory inflow, while minimizing balance challenge and muscle effort. The results from this study contribute to the understanding of plasticity of the balance control mechanisms under posture-challenging conditions.