Reliance on Visual Input for Balance Skill Transfer in Older Adults: EEG Connectome Analysis Using Minimal Spanning Tree.

Abstract

Skill transfer from trained balance exercises is critical to reduce the rate of falls in older adults, who rely more on vision to control postural responses due to age-dependent sensory reweighting. With an electroencephalography (EEG) minimum spanning tree (MST) structure, the purpose of this study was to compare the organization of supraspinal neural networks of transfer effect after postural training using full and intermittent visual feedbacks for older adults. Thirty-two older adults were randomly assigned to the stroboscopic vision (SV) (n = 16; age = 64.7 ± 3.0 years) and control (16; 66.3 ± 2.7 years) groups for balance training on a stabilometer (target task) with on-line visual feedback. Center-of-pressure characteristics and an MST-based connectome of the weighted phase-lag index during the bilateral stance on a foam surface (transfer task) were compared before and after stabilometer training. The results showed that both the SV and control groups showed improvements in postural stability in the trained task (p < 0.001). However, unlike the control group (p = 0.030), the SV group who received intermittent visual feedback during the stabilometer training failed to reduce the size of postural sway in the anteroposterior direction of the postural transfer task (unstable stance on the foam surface) in the post-test (p = 0.694). In addition, network integration for the transfer task in the post-test was absent in the SV group (p > 0.05). For the control group in the post-test, it manifested with training-related increases in leaf fraction in beta band (p = 0.015) and maximum betweenness in alpha band (p = 0.018), but a smaller diameter in alpha (p = 0.006)/beta (p = 0.021) bands and average eccentricity in alpha band (p = 0.028). In conclusion, stabilometer training with stroboscopic vision impairs generalization of postural skill to unstable stance for older adults. Adequate visual information is a key mediating factor of supraspinal neural networks to carry over balance skill in older adults.