Abstract
Older adults with degenerative declines in sensory systems depend strongly on visual input for postural control. By connecting advanced neural imaging and a postural control model, this study investigated the visual effect on the brain functional network that regulates feedback and feedforward processes of the postural system in older adults under somatosensory perturbations. Thirty-six older adults conducted bilateral stance on a foam surface in the eyes-open (EO) and eyes-closed (EC) conditions while their center of pressure (COP) and scalp EEG were recorded. The stochastic COP trajectory was modeled with non-linear stabilogram diffusion analysis (SDA) to characterize shifts in postural control in a continuum of feedback and feedforward processes. The EEG network was analyzed with the phase-lag index (PLI) and minimum spanning tree (MST). The results indicated that visual input rebalanced feedforward and feedback processes for postural sway, resulting in a greater critical point of displacement (CD), short-term effective diffusion coefficients (Ds) and short-term exponent (Hs), but the smaller critical point of time (CT) and long-term exponent (Hl) for the EC state. The EC network demonstrated stronger frontoparietal-occipital connectivity but weaker fronto-tempo-motor connectivity of the theta (4–7 Hz), alpha (8–12 Hz), and beta (13–35 Hz) bands than did the EO network. MST analysis revealed generally greater leaf fraction and maximal betweenness centrality (BCmax) and kappa of the EC network, as compared with those of the EO network. In contrast, the EC network exhibited a smaller diameter and average eccentricity than those of the EO network. The modulation of long-term negative feedback gain of the aged postural system with visual occlusion was positively correlated with leaf fraction, BCmax, and kappa, but negatively correlated with the diameter and average eccentricity for all EEG sub-bands. In conclusion, the aged brain functional network in older adults is tuned to visual information for modulating long-term negative feedback of the postural system under somatosensory perturbations.
Highlights
During upright stance, sensory information from somatosensory, visual, and vestibular systems is integrated to regulate upright stance in a closed-loop fashion
Post hoc analysis revealed that critical point of time (CT) and long-term scaling exponent (Hl) were smaller in the EC condition than in the EO condition (p < 0.001)
The present study was the first to attempt to connect the visual effect on the feedforward–feedback processes of posture control to cortical networks in older adults during an unstable stance
Summary
Sensory information from somatosensory, visual, and vestibular systems is integrated to regulate upright stance in a closed-loop fashion. A closed-loop process prevails and results in anti-persistent postural responses with a negative correlation of COP data of the past and future This stochastic model of SDA can quantify shifts in a continuum of the control regime ranging from feedforward (open-loop) to feedback (closed-loop) processes, in terms of changes in the transition between the short- and long-time intervals (or the critical point). With SDA, older adults (especially elderly fallers) were noted to favor a higher open-loop gain (Ds) to regulate upright stance than did young adults (Amoud et al, 2007; Toosizadeh et al, 2015) In light of their greater critical point, older adults were less sensible to greater sway amplitude before the negative feedback mechanism was called into play for balance stabilization
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