In this study, we tested several hypotheses related to changes in finger interaction and multifinger synergies during multifinger force production tasks in Parkinson's disease. Ten patients with Parkinson's disease, mostly early stage, and 11 healthy control subjects participated in the study. Synergies were defined as covaried adjustment of commands to fingers that stabilized the total force produced by the hand. Both Parkinson's disease patients and control subjects performed accurate isometric force production tasks with the fingers of both the dominant and nondominant hands. The Parkinson's disease patients showed significantly lower maximal finger forces and higher unintended force production (enslaving). These observations suggest that changes in supraspinal control have a major effect on finger individuation. The synergy indexes in the patients were weaker in both steady-state and cyclic force production tasks compared with the controls. These indexes also were stronger in the left (nondominant) hand in support of the dynamic-dominance hypothesis. Half of the patients could not perform the cyclic task at the highest frequency (2 Hz). Anticipatory adjustments of synergies prior to a quick force pulse production were delayed and reduced in the patients compared with the controls. Similar differences were observed between the asymptomatic hands of the patients with symptoms limited to one side of the body and matched hands of control subjects. Our study demonstrates that the elusive changes in motor coordination in Parkinson's disease can be quantified objectively, even in patients at a relatively early stage of the disease. The results suggest an important role of the basal ganglia in synergy formation and demonstrate a previously unknown component of impaired feedforward control in Parkinson's disease reflected in the reduced and delayed anticipatory synergy adjustments.
Two approaches to motor redundancy, optimization and the principle of abundance, seem incompatible. The former predicts a single, optimal solution for each task, while the latter assumes that families of equivalent solutions are used. We explored the two approaches using a four-finger pressing task with the requirement to produce certain combination of total normal force and a linear combination of normal forces that approximated the total moment of force in static conditions. In the first set of trials, many force-moment combinations were used. Principal component (PC) analysis showed that over 90% of finger force variance was accounted for by the first two PCs. The Analytical Inverse Optimization (ANIO) approach was applied to these data resulting in quadratic cost functions with linear terms. Optimal solutions formed a hyperplane (“optimal plane”) in the four-dimensional finger force space. In the second set of trials, only four force-moment combinations were used with multiple repetitions. Finger force variance within each force-moment combination in the second set was analyzed within the uncontrolled manifold (UCM) hypothesis. Most finger force variance was confined to a hyperplane (the UCM) compatible with the required force-moment values. We conclude that there is no absolute optimal behavior, and the ANIO yields the best fit to a family of optimal solutions that differ across trials. The difference in the force producing capabilities of the fingers and in their moment arms may lead to deviations of the “optimal plane” from the sub-space orthogonal to the UCM. We suggest that the ANIO and UCM approaches may be complementary in analysis of motor variability in redundant systems.
We used two methods, analytical inverse optimization (ANIO) and uncontrolled manifold (UCM) analysis of synergies, to explore age-related changes in finger coordination during accurate force and moment of force production tasks. The two methods address two aspects of the control of redundant systems: Finding an optimal solution (an optimal sharing pattern) and using variable solutions across trials (covarying finger forces) that are equally able to solve the task. Young and elderly subjects produced accurate combinations of total force and moment by pressing with the four fingers of the dominant hand on individual force sensors. In session-1, single trials covered a broad range of force–moment combinations. Principal component (PC) analysis showed that the first two PCs explained about 90% and 75% of finger force variance for the young and elderly groups, respectively. The magnitudes of the loading coefficients in the PCs suggested that the young subjects used mechanical advantage to produce moment while elderly subjects did not (confirmed by analysis of moments produced by individual digits). A co-contraction index was computed reflecting the magnitude of moment produced by fingers acting against the required direction of the total moment. This index was significantly higher in the young group. The ANIO approach yielded a quadratic cost function with linear terms. In the elderly group, the contribution of the forces produced by the middle and ring fingers to the cost function value was much smaller than in the young group. The angle between the plane of experimental observations and the plane of optimal solutions (D-angle), was very small (about 1.5°) in the young group and significantly larger (about 5°) in the elderly group. In session-2, four force–moment combinations were used with multiple trials at each. Covariation among finger forces (multifinger synergies) stabilizing total force, total moment, and both was seen in both groups with larger synergy indices in the young group. Multiple regression analysis has shown that, at higher force magnitudes, the synergy indices defined with the UCM method were significantly related to the percent of variance accounted by the first two PCs and to the D-angle computed using the ANIO method. We interpret the results as pointing at a transition with age from synergic control to element-based control (back-to-elements hypothesis). Optimization and analysis of synergies are complementary approaches that focus on two aspects of multidigit coordination, sharing and covariation, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.