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帕金森病对多指任务优化和方差结构的影响。

Effects of Parkinson's disease on optimization and structure of variance in multi-finger tasks.

机构信息

Department of Kinesiology, Rec.Hall-268N, The Pennsylvania State University, University Park, PA, 16802, USA.

出版信息

Exp Brain Res. 2013 Nov;231(1):51-63. doi: 10.1007/s00221-013-3665-3. Epub 2013 Aug 13.

Abstract

We explored the role of the basal ganglia in two components of multi-finger synergies by testing a group of patients with early-stage Parkinson's disease and a group of healthy controls. Synergies were defined as co-varied adjustments of commands to individual fingers that reduced variance of the total force and moment of force. The framework of the uncontrolled manifold hypothesis was used to quantify such co-variation patterns, while average performance across repetitive trials (sharing patterns) was analyzed using the analytical inverse optimization (ANIO) approach. The subjects performed four-finger pressing tasks that involved the accurate production of combinations of the total force and total moment of force and also repetitive trials at two selected combinations of the total force and moment. The ANIO approach revealed significantly larger deviations of the experimental data planes from an optimal plane for the patients compared to the control subjects. The synergy indices computed for total force stabilization were significantly higher in the control subjects compared to the patients; this was not true for synergy indices computed for moment of force stabilization. The differences in the synergy indices were due to the larger amount of variance that affected total force in the patients, while the amount of variance that did not affect total force was comparable between the groups. We conclude that the basal ganglia play an important role in both components of synergies reflecting optimization of the sharing patterns and stability of performance with respect to functionally important variables.

摘要

我们通过测试一组早期帕金森病患者和一组健康对照者,探索了基底神经节在多指协同运动的两个组成部分中的作用。协同运动被定义为对单个手指的指令进行共同变化,从而减少总力和力矩的方差。使用无控制流形假说的框架来量化这种共同变化模式,同时使用分析逆优化 (ANIO) 方法分析重复试验中的平均表现 (共享模式)。研究对象执行涉及总力和总力矩的组合的四指按压任务,并且还在总力和力矩的两个选定组合处进行重复试验。与对照组相比,ANIO 方法显示患者的实验数据平面明显偏离最佳平面,存在较大的偏差。与对照组相比,用于总力稳定的协同指数在患者中明显更高;对于用于力矩稳定的协同指数,则不是这样。协同指数的差异归因于影响患者总力的方差量较大,而不影响总力的方差量在两组之间相当。我们得出结论,基底神经节在反映共享模式优化和性能稳定性的协同运动的两个组成部分中都发挥了重要作用,对于功能重要的变量而言。

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