Shadmehr Reza, Krakauer John W
Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, 410 Traylor Building, 720 Rutland Ave., Baltimore, MD 21205, USA.
Exp Brain Res. 2008 Mar;185(3):359-81. doi: 10.1007/s00221-008-1280-5. Epub 2008 Feb 5.
The study of patients to infer normal brain function has a long tradition in neurology and psychology. More recently, the motor system has been subject to quantitative and computational characterization. The purpose of this review is to argue that the lesion approach and theoretical motor control can mutually inform each other. Specifically, one may identify distinct motor control processes from computational models and map them onto specific deficits in patients. Here we review some of the impairments in motor control, motor learning and higher-order motor control in patients with lesions of the corticospinal tract, the cerebellum, parietal cortex, the basal ganglia, and the medial temporal lobe. We attempt to explain some of these impairments in terms of computational ideas such as state estimation, optimization, prediction, cost, and reward. We suggest that a function of the cerebellum is system identification: to build internal models that predict sensory outcome of motor commands and correct motor commands through internal feedback. A function of the parietal cortex is state estimation: to integrate the predicted proprioceptive and visual outcomes with sensory feedback to form a belief about how the commands affected the states of the body and the environment. A function of basal ganglia is related to optimal control: learning costs and rewards associated with sensory states and estimating the "cost-to-go" during execution of a motor task. Finally, functions of the primary and the premotor cortices are related to implementing the optimal control policy by transforming beliefs about proprioceptive and visual states, respectively, into motor commands.
通过对患者的研究来推断正常脑功能,在神经病学和心理学领域有着悠久的传统。最近,运动系统已成为定量和计算表征的研究对象。本综述的目的是论证损伤方法与理论运动控制可以相互启发。具体而言,可以从计算模型中识别出不同的运动控制过程,并将它们与患者的特定缺陷相对应。在此,我们综述了皮质脊髓束、小脑、顶叶皮质、基底神经节和内侧颞叶受损患者在运动控制、运动学习和高阶运动控制方面的一些损伤情况。我们试图用诸如状态估计、优化、预测、成本和奖励等计算概念来解释其中的一些损伤。我们认为,小脑的一个功能是系统识别:构建内部模型,预测运动指令的感觉结果,并通过内部反馈纠正运动指令。顶叶皮质的一个功能是状态估计:将预测的本体感觉和视觉结果与感觉反馈整合起来,以形成关于指令如何影响身体和环境状态的信念。基底神经节的一个功能与最优控制有关:学习与感觉状态相关的成本和奖励,并在执行运动任务期间估计“未来成本”。最后,初级运动皮质和运动前皮质的功能分别与通过将关于本体感觉和视觉状态的信念转化为运动指令来实施最优控制策略有关。