Computational neurorehabilitation: modeling plasticity and learning to predict recovery.
Computational neurorehabilitation: modeling plasticity and learning to predict recovery.
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DOI：
10.1186/s12984-016-0148-3
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发表时间：
2016-04-30
影响因子：
5.1
通讯作者：
Schweighofer N
中科院分区：
文献类型：
--
作者：
Reinkensmeyer DJ;Burdet E;Casadio M;Krakauer JW;Kwakkel G;Lang CE;Swinnen SP;Ward NS;Schweighofer N
尽管过去 30 年在使用计算方法为医学和神经科学提供信息方面取得了进展，但很少有人尝试对感觉运动康复的机制进行建模。我们认为，通过开发显着神经过程（包括大脑的可塑性和学习系统）的计算模型，并将其整合到康复特定的环境中，将有助于对神经系统恢复的基本理解，并因此在个人层面上做出准确的预测。因此，在这里，我们讨论计算神经康复，这是一个新兴领域，旨在对可塑性和运动学习进行建模，以了解和改善神经损伤患者的运动恢复。我们首先解释用于康复的机器人和可穿戴传感器的出现如何提供数据，使此类模型的开发和测试变得越来越可行。然后，我们回顾此类模型将包含的可塑性和运动学习的关键方面。我们继续讨论计算神经康复模型与当前康复模型基准（基于回归的预后模型）的关系。然后，我们批判性地讨论第一个计算神经康复模型，该模型主要关注中风后上肢的康复建模，并展示即使是简单的模型也如何为未来的研究产生新的想法。最后，我们总结了未来研究的关键方向，预计很快我们将看到运动恢复机械模型的出现，这些模型由临床成像结果提供信息，并由康复治疗的实际运动内容以及基于可穿戴传感器的日常活动记录驱动。
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.