Reinkensmeyer David J, Burdet Etienne, Casadio Maura, Krakauer John W, Kwakkel Gert, Lang Catherine E, Swinnen Stephan P, Ward Nick S, Schweighofer Nicolas
Departments of Anatomy and Neurobiology, Mechanical and Aerospace Engineering, Biomedical Engineering, and Physical Medicine and Rehabilitation, University of California, Irvine, USA.
Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK.
J Neuroeng Rehabil. 2016 Apr 30;13(1):42. doi: 10.1186/s12984-016-0148-3.
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.
尽管在过去30年里，利用计算方法为医学和神经科学提供信息取得了进展，但很少有人尝试对感觉运动康复的潜在机制进行建模。我们认为，通过开发包括大脑可塑性和学习系统在内的显著神经过程的计算模型，并将其整合到特定的康复背景中，将有助于从根本上理解神经恢复，从而在个体层面进行准确预测。因此，在这里我们讨论计算神经康复，这是一个新兴领域，旨在通过对可塑性和运动学习进行建模，来理解和改善神经损伤个体的运动恢复。我们首先解释用于康复的机器人技术和可穿戴传感器的出现如何提供数据，使得此类模型的开发和测试越来越可行。然后，我们回顾此类模型将纳入的可塑性和运动学习的关键方面。接着，我们讨论计算神经康复模型与康复建模的当前基准——基于回归的预后建模之间的关系。然后，我们批判性地讨论了首批计算神经康复模型，这些模型主要专注于对中风后上肢康复进行建模，并展示了即使是简单的模型也如何为未来研究产生了新的思路。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由临床成像结果提供信息、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的运动恢复机制模型的出现。
