Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, CA, USA; Neurology Service, SFVAHCS, San Francisco, CA, USA.
Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, CA, USA; Neurology Service, SFVAHCS, San Francisco, CA, USA.
Neuron. 2022 Aug 3;110(15):2363-2385. doi: 10.1016/j.neuron.2022.06.024.
Stroke is a leading cause of disability. While neurotechnology has shown promise for improving upper limb recovery after stroke, efficacy in clinical trials has been variable. Our central thesis is that to improve clinical translation, we need to develop a common neurophysiological framework for understanding how neurotechnology alters network activity. Our perspective discusses principles for how motor networks, both healthy and those recovering from stroke, subserve reach-to-grasp movements. We focus on neural processing at the resolution of single movements, the timescale at which neurotechnologies are applied, and discuss how this activity might drive long-term plasticity. We propose that future studies should focus on cross-area communication and bridging our understanding of timescales ranging from single trials within a session to across multiple sessions. We hope that this perspective establishes a combined path forward for preclinical and clinical research with the goal of more robust clinical translation of neurotechnology.
中风是导致残疾的主要原因之一。神经技术在改善中风后上肢康复方面显示出了一定的前景,但临床试验的疗效却各不相同。我们的核心观点是,为了提高临床转化,我们需要开发一个通用的神经生理学框架,以了解神经技术如何改变网络活动。我们的观点讨论了运动网络(包括健康的和中风后恢复的网络)如何为伸手抓握运动提供服务的原则。我们关注于单个运动分辨率下的神经处理,以及神经技术应用的时间尺度,并讨论这种活动如何驱动长期的可塑性。我们提出,未来的研究应该侧重于跨区域的通信,并弥合我们对从单次试验到多个试验的时间尺度的理解。我们希望,这种观点为临床前和临床研究建立了一个综合的前进道路,目标是更稳健地将神经技术应用于临床。