Brain-computer interfaces based on intracortical recordings of neural activity for restoration of movement and communication of people with paralysis

T. Milekovic
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引用次数: 2

Abstract

Paralysis has a severe impact on a patient's quality of life and entails a high emotional burden and life-long social and financial costs (‘One Degree of Separation, Paralysis and Spinal Cord Injury in the United States’ 2009; “Towards concerted efforts for treating and curing spinal cord injury” 2002; Arno, Levine, and Memmott 1999). Restoring movement and independence for people with paralysis remains a challenging clinical problem, currently with no viable solution. Recent demonstrations of intracortical brain-computer interfaces, neuroprosthetic devices that create a link between a person and a computer based on invasive recordings of a person's brain activity, have brought hope for their potential to restore movement and communication (Ajiboye et al. 2017; Pandarinath et al. 2017; Gilja et al. 2015; Jarosiewicz et al. 2015; Hochberg et al. 2012; Wodlinger et al. 2015; Collinger et al. 2013; Bouton et al. 2016; Aflalo et al. 2015). While the intracortical brain-computer interfaces have steadily improved over the last decade, the recent success in linking brain activity with the newly developed techniques for spinal cord stimulation look to revolutionize locomotor rehabilitation (Moraud et al. 2016; Wenger et al. 2016; Wenger et al. 2014; van den Brand et al. 2012; Rejc et al. 2016; Angeli et al. 2014; Harkema et al. 2011). Specifically, in a recent study a brain-spine interface — a neuroprostheses using gait states decoded from intracortically recorded neuronal activity to control spinal cord stimulation — restored weight-bearing locomotion of the paralyzed leg as early as six days post-injury in rhesus macaques (Capogrosso et al. 2016). The talk will discuss our progress towards enhancing the capabilities of brain-spine interfaces and demonstrating their use to alleviate motor deficits in other neurological disorders. In parallel, there is an ongoing search for identifying neural features and designing decoding algorithms with the aim to deliver both stable and accurate brain-computer interface control over clinically relevant periods of several months (Jarosiewicz et al. 2015; Vansteensel et al. 2016). The talk will also present our progress in developing techniques to identify stable neural features from intracortical neural recordings of people with tetraplegia and locked-in syndrome. The talk will show the use of these techniques to deliver stable long-term control of neural interfaces. This abstract is based on join work with Flavio Raschella, Giuseppe Schiavone, Matthew Perich, Marco Capogrosso, David Borton, Anish A. Sarma, Fabien Wagner, Eduardo Martin Moraud, Christopher Hitz, Jean-Baptiste Mignardot, Daniel Bacher, John D. Simeral, Jad Saab, Chethan Pandarinath, Brittany L. Sorice, Christine Blabe, Erin M. Oakley, Kathryn R. Tringale, Nicolas Buse, Jerome Gandar, Quentin Barraud, David Xing, Elodie Rey, Simone Duis, Yang Jianzhong, Wai Kin D. Ko, Qin Li, Chuan Qin, Emad Eskandar, Sydney S. Cash, Jaimie M. Henderson, Peter Detemple, Tim Denison, Silvestro Micera, Erwan Bezard, Jocelyne Bloch, Krishna V. Shenoy, John P. Donoghue, Leigh R. Hochberg and Grégoire Courtine.
基于皮层内神经活动记录的脑机接口,用于恢复瘫痪患者的运动和交流
瘫痪严重影响患者的生活质量,并带来沉重的情感负担和终生的社会和经济成本(《美国的一度分离、瘫痪和脊髓损伤》,2009;“共同努力治疗和治愈脊髓损伤”,2002年;Arno, Levine, and Memmott 1999)。恢复瘫痪患者的活动和独立仍然是一个具有挑战性的临床问题,目前没有可行的解决方案。最近皮层内脑机接口的展示,神经假肢装置基于对人脑活动的侵入性记录在人与计算机之间建立联系,为他们恢复运动和沟通的潜力带来了希望(Ajiboye等人,2017;Pandarinath et al. 2017;Gilja et al. 2015;Jarosiewicz et al. 2015;Hochberg et al. 2012;Wodlinger et al. 2015;Collinger et al. 2013;Bouton et al. 2016;Aflalo et al. 2015)。虽然皮质内脑机接口在过去十年中稳步改善,但最近成功地将大脑活动与新开发的脊髓刺激技术联系起来,有望彻底改变运动康复(Moraud et al. 2016;Wenger et al. 2016;Wenger et al. 2014;van den Brand et al. 2012;Rejc et al. 2016;Angeli et al. 2014;Harkema et al. 2011)。具体来说,在最近的一项研究中,脑-脊柱界面——一种神经假体,利用从皮质内记录的神经元活动中解码的步态状态来控制脊髓刺激——在猕猴受伤后6天就恢复了瘫痪腿的负重运动(Capogrosso et al. 2016)。这次演讲将讨论我们在增强脑-脊柱接口能力方面的进展,并展示它们在缓解其他神经系统疾病的运动缺陷方面的应用。与此同时,目前正在研究识别神经特征和设计解码算法,目的是在临床相关的几个月内提供稳定和准确的脑机接口控制(Jarosiewicz等人,2015;Vansteensel et al. 2016)。本次演讲还将介绍我们在开发技术方面的进展,以从四肢瘫痪和闭锁综合征患者的皮质内神经记录中识别稳定的神经特征。这次演讲将展示如何使用这些技术来实现对神经接口的长期稳定控制。这篇摘要是基于与弗拉维奥·拉斯切拉、朱塞佩·斯齐亚沃尼、马修·佩里奇、马可·卡波格罗索、大卫·博尔顿、阿尼什·萨尔马、法比安·瓦格纳、爱德华多·马丁·莫罗德、克里斯托弗·希茨、让-巴蒂斯特·米格纳多、丹尼尔·巴切尔、约翰·d·西莫勒尔、杰德·萨伯、切坦·潘达里纳特、布列塔尼·l·索里斯、克里斯汀·布拉贝、埃琳·m·奥克利、凯瑟琳·r·特林格尔、尼古拉斯·布斯、杰罗姆·甘达尔、昆汀·巴罗、大卫·星、Elodie Rey、西蒙·杜伊斯、杨建中、Wai Kin D. Ko、李琴、琴川、Emad Eskandar, Sydney S. Cash, Jaimie M. Henderson, Peter Detemple, Tim Denison, Silvestro Micera, Erwan Bezard, Jocelyne Bloch, Krishna V. Shenoy, John P. Donoghue, Leigh R. Hochberg和gracimgoire Courtine。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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