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使用闭环超声脑机接口解码运动计划。

Decoding motor plans using a closed-loop ultrasonic brain-machine interface.

机构信息

Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.

David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.

出版信息

Nat Neurosci. 2024 Jan;27(1):196-207. doi: 10.1038/s41593-023-01500-7. Epub 2023 Nov 30.

DOI:10.1038/s41593-023-01500-7
PMID:38036744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10774125/
Abstract

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.

摘要

脑机接口（BMI）使患有慢性瘫痪的人无需任何动作仅凭思维即可控制计算机、机器人等。现有的 BMI 在侵入性、性能、空间覆盖范围和时空分辨率方面存在权衡。功能超声（fUS）神经影像学是一种新兴技术，可以平衡这些属性，并可能补充现有的 BMI 记录技术。在这项研究中，我们使用 fUS 成功实现了闭环超声 BMI。当两只恒河猴进行眼动和手动时，我们从它们的顶后皮质流式传输 fUS 数据。经过训练，猴子可以使用 BMI 控制多达八个运动方向。我们还开发了一种使用以前会话数据对 BMI 进行预训练的方法。这使得猴子能够在随后的几天，甚至是相隔数月的日子里立即进行控制，而无需进行广泛的重新校准。这些发现确立了超声 BMI 的可行性，为一类新的、侵入性更小（硬膜外）的接口铺平了道路，这些接口可以在较长时间内推广，并有望恢复神经损伤患者的功能。

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