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为大规模脑动力学和认知增强制定控制理论目标。

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement.

作者信息

Singh Matthew F, Cole Michael W, Braver Todd S, Ching ShiNung

机构信息

Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, 63130, MO, USA.

Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 07102, NJ, USA.

出版信息

Annu Rev Control. 2022;54:363-376. doi: 10.1016/j.arcontrol.2022.05.001. Epub 2022 Jul 5.

Abstract

The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.

摘要

脑刺激技术的发展为科学家和临床医生直接激活大脑和神经系统提供了一种手段。脑刺激在改善患者特定症状群和受试者行为特征方面已显示出引人关注的潜力。因此,为优化这些技术以及追求更细微的刺激目标(包括改善记忆和注意力等复杂认知功能)奠定了基础。控制理论和工程学将在这些方法的开发中发挥关键作用,指导刺激的计算和算法策略。特别是,要实现这一目标将需要新开发的框架,这些框架不仅要能够控制大脑活动,还要能够控制神经计算和信息处理背后的潜在动力学。在本观点文章中,我们回顾了脑刺激的最新进展,并概述了与认知功能的外部控制相关的挑战和潜在研究途径。

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Whole-brain estimates of directed connectivity for human connectomics.人类连接组学中定向连接的全脑估计。
Neuroimage. 2021 Jan 15;225:117491. doi: 10.1016/j.neuroimage.2020.117491. Epub 2020 Oct 24.

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