Singh Matthew F, Cole Michael W, Braver Todd S, Ching ShiNung
Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, United States.
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.
Front Neuroimaging. 2022 Nov 18;1:982288. doi: 10.3389/fnimg.2022.982288. eCollection 2022.
Transcranial electrical stimulation (tES) technology and neuroimaging are increasingly coupled in basic and applied science. This synergy has enabled individualized tES therapy and facilitated causal inferences in functional neuroimaging. However, traditional tES paradigms have been stymied by relatively small changes in neural activity and high inter-subject variability in cognitive effects. In this perspective, we propose a tES framework to treat these issues which is grounded in dynamical systems and control theory. The proposed paradigm involves a tight coupling of tES and neuroimaging in which M/EEG is used to parameterize generative brain models as well as control tES delivery in a hybrid closed-loop fashion. We also present a novel quantitative framework for cognitive enhancement driven by a new computational objective: shaping how the brain reacts to potential "inputs" (e.g., task contexts) rather than enforcing a fixed pattern of brain activity.
在基础科学和应用科学领域，经颅电刺激（tES）技术与神经成像的结合日益紧密。这种协同作用推动了个体化tES治疗的发展，并促进了功能神经成像中的因果推断。然而，传统的tES范式受到神经活动变化相对较小以及认知效应个体间差异较大的限制。基于此观点，我们提出了一个基于动力系统和控制理论的tES框架来解决这些问题。所提出的范式涉及tES与神经成像的紧密结合，其中M/EEG用于对生成性脑模型进行参数化，并以混合闭环方式控制tES的施加。我们还提出了一个新的定量框架，用于由新的计算目标驱动的认知增强：塑造大脑对潜在“输入”（如任务情境）的反应方式，而不是强制大脑活动形成固定模式。
