Cognitive enhancement through model-based and individualized neurostimulation

项目摘要(摘要) 经颅电刺激(TES)是一种很有前途的非侵入性检测方法 人类认知脑功能的因果机制，并将系统神经科学理论转化为 心理治疗。这一建议利用全脑个性化神经建模来更好地理解和利用 TES调节大脑活动动态和认知功能的因果机制。这个 拟议的研究解决了先前TES认知增强研究的主要局限性，包括不确定 机制、有效性和个体变异性，通过借鉴来自 从而得出大脑与行为之间的紧密联系。特别是，拟议项目的一个主要优势是其 利用介观个性化神经动力学(Mindy)建模方法和平台 由调查团队开发和验证。拟议的项目雄心勃勃地延长了我们的 之前的工作，通过充分利用Mindy平台来验证和测试创新的神经控制工程 分析、预测和处理大规模脑网络之间的因果关系的方法 (突显[SAL]、额顶控制[FPN]、默认模式[DMN])及其对认知功能的影响。我们 根据参与者的静息脑电为每个参与者建立个性化的Mindy模型 (EEG)数据，然后通过一种新颖的闭环TES+EEG刺激进一步优化模型估计 礼宾(目标1)。然后，我们应用Mindy来调节与注意力和 认知控制，借鉴有影响力的三重网络模型，通过优化刺激SAL网络 以模型引导的方式在静息状态下从DMN主导模式转变为FPN主导模式 条件(目标2)。最后，我们将刺激协议扩展到认知任务情境(目标3)，通过 在参与者进行完善的实验时，实施模型引导的FPN-DMN平衡转移 研究注意力和认知控制的范式(AX-CPT)，测试在以下方面的表现增强 理论上可解释的行为标记。这个项目的结果将会有很高的

Project Summary (Abstract) Transcranial electrical stimulation (tES) represents a promising, noninvasive methodology by which to test causal mechanisms of human cognitive brain functions, and to translate systems neuroscience theory into therapy. This proposal utilizes whole-brain individualized neural modeling to better understand and harness the causal mechanisms by which tES modulates brain activity dynamics and cognitive functioning. The proposed research addresses key limitations of previous tES cognitive-enhancement studies, including uncertain mechanisms, efficacy, and individual variability, by drawing on an explicit neurocomputational architecture from which to derive strong brain-behavior linkages. In particular, a primary strength of the proposed project is its utilization of the Mesoscopic Individualized Neurodynamic (MINDy) modeling approach and platform previously developed and validated by the investigative team. The proposed project provides an ambitious extension of our prior work, by fully leveraging the MINDy platform to validate and test an innovative neural control engineering approach for analyzing, predicting, and manipulating causal relationships between large-scale brain networks (salience [SAL], frontoparietal control [FPN], default mode [DMN]) and their influence on cognitive function. We build individualized MINDy models for each participant based on their resting-state electroencephalographic (EEG) data, then further optimize model estimation through a novel closed-loop tES+EEG stimulation protocol (Aim 1). We then apply MINDy to modulate brain network dynamics associated with attention and cognitive control, drawing upon the influential triple-network model, by optimally stimulating the SAL network in a model-guided manner to shift from a DMN-dominant to FPN-dominant mode, under resting-state EEG conditions (Aim 2). Finally, we extend the stimulation protocol to cognitive task contexts (Aim 3), by implementing model-guided shifts of FPN-DMN balance as participants perform a well-established experimental paradigm probing attention and cognitive control (AX-CPT), testing for performance enhancements in terms of theoretically-interpretable behavioral markers. The findings of this project will have high