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脑电源显著网络耦合支持现实世界中的注意力切换。

EEG source derived salience network coupling supports real-world attention switching.

机构信息

Neural Engineering and Translation Labs, Department of Psychiatry, University of California San Diego, USA; Department of Electrical and Computer Engineering, University of California San Diego, USA.

Department of Bioengineering, University of California San Diego, USA.

出版信息

Neuropsychologia. 2023 Jan 7;178:108445. doi: 10.1016/j.neuropsychologia.2022.108445. Epub 2022 Dec 9.

Abstract

While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.

摘要

虽然在受控的实验室环境中已经对选择性注意的大脑机制进行了详细研究,但在现实世界的自我调节任务背景下,这些过程如何运作还不太清楚。在这里,我们研究了一项相当于标准学术测试的真实世界计算机化任务的参与情况,该任务包括以自我调节的方式解决高中水平的问题。在这项任务中,我们使用 EEG 源估计有效耦合来描述从专注于任务状态到分心于任务状态的持续注意力切换的神经机制。具体来说,由于突显网络与持续注意力和注意力切换有关,我们对突显网络的核心节点(前岛叶和前扣带皮层)之间的有效耦合进行了假设驱动的分析。根据我们的假设,我们发现,在注意力从专注于任务的状态向分心于任务的状态的转变过程中,AI-ACC 之间的有效耦合增加。这项研究可能为未来的神经功能为导向的脑机接口的开发提供信息,以增强持续注意力。

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