Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)

蚂蚁 流量(数学) 信息流 考试(生物学) 计算机科学 心理学 人工智能 生物 生态学 物理 计算机网络 机械 语言学 哲学
作者
Keyi Duan,Songyun Xie,Xin Zhang,Xinzhou Xie,Yujie Cui,Ruizhen Liu,Jian Xu
出处
期刊:Brain Sciences [Multidisciplinary Digital Publishing Institute]
卷期号:13 (2): 247-247 被引量:6
标识
DOI:10.3390/brainsci13020247
摘要

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1–30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.
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