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. 2022 Apr 25;39(2):228-236.
doi: 10.7507/1001-5515.202108048.

[Neurovascular coupling analysis of working memory based on electroencephalography and functional near-infrared spectroscopy]

[Article in Chinese]
Wenzheng Liu  1   2 , Hao Zhang  3 , Liu Yang  1   2 , Yue Gu  1   2   4
Affiliations

[Neurovascular coupling analysis of working memory based on electroencephalography and functional near-infrared spectroscopy]

[Article in Chinese]
Wenzheng Liu et al. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. .

Abstract
in English, Chinese

Working memory is an important foundation for advanced cognitive function. The paper combines the spatiotemporal advantages of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the neurovascular coupling mechanism of working memory. In the data analysis, the convolution matrix of time series of different trials in EEG data and hemodynamic response function (HRF) and the blood oxygen change matrix of fNIRS are extracted as the coupling characteristics. Then, canonical correlation analysis (CCA) is used to calculate the cross correlation between the two modal features. The results show that CCA algorithm can extract the similar change trend of related components between trials, and fNIRS activation of frontal pole region and dorsolateral prefrontal lobe are correlated with the delta, theta, and alpha rhythms of EEG data. This study reveals the mechanism of neurovascular coupling of working memory, and provides a new method for fusion of EEG data and fNIRS data.

工作记忆是高级认知功能的重要基础。本文结合脑电图（EEG）和功能近红外成像（fNIRS）的时空优势研究工作记忆的神经血管耦合机制。在数据分析中，提取EEG数据中不同试次的时间序列与血氧动力学响应函数（HRF）卷积后的矩阵和fNIRS的血氧变化矩阵作为耦合的特征。然后，使用典型相关分析（CCA）计算两种模态特征间的交叉关联。结果表明，CCA算法能够提取出相关成分试次间相近的变化趋势，并发现额极区和背外侧前额叶的fNIRS激活与EEG数据的delta、theta和alpha节律相关。本研究揭示了工作记忆下的神经血管耦合机制，为EEG数据和fNIRS数据融合提供了新方法。.

Keywords: Canonical correlation analysis; Electroencephalography; Functional near-infrared spectroscopy; Neurovascular coupling; Working memory.

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Conflict of interest statement

利益冲突声明：本文全体作者均声明不存在利益冲突。

Figures

图 1
图 1
The fusion model of EEG data and fNIRS data EEG数据与fNIRS数据融合模型
图 2
图 2
n-back paradigm n-back范式
图 3
图 3
The steps of fusion analysis of EEG data and fNIRS data EEG数据与fNIRS数据融合分析的步骤
图 4
图 4
AMs results of N200 and P300 under three types of loads 3种负荷下N200和P300的AMs结果
图 5
图 5
Correlation components of EEG data and fNIRS data under 0-back loads 0-back负荷下EEG数据与fNIRS数据的相关成分
图 6
图 6
Correlation components of EEG data and fNIRS data under 1-back loads 1-back负荷下EEG数据与fNIRS数据的相关成分
图 7
图 7
Correlation components of EEG data and fNIRS data under 2-back loads 2-back负荷下EEG数据与fNIRS数据的相关成分
See this image and copyright information in PMC

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