Liu Wenzheng, Zhang Hao, Yang Liu, Gu Yue
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, P. R. China.
Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Apr 25;39(2):228-236. doi: 10.7507/1001-5515.202108048.
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）和功能近红外光谱（fNIRS）的时空优势，探索工作记忆的神经血管耦合机制。在数据分析中，提取EEG数据中不同试验时间序列的卷积矩阵与血流动力学响应函数（HRF）以及fNIRS的血氧变化矩阵作为耦合特征。然后，使用典型相关分析（CCA）计算两个模态特征之间的交叉相关性。结果表明，CCA算法能够提取试验间相关成分的相似变化趋势，且fNIRS在额极区和背外侧前额叶的激活与EEG数据的δ、θ和α节律相关。本研究揭示了工作记忆的神经血管耦合机制，并为EEG数据与fNIRS数据的融合提供了一种新方法。
