Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing, China.
Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing, ChinabBeijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing, China.
J Biomed Opt. 2017 Feb 1;22(2):27004. doi: 10.1117/1.JBO.22.2.027004.
Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed “hyperscanning.” Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.
双人神经科学是一种理解人类社会认知和互动的视角，涉及设计沉浸式社会互动实验以及同时记录两个或更多主体的大脑活动，这一过程被称为“超扫描”。使用新开发的成像技术，已经揭示了各种类型的社会互动的脑间连接或超链接。功能近红外光谱（fNIRS）-超扫描为社会互动的实验范式提供了更自然的环境，最近引起了广泛关注。然而，大多数 fNIRS-超扫描研究都是直接使用传感器数据计算超链接，而忽略了传感器级信号中包含混杂噪声的事实，这可能导致超链接分析的灵敏度和特异性丧失。在这项研究中，基于独立成分分析（ICA），提出了一种源级分析框架来研究 fNIRS 双人神经科学研究中的超链接。在模拟数据集上比较了五种常用的 ICA 算法在提取交互源方面的性能，并在模拟和真实的双人实验中证明了我们提出的方法在超链接分析中的更高的灵敏度和特异性。
