Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies

• Published in: Journal of biomedical optics Vol. 22; no. 2; p. 027004
• Main Authors: Zhao, Yang, Dai, Rui-Na, Xiao, Xiang, Zhang, Zong, Duan, Lian, Li, Zheng, Zhu, Chao-Zhe
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.02.2017
• Subjects: Algorithms; Brain Mapping; Humans; Interpersonal Relations; Neurosciences - instrumentation; Neurosciences - methods; Spectroscopy, Near-Infrared; hyperlink; independent component analysis; functional near-infrared spectroscopy; hyperscanning; noise reduction; social interaction; blind source separation; two-person neuroscience
• ISSN: 1083-3668; 1560-2281; 1560-2281
• 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.