Dynamic inter-brain synchrony in real-life inter-personal cooperation: A functional near-infrared spectroscopy hyperscanning study

• Published in: NeuroImage (Orlando, Fla.) Vol. 238; p. 118263
• Main Authors: Li, Rihui, Mayseless, Naama, Balters, Stephanie, Reiss, Allan L.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.09.2021; Elsevier Limited; Elsevier
• Subjects: Brain research; Brain state; Cognition; Cooperation; Dynamic functional connectivity; Electroencephalography; fNIRS; Hemoglobin; Hyperscanning; I.R. radiation; Infrared spectroscopy; Medical imaging; Social interaction; Spectrum analysis; Verbal communication; Wavelet transforms; Brain state; Dynamic functional connectivity; fNIRS; Cooperation; Hyperscanning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118263

How two brains communicate with each other during social interaction is highly dynamic and complex. Multi-person (i.e., hyperscanning) studies to date have focused on analyzing the entire time series of brain signals to reveal an overall pattern of inter-brain synchrony (IBS). However, this approach does not account for the dynamic nature of social interaction. In the present study, we propose a data-driven approach based on sliding windows and k-mean clustering to capture the dynamic modulation of IBS patterns during interactive cooperation tasks. We used a portable functional near-infrared spectroscopy (fNIRS) system to measure brain hemodynamic response between interacting partners (20 dyads) engaged in a creative design task and a 3D model building task. Results indicated that inter-personal communication during naturalistic cooperation generally presented with a series of dynamic IBS states along the tasks. Compared to the model building task, the creative design task appeared to involve more complex and active IBS between multiple regions in specific dynamic IBS states. In summary, the proposed approach stands as a promising tool to distill complex inter-brain dynamics associated with social interaction into a set of representative brain states with more fine-grained temporal resolution. This approach holds promise for advancing our current understanding of the dynamic nature of neurocognitive processes underlying social interaction.