Inter-brain network underlying spontaneous interpersonal synchronization
Poster No:
653
Submission Type:
Abstract Submission
Authors:
Kohei Miyata1, Takahiko Koike1, Shohei Tsuchimoto2, Kanae Ogasawara1, Hiroki Tanabe3, Norihiro Sadato4, Kazutoshi Kudo5
Institutions:
1RIKEN, Wako, Saitama, 2National institute for physiological sciences, Okazaki, Aichi, 3Nagoya University, Nagoya, Aichi, 4Ritsumeikan University, Kusatsu, Shiga, 5University of Tokyo, Meguro, Tokyo
First Author:
Co-Author(s):
Introduction:
People spontaneously synchronize their body movements when interacting visually and auditorily (Schmidt et al., 1990). Interpersonal motor synchronization is linked to social and affective factors, playing a crucial role in social interactions (Keller et al., 2014). While it has been suggested that the mirror system contributes to this synchronization, the underlying neural mechanisms remain unclear. Since synchronization occurs through real-time interactions between two individuals, a multi-brain approach is necessary to explore its neural basis. A major challenge in this approach is the multiple comparisons problem when assessing activity relationships across regions in two brains. To address this, we leveraged a machine learning approach, connectome-based predictive modeling (CPM), which offers an advantage over traditional inter-brain correlation methods by focusing on identifying the inter-brain network involved in cognitive behaviors without the pitfalls of multiple comparisons (Shen et al., 2017). We extended CPM to inter-brain analysis to identify the inter-brain network underlying sponetanous interpersonal synchronization.
Methods:
We conducted hyperscanning functional magnetic resonance imaging (MRI) to examine brain activity in pairs of individuals drawing a circle. Each participant in twenty-seven pairs was instructed to keep drawing a circle as accurately as possible in the air, at a preferred but constant frequency for six minutes. Their movements were recorded by MRI-compatible video cameras and projected onto a screen in the other scanner, allowing participants to see each other's movements. Participants were not explicitly instructed to synchronize or unsynchronize their movements. The tips of their index fingers were digitized to calculate the synchronization index (SI), which quantifies the phase relationship between their movements. Blood-oxygen-level-dependent signals were recorded from both individuals simultaneously using two MRI scanners. Whole-brain time-series data were anatomically parcellated into 368 regions (Shen et al., 2013). Pearson's correlation coefficients were calculated for each pair of nodes both within and between individuals, creating a 736×736 functional connectivity matrix. These matrices, along with the SI, were used as input variables for CPM. To ensure the inter-brain network reflected real-time interaction and not merely the performance of the same movements, permutation tests were conducted using pseudo-pairs, created by randomly pairing participants from different groups.
Results:
The SI was significantly higher in real pairs compared to pseudo-pairs (Figure 1), confirming that participants synchronized their movements during real-time interaction. The CPM analysis revealed that interpersonal synchronization in real-time interactions was predicted by connectivity within an inter-brain network involving the mirror system, such as the inferior frontal gyrus, and the midcingulate cortex (MCC) which served as the central hub of the network (Figure 2). The network did not include any intra-brain connectivity.
·Figure 1. Synchronization index
·Figure 2. Inter-brain network to build the connectome-based predictive model of interpersonal motor synchronization
Conclusions:
This study provides new insights into the neural basis of interpersonal motor synchronization and demonstrates that CPM is a powerful tool for inter-brain analysis. While previous studies have suggested a role for the mirror system, our study is the first to investigate the inter-brain network, including both cortical and subcortical regions, that underlies motor synchronization using hyperscanning MRI and CPM. A novel finding is that, while the mirror system is part of the network, the MCC serves as a central hub in the inter-brain network underlying spontaneous interpersonal synchronization.
Emotion, Motivation and Social Neuroscience:
Social Interaction 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Motor Behavior:
Mirror System
Motor Behavior Other
Keywords:
ADULTS
FUNCTIONAL MRI
Immitation
Machine Learning
Motor
NORMAL HUMAN
Social Interactions
Other - Interpersonal Synchronization, hyperscanning
1|2Indicates the priority used for review
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Provide references using APA citation style.
Schmidt, R. C., Carello, C., & Turvey, M. T. (1990). Phase transitions and critical fluctuations in the visual coordination of rhythmic movements between people. Journal of Experimental Psychology. Human Perception and Performance, 16(2), 227–247.
Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3), 506–518.
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82(C), 403–415.