Information flow between interacting human brains: Identification, validation, and relationship to social expertise
Proceedings of the National Academy of Sciences of the United States of America
Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals,
... dentifying information flow between the sender's and receiver's temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions. fMRI | hyperscanning | joint attention H uman social interactions have likely shaped brain evolution and are critical for development, health, and society. Defining their neural underpinnings is a key goal of social neuroscience. Interacting dyads, the simplest and fundamental form of human interaction, have been examined with behavioral setups that used real movement interactions during communication in real time as a proxy (1-4), providing mathematical models representing human interaction, goal sharing, mutual engagement, and coordination. To identify the neural systems supporting these behaviors, neuroimaging would be the tool of choice, but studying dyadic interactions with this method is both experimentally and analytically challenging. Consequently, the neural processes underlying human social interactions remain incompletely understood. Experimentally, studying dyads with neuroimaging technology that allows only one participant per scanner provides challenges that have been addressed in the literature in one of two ways. First, the audiovisual experiences of human social contact have been simulated using stimuli such as photographs, recorded videos, or computerized avatars in the absence of human interaction (5-7), or, recently, immersive audiovisual linkups have been used with one of the two participants being scanned (8, 9) . Secondly, pioneering neuroimaging experiments have coupled two scanner sites over the Internet, a setup called hyperscanning, enabling subjects to observe higher-level behavioral responses such as choices made to accept or reject an offer in real time while in the scanners (10, 11). In the current study, we aimed to combine the advantages of these experimental approaches by enabling two humans to see (and possibly hear) each other in a hyperscanning framework, enabling an immersive social interaction while both participant's brains are imaged. To do so, we implemented a setup with delay-free data transmission and precisely synchronized data acquisition, in addition to a live video stream provided between scanner sites during the entire session (Fig. 1A) . While real-time video transmission is not an indispensable requirement for the study of all forms of social interaction, it is a naturalistic presentation method for visual social stimuli in the scanner, and likely helpful for the study of interactions involving changes in eye gaze and facial expressions, although the advantages of the precise temporal synchronization are partially mitigated by the low temporal resolution of the blood oxygen level-dependent (BOLD) response and the sampling frequency of functional MRI (fMRI) experiments. Analytically, extracting and testing for information flow in the resulting joint neuroimaging data are not straightforward. In this paper, we describe a general analysis framework for this problem that makes only minimal a priori assumptions. Importantly, using permutation testing, we also aim to address the open question of whether there is anything neurally specific or even unique about Significance Social interaction is the likely driver of human brain evolution, critical for health, and underlies phenomena as varied as childhood development, stock market behavior, and much of what is studied in the humanities. However, appropriate experimental methods to study the underlying brain processes are still developing and technically challenging. Here, we extend previous pioneering approaches in neuroimaging [functional MRI (fMRI) hyperscanning] to provide a method for studying information flow between interacting humans in a two-person approach. A scan environment enabling synchronized data acquisition and interaction-based fMRI tasks is described. We provide a generally applicable analysis method to identify interacting brain systems. Specific social brain systems are identified as drivers of interaction in humans, and we find a link to a measure of social expertise.