Making new connections: An fNIRS machine learning classification study of neural synchrony in the default mode network

Successfully making connections with others is crucial to navigating the social world and general well-being, yet little is known about connection formation and its neurocognitive underpinnings. Increasingly, neuroscientists use interpersonal ‘neural synchrony’ within the default mode network (DMN) to measure when two or more people subjectively experience something in similar ways. DMN synchrony as ‘seeing eye-to-eye’ is typically observed when multiple people are passively observing the same stimulus. In this study, we tested whether the same DMN synchrony as ‘seeing eye-to-eye’ pattern holds during social interactions. We conducted a between-subject naturalistic experiment with 70 pairs of strangers engaged in either shallow or deep conversations while brain activity was measured with functional near infrared spectroscopy (fNIRS). Stranger dyads successfully formed connections, as indicated by composite connection scores. Replicating Kardas et al. (2021), those in the deep conversation condition felt more connected than those in the shallow conversation condition. DMN neural synchrony significantly predicted self-reported connection, with synchrony in the DMN subregions of medial prefrontal cortex (mPFC) and right temporoparietal junction (TPJ) each correlating significantly with connection. Using machine learning classification, we distinguished high-versus low-connection dyads based on DMN neural synchrony and the perceived depth of conversation with 64.5% accuracy across 1,000 iterations. This effect was primarily carried by right TPJ, which alone classified connection strength at 62.6% accuracy. We consider implications related to the growing loneliness crisis and the importance of understanding how social connections can be formed and fostered in an era of increased social isolation.