Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female–Male Relationship

Abstract

Prediction of the initial compatibility of heterosexual individuals based on self-reported traits and preferences has not been successful, even with significantly developed information technology. To overcome the limitations of self-reported measures and predict compatibility, we used functional connectivity profiles from resting-state functional magnetic resonance imaging (fMRI) data that carry rich individual-specific information sufficient to predict psychological constructs and activation patterns during social cognitive tasks. Several days after collecting data from resting-state fMRIs, participants undertook a speed-dating experiment in which they had a 3-min speed date with every other opposite-sex participant. Our machine learning algorithm successfully predicted whether pairs in the experiment were compatible or not using (dis)similarity of functional connectivity profiles obtained before the experiment. The similarity and dissimilarity of functional connectivity between individuals and these multivariate relationships contributed to the prediction, hence suggesting the importance of complementarity (observed as dissimilarity) as well as the similarity between an individual and a potential partner during the initial attraction phase. The result indicates that the salience network, limbic areas, and cerebellum are especially important for the feeling of compatibility. This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.

Neural Processing of Facial Attractiveness and Romantic Love: An Overview and Suggestions for Future Empirical Studies

“…Recent neuroimaging studies using the speed-dating paradigm have further provided intriguing findings, including synchronization of brain activities between the two people who are interacting ( Yuan and Liu, 2022 ), the automatic formation of a “reflected impression” on the speed-dating partner (i.e., the degree to which participants expect the partner to positively view them; Ito et al, 2020 ), and similarities of functional connectivity profiles obtained from resting-state fMRI data that are predictive of the compatibility of the impressions between the two partners ( Kajimura et al, 2021 ). On the other hand, no previous studies have directly revealed how our brains integrate perceived facial attractiveness into initial romantic attraction.…”

The Brain Understands Social Relationships: The Emerging Field of Functional-Connectome-Based Interpersonal Research

“… 14 However, correlation values are insufficient features for machine learning algorithms, because they aggregated the information content of resting-state functional connectome patterns into a single index. In this study, the authors newly defined “connectome similarity” (“Connectivity difference calculation” in Figure 1 ; although this figure is for Kajimura et al 10 , the definition of connectome similarity is the same. ), which has the same number of elements as the vectorized functional connectome, by calculating the absolute value of Euclidean distance for each element of the vectorized functional connectome.…”

“…In this study, 10 similar to Hyon et al, 9 we focused on the possibility that the functional connectome can predict tendencies of behaviors and thoughts that cannot be captured by self-report indices and constructed a machine learning algorithm to predict compatibility based on similarities and differences in functional connectome patterns between 2 individuals ( Figure 1 ). The connectome similarity defined in this study is the same as that of Hyon et al 9 …”

“…In this context, research objectives go beyond the within-individual prediction; parameters tied to dyadic relationships such as compatibility become the main targets of prediction. This paper introduces 2 recent studies published at similar time periods, 9 , 10 which investigated psychological phenomena that have been explained intuitively but not yet scientifically proven: distance among individuals in a social network 9 and the compatibility of a dyad. 10 These studies defined new brain science indices and opened up a new era of interpersonal research using state-of-the-art methodology.…”

“…This paper introduces 2 recent studies published at similar time periods, 9 , 10 which investigated psychological phenomena that have been explained intuitively but not yet scientifically proven: distance among individuals in a social network 9 and the compatibility of a dyad. 10 These studies defined new brain science indices and opened up a new era of interpersonal research using state-of-the-art methodology. Specifically, these studies are significant in that the methods proposed in these studies enabled the application of machine learning algorithms using neuroimaging data and prediction of psychological phenomena caused by the interaction between individuals that cannot be predicted by self-reported psychological indices.…”