Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female–Male Relationship. (18th June 2021)

• Record Type: Journal Article
• Authors: Kajimura, Shogo; Ito, Ayahito; Izuma, Keise
• Is Part Of: Cerebral cortex. Volume 31:Number 11(2021)
• Page Start: 5077
• Page End: 5089
• Publication Date: 2021-06-18
• Subjects: functional connectivity -- machine learning -- resting-state fMRI -- romantic relationship -- speed-dating; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhab143
• Languages: English
• ISSNs: 1047-3211

Abstract

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.