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Deep learning identifies partially overlapping subnetworks in the human social brain

Research output: Contribution to journalArticlepeer-review

Hannah Kiesow, R. Nathan Spreng, Avram J. Holmes, M. Mallar Chakravarty, Andre F. Marquand, B. T.Thomas Yeo, Danilo Bzdok

Original languageEnglish
Article number65
JournalCommunications Biology
Volume4
Issue number1
DOIs
PublishedDec 2021

King's Authors

Abstract

Complex social interplay is a defining property of the human species. In social neuroscience, many experiments have sought to first define and then locate ‘perspective taking’, ‘empathy’, and other psychological concepts to specific brain circuits. Seldom, bottom-up studies were conducted to first identify explanatory patterns of brain variation, which are then related to psychological concepts; perhaps due to a lack of large population datasets. In this spirit, we performed a systematic de-construction of social brain morphology into its elementary building blocks, involving ~10,000 UK Biobank participants. We explored coherent representations of structural co-variation at population scale within a recent social brain atlas, by translating autoencoder neural networks from deep learning. The learned subnetworks revealed essential patterns of structural relationships between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as the experience of social isolation. As a consequence of our population-level evidence, spatially overlapping subsystems of the social brain probably relate to interindividual differences in everyday social life.

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