ArticleCommunications biology2021
Deep learning identifies partially overlapping subnetworks in the human social brain.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Predicting Empathy and Other Mental States During VR Sessions Using Sensor Data and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings.Psychoradiology · 2023Review
- Prefrontal circuits guiding social preference: Implications in autism spectrum disorder.Neuroscience and biobehavioral reviews · 2022Review
- A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.PLoS biology · 2022Article
- Pattern learning reveals brain asymmetry to be linked to socioeconomic status.Cerebral cortex communications · 2022Article
- Organization of the social cognition network predicts future depression and interpersonal impairment: a prospective family-based study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- Dissecting the midlife crisis: disentangling social, personality and demographic determinants in social brain anatomy.Communications biology · 2021Article
- Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 6 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.