Deep learning identifies partially overlapping subnetworks in the human social brain.
|Title||Deep learning identifies partially overlapping subnetworks in the human social brain.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Kiesow H, R Spreng N, Holmes AJ, Chakravarty MM, Marquand AF, Yeo BTThomas, Bzdok D|
|Date Published||2021 01 14|
|Keywords||Algorithms, Brain, Databases, Factual, Deep Learning, Female, Humans, Image Processing, Computer-Assisted, Life Style, Male, Middle Aged, Neural Networks, Computer, Social Interaction|
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.
|Alternate Journal||Commun Biol|
|PubMed Central ID||PMC7809473|
|Grant List||R01 AG068563 / AG / NIA NIH HHS / United States |
438531 / / CIHR / Canada