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Hidden population modes in social brain morphology: Its parts are more than its sum

View ORCID ProfileHannah Kiesow, View ORCID ProfileR. Nathan Spreng, View ORCID ProfileAvram J. Holmes, View ORCID ProfileM. Mallar Chakravarty, View ORCID ProfileAndre F. Marquand, View ORCID ProfileB.T. Thomas Yeo, View ORCID ProfileDanilo Bzdok
doi: https://doi.org/10.1101/2020.08.07.241497
Hannah Kiesow
1Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Germany
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R. Nathan Spreng
2Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
3Departments of Psychiatry and Psychology, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Avram J. Holmes
4Department of Psychology, Yale University, New Haven, CT, USA
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M. Mallar Chakravarty
5Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
6Department of Psychiatry, McGill University, Montreal, QC, Canada
7Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
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Andre F. Marquand
8Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
9Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
10Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, De Crespigny Park, London, UK
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B.T. Thomas Yeo
11Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore 119077, Singapore
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Danilo Bzdok
12Department of Biomedical Engineering, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada
13Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
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  • For correspondence: danilo.bzdok@mcgill.ca
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Abstract

The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map ‘perspective taking’, ‘empathy’, and other canonical psychological constructs to distinguishable brain circuits. This predominant research paradigm was seldom complemented by bottom-up studies of the unknown sources of variation that add up to measures of social brain structure; perhaps due to a lack of large population datasets. We aimed at a systematic de-construction of social brain morphology into its elementary building blocks in the UK Biobank cohort (n=~10,000). Coherent patterns of structural co-variation were explored within a recent atlas of social brain locations, enabled through translating autoencoder algorithms from deep learning. The artificial neural networks learned rich subnetwork representations that became apparent from social brain variation at population scale. The learned subnetworks carried essential information about the co-dependence configurations 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 feelings of loneliness. Our population-level evidence indicates that hidden subsystems of the social brain underpin interindividual variation in dissociable aspects of social lifestyle.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 07, 2020.
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Hidden population modes in social brain morphology: Its parts are more than its sum
Hannah Kiesow, R. Nathan Spreng, Avram J. Holmes, M. Mallar Chakravarty, Andre F. Marquand, B.T. Thomas Yeo, Danilo Bzdok
bioRxiv 2020.08.07.241497; doi: https://doi.org/10.1101/2020.08.07.241497
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Hidden population modes in social brain morphology: Its parts are more than its sum
Hannah Kiesow, R. Nathan Spreng, Avram J. Holmes, M. Mallar Chakravarty, Andre F. Marquand, B.T. Thomas Yeo, Danilo Bzdok
bioRxiv 2020.08.07.241497; doi: https://doi.org/10.1101/2020.08.07.241497

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