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Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning

  • Luigi A. Maglanoc
    Correspondence
    Address correspondence to Luigi A. Maglanoc, Ph.D., Department of Psychology, University of Oslo, Pb. 1094, Blindern 0317, Oslo, Norway.
    Affiliations
    Department of Psychology, University of Oslo, Oslo, Norway

    Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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  • Tobias Kaufmann
    Affiliations
    Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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  • Dennis van der Meer
    Affiliations
    Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

    School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
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  • Andre F. Marquand
    Affiliations
    Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

    Department of Neuroimaging, Institute of Psychiatry, King’s College London, London, United Kingdom
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  • Thomas Wolfers
    Affiliations
    Department of Psychology, University of Oslo, Oslo, Norway

    Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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  • Rune Jonassen
    Affiliations
    Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
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  • Eva Hilland
    Affiliations
    Department of Psychology, University of Oslo, Oslo, Norway

    Division of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
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  • Ole A. Andreassen
    Affiliations
    Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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  • Nils Inge Landrø
    Affiliations
    Department of Psychology, University of Oslo, Oslo, Norway
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  • Lars T. Westlye
    Correspondence
    Lars Westlye, Ph.D., Department of Psychology, University of Oslo, Pb. 1094, Blindern 0317, Oslo, Norway.
    Affiliations
    Department of Psychology, University of Oslo, Oslo, Norway

    Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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      Abstract

      Background

      Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge.

      Methods

      In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging–based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia.

      Results

      Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits.

      Conclusions

      These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging–based brain connectomics.

      Keywords

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