@article{eab9ee9804764546ac0e0d080f43e8a7,
title = "Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning",
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 = "Brain networks, fMRI, Functional connectivity, Human traits, Machine learning, Polygenic scores, GENOME-WIDE ASSOCIATION, FUNCTIONAL CONNECTIVITY, SCHIZOPHRENIA, INTELLIGENCE, DEPRESSION, NETWORK, HEALTH, RISK, AGE, DISORDERS",
author = "Maglanoc, {Luigi A.} and Tobias Kaufmann and {van der Meer}, Dennis and Marquand, {Andre F.} and Thomas Wolfers and Rune Jonassen and Eva Hilland and Andreassen, {Ole A.} and Landro, {Nils Inge} and Westlye, {Lars T.}",
note = "Funding Information: The authors were funded by the Research Council of Norway (Grant Nos. 213837 , 223723 , 229129 , 204966 , and 249795 [to LTW]), the South-Eastern Norway Regional Health Authority (Grant Nos. 2014097 and 2015073 [to LTW] and 2016083 and 2017112 [to OAA]), the European Research Council under the European Union{\textquoteright}s Horizon 2020 research and innovation program (ERC StG, Grant No. 802998 [to LTW]), the Department of Psychology at the University of Oslo (to LTW, NIL), and the foundation KG Jebsen Stiftelsen (to OAA). The permutation testing was performed using resources provided by Uninett Sigma2, the national infrastructure for high-performance computing and data storage in Norway. Funding Information: The authors were funded by the Research Council of Norway (Grant Nos. 213837, 223723, 229129, 204966, and 249795 [to LTW]), the South-Eastern Norway Regional Health Authority (Grant Nos. 2014097 and 2015073 [to LTW] and 2016083 and 2017112 [to OAA]), the European Research Council under the European Union's Horizon 2020 research and innovation program (ERC StG, Grant No. 802998 [to LTW]), the Department of Psychology at the University of Oslo (to LTW, NIL), and the foundation KG Jebsen Stiftelsen (to OAA). The permutation testing was performed using resources provided by Uninett Sigma2, the national infrastructure for high-performance computing and data storage in Norway. This research was conducted using the UK Biobank Resource (access code 27412). This article was published as a preprint on bioRxiv: doi: https://doi.org/10.1101/609586. OAA has previously received speakers honoraria from Lundbeck and Sunovion. NIL has previously received consultancy fees and travel expenses from Lundbeck. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: {\textcopyright} 2019 Society of Biological Psychiatry",
year = "2020",
month = apr,
day = "15",
doi = "10.1016/j.biopsych.2019.10.011",
language = "English",
volume = "87",
pages = "717--726",
journal = "Biological Psychiatry",
issn = "0006-3223",
publisher = "Elsevier Science",
number = "8",
}