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    Predicting alcohol dependence frommulti-sitebrain structural measures

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    Author
    Hahn, S; Mackey, S; Cousijn, J; Foxe, JJ; Heinz, A; Hester, R; Hutchinson, K; Kiefer, F; Korucuoglu, O; Lett, T; ...
    Date
    2020-10-16
    Source Title
    Human Brain Mapping
    Publisher
    WILEY
    University of Melbourne Author/s
    Hester, Robert; Schmaal, Lianne
    Affiliation
    Melbourne School of Psychological Sciences
    Centre for Youth Mental Health
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Hahn, S., Mackey, S., Cousijn, J., Foxe, J. J., Heinz, A., Hester, R., Hutchinson, K., Kiefer, F., Korucuoglu, O., Lett, T., Li, C. -S. R., London, E., Lorenzetti, V., Maartje, L., Momenan, R., Orr, C., Paulus, M., Schmaal, L., Sinha, R. ,... Garavan, H. (2020). Predicting alcohol dependence frommulti-sitebrain structural measures. HUMAN BRAIN MAPPING, https://doi.org/10.1002/hbm.25248.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/252616
    DOI
    10.1002/hbm.25248
    Abstract
    To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

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