Journal article

Predicting alcohol dependence frommulti-sitebrain structural measures

Sage Hahn, Scott Mackey, Janna Cousijn, John J Foxe, Andreas Heinz, Robert Hester, Kent Hutchinson, Falk Kiefer, Ozlem Korucuoglu, Tristram Lett, Chiang-Shan R Li, Edythe London, Valentina Lorenzetti, Luijten Maartje, Reza Momenan, Catherine Orr, Martin Paulus, Lianne Schmaal, Rajita Sinha, Zsuzsika Sjoerds Show all

Human Brain Mapping | WILEY | Published : 2020

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..

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Grants

Awarded by Division of Advanced Cyberinfrastructure


Awarded by National Institute of Mental Health


Awarded by National Institute on Alcohol Abuse and Alcoholism


Awarded by National Institute on Drug Abuse


Awarded by National Institutes of Health


Awarded by Nederlandse Organisatie voor Wetenschappelijk Onderzoek


Funding Acknowledgements

Division of Advanced Cyberinfrastructure, Grant/Award Number: OAC-1827314; National Institute of Mental Health, Grant/Award Number: R01 DA018307; National Institute on Alcohol Abuse and Alcoholism, Grant/Award Numbers: R01-AA013892, ZIA AA000125-04 DICB; National Institute on Drug Abuse, Grant/Award Numbers: PL30-1DA024859-01, R01-DA014100, R01-DA020726, R01DA047119, T32DA043593, UL1-RR24925-01; National Institutes of Health, Grant/Award Number: U54 EB020403; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: VICI grant 453.08.01, VIDI grant 016.08.322, ZonMW grant 31160003, ZonMW grant 31160004, ZonMW grant 31180002, ZonMW grant 91676084; Philip Morris International