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Predicting alcohol dependence from multi-site brain structural measures
Hahn, Sage Mackey, Scott Cousijn, Janna Foxe, John J. Heinz, Andreas Hester, Robert Hutchinson, Kent Kiefer, Falk Korucuoglu, Ozlem Lett, Tristram
Hahn, Sage
Mackey, Scott
Cousijn, Janna
Foxe, John J.
Heinz, Andreas
Hester, Robert
Hutchinson, Kent
Kiefer, Falk
Korucuoglu, Ozlem
Lett, Tristram
Author
Hahn, Sage
Mackey, Scott
Cousijn, Janna
Foxe, John J.
Heinz, Andreas
Hester, Robert
Hutchinson, Kent
Kiefer, Falk
Korucuoglu, Ozlem
Lett, Tristram
Li, Chiang-Shan R.
London, Edythe
Lorenzetti, Valentina
Maartje, Luijten
Momenan, Reza
Orr, Catherine
Paulus, Martin
Schmaal, Lianne
Sinha, Rajita
Sjoerds, Zsuzsika
Stein, Dan J.
Stein, Elliot
van Holst, Ruth J.
Veltman, Dick
Walter, Henrik
Wiers, Reinout W.
Yucel, Murat
Thompson, Paul M.
Conrod, Patricia
Allgaier, Nicholas
Garavan, Hugh
Mackey, Scott
Cousijn, Janna
Foxe, John J.
Heinz, Andreas
Hester, Robert
Hutchinson, Kent
Kiefer, Falk
Korucuoglu, Ozlem
Lett, Tristram
Li, Chiang-Shan R.
London, Edythe
Lorenzetti, Valentina
Maartje, Luijten
Momenan, Reza
Orr, Catherine
Paulus, Martin
Schmaal, Lianne
Sinha, Rajita
Sjoerds, Zsuzsika
Stein, Dan J.
Stein, Elliot
van Holst, Ruth J.
Veltman, Dick
Walter, Henrik
Wiers, Reinout W.
Yucel, Murat
Thompson, Paul M.
Conrod, Patricia
Allgaier, Nicholas
Garavan, Hugh
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.
Keywords
addiction, alcohol dependence, genetic algorithm, machine learning, multi-site, prediction, structural MRI
Date
2020
Type
Journal article
Journal
Human Brain Mapping
Book
Volume
Issue
Page Range
1-11
Article Number
ACU Department
School of Behavioural and Health Sciences
Faculty of Health Sciences
Faculty of Health Sciences
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open