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  Predicting alcohol dependence from multi‐sitebrain structural measures

Hahn, S., Mackey, S., Cousijn, J., Foxe, J. J., Heinz, A., Hester, R., et al. (2022). Predicting alcohol dependence from multi‐sitebrain structural measures. Human Brain Mapping, 43(1), 555-565. doi:10.1002/hbm.25248.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0007-4D33-A Versions-Permalink: https://hdl.handle.net/21.11116/0000-000A-3A4B-2
Genre: Zeitschriftenartikel

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 Urheber:
Hahn, Sage, Autor
Mackey, Scott, Autor
Cousijn, Janna, Autor
Foxe, John J., Autor
Heinz, Andreas, Autor
Hester, Robert, Autor
Hutchinson, Kent, Autor
Kiefer, Falk, Autor
Korucuoglu, Ozlem, Autor
Lett, Tristram, Autor
Li, Chiang‐Shan R., Autor
London, Edythe, Autor
Lorenzetti, Valentina, Autor
Maartje, Luijten, Autor
Momenan, Reza, Autor
Orr, Catherine, Autor
Paulus, Martin, Autor
Schmaal, Lianne, Autor
Sinha, Rajita, Autor
Sjoerds, Zsuzsika1, Autor           
Stein, Dan J., AutorStein, Elliot, AutorHolst, Ruth J., AutorVeltman, Dick, AutorWalter, Henrik, AutorWiers, Reinout W., AutorYucel, Murat, AutorThompson, Paul M., AutorConrod, Patricia, AutorAllgaier, Nicholas, AutorGaravan, Hugh, Autor mehr..
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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Schlagwörter: Addiction; Alcohol dependence; Genetic algorithm; Machine learning; Multi‐site; Prediction; Structural MRI
 Zusammenfassung: 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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Sprache(n): eng - English
 Datum: 2020-09-212020-06-122020-10-062020-10-162022-01
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1002/hbm.25248
Anderer: epub 2020
PMID: 33064342
 Art des Abschluß: -

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Projektinformation

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

Quelle 1

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Titel: Human Brain Mapping
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: New York : Wiley-Liss
Seiten: - Band / Heft: 43 (1) Artikelnummer: - Start- / Endseite: 555 - 565 Identifikator: ISSN: 1065-9471
CoNE: https://pure.mpg.de/cone/journals/resource/954925601686