An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis

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Abstract

Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.

Original languageEnglish (US)
Article number435
JournalCommunications biology
Volume4
Issue number1
DOIs
StatePublished - Mar 31 2021

Bibliographical note

Funding Information:
E.R. is supported by National Institutes of Mental Health (NIMH) grant T32-MH115866. E.K. received support for this work from the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR000114. A.Z. is supported by the P30 DA048742-01A1 of the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Institutes of Mental Health. The authors thank Matthew Kushner for valuable input and editorial assistance.

Publisher Copyright:
© 2021, The Author(s).

PubMed: MeSH publication types

  • Journal Article

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