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.
Highlights
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
Exploratory factor analysis: decomposing the phenotypic space measured in the Human Connectome Project (HCP)
To reduce the phenotypic space measured in the HCP to a set of underlying domains, we conducted an exploratory factor analysis (EFA) in the entire HCP sample that had complete phenotypic data (n = 933, 53.5% females)
Summary
Exploratory factor analysis: decomposing the phenotypic space measured in the HCP. To reduce the phenotypic space measured in the HCP to a set of underlying domains, we conducted an exploratory factor analysis (EFA) in the entire HCP sample that had complete phenotypic data (n = 933, 53.5% females). From this point, causal influences propagated from Fluid Cognition to Visuospatial Processing and Crystalized IQ, replicating a well-studied effect that individuals high in fluid cognitive ability will be high in crystalized intelligence[25,26]. From there causal influences proceeded to more specific cognitive measures, including Working Memory, Language Task Performance (verbal and math ability), and Delay Discounting. We found a direct link between Crystalized IQ and Delay Discounting, such that individuals higher in Crystalized IQ exhibited lower (less impulsive) discounting rates These cognitive measures were in turn causally linked to affective, social, and psychiatric factors. Our results support a causal role for cognitive and affective influences on AUD, while expanding our understanding of the complex multifactorial space contributing to AUD
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