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

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.

Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

“…These results converge with previous studies and systematic reviews that have demonstrated that CUD is characterized by changes in the brain's memory system (32,33,78), the reward and salience networks (32,34), and the occipital lobe (79,80). These results also demonstrate changes in the brain's reward/approach-related system, a domain that was not captured well by the behavioral assessments or neuroimaging tasks used in this study (see Rawls and colleagues [11] for a more in depth discussion). Thus, the current evidence supports the triadic models of cannabis addiction by indicating changes in the brain's reward/approach system, deficits in executive function, specifically in working memory function and verbal IQ, and a role of negative affect, specifically of externalizing symptoms and aggression.…”

“…The identified factors included environmental, personality, mental health, neurocognitive and brain measures, demonstrating the complexity of the factors involved in CUD. Overall, the current results confirm the importance of multi-domain investigations into the factors underlying drug addiction, as in our previous empirical investigation of multi-domain factors in substance use disorders (11).…”

“…Overall, the current results confirm the importance of multi-domain investigations into the factors underlying drug addiction, as in our previous empirical investigation of multi-domain factors in substance use disorders (11).…”

“…Overall, the classification models achieved high accuracy, which in itself was remarkable, since the used dataset was not designed to assess substance use and dependence (see Rawls and colleagues [11] for a more in-depth discussion of the assessments and how they relate to addiction). The current results further confirmed that a small number of factors of the more than one thousand included model factors, consistently provided a unique and additive contribution to the classification performance, beyond other factors in the model.…”

“…Only very few addiction theories have moved beyond this triadic-mechanism framework [e.g. see “vulnerabilities in decision making” (10) as an example], and even less empirical work has been done using a multi-domain data-driven approach [e.g., see “An Integrated Multimodal Model of Alcohol Use Disorder” (11) as an example]. However, separate empirical investigations strongly suggest the involvement of a myriad of different factors.…”