An Integrated Multimodal Model of Alcohol Use Disorder Generated by Data-Driven Causal Discovery Analysis

Abstract

Objective: 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 multifactorial mechanisms driving AUD. Methods: We used Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). Our outcome variable was number of AUD symptoms. We applied exploratory factor analysis (EFA) to parse phenotypic measures into underlying constructs, and assessed functional connectivity within 12 resting-state brain networks as an indicator of brain function. We then employed data-driven CDA to generate an integrated model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity. Results: EFA extracted 18 factors representing the wide HCP phenotypic space (100 measures). CDA produced an integrated multimodal model, highlighting a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain function to cognition (fluid/crystalized cognition, language & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Every edge in the model was present at p < .001, and the SEM model overall provided a good fit (RMSEA = .06, Tucker-Lewis Index = .91). Conclusions: Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that traditional addiction models need to be expanded to highlight the importance of social factors, amongst others. Results further demonstrated that it is possible to extract a limited set of causal factors of AUD, which can inform future research aimed at tracking factors that dynamically predict alcohol use trajectories. Lastly, the presented model identified potential treatment targets for AUD, including neuromodulation of the frontoparietal network, cognitive/affective interventions, and social interventions.

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.…”

“…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.…”

Negative Urgency as a Risk Factor for Hazardous Alcohol Use: Dual Influences of Cognitive Control and Reinforcement Processing

“…Our results suggest interventions that enhance efficiency in an individual’s use of cortical resources for cognitive control and reinforcement processing (i.e., identification of salient stimuli and shifting of attention toward or away from such stimuli) may significantly impact their engagement in hazardous alcohol use. Importantly, previous research by our group has shown that PFC function might have a downstream causal impact on severity of alcohol use disorder (Rawls et al, 2021), suggesting that interventions focused on PFC function could improve cognitive function in individuals with problematic alcohol use.…”

The Resting-State Causal Human Connectome is Characterized by Hub Connectivity of Executive and Attentional Networks

“…The discovered hub role of the frontoparietal network in the causal human connectome is especially attractive, as brain-based therapies for psychiatric conditions typically impact or directly stimulate nodes in the frontoparietal network (Belsher et al, 2021; Ferrarelli and Phillips, 2021; Fitzgerald, 2021; Song et al, 2019; Voigt et al, 2021; Zhang et al, 2021; Zilverstand et al, 2017, 2016). Previously, we even demonstrated that connectivity in the frontoparietal network has downstream causal effects on the severity of alcohol use disorder (Rawls et al, 2021). As it is applied on a single-subject basis, the GANGO method could potentially even enable individualized causal connectivity-based neuromodulation targeting.…”