Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

Abstract Background Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy. Methods In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment. Results We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption. Conclusions Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions – setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

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

10.1101/2020.09.21.306761 ◽

2020 ◽

Author(s):

Eric Rawls ◽

Erich Kummerfeld ◽

Anna Zilverstand

Keyword(s):

Alcohol Use ◽

Brain Function ◽

Alcohol Use Disorder ◽

Network Connectivity ◽

Data Driven ◽

Causal Discovery ◽

Outcome Variable ◽

Future Research ◽

Affective Factors ◽

Human Connectome Project

AbstractObjectiveAlcohol 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.MethodsWe 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.ResultsEFA 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).ConclusionsOur 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.

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