An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis.
Commun Biol
; 4(1): 435, 2021 03 31.
Article
en En
| MEDLINE
| ID: mdl-33790384
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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Encéfalo
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Alcoholismo
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Commun Biol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos