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

Rawls and colleagues use an advanced statistical approach to identify causal neurobehavioral mechanisms underlying Alcohol Use Disorder. Their findings support current multifactorial models of addiction, but also highlight the importance of social factors in addiction maintenance.

Bibliographic Details
Main Authors:	Eric Rawls, Erich Kummerfeld, Anna Zilverstand
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-03-01
Series:	Communications Biology
Online Access:	https://doi.org/10.1038/s42003-021-01955-z

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spelling	doaj-6f07ae07a7674b24a0447204712add142021-04-04T11:27:32ZengNature Publishing GroupCommunications Biology2399-36422021-03-014111210.1038/s42003-021-01955-zAn integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysisEric Rawls0Erich Kummerfeld1Anna Zilverstand2Department of Psychiatry and Behavioral Sciences, University of MinnesotaInstitute for Health Informatics, University of MinnesotaDepartment of Psychiatry and Behavioral Sciences, University of MinnesotaRawls and colleagues use an advanced statistical approach to identify causal neurobehavioral mechanisms underlying Alcohol Use Disorder. Their findings support current multifactorial models of addiction, but also highlight the importance of social factors in addiction maintenance.https://doi.org/10.1038/s42003-021-01955-z
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Eric Rawls Erich Kummerfeld Anna Zilverstand
spellingShingle	Eric Rawls Erich Kummerfeld Anna Zilverstand An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis Communications Biology
author_facet	Eric Rawls Erich Kummerfeld Anna Zilverstand
author_sort	Eric Rawls
title	An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
title_short	An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
title_full	An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
title_fullStr	An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
title_full_unstemmed	An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
title_sort	integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
publisher	Nature Publishing Group
series	Communications Biology
issn	2399-3642
publishDate	2021-03-01
description	Rawls and colleagues use an advanced statistical approach to identify causal neurobehavioral mechanisms underlying Alcohol Use Disorder. Their findings support current multifactorial models of addiction, but also highlight the importance of social factors in addiction maintenance.
url	https://doi.org/10.1038/s42003-021-01955-z
work_keys_str_mv	AT ericrawls anintegratedmultimodalmodelofalcoholusedisordergeneratedbydatadrivencausaldiscoveryanalysis AT erichkummerfeld anintegratedmultimodalmodelofalcoholusedisordergeneratedbydatadrivencausaldiscoveryanalysis AT annazilverstand anintegratedmultimodalmodelofalcoholusedisordergeneratedbydatadrivencausaldiscoveryanalysis AT ericrawls integratedmultimodalmodelofalcoholusedisordergeneratedbydatadrivencausaldiscoveryanalysis AT erichkummerfeld integratedmultimodalmodelofalcoholusedisordergeneratedbydatadrivencausaldiscoveryanalysis AT annazilverstand integratedmultimodalmodelofalcoholusedisordergeneratedbydatadrivencausaldiscoveryanalysis
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An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis

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