Modeling motivation for alcohol in humans using traditional and machine learning approaches

Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to br...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Addiction biology 2021-05, Vol.26 (3), p.e12949-n/a
Hauptverfasser: Grodin, Erica N., Montoya, Amanda K., Bujarski, Spencer, Ray, Lara A.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page n/a
container_issue 3
container_start_page e12949
container_title Addiction biology
container_volume 26
creator Grodin, Erica N.
Montoya, Amanda K.
Bujarski, Spencer
Ray, Lara A.
description Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self‐administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive‐ratio alcohol self‐administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20–3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self‐administration. K‐means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self‐administration trajectories. The data were analyzed using two approaches: a theory‐driven test of a‐priori predictors and a data‐driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data‐driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self‐administration. Additional application of this model to AUD translational science and treatment development appear warranted. K‐means clustering was used to identify motivated and unmotivated self‐administration trajectories for heavy drinkers. Data driven models found that steeper delay discounting and higher phasic alcohol craving were predictors of motivated alcohol self‐administration. This combination of traditional and novel analytic approaches should be applied to AUD translational science and treatment development.
doi_str_mv 10.1111/adb.12949
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2428418075</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2428418075</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3889-b1b5aebc10a2cccc22761d0f92abb88e915ad26ad924a21c2c64087c90b1a99e3</originalsourceid><addsrcrecordid>eNp10LtOwzAUBmALgWgpDLwAssQCQ1pfcvNYylUqYoGJITpxHOrKiYvdgPr2OLQwIHEWW9an3zo_QqeUjGmYCVTlmDIRiz00pDwVEU0J2e_vSRKljCYDdOT9khDKsoQfogFnGUvylA_R66OtlNHtG27sWn_AWtsW19ZhMNIurMG6xYuugdbjzvds7aDSvQKDoa1wA3KhW4WNAtf2AFYrZ8Oj8sfooAbj1cnuHKGX25vn2X00f7p7mE3nkeR5LqKSlgmoUlICTIZhLEtpRWrBoCzzXAmaQMVSqASLgVHJZBqTPJOClBSEUHyELra54eP3Tvl10WgvlTHQKtv5gsUsj2lOwu4jdP6HLm3nwi5BJZQTHvM4Dupyq6Sz3jtVFyunG3CbgpKib7wIjRffjQd7tkvsykZVv_Kn4gAmW_Cpjdr8n1RMr6-2kV-0yoqR</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2513034344</pqid></control><display><type>article</type><title>Modeling motivation for alcohol in humans using traditional and machine learning approaches</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Grodin, Erica N. ; Montoya, Amanda K. ; Bujarski, Spencer ; Ray, Lara A.</creator><creatorcontrib>Grodin, Erica N. ; Montoya, Amanda K. ; Bujarski, Spencer ; Ray, Lara A.</creatorcontrib><description>Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self‐administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive‐ratio alcohol self‐administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20–3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self‐administration. K‐means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self‐administration trajectories. The data were analyzed using two approaches: a theory‐driven test of a‐priori predictors and a data‐driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data‐driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self‐administration. Additional application of this model to AUD translational science and treatment development appear warranted. K‐means clustering was used to identify motivated and unmotivated self‐administration trajectories for heavy drinkers. Data driven models found that steeper delay discounting and higher phasic alcohol craving were predictors of motivated alcohol self‐administration. This combination of traditional and novel analytic approaches should be applied to AUD translational science and treatment development.</description><identifier>ISSN: 1355-6215</identifier><identifier>EISSN: 1369-1600</identifier><identifier>DOI: 10.1111/adb.12949</identifier><identifier>PMID: 32725863</identifier><language>eng</language><publisher>United States: John Wiley &amp; Sons, Inc</publisher><subject>Addictions ; Alcohol ; alcohol seeking ; delay discounting ; Learning algorithms ; Machine learning ; Motivation ; progressive ratio ; Risk factors</subject><ispartof>Addiction biology, 2021-05, Vol.26 (3), p.e12949-n/a</ispartof><rights>2020 Society for the Study of Addiction</rights><rights>2020 Society for the Study of Addiction.</rights><rights>2021 Society for the Study of Addiction</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3889-b1b5aebc10a2cccc22761d0f92abb88e915ad26ad924a21c2c64087c90b1a99e3</citedby><cites>FETCH-LOGICAL-c3889-b1b5aebc10a2cccc22761d0f92abb88e915ad26ad924a21c2c64087c90b1a99e3</cites><orcidid>0000-0001-9316-8184 ; 0000-0002-5734-9444 ; 0000-0001-5528-4918</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fadb.12949$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fadb.12949$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32725863$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Grodin, Erica N.</creatorcontrib><creatorcontrib>Montoya, Amanda K.</creatorcontrib><creatorcontrib>Bujarski, Spencer</creatorcontrib><creatorcontrib>Ray, Lara A.</creatorcontrib><title>Modeling motivation for alcohol in humans using traditional and machine learning approaches</title><title>Addiction biology</title><addtitle>Addict Biol</addtitle><description>Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self‐administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive‐ratio alcohol self‐administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20–3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self‐administration. K‐means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self‐administration trajectories. The data were analyzed using two approaches: a theory‐driven test of a‐priori predictors and a data‐driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data‐driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self‐administration. Additional application of this model to AUD translational science and treatment development appear warranted. K‐means clustering was used to identify motivated and unmotivated self‐administration trajectories for heavy drinkers. Data driven models found that steeper delay discounting and higher phasic alcohol craving were predictors of motivated alcohol self‐administration. This combination of traditional and novel analytic approaches should be applied to AUD translational science and treatment development.</description><subject>Addictions</subject><subject>Alcohol</subject><subject>alcohol seeking</subject><subject>delay discounting</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Motivation</subject><subject>progressive ratio</subject><subject>Risk factors</subject><issn>1355-6215</issn><issn>1369-1600</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp10LtOwzAUBmALgWgpDLwAssQCQ1pfcvNYylUqYoGJITpxHOrKiYvdgPr2OLQwIHEWW9an3zo_QqeUjGmYCVTlmDIRiz00pDwVEU0J2e_vSRKljCYDdOT9khDKsoQfogFnGUvylA_R66OtlNHtG27sWn_AWtsW19ZhMNIurMG6xYuugdbjzvds7aDSvQKDoa1wA3KhW4WNAtf2AFYrZ8Oj8sfooAbj1cnuHKGX25vn2X00f7p7mE3nkeR5LqKSlgmoUlICTIZhLEtpRWrBoCzzXAmaQMVSqASLgVHJZBqTPJOClBSEUHyELra54eP3Tvl10WgvlTHQKtv5gsUsj2lOwu4jdP6HLm3nwi5BJZQTHvM4Dupyq6Sz3jtVFyunG3CbgpKib7wIjRffjQd7tkvsykZVv_Kn4gAmW_Cpjdr8n1RMr6-2kV-0yoqR</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Grodin, Erica N.</creator><creator>Montoya, Amanda K.</creator><creator>Bujarski, Spencer</creator><creator>Ray, Lara A.</creator><general>John Wiley &amp; Sons, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7T5</scope><scope>7TM</scope><scope>H94</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9316-8184</orcidid><orcidid>https://orcid.org/0000-0002-5734-9444</orcidid><orcidid>https://orcid.org/0000-0001-5528-4918</orcidid></search><sort><creationdate>202105</creationdate><title>Modeling motivation for alcohol in humans using traditional and machine learning approaches</title><author>Grodin, Erica N. ; Montoya, Amanda K. ; Bujarski, Spencer ; Ray, Lara A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3889-b1b5aebc10a2cccc22761d0f92abb88e915ad26ad924a21c2c64087c90b1a99e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Addictions</topic><topic>Alcohol</topic><topic>alcohol seeking</topic><topic>delay discounting</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Motivation</topic><topic>progressive ratio</topic><topic>Risk factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Grodin, Erica N.</creatorcontrib><creatorcontrib>Montoya, Amanda K.</creatorcontrib><creatorcontrib>Bujarski, Spencer</creatorcontrib><creatorcontrib>Ray, Lara A.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Immunology Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Addiction biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Grodin, Erica N.</au><au>Montoya, Amanda K.</au><au>Bujarski, Spencer</au><au>Ray, Lara A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling motivation for alcohol in humans using traditional and machine learning approaches</atitle><jtitle>Addiction biology</jtitle><addtitle>Addict Biol</addtitle><date>2021-05</date><risdate>2021</risdate><volume>26</volume><issue>3</issue><spage>e12949</spage><epage>n/a</epage><pages>e12949-n/a</pages><issn>1355-6215</issn><eissn>1369-1600</eissn><abstract>Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self‐administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive‐ratio alcohol self‐administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20–3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self‐administration. K‐means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self‐administration trajectories. The data were analyzed using two approaches: a theory‐driven test of a‐priori predictors and a data‐driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data‐driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self‐administration. Additional application of this model to AUD translational science and treatment development appear warranted. K‐means clustering was used to identify motivated and unmotivated self‐administration trajectories for heavy drinkers. Data driven models found that steeper delay discounting and higher phasic alcohol craving were predictors of motivated alcohol self‐administration. This combination of traditional and novel analytic approaches should be applied to AUD translational science and treatment development.</abstract><cop>United States</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>32725863</pmid><doi>10.1111/adb.12949</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-9316-8184</orcidid><orcidid>https://orcid.org/0000-0002-5734-9444</orcidid><orcidid>https://orcid.org/0000-0001-5528-4918</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1355-6215
ispartof Addiction biology, 2021-05, Vol.26 (3), p.e12949-n/a
issn 1355-6215
1369-1600
language eng
recordid cdi_proquest_miscellaneous_2428418075
source Wiley Online Library Journals Frontfile Complete
subjects Addictions
Alcohol
alcohol seeking
delay discounting
Learning algorithms
Machine learning
Motivation
progressive ratio
Risk factors
title Modeling motivation for alcohol in humans using traditional and machine learning approaches
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T22%3A25%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modeling%20motivation%20for%20alcohol%20in%20humans%20using%20traditional%20and%20machine%20learning%20approaches&rft.jtitle=Addiction%20biology&rft.au=Grodin,%20Erica%20N.&rft.date=2021-05&rft.volume=26&rft.issue=3&rft.spage=e12949&rft.epage=n/a&rft.pages=e12949-n/a&rft.issn=1355-6215&rft.eissn=1369-1600&rft_id=info:doi/10.1111/adb.12949&rft_dat=%3Cproquest_cross%3E2428418075%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2513034344&rft_id=info:pmid/32725863&rfr_iscdi=true