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dc.rights.licenseopenen_US
dc.contributor.authorJADHAV, Kshitij S
dc.contributor.authorBOURY JAMOT, Benjamin
hal.structure.identifierNeurocentre Magendie : Physiopathologie de la Plasticité Neuronale [U1215 Inserm - UB]
dc.contributor.authorDEROCHE-GAMONET, Veronique
IDREF: 119544008
dc.contributor.authorBELIN, David
dc.contributor.authorBOUTREL, Benjamin
dc.date.accessioned2022-11-18T08:37:16Z
dc.date.available2022-11-18T08:37:16Z
dc.date.issued2022-10-10
dc.identifier.issn1460-9568en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/170322
dc.description.abstractEnOver the last few decades, there has been a progressive transition from a categorical to a dimensional approach to psychiatric disorders. Especially in the case of substance use disorders, interest in the individual vulnerability to transition from controlled to compulsive drug taking warrants the development of novel dimension-based objective stratification tools. Here we drew on a multidimensional preclinical model of addiction, namely the 3-criteria model, previously developed to identify the neurobehavioural basis of the individual's vulnerability to switch from controlled to compulsive drug taking, to test a machine-learning assisted classifier objectively to identify individual subjects as vulnerable/resistant to addiction. Datasets from our previous studies on addiction-like behaviour for cocaine or alcohol were fed into a variety of machine-learning algorithms to develop a classifier that identifies resilient and vulnerable rats with high precision and reproducibility irrespective of the cohort to which they belong. A classifier based on K-median or K-mean-clustering (for cocaine or alcohol, respectively) followed by artificial neural networks emerged as a highly reliable and accurate tool to predict if a single rat is vulnerable/resilient to addiction. Thus, each rat previously characterized as displaying 0-criterion (i.e., resilient) or 3-criteria (i.e., vulnerable) in individual cohorts was correctly labelled by this classifier. The present machine-learning-based classifier objectively labels single individuals as resilient or vulnerable to developing addiction-like behaviour in a multisymptomatic preclinical model of addiction-like behaviour in rats. This novel dimension-based classifier increases the heuristic value of these preclinical models while providing proof of principle to deploy similar tools for the future of diagnosis of psychiatric disorders.
dc.description.sponsorshipCOCAINE ADDICTION: A TRANSLATIONAL STUDY TO IDENTIFY AND CHARACTERIZE DYSFUNCTIONAL NEURONAL NETWORKS - ANR-13-NEUR-0002en_US
dc.description.sponsorshipBordeaux Region Aquitaine Initiative for Neuroscience - ANR-10-LABX-0043en_US
dc.description.sponsorshipInnovations instrumentales et procédurales en psychopathologie expérimentale chez le rongeur - ANR-10-EQPX-0008en_US
dc.language.isoENen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subject.enAddiction
dc.subject.enClustering
dc.subject.enIndividual vulnerability
dc.subject.enMachine learning
dc.subject.enNeural networks
dc.subject.enSubstance use disorder
dc.title.enTowards a machine-learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction-like behaviour in individual rats.
dc.title.alternativeEur J Neuroscien_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1111/ejn.15839en_US
dc.subject.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]en_US
dc.identifier.pubmed36215170en_US
bordeaux.journalEuropean Journal of Neuroscienceen_US
bordeaux.hal.laboratoriesNeurocentre Magendie - U1215en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamPsychobiologie de la vulnérabilité à l'addiction aux droguesen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDMedical Research Councilen_US
bordeaux.import.sourcepubmed
hal.identifierhal-03859112
hal.version1
hal.date.transferred2022-11-18T08:37:21Z
hal.exporttrue
workflow.import.sourcepubmed
dc.rights.ccCC BY-NC-NDen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=European%20Journal%20of%20Neuroscience&rft.date=2022-10-10&rft.eissn=1460-9568&rft.issn=1460-9568&rft.au=JADHAV,%20Kshitij%20S&BOURY%20JAMOT,%20Benjamin&DEROCHE-GAMONET,%20Veronique&BELIN,%20David&BOUTREL,%20Benjamin&rft.genre=article


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