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Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats
Over 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 d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10092243/ https://www.ncbi.nlm.nih.gov/pubmed/36215170 http://dx.doi.org/10.1111/ejn.15839 |
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author | Jadhav, Kshitij S. Boury Jamot, Benjamin Deroche‐Gamonet, Veronique Belin, David Boutrel, Benjamin |
author_facet | Jadhav, Kshitij S. Boury Jamot, Benjamin Deroche‐Gamonet, Veronique Belin, David Boutrel, Benjamin |
author_sort | Jadhav, Kshitij S. |
collection | PubMed |
description | Over 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. |
format | Online Article Text |
id | pubmed-10092243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100922432023-04-13 Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats Jadhav, Kshitij S. Boury Jamot, Benjamin Deroche‐Gamonet, Veronique Belin, David Boutrel, Benjamin Eur J Neurosci Clinical and Translational Neuroscience Over 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. John Wiley and Sons Inc. 2022-11-01 2022-12 /pmc/articles/PMC10092243/ /pubmed/36215170 http://dx.doi.org/10.1111/ejn.15839 Text en © 2022 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Clinical and Translational Neuroscience Jadhav, Kshitij S. Boury Jamot, Benjamin Deroche‐Gamonet, Veronique Belin, David Boutrel, Benjamin Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats |
title | Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats |
title_full | Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats |
title_fullStr | Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats |
title_full_unstemmed | Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats |
title_short | Towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction‐like behaviour in individual rats |
title_sort | towards a machine‐learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: evidence from studies on addiction‐like behaviour in individual rats |
topic | Clinical and Translational Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10092243/ https://www.ncbi.nlm.nih.gov/pubmed/36215170 http://dx.doi.org/10.1111/ejn.15839 |
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