Cargando…

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an “explainable” machine learning approach that...

Descripción completa

Detalles Bibliográficos
Autores principales: Niklason, Gregory R., Rawls, Eric, Ma, Sisi, Kummerfeld, Erich, Maxwell, Andrea M., Brucar, Leyla R., Drossel, Gunner, Zilverstand, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482622/
https://www.ncbi.nlm.nih.gov/pubmed/36115920
http://dx.doi.org/10.1038/s41598-022-19804-2
_version_ 1784791494581813248
author Niklason, Gregory R.
Rawls, Eric
Ma, Sisi
Kummerfeld, Erich
Maxwell, Andrea M.
Brucar, Leyla R.
Drossel, Gunner
Zilverstand, Anna
author_facet Niklason, Gregory R.
Rawls, Eric
Ma, Sisi
Kummerfeld, Erich
Maxwell, Andrea M.
Brucar, Leyla R.
Drossel, Gunner
Zilverstand, Anna
author_sort Niklason, Gregory R.
collection PubMed
description Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an “explainable” machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley’s Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.
format Online
Article
Text
id pubmed-9482622
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-94826222022-09-19 Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder Niklason, Gregory R. Rawls, Eric Ma, Sisi Kummerfeld, Erich Maxwell, Andrea M. Brucar, Leyla R. Drossel, Gunner Zilverstand, Anna Sci Rep Article Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an “explainable” machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley’s Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men. Nature Publishing Group UK 2022-09-17 /pmc/articles/PMC9482622/ /pubmed/36115920 http://dx.doi.org/10.1038/s41598-022-19804-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Niklason, Gregory R.
Rawls, Eric
Ma, Sisi
Kummerfeld, Erich
Maxwell, Andrea M.
Brucar, Leyla R.
Drossel, Gunner
Zilverstand, Anna
Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder
title Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder
title_full Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder
title_fullStr Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder
title_full_unstemmed Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder
title_short Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder
title_sort explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of cannabis use disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482622/
https://www.ncbi.nlm.nih.gov/pubmed/36115920
http://dx.doi.org/10.1038/s41598-022-19804-2
work_keys_str_mv AT niklasongregoryr explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT rawlseric explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT masisi explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT kummerfelderich explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT maxwellandream explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT brucarleylar explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT drosselgunner explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder
AT zilverstandanna explainablemachinelearninganalysisrevealssexandgenderdifferencesinthephenotypicandneurobiologicalmarkersofcannabisusedisorder