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

Gregory Niklason, Eric Rawls, Sisi Ma, Erich Kummerfeld, Andrea Maxwell, Leyla R Brucar, Gunner Drossel, Anna Zilverstand

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number15624
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. ER was supported by a postdoctoral training grant from the National Institutes of Mental Health (NIMH; T32 MH115866). GD was supported by a predoctoral training grant from the National Institute on Drug Abuse (NIDA; T32 DA007234). AMM was supported by a predoctoral training grant from the National Institute of Neurological Disorders and Stroke (NINDS; T32 NS105604-04). SM and EK received support for this work from the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number (NCATS; UL1TR000114).

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

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