Post-traumatic stress comorbidity in substance use disorder: machine learning analyses of phenotypic drivers
Author:
Funder
U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41386-024-01969-1.pdf
Reference6 articles.
1. Volkow ND, Koob G, Baler R. Biomarkers in substance use disorders. ACS Chem Neurosci. 2015;6:522–5.
2. Barenholtz E, Fitzgerald ND, Hahn WE. Machine-learning approaches to substance-abuse research: Emerging trends and their implications. Curr Opin Psychiatry. 2020;33:334–42.
3. Houghton DC, Spratt HM, Keyser-Marcus L, Bjork JM, Neigh GN, Cunningham KA, et al. Behavioral and neurocognitive factors distinguishing post-traumatic stress comorbidity in substance use disorder. Transl Psychiatry. 2023;13:296.
4. Keyser-Marcus LA, Ramey T, Bjork J, Adams A, Moeller FG. Development and feasibility study of an addiction-focused phenotyping assessment battery. Am J Addict. 2021;30:398–405.
5. Rezapour T, Rafei P, Baldacchino A, Conrod PJ, Dom G, Fishbein DH, et al. Neuroscience-informed classification of prevention interventions in substance use disorders: An RDoC-based approach. Neurosci Biobehav Rev. 2024;159:105578.
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