Identification and external validation of a problem cannabis risk network
Background - Cannabis use is common, particularly during emerging adulthood when brain development is ongoing, and its use is associated with harmful outcomes for a subset of people. An improved understanding of the neural mechanisms underlying risk for problem-level use is critical to facilitate th...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
15 October 2025
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| In: |
Biological psychiatry
Year: 2025, Volume: 98, Issue: 8, Pages: 586-596 |
| ISSN: | 1873-2402 |
| DOI: | 10.1016/j.biopsych.2025.01.022 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.biopsych.2025.01.022 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0006322325000654 |
| Author Notes: | Sarah D. Lichenstein, Brian D. Kiluk, Marc N. Potenza, Hugh Garavan, Bader Chaarani, Tobias Banaschewski, Arun L.W. Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Luise Poustka, Sarah Hohmann, Nathalie Holz, Christian Baeuchl, Michael N. Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann, Godfrey Pearlson, and Sarah W. Yip |
MARC
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| 245 | 1 | 0 | |a Identification and external validation of a problem cannabis risk network |c Sarah D. Lichenstein, Brian D. Kiluk, Marc N. Potenza, Hugh Garavan, Bader Chaarani, Tobias Banaschewski, Arun L.W. Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Luise Poustka, Sarah Hohmann, Nathalie Holz, Christian Baeuchl, Michael N. Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann, Godfrey Pearlson, and Sarah W. Yip |
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| 520 | |a Background - Cannabis use is common, particularly during emerging adulthood when brain development is ongoing, and its use is associated with harmful outcomes for a subset of people. An improved understanding of the neural mechanisms underlying risk for problem-level use is critical to facilitate the development of more effective prevention and treatment approaches. - Methods - In the current study, we applied a whole-brain, data-driven, machine learning approach to identify neural features predictive of problem-level cannabis use in a nonclinical sample of college students (n = 191, 58% female) based on reward task functional connectivity data. We further examined whether the identified network would generalize to predict cannabis use in an independent sample of European adolescents/emerging adults (n = 1320, 53% female), whether it would predict clinical characteristics among adults seeking treatment for cannabis use disorder (n = 33, 9% female), and whether it was specific for predicting cannabis versus alcohol use outcomes across datasets. - Results - Results demonstrated identification of a problem cannabis risk network, which generalized to predict cannabis use in an independent sample of adolescents and was linked to increased addiction severity and poorer treatment outcome in a third sample of treatment-seeking adults. Furthermore, the identified network was specific for predicting cannabis versus alcohol use outcomes across all 3 datasets. - Conclusions - Findings provide insight into neural mechanisms of risk for problem-level cannabis use among adolescents/emerging adults. Future work is needed to assess whether targeting this network can improve prevention and treatment outcomes. | ||
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| 650 | 4 | |a External replication | |
| 650 | 4 | |a Functional connectivity | |
| 650 | 4 | |a Marijuana | |
| 650 | 4 | |a Neuromarker | |
| 650 | 4 | |a Predictive modeling | |
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