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Distinct neural networks predict cocaine versus cannabis treatment outcomes

Molecular Psychiatry volume 28, pages 3365–3372 (2023)Cite this article

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Abstract

Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: “Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)”, registration number: NCT01442597. “Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management”, registration number: NCT00350649. “Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)”, registration number: NCT01406899.

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Fig. 1: Cannabis abstinence network.
Fig. 2: Specificity of cannabis and cocaine abstinence networks.

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Data availability

Preliminary data on the cannabis abstinence network were presented at the Society for Biological Psychiatry’s 2022 Annual Scientific Meeting, New Orleans, LA.

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Acknowledgements

The current work was supported by K08DA051667 (SL), Women’s Health Research at Yale (SL), T32DA007238 (RK), R01DA039136 (MNP), K02AA027300 (BK), and YSM Office of Team Science (SWY, SL). Data collection was supported by P50DA09241 (MNP, BK), R01DA020908 (MNP), R01DA035058 (MNP) and R01DA019039 (MNP).

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Authors and Affiliations

  1. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

    Sarah D. Lichenstein, Robert Kohler, Marc N. Potenza, Brian Kiluk & Sarah W. Yip

  2. Stripe, San Francisco, CA, USA

    Fengdan Ye

  3. Child Study Center, Yale School of Medicine, New Haven, CT, USA

    Marc N. Potenza & Sarah W. Yip

  4. Connecticut Mental Health Center, New Haven, CT, USA

    Marc N. Potenza

  5. Connecticut Council on Problem Gambling, Wethersfield, CT, USA

    Marc N. Potenza

  6. Wu Tsai Institute, Yale University, New Haven, CT, USA

    Marc N. Potenza

  7. Department of Neuroscience, Yale University, New Haven, CT, USA

    Marc N. Potenza

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  1. Sarah D. Lichenstein
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Contributions

SDL, BK, and SWY collaborated on conception and design of the current analyses. MNP and BK oversaw acquisition of the data. SDL, RK, FY, BK, and SWY contributed to analysis and interpretation of the data. SDL and FY drafted the article. All authors revised the article critically for important intellectual content and provided final approval for the version to be published.

Corresponding author

Correspondence to Sarah D. Lichenstein.

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Competing interests

SDL, RK and FY report no competing financial interests in relation to the work described. MNP has consulted for Opiant Therapeutics, Game Day Data, Baria-Tek, the Addiction Policy Forum, AXA and Idorsia Pharmaceuticals; has been involved in a patent application with Yale University and Novartis; has received research support from Mohegan Sun Casino and the Connecticut Council on Problem Gambling; has participated in surveys, mailings or telephone consultations related to drug addiction, impulse-control disorders or other health topics; has consulted for and/or advised gambling and legal entities on issues related to impulse-control/addictive disorders; has provided clinical care in a problem gambling services program; has performed grant reviews for research-funding agencies; has edited journals and journal sections; has given academic lectures in grand rounds, CME events and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. BK is a consultant to CBT4CBT LLC, which makes versions of CBT4CBT (one of the treatments evaluated in the parent RCTs included in this study) available to qualified clinical providers and organizations on a commercial basis. SWF is a consultant for Sparian Biosciences.

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Lichenstein, S.D., Kohler, R., Ye, F. et al. Distinct neural networks predict cocaine versus cannabis treatment outcomes. Mol Psychiatry 28, 3365–3372 (2023). https://doi.org/10.1038/s41380-023-02120-0

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