Distinct neural networks predict cocaine versus cannabis treatment outcomes

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 .

Figure 1.. Cannabis Abstinence Network.

Panel A displays model performance for positive, negative, and combined cannabis abstinence network CPM models. Panel B illustrates network anatomy based on overlap with macroscale brain regions; edges of the positive network are depicted with red lines and edges of the negative network are depicted with blue lines. Panel C illustrates network anatomy based on overlap with canonical neural networks. Darker shading indicates that network connections account for a greater percentage of the total network.

Figure 2.. Specificity of Cannabis and Cocaine Abstinence Networks.

Panel A illustrates the anatomical specificity of cocaine and cannabis abstinence networks. Cells shaded in green represent network connections that are more characteristic of the cannabis versus cocaine abstinence network and cells shaded in orange represent network connections that are more characteristic of the cocaine versus cannabis abstinence networks. Panel B illustrates the substance specificity of abstinence networks by depicting the effect size for each network for predicting cocaine and cannabis abstinence.