Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
Stripe, San Francisco, CA, USA.
Mol Psychiatry. 2023 Aug;28(8):3365-3372. doi: 10.1038/s41380-023-02120-0. Epub 2023 Jun 12.
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 .
治疗药物使用障碍（SUD）患者的效果因人而异，可能需要更个体化的方法。经过交叉验证的机器学习方法非常适合探究治疗效果的神经机制。我们之前的工作应用了一种这样的方法，即基于连接组的预测建模（CPM），以识别可卡因和阿片类药物戒断的可分离和特定物质的神经网络。在研究 1 中，我们旨在通过在认知行为治疗 SUD 试验的 43 名独立参与者样本中测试可卡因网络的预测能力，并评估其预测大麻戒断的能力，来复制和扩展之前的工作。在研究 2 中，CPM 被应用于识别独立的大麻戒断网络。为了有一个合并的 33 名大麻使用障碍患者的样本，确定了额外的参与者。参与者在治疗前后接受 fMRI 扫描。使用 53 名同时患有可卡因和阿片类药物使用障碍的额外样本和 38 名对照受试者来评估相对于无 SUD 患者的物质特异性和网络强度。结果表明可卡因网络预测未来可卡因戒断的第二次外部复制，但它不能推广到大麻戒断。一个独立的 CPM 确定了一个新的大麻戒断网络，它是（i）与可卡因网络解剖学不同，（ii）专门用于预测大麻戒断，并且（iii）在治疗反应者中网络强度相对于对照参与者明显更强。结果为戒断的神经预测因子的物质特异性提供了进一步的证据，并为成功的大麻治疗的神经机制提供了深入的了解，从而确定了新的治疗靶点。临床试验注册：“基于计算机的认知行为治疗网络（Man VS Machine）”，注册号：NCT01442597。“最大限度地提高认知行为疗法和条件管理的效果”，注册号：NCT00350649。“认知行为疗法的基于计算机的培训（CBT4CBT）”，注册号：NCT01406899。
