可卡因使用障碍的判别性功能连接特征与经颅磁刺激治疗反应的联系

Kanhao Zhao, Gregory A. Fonzo, Hua Xie, Desmond J. Oathes, Corey J. Keller, Nancy B. Carlisle, Amit Etkin, Eduardo A. Garza-Villarreal, Yu Zhang
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引用次数: 0

摘要

可卡因使用障碍(CUD)很普遍,重复经颅磁刺激(rTMS)在减少渴求方面显示出前景。然而,可卡因使用障碍特异性功能连接特征与治疗反应之间的联系仍不清楚。在此,我们从功能性磁共振成像中发现了一种有效的功能连接特征,可用于区分 CUD,并成功地进行了独立复制。我们发现,在CUD患者中,视觉和背侧注意力网络内部以及额顶控制和腹侧注意力网络之间的连接性增强,同时默认模式和边缘网络之间的连接性降低。这些连接与吸毒史和认知障碍有关。利用一项随机临床试验的数据,我们还确定了这些功能连接对于经颅磁刺激治疗 CUD 结果的预后价值,尤其是涉及额顶控制和默认模式网络的功能连接。我们的研究结果揭示了 CUD 的神经生物学机制,并将功能连接生物标志物与经颅磁刺激治疗反应联系起来,为未来的治疗开发提供了潜在靶点。作者利用机器学习模型,通过静息态功能磁共振成像功能连接数据,将可卡因使用障碍和有多种药物使用史的患者与健康对照组区分开来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response

Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response
Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development. The authors used a machine learning model to distinguish patients with cocaine use disorder and polysubstance use history from healthy controls, using resting-state functional magnetic resonance imaging functional connectivity data.
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