Department of Psychiatry (Garrison, Sinha, Potenza), Child Study Center (Sinha, Potenza, Scheinost), n.; Department of Neuroscience (Sinha, Potenza), Wu Tsai Institute (Potenza, Scheinost), Department of Biomedical Engineering (Gao, Liang, Scheinost), and Department of Statistics and Data Science (Scheinost), Yale University, New Haven, Conn.; Connecticut Mental Health Center, New Haven, Conn. (Potenza); Connecticut Council on Problem Gambling, Hartford, Conn. (Potenza).
Am J Psychiatry. 2023 Jun 1;180(6):445-453. doi: 10.1176/appi.ajp.21121207. Epub 2023 Mar 29.
Craving is a central construct in the study of motivation and human behavior and is also a clinical symptom of substance and non-substance-related addictive disorders. Thus, craving represents a target for transdiagnostic modeling.
The authors applied connectome-based predictive modeling (CPM) to functional connectivity data in a large (N=274) transdiagnostic sample of individuals with and without substance use-related conditions, to predict self-reported craving. Functional connectomes derived from three guided imagery conditions of personalized appetitive, stress, and neutral-relaxing experiences were used to predict craving rated before and after each imagery condition. The generalizability of the "craving network" was tested in an independent sample using functional connectomes derived from a cue-induced craving task collected before and after fasting to predict craving rated during fasting.
CPM successfully predicted craving, thereby identifying a transdiagnostic "craving network." Anatomical localization of model contribution suggested that the strongest predictors of craving were regions of the salience, subcortical, and default mode networks. As external validation, in an independent sample, the "craving network" predicted food craving during fasting using data from a cue-induced craving task.
These data provide a transdiagnostic perspective to a key phenomenological feature of addictive disorders-craving-and identify a common "craving network" across individuals with and without substance use-related disorders, thereby suggesting a neural signature for craving or urge for motivated behaviors.
渴望是动机和人类行为研究中的一个核心概念，也是物质和非物质相关成瘾障碍的临床症状。因此，渴望代表了跨诊断模型的一个目标。
作者将连接组预测模型（CPM）应用于有和没有物质使用相关障碍的个体的大型（N=274）跨诊断样本的功能连接数据，以预测自我报告的渴望。从个性化的食欲、压力和中性放松体验的三个引导想象条件中得出的功能连接组被用来预测每个想象条件前后的渴望评分。通过使用在禁食前后收集的提示诱发渴望任务的功能连接组来预测禁食期间的渴望评分，在独立样本中测试了“渴望网络”的通用性。
CPM 成功地预测了渴望，从而确定了一个跨诊断的“渴望网络”。模型贡献的解剖定位表明，渴望的最强预测因子是显着性、皮质下和默认模式网络的区域。作为外部验证，在一个独立的样本中，使用来自提示诱发渴望任务的数据，“渴望网络”预测了禁食期间的食物渴望。
这些数据为成瘾障碍的一个关键现象特征——渴望——提供了一个跨诊断的视角，并在有和没有物质使用相关障碍的个体中确定了一个共同的“渴望网络”，从而为渴望或动机行为的冲动提供了一个神经特征。
