Zhang Hang-Bin, Yu Quanhao, Zhang Xinyuan, Zhang Yi, Huang Taicheng, Ding Jinjun, Yan Lan, Cao Xinyu, Yin Lu, Liu Yi, Yuan Ti-Fei, Luo Wenbo, Zhao Di
Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine and School of Psychology, Shanghai, China.
Da Lian Shan Institute of Addiction Rehabilitation, Nanjing, Jiangsu, China.
Int J Clin Health Psychol. 2025 Jan-Mar;25(1):100551. doi: 10.1016/j.ijchp.2025.100551. Epub 2025 Feb 8.
Methamphetamine use disorder (MUD) is characterized by prominent psychological craving and its relapsing nature. Previous studies have linked trait impulsivity and abstinence duration to drug use, but the neural substrates of drug cue-induced craving and its relationship with these traits remain unclear in MUD.
We acquired high-density resting-state electroencephalography (EEG) after participants watched a five-minute video demonstrating methamphetamine use. Combining precise source imaging to reconstruct brain activities with connectome predictive modeling (CPM), we built a craving-specific network within beta band activity from two independent MUD cohorts (N=144 for model development and N=47 for validation).
This network reveals a unified neural signature for craving in MUD, spanning multiple brain networks including the medial prefrontal, frontal parietal, and subcortical networks. Our findings underscored the mediating role of this craving connectome profile in modulating the relationship between abstinence duration and craving intensity. Moreover, trait impulsivity mediated the relationship between the EEG-derived craving connectome and cue-induced craving.
This study presents a novel predictive model that utilizes sourced connectivity from high-density EEG of resting-state recording to successfully predict methamphetamine craving in abstinent individuals with MUD. These results shed light on the cognitive organization involved in craving, involving cognitive control, attention, and reward reactivity. A comprehensive analysis reveals EEG data's capacity to decipher craving's complex dynamics, facilitating improved understanding and targeted treatments for substance use disorders.
甲基苯丙胺使用障碍（MUD）的特征是强烈的心理渴望及其复发性。先前的研究已将特质冲动性和戒断持续时间与药物使用联系起来，但在MUD中，药物线索诱发的渴望的神经基质及其与这些特质的关系仍不清楚。
在参与者观看一段展示甲基苯丙胺使用的五分钟视频后，我们采集了高密度静息态脑电图（EEG）。结合精确源成像以重建大脑活动与连接组预测模型（CPM），我们在两个独立的MUD队列（模型开发N = 144，验证N = 47）的β波段活动中构建了一个特定于渴望的网络。
该网络揭示了MUD中渴望的统一神经特征，跨越多个脑网络，包括内侧前额叶、额顶叶和皮质下网络。我们的研究结果强调了这种渴望连接组特征在调节戒断持续时间与渴望强度之间关系中的中介作用。此外，特质冲动性介导了脑电图衍生的渴望连接组与线索诱发的渴望之间的关系。
本研究提出了一种新颖的预测模型，该模型利用静息态记录的高密度脑电图的源连接性，成功预测了患有MUD的戒断个体的甲基苯丙胺渴望。这些结果揭示了参与渴望的认知组织，涉及认知控制、注意力和奖励反应性。综合分析揭示了脑电图数据解读渴望复杂动态的能力，有助于改善对物质使用障碍的理解和针对性治疗。
