An electroencephalography connectome predictive model of craving for methamphetamine

• Hang-Bin Zhang ^[1] ; Quanhao Yu ^[1] ; Xinyuan Zhang ^[1] ; Yi Zhang ^[1] ; Taicheng Huang ^[1] ; Jinjun Ding ^[1] ; Lan Yan ^[1] ; Xinyu Cao ^[3] ; Lu Yin ^[4] ; Yi Liu ^[5] ; Ti-Fei Yuan ^[1] ; Wenbo Luo ^[2] ; Di Zhao ^[1]
  1. [1] Shanghai Jiao Tong University

     Shanghai Jiao Tong University

     China

     
  2. [2] Liaoning Normal University

     Liaoning Normal University

     China

     
  3. [3] Da Lian Shan Institute of Addiction Rehabilitation, Nanjing, Jiangsu, China
  4. [4] Tian Tang He Institute of Addiction Rehabilitation, Beijing, China
  5. [5] Tai Hu Institute of Addiction Rehabilitation, Suzhou, Jiangsu, China

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• Localización: International journal of clinical and health psychology, ISSN 1697-2600, Vol. 25, Nº. 1, 2025, págs. 161-170
• Idioma: inglés
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• Resumen

  • Background 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.

    Methods 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).

    Results 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.

    Conclusion 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.