Electroencephalography microstates predict 12-h abstinence-induced craving changes in young smokers.

Journal: Addictive behaviors
Published Date: May 24, 2025

Abstract

Nicotine abstinence inhibits the function of the mesolimbic dopamine system to enhance craving. EEG microstates may provide spatiotemporal characteristics of global brain neuronal activity. However, little is known about the temporal dynamics and spatial topography of microstates in young smokers after abstinence. At the same time, in order to explore the neurophysiological mechanisms of craving induced by smoking abstinence, baseline microstates indicators were applied to predict craving changes. This study compared the microstates characteristics in 53 young male smokers and 48 matched nonsmokers. A 12-hour smoking abstinence procedure was designed for smokers, and their craving levels were measured using the Questionnaire on Smoking Urges (QSU). Furthermore, smokers were divided into high-craving and low-craving groups based on whether their craving changes increased after abstinence. We investigated the differences of microstates indicators before and after abstinence, and explored the relationships between baseline EEG microstates characteristics and smoking craving changes. The machine learning methods were used to predict abstinence-induced craving changes. We observed that the 12-h abstinence procedure significantly decreased the explained variance, duration, occurrence and coverage of microstates class D in 53 smokers. Craving changes induced by abstinence were significantly positively correlated with the explained variance, duration, occurrence and coverage of class D at baseline. The baseline microstates characteristics in smokers predicted abstinence-induced craving changes with an accuracy of 70.18%. These findings suggest that EEG microstates features can serve as key functional biomarkers for abstinence-induced craving in young smokers, providing novel insights for developing personalized abstinence intervention strategies based on EEG characteristics.

Authors

  • Fang Dong
    Key Laboratory of Coastal Biology and Bioresource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China. fdong@yic.ac.cn.
  • Zhimin Zhou
    Department of Medical Imaging, Affiliated Hospital of Putian University, Putian, 351100, China.
  • Zhiwei Ren
    School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China.
  • Yongxin Cheng
    School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China.
  • Yuxin Ma
    School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China.
  • Juan Wang
    Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Ting Xue
    Department of Stomatology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China. Electronic address: xxtt.589@163.com.
  • Dahua Yu
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Gengdi Huang
    Department of Addiction Medicine, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen 518118, China; State Key Laboratory of Chemical Oncogenomics, Guangdong Provincial Key Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China. Electronic address: gengdi_huang@163.com.
  • Kai Yuan
    College of Animal Science and Veterinary Medicine, Shanxi Agricultural University, Shanxi Province, 030801, China.
  • Xiaoqi Lu
    School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China. Electronic address: lxiaoqi@imut.edu.cn.

Keywords

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