Comparison of brain functions between healthy participants and methamphetamine users with various addiction histories:Data analysis based on EEG and fNIRS

作     者：Xuelin Gu Xiaoou Li Banghua Yang 

作者机构：College of Medical InstrumentsShanghai University of Medicine&Health SciencesShanghai 201318P.R.China School of Mechanical and Electrical Engineering and AutomationShanghai UniversityShanghai 200444P.R.China 

出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)

年 卷 期：2024年第17卷第3期

页      面：41-55页

核心收录：

学科分类：1002[医学-临床医学] 1010[医学-医学技术（可授医学、理学学位）] 10[医学] 0702[理学-物理学] 

基　　金：supported by Shanghai Municipal Science and Technology Plan Project(No.22010502400) National Natural Science Foundation of China(Nos.82072228,92048205,and 62376149). 

主　　题：Drug addiction history electroencephalogram functional near-infrared spectroscopy isolated effective coherence addiction history classification 

摘      要：The electroencephalogram(EEG)rhythm and functional near-infrared spectroscopy(fNIRS)activation levels have not been compared between a healthy control group(HCG)and methamphetamine user group(MUG)with different addiction histories.This study used 64-electrode EEG and fNIRS to conduct an experiment that analyzed the resting and craving states.The EEG and fNIRS data of 56 participants were collected,including 14 healthy participants,14 methamphetamine users with an addiction history of 0.5–5 years,14 users with an addiction history of 5–10 years,and 14 users with an addiction history of 10–15 years.Isolated effective coherence(iCoh)within the brain network was used to process the EEG data.Statistical analysis was performed to compare differences in iCoh among the delta,theta,alpha,beta,and gamma bands and explore oxyhemoglobin activation levels in the ventrolateral prefrontal cortex,dorsolateral prefrontal cortex,orbitofrontal cortex,and frontopolar prefrontal cortex(FPC)of the control group.Finally,the Kmeans,Gaussian mixed model(GMM),linear discriminant analysis(LDA),support vector machine(SVM),Bayes,and convolutional neural networks(CNN)algorithms were used to classify methamphetamine users based on drug and neutral images.A 3-class accuracy was achieved.Changes in EEG and fNIRS activation levels of HCG and MUG with varied addiction histories were demonstrated.