School of Psychology, Northwest Normal University, Lanzhou 730000, China.
School of Psychology, Northwest Normal University, Lanzhou 730000, China.
Psychiatry Res Neuroimaging. 2023 Mar;329:111594. doi: 10.1016/j.pscychresns.2023.111594. Epub 2023 Jan 28.
The effects of transcranial magnetic stimulation in treating substance use disorders are gaining attention; however, most existing studies used subjective measures to examine the treatment effects. Objective electroencephalography (EEG)-based microstate analysis is important for measuring the efficacy of transcranial magnetic stimulation in patients with heroin addiction. We investigated dynamic brain activity changes in individuals with heroin addiction after transcranial magnetic stimulation using microstate indicators. Thirty-two patients received intermittent theta-burst stimulation (iTBS) over the left dorsolateral prefrontal cortex. Resting-state EEG data were collected pre-intervention and 10 days post-intervention. The feature values of the significantly different microstate classes were computed using a K-means clustering algorithm. Four EEG microstate classes (A-D) were noted. There were significant increases in the duration, occurrence, and contribution of microstate class A after the iTBS intervention. K-means classification accuracy reached 81.5%. The EEG microstate is an effective improvement indicator in patients with heroin addiction treated with iTBS. Microstates were examined using machine learning; this method effectively classified the pre- and post-intervention cohorts among patients with heroin addiction and healthy individuals. Using EEG microstate to measure heroin addiction and further exploring the effect of iTBS in patients with heroin addiction merit clinical investigation.
经颅磁刺激治疗物质使用障碍的效果引起了关注；然而，大多数现有研究使用主观测量来检查治疗效果。基于客观脑电图 (EEG) 的微状态分析对于测量海洛因成瘾患者经颅磁刺激的疗效很重要。我们使用微状态指标研究了经颅磁刺激后海洛因成瘾者大脑活动的动态变化。32 名患者接受了左侧背外侧前额叶的间歇性经颅磁刺激（iTBS）。在干预前和干预后 10 天采集静息态 EEG 数据。使用 K-均值聚类算法计算显著不同微状态类别的特征值。注意到四个 EEG 微状态类（A-D）。iTBS 干预后，微状态类 A 的持续时间、出现和贡献显著增加。K-均值分类准确率达到 81.5%。经 iTBS 治疗后，海洛因成瘾患者的 EEG 微状态是一种有效的改善指标。使用机器学习对微状态进行了检查；这种方法有效地对海洛因成瘾患者和健康个体的干预前后队列进行了分类。使用 EEG 微状态来衡量海洛因成瘾，并进一步探索 iTBS 对海洛因成瘾患者的影响，值得临床研究。
