Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.

中文翻译：

---

使用BOLD信号中的区域同质性识别甲基苯丙胺依赖性

甲基苯丙胺是一种高度成瘾的滥用药物，它将在精神和身体上引起一系列异常后果。本文旨在研究区域同质性异常（ReHo）是否可以作为使用机器学习方法从控制对象中区分出具有甲基苯丙胺依赖（MAD）的个体的有效特征。我们利用静息状态功能磁共振成像来测量41位MAD患者和42位年龄和性别匹配的对照对象的区域同质性，发现与对照对象相比，MAD患者在右内侧额中上回的ReHo值较低但右颞下梭形的ReHo值较高。此外，AdaBoost分类器是一种非常有效的机器学习集成学习，用来对来自具有异常ReHo值的对照受试者的MAD患者进行分类。通过使用留一法交叉验证方法，我们获得了超过84.3％的准确性，这意味着我们可以通过机器学习方法在ReHo值中将MAD个体与对照个体区分开。简而言之，我们的研究结果表明，AdaBoost分类器-神经影像学方法可能是寻找一个人是否沉迷于甲基苯丙胺的有前途的方法，而且，本文表明，静息状态fMRI应该被视为一种生物标记物，用于评估MAD的无创且有效的辅助工具。这意味着我们几乎可以通过机器学习方法在ReHo值中将MAD个体与对照个体区分开。简而言之，我们的研究结果表明，AdaBoost分类器-神经影像学方法可能是寻找一个人是否沉迷于甲基苯丙胺的有前途的方法，而且，本文表明，静息状态fMRI应该被视为一种生物标记物，用于评估MAD的无创且有效的辅助工具。这意味着我们几乎可以通过机器学习方法在ReHo值中将MAD个体与对照个体区分开。简而言之，我们的研究结果表明，AdaBoost分类器-神经影像学方法可能是寻找一个人是否沉迷于甲基苯丙胺的有前途的方法，而且，本文表明，静息状态fMRI应该被视为一种生物标记物，用于评估MAD的无创且有效的辅助工具。