Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–15 Hz), beta (15–30 Hz), gamma (30–45 Hz) and wideband (1–45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.

中文翻译：

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使用静息态脑电图对甲基苯丙胺使用障碍进行计算机辅助分类和表征。

甲基苯丙胺（meth）具有很强的成瘾性，并且与世界上的高犯罪率密切相关。由于冰毒戒断非常痛苦且困难，大多数滥用者在传统治疗中再次滥用。因此，开发基于大脑功能连接的准确数据驱动方法可能有助于对冰毒依赖的神经特征进行分类和表征，以优化治疗。因此，在本研究中，使用静息态脑电图计算功能连接来对冰毒依赖进行分类。首先，通过加权相位滞后指数构建了 36 名冰毒依赖者和 24 名正常对照的大脑功能连接网络（FCN），在六个频段：δ（1-4 Hz）、θ（4-8 Hz）、α（8 –15 Hz)、beta (15–30 Hz)、gamma (30–45 Hz) 和宽带 (1–45 Hz)。然后，使用 FCN 图形指标和连接值的显着差异来区分两组。支持向量机分类器在区分 MDI 和 NC 方面具有最佳性能，准确度为 93%，灵敏度为 100%，特异性为 83%，F 分数为 0.94。当所选功能是 Beta 频段中的连接值和图形指标的组合时，会产生最佳性能。