Publication / RCBTR
Published Date: 2019/12/01
Published By: Dr.Bahador Makki Abadi
Published At: Cognitive neurodynamics
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Published URL: https://link.springer.com/article/10.1007/s11571-019-09550-z

Hassan Khajehpour, Fahimeh Mohagheghian, Hamed Ekhtiari, Bahador Makkiabadi, Amir Homayoun Jafari, Ehsan Eqlimi, Mohammad Hossein Harirchian

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