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Computer-Aided Classifying and Characterizing of Methamphetamine Use Disorder Using Resting-State Eeg Publisher



Khajehpour H1, 6 ; Mohagheghian F4 ; Ekhtiari H2, 3 ; Makkiabadi B1, 6 ; Jafari AH1, 6 ; Eqlimi E1, 6 ; Harirchian MH5
Authors

Source: Cognitive Neurodynamics Published:2019


Abstract

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. © 2019, Springer Nature B.V.
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