Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG
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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.
KeywordsSupport vector machine Weighted phase lag index Functional brain connectivity network Electroencephalography Meth dependence
The authors wish to thank TUMS and CSTC for financial support of this research and also National Brain Mapping Laboratory (NBML) for their instrumental support.
This work was supported in part by Tehran University of Medical Sciences (TUMS) (https://www.tums.ac.ir/?lang=en), project Grant No. of 95-02-30-32441, and also by Cognitive Sciences and Technologies Council (CSTC) (http://cogc.ir/?lang=2) Grant No. of 4517. The funders has played no role in the research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
The experimental was reviewed and approved by the ethics committee of Tehran University of Medical Sciences (Iran) (Ethical Committee Approval Code: IR.TUMS.MEDICINE.REC.1395.1621).
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