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Cognitive Neurodynamics

, Volume 13, Issue 6, pp 519–530 | Cite as

Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG

  • Hassan Khajehpour
  • Fahimeh Mohagheghian
  • Hamed Ekhtiari
  • Bahador MakkiabadiEmail author
  • Amir Homayoun Jafari
  • Ehsan Eqlimi
  • Mohammad Hossein Harirchian
Research Article
  • 78 Downloads

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.

Keywords

Support vector machine Weighted phase lag index Functional brain connectivity network Electroencephalography Meth dependence 

Notes

Acknowledgements

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.

Funding

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.

Ethical approval

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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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical Sciences (TUMS)TehranIran
  2. 2.Laureate Institute for Brain Research (LIBR)TulsaUSA
  3. 3.Iranian National Center for Addiction Studies (INCAS)Tehran University of Medical Sciences (TUMS)TehranIran
  4. 4.Department of Medical Physics and Biomedical Engineering, School of MedicineShahid Beheshti University of Medical Sciences (SBMU)TehranIran
  5. 5.Iranian Center of Neurological Research, Neuroscience InstituteTehran University of Medical Sciences (TUMS)TehranIran
  6. 6.Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT)Tehran University of Medical Sciences (TUMS)TehranIran

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