Skip to main content

Advertisement

SpringerLink
Log in

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

  • Research Article
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Ahmadlou M, Ahmadi K, Rezazade M, Azad-Marzabadi E (2013) Global organization of functional brain connectivity in methamphetamine abusers. Clin Neurophysiol 124(6):1122–1131

    Article  PubMed  Google Scholar 

  • Alvar AA, Deevband MR, Ashtiyani M (2017) Neutron spectrum unfolding using radial basis function neural networks. Appl Radiat Isot 129:35–41

    Article  CAS  PubMed  Google Scholar 

  • Bae Y, Yoo BW, Lee JC, Kim HC (2017) Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism. Physiol Meas 38(5):759

    Article  PubMed  Google Scholar 

  • Bauer LO (2001) Predicting relapse to alcohol and drug abuse via quantitative electroencephalography. Neuropsychopharmacology 25(3):332–340

    Article  CAS  PubMed  Google Scholar 

  • Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186

    Article  CAS  PubMed  Google Scholar 

  • Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

  • Choi JS, Park SM, Lee J, Hwang JY, Jung HY, Choi SW, Kim DJ, Oh S, Lee JY (2013) Resting-state beta and gamma activity in Internet addiction. Int J Psychophysiol 89(3):328–333

    Article  PubMed  Google Scholar 

  • Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  PubMed  Google Scholar 

  • Dunning JP, Parvaz MA, Hajcak G, Maloney T, Alia-Klein N, Woicik PA, Telang F, Wang GJ, Volkow ND, Goldstein RZ (2011) Motivated attention to cocaine and emotional cues in abstinent and current cocaine users—an ERP study. Eur J Neurosci 33(9):1716–1723

    Article  PubMed  PubMed Central  Google Scholar 

  • Ewald A, Aristei S, Nolte G, Rahman RA (2012) Brain oscillations and functional connectivity during overt language production. Front Psychol 3:166

    Article  PubMed  PubMed Central  Google Scholar 

  • Fein G, Allen J (2005) EEG spectral changes in treatment-naive, actively drinking alcoholics. Alcohol Clin Exp Res 29(4):538–546

    Article  PubMed  PubMed Central  Google Scholar 

  • Fingelkurts AA, Fingelkurts AA, Kivisaari R, Autti T, Borisov S, Puuskari V, Jokela O, Kahkonen S (2006) Increased local and decreased remote functional connectivity at EEG alpha and beta frequency bands in opioid-dependent patients. Psychopharmacology 188(1):42–52

    Article  CAS  PubMed  Google Scholar 

  • Franken IH, Stam CJ, Hendriks VM, van den Brink W (2004) Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients. Neuropsychobiology 49(2):105–110

    Article  CAS  PubMed  Google Scholar 

  • González GF, Van der Molen M, Žarić G, Bonte M, Tijms J, Blomert L, Stam C, Van der Molen M (2016) Graph analysis of EEG resting state functional networks in dyslexic readers. Clin Neurophysiol 127(9):3165–3175

    Article  Google Scholar 

  • Günther W, Müller N, Knesewitsch P, Haag C, Trapp W, Banquet J-P, Stieg C, Alper KR (1997) Functional EEG mapping and SPECT in detoxified male alcoholics. Eur Arch Psychiatry Clin Neurosci 247(3):128–136

    Article  PubMed  Google Scholar 

  • Hardmeier M, Hatz F, Bousleiman H, Schindler C, Stam CJ, Fuhr P (2014) Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PLoS ONE 9(10):e108648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haufe S, Nikulin VV, Müller K-R, Nolte G (2013) A critical assessment of connectivity measures for EEG data: a simulation study. Neuroimage 64:120–133

    Article  PubMed  Google Scholar 

  • Herning RI, Better W, Cadet JL (2008) EEG of chronic marijuana users during abstinence: relationship to years of marijuana use, cerebral blood flow and thyroid function. Clin Neurophysiol 119(2):321–331

    Article  PubMed  Google Scholar 

  • Herrera-Diaz A, Mendoza-Quinones R, Melie-Garcia L, Martinez-Montes E, Sanabria-Diaz G, Romero-Quintana Y, Salazar-Guerra I, Carballoso-Acosta M, Caballero-Moreno A (2016) Functional connectivity and quantitative EEG in women with alcohol use disorders: a resting-state study. Brain Topogr 29(3):368–381

    Article  PubMed  Google Scholar 

  • Hjorth B (1975) An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalogr Clin Neurophysiol 39(5):526–530

    Article  CAS  PubMed  Google Scholar 

  • Hu B, Dong Q, Hao Y, Zhao Q, Shen J, Zheng F (2017) Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects. J Neural Eng 14(4):046002

    Article  PubMed  Google Scholar 

  • Huang Y, Mohan A, De Ridder D, Sunaert S, Vanneste S (2018) The neural correlates of the unified percept of alcohol-related craving: a fMRI and EEG study. Sci Rep 8(1):923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jena SK (2015) Examination stress and its effect on EEG. Int J Med Sci Public Health 11(4):1493–1497

    Article  Google Scholar 

  • Jiang G, Wen X, Qiu Y, Zhang R, Wang J, Li M, Ma X, Tian J, Huang R (2013) Disrupted topological organization in whole-brain functional networks of heroin-dependent individuals: a resting-state FMRI study. PLoS ONE 8(12):e82715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim YJ, Lee JY, Oh S, Park M, Jung HY, Sohn BK, Choi SW, Kim DJ, Choi JS (2017) Associations between prospective symptom changes and slow-wave activity in patients with Internet gaming disorder: a resting-state EEG study. Medicine 96(8):e6178

    Article  PubMed  PubMed Central  Google Scholar 

  • Lee JY, Park SM, Kim YJ, Kim DJ, Choi S-W, Kwon JS, Choi J-S (2017) Resting-state EEG activity related to impulsivity in gambling disorder. J Behav Addict 6(3):387–395

    Article  PubMed  PubMed Central  Google Scholar 

  • Ma N, Liu Y, Li N, Wang C-X, Zhang H, Jiang X-F, Xu H-S, Fu X-M, Hu X, Zhang D-R (2010) Addiction related alteration in resting-state brain connectivity. Neuroimage 49(1):738–744

    Article  PubMed  Google Scholar 

  • McKetin R, McLaren J, Lubman DI, Hides L (2006) The prevalence of psychotic symptoms among methamphetamine users. Addiction 101(10):1473–1478

    Article  PubMed  Google Scholar 

  • Mohagheghian F, Makkiabadi B, Jalilvand H, Khajehpoor H, Samadzadehaghdam N, Eqlimi E, Deevband M (2018) Computer-aided tinnitus detection based on brain network analysis of EEG functional connectivity. J Biomed Phys Eng

  • Motlagh F, Ibrahim F, Rashid R, Seghatoleslam T, Habil H (2017) Investigation of brain electrophysiological properties among heroin addicts: quantitative EEG and event-related potentials. J Neurosci Res 95(8):1633–1646

    Article  CAS  PubMed  Google Scholar 

  • Mumtaz W, Vuong PL, Xia L, Malik AS, Rashid RBA (2017) An EEG-based machine learning method to screen alcohol use disorder. Cogn Neurodyn 11(2):161–171

    Article  PubMed  Google Scholar 

  • Mumtaz W, Kamel N, Ali SSA, Malik AS (2018a) An EEG-based functional connectivity measure for automatic detection of alcohol use disorder. Artif Intell Med 84:79–89

    Article  PubMed  Google Scholar 

  • Mumtaz W, Vuong PL, Malik AS, Rashid RBA (2018b) A review on EEG-based methods for screening and diagnosing alcohol use disorder. Cogn Neurodyn 12(2):141–156

    Article  PubMed  Google Scholar 

  • Newson JJ, Thiagarajan TC (2018) EEG frequency bands in psychiatric disorders: a review of resting state studies. Front Hum Neurosci 12:521

    Article  PubMed  Google Scholar 

  • Ojala M, Garriga GC (2010) Permutation tests for studying classifier performance. J Mach Learn Res 11(Jun):1833–1863

    Google Scholar 

  • Oostenveld R, Fries P, Maris E, Schoffelen J-M (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011:1

    Article  Google Scholar 

  • Park SM, Lee JY, Kim YJ, Lee JY, Jung HY, Sohn BK, Kim DJ, Choi JS (2017) Neural connectivity in Internet gaming disorder and alcohol use disorder: a resting-state EEG coherence study. Sci Rep 7(1):1333

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Polunina AG, Davydov DM (2004) EEG spectral power and mean frequencies in early heroin abstinence. Prog Neuropsychopharmacol Biol Psychiatry 28(1):73–82

    Article  CAS  PubMed  Google Scholar 

  • Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Bauer LO, Rohrbaugh J, O’connor SJ, Kuperman S, Reich T (2002) Beta power in the EEG of alcoholics. Biol Psychiat 52(8):831–842

    Article  PubMed  Google Scholar 

  • Rangaswamy M, Porjesz B, Chorlian DB, Choi K, Jones KA, Wang K, Rohrbaugh J, O’Connor S, Kuperman S, Reich T (2003) Theta power in the EEG of alcoholics. Alcohol Clin Exp Res 27(4):607–615

    Article  PubMed  Google Scholar 

  • Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  PubMed  Google Scholar 

  • Saletu-Zyhlarz GM, Arnold O, Anderer P, Oberndorfer S, Walter H, Lesch OM, Böning J, Saletu B (2004) Differences in brain function between relapsing and abstaining alcohol-dependent patients, evaluated by EEG mapping. Alcohol Alcohol 39(3):233–240

    Article  PubMed  Google Scholar 

  • Shahmohammadi F, Golesorkhi M, Kashani MMR, Sangi M, Yoonessi A, Yoonessi A (2016) Neural correlates of craving in methamphetamine abuse. Basic Clin Neurosci 7(3):221

    PubMed  PubMed Central  Google Scholar 

  • Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 5(1):2951–2959

    Google Scholar 

  • Son KL, Choi JS, Lee J, Park SM, Lim JA, Lee JY, Kim SN, Oh S, Kim DJ, Kwon JS (2015) Neurophysiological features of Internet gaming disorder and alcohol use disorder: a resting-state EEG study. Transl Psychiatry 5:e628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Vapnik V (2013) The nature of statistical learning theory. Springer, Berlin

    Google Scholar 

  • Vinck M, Oostenveld R, Van Wingerden M, Battaglia F, Pennartz CM (2011) An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55(4):1548–1565

    Article  PubMed  Google Scholar 

  • Wang GY, Kydd R, Wouldes TA, Jensen M, Russell BR (2015a) Changes in resting EEG following methadone treatment in opiate addicts. Clin Neurophysiol 126(5):943–950

    Article  PubMed  Google Scholar 

  • Wang Z, Suh J, Li Z, Li Y, Franklin T, O’Brien C, Childress AR (2015b) A hyper-connected but less efficient small-world network in the substance-dependent brain. Drug Alcohol Depend 152:102–108

    Article  PubMed  PubMed Central  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393(6684):440

    Article  CAS  PubMed  Google Scholar 

  • Wetherill RR, Rao H, Hager N, Wang J, Franklin TR, Fan Y (2018) Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI. Addict Biol 24(4):811–821

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhao Q, Jiang H, Hu B, Li Y, Zhong N, Li M, Lin W, Liu Q (2017) Nonlinear dynamic complexity and sources of resting-state eeg in abstinent heroin addicts. IEEE Trans Nanobiosci 16(5):349–355

    Article  Google Scholar 

  • Zilverstand A, Huang AS, Alia-Klein N, Goldstein RZ (2018) Neuroimaging impaired response inhibition and salience attribution in human drug addiction: a systematic review. Neuron 98(5):886–903

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–577

    CAS  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran

    Hassan Khajehpour, Bahador Makkiabadi, Amir Homayoun Jafari & Ehsan Eqlimi

  2. Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA

    Hamed Ekhtiari

  3. Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran

    Hamed Ekhtiari

  4. Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran

    Fahimeh Mohagheghian

  5. Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran

    Mohammad Hossein Harirchian

  6. Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran

    Hassan Khajehpour, Bahador Makkiabadi, Amir Homayoun Jafari & Ehsan Eqlimi

Authors
  1. Hassan Khajehpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fahimeh Mohagheghian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamed Ekhtiari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bahador Makkiabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amir Homayoun Jafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ehsan Eqlimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mohammad Hossein Harirchian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bahador Makkiabadi.

Ethics declarations

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).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khajehpour, H., Mohagheghian, F., Ekhtiari, H. et al. Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG. Cogn Neurodyn 13, 519–530 (2019). https://doi.org/10.1007/s11571-019-09550-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-019-09550-z

Keywords

Search

Navigation