The quantification of the effect of substance abuse on brain behavior based on electroencephalogram signals: A comprehensive review

Document Type : Review Article

Authors

1 Ph.D. student in medical engineering, Hakim Sabzevari University, Sabzevar, Iran.

2 Assistant professor, Hakim Sabzevari University, Sabzevar, Iran.

Abstract

Introduction: Substance abuse has become a significant health problem for individuals and society. This study aimed to investigate the effects of substance abuse on brain waves.
Materials and Methods: Articles used were systematically searched in Google Scholar, Scopus, Thomson Reuters, ScienceDirect, PubMed, and IEEE databases. The result of this search was 420 articles. Keywords [Substance-Related Disorders MESH) AND (EEG MESH)], ["substance-related disorder" AND "EEG"], ["drug dependence" AND "EEG"], ["Substance abuse" AND "EEG"], ["Opioid" AND "EEG"], ["Cannabis" AND "EEG"] and ["Methamphetamine" AND "EEG"] were used for the search. After removing irrelevant and duplicate articles, we included 22 full-text articles in the study.
Results: The articles examined the effects of substance abuse on brain waves with three approaches. The first approach is the event-related potentials technique. The second approach is to investigate the functional connections between different brain parts. The third approach analyzes EEG signals from various channels to select biomarkers to detect substance abuse.
Conclusion: According to the present findings, it is suggested that policymakers and community health managers increase public awareness of the harms of substance abuse. Researchers in health should also discover and develop new diagnostic methods and treatment strategies according to the damage caused to the brain of substance abusers.

Keywords


  1. World Drug Report 2021_Annex 2021. [cited 2021]. Available from: https://www.unodc.org/unodc/en/data-and-analysis/wdr2021_annex.html.
  2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Am J Psychiatry 2013; 21(21): 591-643.
  3. Keihani A, Ekhtiari H, Batouli SAH, Shahbabaie A, Sadighi N, Mirmohammad M, et al. Lower gray matter density in the anterior cingulate cortex and putamen can be traceable in chronic heroin dependents after over three months of successful abstinence. Iran J Radiol 2017; 14(3): e41858.
  4. Farnia V, Farshchian F, Farshchian N, Alikhani M, Pormehr R, Golshani S, et al. A voxel-based morphometric brain study of patients with methamphetamine dependency: A case controlled study. NeuroQuantology 2018; 16(12): 57-62.
  5. Vuletic D, Dupont P, Robertson F, Warwick J, Zeevaart JR, Stein DJ. Methamphetamine dependence with and without psychotic symptoms: A multi-modal brain imaging study. Neuroimage Clin 2018; 20: 1157-62.
  6. Huhn A, Meyer R, Harris J, Ayaz H, Deneke E, Stankoski D, et al. Evidence of anhedonia and differential reward processing in prefrontal cortex among post-withdrawal patients with prescription opiate dependence. Brain Res Bull 2016; 123: 102-9.
  7. Sadeghi AZ, Jafari AH, Oghabian MA, Salighehrad HR, Batouli SAH, Raminfard S, et al. Changes in effective connectivity network patterns in drug abusers, treated with different methods. Basic Clin Neurosci 2017; 8(4): 285.
  8. Khajehpour H, Mohagheghian F, Ekhtiari H, Makkiabadi B, Jafari AH, Eqlimi E, et al. Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG. Cogn Neurodyn 2019; 13(6): 519-30.
  9. Li X, Zhou Y, Zhang G, Lu Y, Zhou C, Wang H. Behavioral and brain reactivity associated with drug-related and non-drug-related emotional stimuli in methamphetamine addicts. Front Hum Neurosci 2022; 16: 894-911.
  10. Crane NA, Funkhouser CJ, Burkhouse KL, Klumpp H, Phan KL, Shankman SA. Cannabis users demonstrate enhanced neural reactivity to reward: An event-related potential and time-frequency EEG study. Addict Behav 2021; 113: 106669.
  11. Macatee RJ, Okey SA, Albanese BJ, Schmidt NB, Cougle JR. Distress intolerance moderation of motivated attention to cannabis and negative stimuli after induced stress among cannabis users: An ERP study. Addict Biol 2019; 24(4): 717-29.
  12. Wei S, Zheng Y, Li Q, Dai W, Sun J, Wu H, et al. Enhanced neural responses to monetary rewards in methamphetamine use disordered individuals compared to healthy controls. Physiol Behav 2018; 195: 118-27.
  13. Haifeng J, Wenxu Z, Hong C, Chuanwei L, Jiang D, Haiming S, et al. P300 event-related potential in abstinent methamphetamine-dependent patients. Physiol Behav 2015; 149: 142-8.
  14. Shahmohammadi F, Golesorkhi M, Kashani MMR, Sangi M, Yoonessi A, Yoonessi A. Neural correlates of craving in methamphetamine abuse. Basic Clin Neurosci 2016; 7(3): 221.
  15. Morie KP, De Sanctis P, Garavan H, Foxe JJ. Executive dysfunction and reward dysregulation: A high-density electrical mapping study in cocaine abusers. Neuropharmacology 2014; 85: 397-407.
  16. Fink BC, Steele VR, Maurer MJ, Fede SJ, Calhoun VD, Kiehl KA. Brain potentials predict substance abuse treatment completion in a prison sample. Brain Behav 2016; 6(8): e00501.
  17. Imperatori C, Massullo C, Carbone GA, Panno A, Giacchini M, Capriotti C, et al. Increased resting state triple network functional connectivity in undergraduate problematic cannabis users: A preliminary EEG coherence study. Brain Sci 2020; 10(3): 136.
  18. Prashad S, Dedrick ES, Filbey FM. Cannabis users exhibit increased cortical activation during resting state compared to non-users. Neuroimage 2018; 179: 176-86.
  19. Khajehpour H, Makkiabadi B, Ekhtiari H, Bakht S, Noroozi A, Mohagheghian F. Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG. PloS One 2019; 14(12): e0226249.
  20. Ahmadlou M, Ahmadi K, Rezazade M, Azad-Marzabadi E. Global organization of functional brain connectivity in methamphetamine abusers. Clin Neurophysiol 2013; 124(6): 1122-31.
  21. Capecci E, Kasabov N, Wang GY. Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment. Neural Netw 2015; 68: 62-77.
  22. Doborjeh MG, Wang GY, Kasabov NK, Kydd R, Russell B. A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not treated subjects. IEEE Trans Biomed Eng 2015; 63(9): 1830-41.
  23. Coullaut-Valera R, Arbaiza I, Bajo R, Arrúe R, López ME, Coullaut-Valera J, et al. Drug polyconsumption is associated with increased synchronization of brain electrical-activity at rest and in a counting task. Int J Neural Syst 2014; 24: 1450005.
  24. Laprevote V, Bon L, Krieg J, Schwitzer T, Bourion-Bedes S, Maillard L, et al. Association between increased EEG signal complexity and cannabis dependence. Eur Neuropsychopharmacol 2017; 27(12): 1216-22.
  25. Yun K, Park H-K, Kwon D-H, Kim Y-T, Cho S-N, Cho H-J, et al. Decreased cortical complexity in methamphetamine abusers. Psychiatr Res Neuroimag 2012; 201(3): 226-32.
  26. Chen T, Su H, Zhong N, Tan H, Li X, Meng Y, et al. Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates. BMC Psychiatry 2020; 20(1): 1-11.
  27. Erguzel TT, Uyulan C, Unsalver B, Evrensel A, Cebi M, Noyan CO, et al. Entropy: A promising EEG biomarker dichotomizing subjects with opioid use disorder and healthy controls. Clin EEG Neurosci 2020; 51(6): 373-81.
  28. Minnerly C, Shokry IM, To W, Callanan JJ, Tao R. Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine-and alcohol-use disorders. PloS One 2021; 16(9): e0248794.
  29. Moreno-Alcázar A, Gonzalvo B, Canales-Rodríguez EJ, Blanco L, Bachiller D, Romaguera A, et al. Larger gray matter volume in the basal ganglia of heavy cannabis users detected by voxel-based morphometry and subcortical volumetric analysis. Front Psychiatry 2018; 3(9): 175.
  30. Bosnyak D, McDonald AC, Gasperin Haaz I, Qi W, Crowley DC, Guthrie N, et al. Use of a novel EEG-based objective test, the Cognalyzer®, in quantifying the strength and determining the action time of cannabis psychoactive effects and factors that may influence them within an observational study framework. Neurol Ther 2022; 11(1): 51-72.
  31. McDonald AC, Gasperin Haaz I, Qi W, Crowley DC, Guthrie N, Evans M, et al. Sensitivity, specificity and accuracy of a novel EEG-based objective test, the Cognalyzer®, in detecting cannabis psychoactive effects. Adv Ther 2021; 38(5): 2513-31.
  32. Gu X, Yang B, Gao S, Yan LF, Xu D, Wang W. Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning. Math Biosci Eng 2021; 18: 6926-40.