Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

English version

Autoři: Hassan Khajehpour ^aff001; Bahador Makkiabadi ^aff001; Hamed Ekhtiari ^aff003; Sepideh Bakht ^aff005; Alireza Noroozi ^aff004; Fahimeh Mohagheghian ^aff007
Působiště autorů: Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran ^aff001; Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran ^aff002; Laureate Institute for Brain Research (LIBR), Tulsa, OK, United States of America ^aff003; Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran ^aff004; Department of Cognitive Psychology, Institute for Cognitive Sciences Studies (ICSS), Tehran, Iran ^aff005; Neuroscience and Addiction Studies Department, School of Advanced Technologies in Medicine (SATiM), Tehran University of Medical Sciences (TUMS), Tehran, Iran ^aff006; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America ^aff007
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226249

Souhrn

This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.

Klíčová slova:

Psychological stress – Functional magnetic resonance imaging – Electroencephalography – Addiction – Neural networks – Impulsivity – Clustering coefficients – Drug addiction

Zdroje

1. Noroozi A, Malekinejad M, Rahimi-Movaghar A. Factors Influencing Transition to Shisheh (Methamphetamine) among Young People Who Use Drugs in Tehran: A Qualitative Study. Journal of psychoactive drugs. 2018;50(3):214–23. doi: 10.1080/02791072.2018.1425808 29377788

2. United Nations Office on Drugs and Crime, World Drug Report 2016 (United Nations publication, Sales No. E.16.XI.7).

3. Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature reviews neuroscience. 2011;12(11):652. doi: 10.1038/nrn3119 22011681

4. Jiang G, Wen X, Qiu Y, Zhang R, Wang J, Li M, et al. Disrupted topological organization in whole-brain functional networks of heroin-dependent individuals: a resting-state FMRI study. PLoS One. 2013;8(12):e82715. doi: 10.1371/journal.pone.0082715 24358220

5. Ma N, Liu Y, Li N, Wang C-X, Zhang H, Jiang X-F, et al. Addiction related alteration in resting-state brain connectivity. Neuroimage. 2010;49(1):738–44. doi: 10.1016/j.neuroimage.2009.08.037 19703568

6. Başar E, Schmiedt-Fehr C, Mathes B, Femir B, Emek-Savaş D, Tülay E, et al. What does the broken brain say to the neuroscientist? Oscillations and connectivity in schizophrenia, Alzheimer's disease, and bipolar disorder. International Journal of Psychophysiology. 2016;103:135–48. doi: 10.1016/j.ijpsycho.2015.02.004 25660302

7. Sutherland MT, McHugh MJ, Pariyadath V, Stein EA. Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage. 2012;62(4):2281–95. doi: 10.1016/j.neuroimage.2012.01.117 22326834

8. Mohan A, De Ridder D, Vanneste S. Graph theoretical analysis of brain connectivity in phantom sound perception. Scientific reports. 2016;6:19683. doi: 10.1038/srep19683 26830446

9. Wang Z, Suh J, Li Z, Li Y, Franklin T, O’Brien C, et al. A hyper-connected but less efficient small-world network in the substance-dependent brain. Drug and alcohol dependence. 2015;152:102–8. doi: 10.1016/j.drugalcdep.2015.04.015 25957794

10. Mahmoodi M, Abadi BM, Khajepur H, Harirchian MH, editors. A robust beamforming approach for early detection of readiness potential with application to brain-computer interface systems. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2017: IEEE.

11. Maryam Yasaminshirazi MA. Neuroimaging Findings in Methamphetamine Abusers. Addict Res Ther 2016.

12. Newton TF, Cook IA, Kalechstein AD, Duran S, Monroy F, Ling W, et al. Quantitative EEG abnormalities in recently abstinent methamphetamine dependent individuals. Clinical Neurophysiology. 2003;114(3):410–5. doi: 10.1016/s1388-2457(02)00409-1 12705421

13. Ahmadlou M, Ahmadi K, Rezazade M, Azad-Marzabadi E. Global organization of functional brain connectivity in methamphetamine abusers. Clinical neurophysiology. 2013;124(6):1122–31. doi: 10.1016/j.clinph.2012.12.003 23332777

14. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69. doi: 10.1016/j.neuroimage.2009.10.003 19819337

15. Sporns O, Honey CJ. Small worlds inside big brains. Proceedings of the National Academy of Sciences. 2006;103(51):19219–20.

16. Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences. 2006;103(51):19518–23.

17. Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological psychiatry. 2011;70(4):334–42. doi: 10.1016/j.biopsych.2011.05.018 21791259

18. Li C, Huang B, Zhang R, Ma Q, Yang W, Wang L, et al. Impaired topological architecture of brain structural networks in idiopathic Parkinson’s disease: a DTI study. Brain imaging and behavior. 2017;11(1):113–28. doi: 10.1007/s11682-015-9501-6 26815739

19. Wang J, Zuo X, Dai Z, Xia M, Zhao Z, Zhao X, et al. Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biological psychiatry. 2013;73(5):472–81. doi: 10.1016/j.biopsych.2012.03.026 22537793

20. Ma S, Calhoun VD, Eichele T, Du W, Adalı T. Modulations of functional connectivity in the healthy and schizophrenia groups during task and rest. Neuroimage. 2012;62(3):1694–704. doi: 10.1016/j.neuroimage.2012.05.048 22634855

21. Mohagheghian F, Makkiabadi B, Jalilvand H, Khajehpoor H, Samadzadehaghdam N, Eqlimi E, et al. Computer-aided tinnitus detection based on brain network analysis of EEG functional connectivity. Journal of Biomedical Physics and Engineering. 2018.

22. Yuan K, Qin W, Liu J, Guo Q, Dong M, Sun J, et al. Altered small-world brain functional networks and duration of heroin use in male abstinent heroin-dependent individuals. Neuroscience Letters. 2010;477(1):37–42. doi: 10.1016/j.neulet.2010.04.032 20417253

23. Hsu T-W, Wu CW, Cheng Y-F, Chen H-L, Lu C-H, Cho K-H, et al. Impaired small-world network efficiency and dynamic functional distribution in patients with cirrhosis. PLoS One. 2012;7(5):e35266. doi: 10.1371/journal.pone.0035266 22563460

24. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS computational biology. 2007;3(2):e17. doi: 10.1371/journal.pcbi.0030017 17274684

25. Meunier D, Achard S, Morcom A, Bullmore E. Age-related changes in modular organization of human brain functional networks. Neuroimage. 2009;44(3):715–23. doi: 10.1016/j.neuroimage.2008.09.062 19027073

26. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods. 2004;134(1):9–21. doi: 10.1016/j.jneumeth.2003.10.009 15102499

27. Vinck M, Oostenveld R, Van Wingerden M, Battaglia F, Pennartz CM. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011;55(4):1548–65. doi: 10.1016/j.neuroimage.2011.01.055 21276857

28. González GF, Van der Molen M, Žarić G, Bonte M, Tijms J, Blomert L, et al. Graph analysis of EEG resting state functional networks in dyslexic readers. Clinical Neurophysiology. 2016;127(9):3165–75. doi: 10.1016/j.clinph.2016.06.023 27476025

29. Hardmeier M, Hatz F, Bousleiman H, Schindler C, Stam CJ, Fuhr P. 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. 2014;9(10):e108648. doi: 10.1371/journal.pone.0108648 25286380

30. Xing M, Tadayonnejad R, MacNamara A, Ajilore O, DiGangi J, Phan KL, et al. Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder. NeuroImage: Clinical. 2017;13:24–32.

31. Ewald A, Aristei S, Nolte G, Rahman RA. Brain oscillations and functional connectivity during overt language production. Frontiers in psychology. 2012;3:166. doi: 10.3389/fpsyg.2012.00166 22701106

32. Haufe S, Nikulin VV, Müller K-R, Nolte G. A critical assessment of connectivity measures for EEG data: a simulation study. Neuroimage. 2013;64:120–33. doi: 10.1016/j.neuroimage.2012.09.036 23006806

33. Hjorth B. An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalography and clinical neurophysiology. 1975;39(5):526–30. doi: 10.1016/0013-4694(75)90056-5 52448

34. Hu B, Dong Q, Hao Y, Zhao Q, Shen J, Zheng F. Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects. Journal of neural engineering. 2017;14(4):046002. doi: 10.1088/1741-2552/aa6c6f 28397708

35. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’networks. nature. 1998;393(6684):440. doi: 10.1038/30918 9623998

36. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009;10(3):186. doi: 10.1038/nrn2575 19190637

37. Beudel M, Tjepkema-Cloostermans MC, Boersma JH, van Putten MJ. Small-world characteristics of EEG patterns in post-anoxic encephalopathy. Frontiers in neurology. 2014;5:97. doi: 10.3389/fneur.2014.00097 24982649

38. Mohan A, De Ridder D, Vanneste S. Emerging hubs in phantom perception connectomics. NeuroImage: Clinical. 2016;11:181–94.

39. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24(Suppl D):5–12.

40. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89. doi: 10.1006/nimg.2001.0978 11771995

41. Pascual-Marqui RD, Lehmann D, Koukkou M, Kochi K, Anderer P, Saletu B, et al. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philosophical transactions Series A, Mathematical, physical, and engineering sciences. 2011;369(1952):3768–84. doi: 10.1098/rsta.2011.0081 21893527

42. Imperatori C, Della Marca G, Brunetti R, Carbone GA, Massullo C, Valenti EM, et al. Default Mode Network alterations in alexithymia: an EEG power spectra and connectivity study. Scientific reports. 2016;6:36653. doi: 10.1038/srep36653 27845326

43. Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one. 2013;8(7):e68910. doi: 10.1371/journal.pone.0068910 23861951

44. Yun K, Park HK, Kwon DH, Kim YT, Cho SN, Cho HJ, et al. Decreased cortical complexity in methamphetamine abusers. Psychiatry research. 2012;201(3):226–32. doi: 10.1016/j.pscychresns.2011.07.009 22445216

45. 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. Cognitive Neurodynamics. 2019:1–12. doi: 10.1007/s11571-018-9509-x

46. Mantini D, Vanduffel W. Emerging roles of the brain’s default network. The Neuroscientist. 2013;19(1):76–87. doi: 10.1177/1073858412446202 22785104

47. Engel AK, Fries P, Singer W. Dynamic predictions: oscillations and synchrony in top–down processing. Nature Reviews Neuroscience. 2001;2(10):704. doi: 10.1038/35094565 11584308

48. Engel AK, König P, Kreiter AK, Schillen TB, Singer W. Temporal coding in the visual cortex: new vistas on integration in the nervous system. Trends Neurosci. 1992;15(6):218–26. doi: 10.1016/0166-2236(92)90039-b 1378666

49. Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in cognitive sciences. 2005;9(10):474–80. doi: 10.1016/j.tics.2005.08.011 16150631

50. Fries P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annual review of neuroscience. 2009;32:209–24. doi: 10.1146/annurev.neuro.051508.135603 19400723

51. Jensen O, Kaiser J, Lachaux J-P. Human gamma-frequency oscillations associated with attention and memory. Trends in neurosciences. 2007;30(7):317–24. doi: 10.1016/j.tins.2007.05.001 17499860

52. Neuner I, Arrubla J, Werner CJ, Hitz K, Boers F, Kawohl W, et al. The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study. PLoS One. 2014;9(2):e88214. doi: 10.1371/journal.pone.0088214 24505434

53. Burgess AP, Gruzelier JH. Short duration synchronization of human theta rhythm during recognition memory. Neuroreport. 1997;8(4):1039–42. doi: 10.1097/00001756-199703030-00044 9141088

54. Knyazev GG. Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience & Biobehavioral Reviews. 2007;31(3):377–95.

55. Steriade M, McCormick DA, Sejnowski TJ. Thalamocortical oscillations in the sleeping and aroused brain. Science. 1993;262(5134):679–85. doi: 10.1126/science.8235588 8235588

56. Ronconi L, Oosterhof NN, Bonmassar C, Melcher D. Multiple oscillatory rhythms determine the temporal organization of perception. Proceedings of the National Academy of Sciences. 2017:201714522.

57. Engel AK, Fries P. Beta-band oscillations—signalling the status quo? Current opinion in neurobiology. 2010;20(2):156–65. doi: 10.1016/j.conb.2010.02.015 20359884

58. Csicsvari J, Jamieson B, Wise KD, Buzsáki G. Mechanisms of gamma oscillations in the hippocampus of the behaving rat. Neuron. 2003;37(2):311–22. doi: 10.1016/s0896-6273(02)01169-8 12546825

59. Alcaro A, Panksepp J. The SEEKING mind: primal neuro-affective substrates for appetitive incentive states and their pathological dynamics in addictions and depression. Neuroscience & Biobehavioral Reviews. 2011;35(9):1805–20.

60. HajiHosseini A, Rodríguez-Fornells A, Marco-Pallarés J. The role of beta-gamma oscillations in unexpected rewards processing. Neuroimage. 2012;60(3):1678–85. doi: 10.1016/j.neuroimage.2012.01.125 22330314

61. Zilverstand A, Huang AS, Alia-Klein N, Goldstein RZ. Neuroimaging Impaired Response Inhibition and Salience Attribution in Human Drug Addiction: A Systematic Review. Neuron. 2018;98(5):886–903. doi: 10.1016/j.neuron.2018.03.048 29879391

62. Goldstein RZ, Volkow ND. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. American Journal of Psychiatry. 2002;159(10):1642–52. doi: 10.1176/appi.ajp.159.10.1642 12359667

63. Walsh ND, Phillips ML. Interacting outcome retrieval, anticipation, and feedback processes in the human brain. Cerebral Cortex. 2009;20(2):271–81. doi: 10.1093/cercor/bhp098 19429861

64. Ishai A, Ungerleider LG, Martin A, Schouten JL, Haxby JV. Distributed representation of objects in the human ventral visual pathway. Proceedings of the National Academy of Sciences. 1999;96(16):9379–84.

65. Herath P, Kinomura S, Roland PE. Visual recognition: evidence for two distinctive mechanisms from a PET study. Human brain mapping. 2001;12(2):110–9. doi: 10.1002/1097-0193(200102)12:2<110::aid-hbm1008>3.0.co;2-0 11169875

66. Ray S, Hanson C, Hanson SJ, Bates ME. fMRI BOLD response in high-risk college students (part 1): during exposure to alcohol, marijuana, polydrug and emotional picture cues. Alcohol and alcoholism. 2010;45(5):437–43. doi: 10.1093/alcalc/agq042 20729530

67. Tau GZ, Marsh R, Wang Z, Torres-Sanchez T, Graniello B, Hao X, et al. Neural correlates of reward-based spatial learning in persons with cocaine dependence. Neuropsychopharmacology. 2014;39(3):545. doi: 10.1038/npp.2013.189 23917430

68. Chen Q, Zheng D, Cui S, Yan K-J, Fan C-x, Zhang G-f, et al. Disrupted Resting-State Brain Functional Architecture in Amphetamine-Type Stimulant Abusers. Neuropsychiatry. 2018;8(1):249–60.

69. BassettDS B. Humanbrainnetworksinhealthanddi ⋅ sease. CurrentOpinioninNeurology. 2009;22(4):340.

70. Balconi M, Campanella S, Finocchiaro R. Web addiction in the brain: Cortical oscillations, autonomic activity, and behavioral measures. Journal of behavioral addictions. 2017;6(3):334–44. doi: 10.1556/2006.6.2017.041 28718301

71. Hanlon CA, Dowdle LT, Naselaris T, Canterberry M, Cortese BM. Visual cortex activation to drug cues: a meta-analysis of functional neuroimaging papers in addiction and substance abuse literature. Drug and alcohol dependence. 2014;143:206–12. doi: 10.1016/j.drugalcdep.2014.07.028 25155889

72. Florin E, Gross J, Pfeifer J, Fink GR, Timmermann L. The effect of filtering on Granger causality based multivariate causality measures. Neuroimage. 2010;50(2):577–88. doi: 10.1016/j.neuroimage.2009.12.050 20026279

73. Park SM, Lee JY, Kim YJ, Lee J-Y, Jung HY, Sohn BK, et al. Neural connectivity in Internet gaming disorder and alcohol use disorder: a resting-state EEG coherence study. Scientific reports. 2017;7(1):1333. doi: 10.1038/s41598-017-01419-7 28465521

74. Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nature reviews genetics. 2011;12(1):56. doi: 10.1038/nrg2918 21164525

75. Zeng H, Su D, Wang P, Wang M, Vollstadt-Klein S, Chen Q, et al. The Action Representation Elicited by Different Types of Drug-Related Cues in Heroin-Abstinent Individuals. Frontiers in behavioral neuroscience. 2018;12:123. doi: 10.3389/fnbeh.2018.00123 30013467

76. Lee JY, Park SM, Kim YJ, Kim DJ, Choi S-W, Kwon JS, et al. Resting-state EEG activity related to impulsivity in gambling disorder. Journal of behavioral addictions. 2017;6(3):387–95. doi: 10.1556/2006.6.2017.055 28856896

77. Jena SK. Examination stress and its effect on EEG. Int J Med Sci Pub Health. 2015;11(4):1493–7.

78. Dluzen DE, Liu B. Gender differences in methamphetamine use and responses: a review. Gender medicine. 2008;5(1):24–35. doi: 10.1016/s1550-8579(08)80005-8 18420163

79. Berman S, O'Neill J, Fears S, Bartzokis G, London ED. Abuse of amphetamines and structural abnormalities in the brain. Annals of the New York Academy of Sciences. 2008;1141:195–220. doi: 10.1196/annals.1441.031 18991959

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

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