Advertisement

Current Behavioral Neuroscience Reports

, Volume 6, Issue 1, pp 1–11 | Cite as

Current Understanding of the Neurobiology of Opioid Use Disorder: an Overview

  • Hestia Moningka
  • Sarah Lichenstein
  • Sarah W. YipEmail author
Addictions (M Potenza and M Brand, Section Editors)
  • 22 Downloads
Part of the following topical collections:
  1. Topical Collection on Addictions

Abstract

Purpose of Review

This review provides an overview of the neurobiological mechanisms underlying opioid use disorder (OUD) drawing from genetic, functional, and structural magnetic resonance imaging (MRI) research.

Recent Findings

Preliminary evidence suggests an association between OUD and specific variants of the DRD2, δ-opioid receptor 1 (OPRD1), and μ-opioid receptor 1 (OPRM1) genes. Additionally, MRI research indicates functional and structural alterations in striatal and corticolimbic brain regions and pathways underlying reward, emotion/stress, and cognitive control processes among individuals with OUD.

Summary

Individual differences in genetic and functional and structural brain-based features are correlated with differences in OUD severity and treatment outcomes, and therefore may potentially one day be used to inform OUD treatment selection. However, given the heterogeneous findings reported, further longitudinal research across different stages of opioid addiction is needed to yield a convergent characterization of OUD and improve treatment and prevention.

Keywords

Addiction Neuroimaging Resting state fMRI Voxel-based morphometry Diffusion-weighted imaging 

Notes

Funding

This work was supported by NIDA grants T32 DA022975, K01DA039299 and R21DA045969.

Compliance with Ethical Standards

Conflict of Interest

Hestia Moningka, Sarah Lichenstein, and Sarah Yip declare no conflicts of interest relevant to this manuscript.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, VA: American Psychiatric Publishing; 2013.Google Scholar
  2. 2.
    Hadland SE, Wharam JF, Schuster MA, Zhang F, Samet JH, Larochelle MR. Trends in receipt of buprenorphine and naltrexone for opioid use disorder among adolescents and young adults, 2001-2014. JAMA Pediatr. 2017;171(8):747–55.Google Scholar
  3. 3.
    O'Donnell JK, Gladden RM, Seth P. Trends in deaths involving heroin and synthetic opioids excluding methadone, and law enforcement drug product reports, by census region - United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66(34):897–903.Google Scholar
  4. 4.
    Schuckit MA. Treatment of opioid-use disorders. N Engl J Med. 2016;375(4):357–68.Google Scholar
  5. 5.
    Bart G. Maintenance medication for opiate addiction: the Foundation of Recovery. J Addict Dis. 2012;31(3):207–25.Google Scholar
  6. 6.
    Dugosh K, Abraham A, Seymour B, McLoyd K, Chalk M, Festinger D. A systematic review on the use of psychosocial interventions in conjunction with medications for the treatment of opioid addiction. J Addict Med. 2016;10(2):91–101.Google Scholar
  7. 7.
    Kouimtsidis C, Reynolds M, Coulton S, Drummond C. How does cognitive behaviour therapy work with opioid-dependent clients? Results of the UKCBTMM study. Drugs: Educ Prev Polic. 2012;19(3):253–8.Google Scholar
  8. 8.
    Carroll KM, Ball SA, Martino S, Nich C, Babuscio TA, Nuro KF, et al. Computer-assisted delivery of cognitive-behavioral therapy for addiction: a randomized trial of CBT4CBT. Am J Psychiatry. 2008;165(7):881–8.Google Scholar
  9. 9.
    Hser YI, Mooney LJ, Saxon AJ, Miotto K, Bell DS, Zhu Y, et al. High mortality among patients with opioid use disorder in a large healthcare system. J Addict Med. 2017;11(4):315–9.Google Scholar
  10. 10.
    Hendershot CS, Witkiewitz K, George WH, Marlatt GA. Relapse prevention for addictive behaviors. Subst Abuse Treat Prev Policy. 2011;6:17.Google Scholar
  11. 11.
    Weiss RD, Potter JS, Fiellin DA, Byrne M, Connery HS, Dickinson W, et al. Adjunctive counseling during brief and extended buprenorphine-naloxone treatment for prescription opioid dependence: a 2-phase randomized controlled trial. Arch Gen Psychiatry. 2011;68(12):1238–46.Google Scholar
  12. 12.
    Woody GE, Poole SA, Subramaniam G, Dugosh K, Bogenschutz M, Abbott P, et al. Extended vs short-term buprenorphine-naloxone for treatment of opioid-addicted youth: a randomized trial. JAMA. 2008;300(17):2003–11.Google Scholar
  13. 13.
    Bertschy G. Methadone maintenance treatment: an update. Eur Arch Psychiatry Clin Neurosci. 1995;245(2):114–24.Google Scholar
  14. 14.
    Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010;35(1):217–38.Google Scholar
  15. 15.
    Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med. 2016;22(11):1248–55.Google Scholar
  16. 16.
    •• Darcq E, Kieffer BL. Opioid receptors: drivers to addiction? Nat Rev Neurosci. 2018;19(8):499–514 This review discusses the role of the opioid receptors in addiction and the translational potential of genetic, pharmacological and neuroimaging research in OUD. Google Scholar
  17. 17.
    Pert CB, Snyder SH. Properties of opiate-receptor binding in rat brain. Proc Natl Acad Sci. 1973;70(8):2243–7.Google Scholar
  18. 18.
    Lord JA, Waterfield AA, Hughes J, Kosterlitz HW. Endogenous opioid peptides: multiple agonists and receptors. Nature. 1977;267(5611):495–9.Google Scholar
  19. 19.
    Benarroch EE. Endogenous opioid systems: current concepts and clinical correlations. Neurology. 2012;79(8):807–14.Google Scholar
  20. 20.
    Al-Hasani R, Bruchas MR. Molecular mechanisms of opioid receptor-dependent signaling and behavior. Anesthesiology. 2011;115(6):1363–81.Google Scholar
  21. 21.
    Toll L, Bruchas MR, Cox BM, Zaveri NT. Nociceptin/orphanin FQ receptor structure, signaling, ligands, functions, and interactions with opioid systems. Pharmacol Rev. 2016;68(2):419–57.Google Scholar
  22. 22.
    Mansour A, Hoversten MT, Taylor LP, Watson SJ, Akil H. The cloned mu, delta and kappa receptors and their endogenous ligands: evidence for two opioid peptide recognition cores. Brain Res. 1995;700(1–2):89–98.Google Scholar
  23. 23.
    Peng J, Sarkar S, Chang SL. Opioid receptor expression in human brain and peripheral tissues using absolute quantitative real-time RT-PCR. Drug Alcohol Depend. 2012;124(3):223–8.Google Scholar
  24. 24.
    Raynor K, Kong H, Chen Y, Yasuda K, Yu L, Bell GI, et al. Pharmacological characterization of the cloned kappa-, delta-, and mu-opioid receptors. Mol Pharmacol. 1994;45(2):330–4.Google Scholar
  25. 25.
    Fields HL, Margolis EB. Understanding Opioid Reward. Trends Neurosci. 2015;38(4):217–25.Google Scholar
  26. 26.
    Le Merrer J, Becker JA, Befort K, Kieffer BL. Reward processing by the opioid system in the brain. Physiol Rev. 2009;89(4):1379–412.Google Scholar
  27. 27.
    Richards EM, Mathews DC, Luckenbaugh DA, Ionescu DF, Machado-Vieira R, Niciu MJ, et al. A randomized, placebo-controlled pilot trial of the delta opioid receptor agonist AZD2327 in anxious depression. Psychopharmacology. 2016;233(6):1119–30.Google Scholar
  28. 28.
    Ayanga D, Shorter D, Kosten TR. Update on pharmacotherapy for treatment of opioid use disorder. Expert Opin Pharmacother. 2016;17(17):2307–18.Google Scholar
  29. 29.
    Connery HS. Medication-assisted treatment of opioid use disorder: review of the evidence and future directions. Harv Rev Psychiatry. 2015;23(2):63–75.Google Scholar
  30. 30.
    Mattick RP, Breen C, Kimber J, Davoli M. Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database Syst Rev. 2009;3:CD002209.Google Scholar
  31. 31.
    Mattick RP, Breen C, Kimber J, Davoli M. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev. 2014;2:CD002207.Google Scholar
  32. 32.
    D'Onofrio G, O'Connor PG, Pantalon MV, Chawarski MC, Busch SH, Owens PH, et al. Emergency department-initiated buprenorphine/naloxone treatment for opioid dependence: a randomized clinical trial. JAMA. 2015;313(16):1636–44.Google Scholar
  33. 33.
    • Jarvis BP, Holtyn AF, Subramaniam S, Tompkins DA, Oga EA, Bigelow GE, et al. Extended-release injectable naltrexone for opioid use disorder: a systematic review. Addiction. 2018;113(7):1188–209 This systematic review examined extended-release injectable naltrexone (XR-NTX) for opioid use disorder, with regards to induction and adherence rates to XR-NTX as well as its effect on opioid use outcomes. Google Scholar
  34. 34.
    Minozzi S, Amato L, Vecchi S, Davoli M, Kirchmayer U, Verster A. Oral naltrexone maintenance treatment for opioid dependence. Cochrane Database Syst Rev. 2011;4:CD001333.Google Scholar
  35. 35.
    Krupitsky E, Nunes EV, Ling W, Illeperuma A, Gastfriend DR, Silverman BL. Injectable extended-release naltrexone for opioid dependence: a double-blind, placebo-controlled, multicentre randomised trial. Lancet. 2011;377(9776):1506–13.Google Scholar
  36. 36.
    • Tanum L, Solli KK, Latif ZE, Benth JS, Opheim A, Sharma-Haase K, et al. Effectiveness of Injectable Extended-Release Naltrexone vs Daily Buprenorphine-Naloxone for Opioid Dependence: A Randomized Clinical Noninferiority Trial. JAMA Psychiatry. 2017;74(12):1197–205 This randomized clinical trial demonstrates that injectable extended-release naltrexone is as effective as buprenorphine-naloxone in maintaining short-term abstinence from opioids. Google Scholar
  37. 37.
    Carroll KM, Nich C, Frankforter TL, Yip SW, Kiluk BD, DeVito EE, et al. Accounting for the uncounted: physical and affective distress in individuals dropping out of oral naltrexone treatment for opioid use disorder. Drug Alcohol Depend. 2018;192:264–70.Google Scholar
  38. 38.
    Samples H, Williams AR, Olfson M, Crystal S. Risk factors for discontinuation of buprenorphine treatment for opioid use disorders in a multi-state sample of Medicaid enrollees. J Subst Abus Treat. 2018;95:9–17.Google Scholar
  39. 39.
    Berrettini W. A brief review of the genetics and pharmacogenetics of opioid use disorders. Dialogues Clin Neurosci. 2017;19(3):229–36.Google Scholar
  40. 40.
    Clarke TK, Weiss AR, Ferarro TN, Kampman KM, Dackis CA, Pettinati HM, et al. The dopamine receptor D2 (DRD2) SNP rs1076560 is associated with opioid addiction. Ann Hum Genet. 2014;78(1):33–9.Google Scholar
  41. 41.
    Bart G, Heilig M, LaForge K, Pollak L, Leal S, Ott J, et al. Substantial attributable risk related to a functional mu-opioid receptor gene polymorphism in association with heroin addiction in Central Sweden. Mol Psychiatry. 2004;9(6):547–9.Google Scholar
  42. 42.
    Drakenberg K, Nikoshkov A, Horváth MC, Fagergren P, Gharibyan A, Saarelainen K, et al. μ opioid receptor A118G polymorphism in association with striatal opioid neuropeptide gene expression in heroin abusers. Proc Natl Acad Sci. 2006;103(20):7883–8.Google Scholar
  43. 43.
    Coller JK, Beardsley J, Bignold J, Li Y, Merg F, Sullivan T, et al. Lack of association between the A118G polymorphism of the mu opioid receptor gene (OPRM1) and opioid dependence: a meta-analysis. Pharmacogenomics Pers Med. 2009;2:9–19.Google Scholar
  44. 44.
    Haerian BS, Haerian MS. OPRM1 rs1799971 polymorphism and opioid dependence: evidence from a meta-analysis. Pharmacogenomics. 2013;14(7):813–24.Google Scholar
  45. 45.
    Woodcock EA, Lundahl LH, Burmeister M, Greenwald MK. Functional mu opioid receptor polymorphism (OPRM1 A118G) associated with heroin use outcomes in Caucasian males: a pilot study. Am J Addict. 2015;24(4):329–35.Google Scholar
  46. 46.
    Crist RC, Clarke TK, Ang A, Ambrose-Lanci LM, Lohoff FW, Saxon AJ, et al. An intronic variant in OPRD1 predicts treatment outcome for opioid dependence in African-Americans. Neuropsychopharmacology. 2013;38(10):2003–10.Google Scholar
  47. 47.
    Clarke TK, Crist RC, Ang A, Ambrose-Lanci LM, Lohoff FW, Saxon AJ, et al. Genetic variation in OPRD1 and the response to treatment for opioid dependence with buprenorphine in European-American females. Pharmacogenomics J. 2014;14(3):303–8.Google Scholar
  48. 48.
    Crist RC, Doyle GA, Nelson EC, Degenhardt L, Martin NG, Montgomery GW, et al. A polymorphism in the OPRM1 3′-untranslated region is associated with methadone efficacy in treating opioid dependence. Pharmacogenomics J. 2016;18:173.Google Scholar
  49. 49.
    • Smith AH, Jensen KP, Li J, Nunez Y, Farrer LA, Hakonarson H, et al. Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1. Mol Psychiatry. 2017;22(3):346–52 This genome-wide association study identified a significant association between methadone dose and a single nucleotide polymorphism closely located to the OPRM1 gene in African Americans, but not European Americans. Google Scholar
  50. 50.
    Wise RA, Koob GF. The development and maintenance of drug addiction. Neuropsychopharmacology. 2014;39(2):254–62.Google Scholar
  51. 51.
    Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci. 2005;8(11):1481–9.Google Scholar
  52. 52.
    Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev. 1993;18(3):247–91.Google Scholar
  53. 53.
    Badiani A, Belin D, Epstein D, Calu D, Shaham Y. Opiate versus psychostimulant addiction: the differences do matter. Nat Rev Neurosci. 2011;12:685–700.Google Scholar
  54. 54.
    Ettenberg A, Pettit HO, Bloom FE, Koob GF. Heroin and cocaine intravenous self-administration in rats: mediation by separate neural systems. Psychopharmacology. 1982;78(3):204–9.Google Scholar
  55. 55.
    Avvisati R, Contu L, Stendardo E, Michetti C, Montanari C, Scattoni ML, et al. Ultrasonic vocalization in rats self-administering heroin and cocaine in different settings: evidence of substance-specific interactions between drug and setting. Psychopharmacology. 2016;233(8):1501–11.Google Scholar
  56. 56.
    De Pirro S, Galati G, Pizzamiglio L, Badiani A. The affective and neural correlates of heroin vs. cocaine use in addiction are influenced by environmental setting but in opposite directions. Journal of Neuroscience. 2018;38(22):5182–95.Google Scholar
  57. 57.
    Hartwell KJ, Back SE, McRae-Clark AL, Shaftman SR, Brady KT. Motives for using: a comparison of prescription opioid, marijuana and cocaine dependent individuals. Addict Behav. 2012;37(4):373–8.Google Scholar
  58. 58.
    Epstein DH, Willner-Reid J, Vahabzadeh M, Mezghanni M, Lin JL, Preston KL. Real-time electronic diary reports of cue exposure and mood in the hours before cocaine and heroin craving and use. Arch Gen Psychiatry. 2009;66(1):88–94.Google Scholar
  59. 59.
    • Ahn W-Y, Vassileva J. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug Alcohol Depend. 2016;161:247–57 This study used a machine-learning approach to identify multivariate substance-specific markers that classify heroin and amphetamine dependence respectively. Google Scholar
  60. 60.
    Ahn WY, Ramesh D, Moeller FG, Vassileva J. Utility of machine-learning approaches to identify behavioral markers for substance use disorders: impulsivity dimensions as predictors of current cocaine dependence. Front Psychiatry. 2016;7:34.Google Scholar
  61. 61.
    •• Moningka H, Lichenstein S, Worhunsky PD, DeVito EE, Scheinost D, Yip SW. Can neuroimaging help combat the opioid epidemic? A systematic review of clinical and pharmacological challenge fMRI studies with recommendations for future research. Neuropsychopharmacology. 2018;44:259–73. This is a systematic review of fMRI studies in opioid use disorder, including studies comparing opioid-dependent and healthy control participants as well as studies on opioid medications, treatment and abstinence effects. Google Scholar
  62. 62.
    Li Q, Li W, Wang H, Wang Y, Zhang Y, Zhu J, et al. Predicting subsequent relapse by drug-related cue-induced brain activation in heroin addiction: an event-related functional magnetic resonance imaging study. Addict Biol. 2015;20(5):968–78.Google Scholar
  63. 63.
    Li Q, Yang WC, Wang YR, Huang YF, Li W, Zhu J, et al. Abnormal function of the posterior cingulate cortex in heroin addicted users during resting-state and drug-cue stimulation task. Chin Med J. 2013;126(4):734–9.Google Scholar
  64. 64.
    Lou M, Wang E, Shen Y, Wang J. Cue-elicited craving in heroin addicts at different abstinent time: an fMRI pilot study. Subst Use Misuse. 2012;47(6):631–9.Google Scholar
  65. 65.
    •• Volkow ND, Koob GF, McLellan AT. Neurobiologic Advances from the Brain Disease Model of Addiction. N Engl J Med. 2016;374(4):363–71 This article briefly summarizes current knowledge on neuroscience addiction research, including conceptual frameworks and neural circuitry underlying addiction. Google Scholar
  66. 66.
    Wang ZX, Zhang JX, Wu QL, Liu N, Hu XP, Chan RC, et al. Alterations in the processing of non-drug-related affective stimuli in abstinent heroin addicts. NeuroImage. 2010;49(1):971–6.Google Scholar
  67. 67.
    Zijlstra F, Veltman DJ, Booij J, van den Brink W, Franken IH. Neurobiological substrates of cue-elicited craving and anhedonia in recently abstinent opioid-dependent males. Drug Alcohol Depend. 2009;99(1–3):183–92.Google Scholar
  68. 68.
    Yip SW, DeVito EE, Kober H, Worhunsky PD, Carroll KM, Potenza MN. Anticipatory reward processing among cocaine-dependent individuals with and without concurrent methadone-maintenance treatment: relationship to treatment response(). Drug Alcohol Depend. 2016;166:134–42.Google Scholar
  69. 69.
    Gradin VB, Baldacchino A, Balfour D, Matthews K, Steele JD. Abnormal brain activity during a reward and loss task in opiate-dependent patients receiving methadone maintenance therapy. Neuropsychopharmacology. 2014;39(4):885–94.Google Scholar
  70. 70.
    Shi Z, Wang A-L, Jagannathan K, Fairchild VP, O’Brien CP, Childress AR, et al. Effects of extended-release naltrexone on the brain response to drug-related stimuli in patients with opioid use disorder. J Psychiatry Neurosci: JPN. 2018;43(4):254–61.Google Scholar
  71. 71.
    Wang A-L, Lowen SB, Elman I, Shi Z, Fairchild VP, Bouril A, et al. Sustained opioid antagonism modulates striatal sensitivity to baby schema in opioid use disorder. J Subst Abus Treat. 2018;85:70–7.Google Scholar
  72. 72.
    Smoski MJ, Salsman N, Wang L, Smith V, Lynch TR, Dager SR, et al. Functional imaging of emotion reactivity in opiate-dependent borderline personality disorder. Personal Disord. 2011;2(3):230–41.Google Scholar
  73. 73.
    Schmidt A, Borgwardt S, Gerber H, Wiesbeck GA, Schmid O, Riecher-Rossler A, et al. Acute effects of heroin on negative emotional processing: relation of amygdala activity and stress-related responses. Biol Psychiatry. 2014;76(4):289–96.Google Scholar
  74. 74.
    Fu LP, Bi GH, Zou ZT, Wang Y, Ye EM, Ma L, et al. Impaired response inhibition function in abstinent heroin dependents: an fMRI study. Neurosci Lett. 2008;438(3):322–6.Google Scholar
  75. 75.
    Yucel M, Lubman DI, Harrison BJ, Fornito A, Allen NB, Wellard RM, et al. A combined spectroscopic and functional MRI investigation of the dorsal anterior cingulate region in opiate addiction. Mol Psychiatry. 2007;12(7):611 91-702.Google Scholar
  76. 76.
    Schmidt A, Walter M, Gerber H, Schmid O, Smieskova R, Bendfeldt K, et al. Inferior frontal cortex modulation with an acute dose of heroin during cognitive control. Neuropsychopharmacology. 2013;38(11):2231–9.Google Scholar
  77. 77.
    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.Google Scholar
  78. 78.
    Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537–41.Google Scholar
  79. 79.
    Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8(9):700–11.Google Scholar
  80. 80.
    • Li Q, Liu J, Wang W, Wang Y, Li W, Chen J, et al. Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men. J Psychiatry Neurosci: JPN. 2018;43(1):48–57 This study investigated how resting-state functional connectivity among the salience, default mode and executive control networks correlate with heroin relapse behaviour. Google Scholar
  81. 81.
    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.Google Scholar
  82. 82.
    Upadhyay J, Maleki N, Potter J, Elman I, Rudrauf D, Knudsen J, et al. Alterations in brain structure and functional connectivity in prescription opioid-dependent patients. Brain. 2010;133(Pt 7):2098–114.Google Scholar
  83. 83.
    Wang PW, Lin HC, Liu GC, Yang YH, Ko CH, Yen CF. Abnormal interhemispheric resting state functional connectivity of the insula in heroin users under methadone maintenance treatment. Psychiatry Res Neuroimaging. 2016;255:9–14.Google Scholar
  84. 84.
    Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214(5–6):655–67.Google Scholar
  85. 85.
    Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A dual-networks architecture of top-down control. Trends Cogn Sci. 2008;12(3):99–105.Google Scholar
  86. 86.
    Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(2):676–82.Google Scholar
  87. 87.
    Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201–15.Google Scholar
  88. 88.
    Fox MD, Zhang D, Snyder AZ, Raichle ME. The global signal and observed Anticorrelated resting state brain networks. J Neurophysiol. 2009;101(6):3270–83.Google Scholar
  89. 89.
    Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A. 2008;105(34):12569–74.Google Scholar
  90. 90.
    Lerman C, Gu H, Loughead J, Ruparel K, Yang Y, Stein EA. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function. JAMA Psychiatry. 2014;71(5):523–30.Google Scholar
  91. 91.
    Li Q, Li Z, Li W, Zhang Y, Wang Y, Zhu J, et al. Disrupted default mode network and basal craving in male heroin-dependent individuals: a resting-state fMRI study. J Clin Psychiatry. 2016;77(10):e1211–e7.Google Scholar
  92. 92.
    Li W, Li Q, Wang D, Xiao W, Liu K, Shi L, et al. Dysfunctional default mode network in methadone treated patients who have a higher heroin relapse risk. Sci Rep. 2015;5:15181.Google Scholar
  93. 93.
    Wang W, Wang YR, Qin W, Yuan K, Tian J, Li Q, et al. Changes in functional connectivity of ventral anterior cingulate cortex in heroin abusers. Chin Med J. 2010;123(12):1582–8.Google Scholar
  94. 94.
    Ma X, Qiu Y, Tian J, Wang J, Li S, Zhan W, et al. Aberrant default-mode functional and structural connectivity in heroin-dependent individuals. PLoS One. 2015;10(4):e0120861.Google Scholar
  95. 95.
    Yuan K, Qin W, Dong M, Liu J, Sun J, Liu P, et al. Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals. Neurosci Lett. 2010;482(2):101–5.Google Scholar
  96. 96.
    Liu J, Liang J, Qin W, Tian J, Yuan K, Bai L, et al. Dysfunctional connectivity patterns in chronic heroin users: an fMRI study. Neurosci Lett. 2009;460(1):72–7.Google Scholar
  97. 97.
    Xie C, Shao Y, Fu L, Goveas J, Ye E, Li W, et al. Identification of hyperactive intrinsic amygdala network connectivity associated with impulsivity in abstinent heroin addicts. Behav Brain Res. 2011;216(2):639–46.Google Scholar
  98. 98.
    Lyoo IK, Pollack MH, Silveri MM, Ahn KH, Diaz CI, Hwang J, et al. Prefrontal and temporal gray matter density decreases in opiate dependence. Psychopharmacology. 2006;184(2):139–44.Google Scholar
  99. 99.
    Yuan Y, Zhu Z, Shi J, Zou Z, Yuan F, Liu Y, et al. Gray matter density negatively correlates with duration of heroin use in young lifetime heroin-dependent individuals. Brain Cogn. 2009;71(3):223–8.Google Scholar
  100. 100.
    Y-w Q, G-h J, H-h S, X-f L, J-z T, L-m L, et al. The impulsivity behavior is correlated with prefrontal cortex gray matter volume reduction in heroin-dependent individuals. Neurosci Lett. 2013;538:43–8.Google Scholar
  101. 101.
    Seifert CL, Magon S, Sprenger T, Lang UE, Huber CG, Denier N, et al. Reduced volume of the nucleus accumbens in heroin addiction. Eur Arch Psychiatry Clin Neurosci. 2015;265(8):637–45.Google Scholar
  102. 102.
    • Wollman SC, Alhassoon OM, Hall MG, Stern MJ, Connors EJ, Kimmel CL, et al. Gray matter abnormalities in opioid-dependent patients: A neuroimaging meta-analysis. The American Journal of Drug and Alcohol Abuse. 2017;43(5):505–17 This meta-analysis demonstrates that opioid-dependent individuals exhibited significantly decreased grey matter in fronto-cerebellar and fronto-insular regions compared to healthy individuals. Google Scholar
  103. 103.
    Wang X, Li B, Zhou X, Liao Y, Tang J, Liu T, et al. Changes in brain gray matter in abstinent heroin addicts. Drug Alcohol Depend. 2012;126(3):304–8.Google Scholar
  104. 104.
    Tolomeo S, Gray S, Matthews K, Steele J, Baldacchino A. Multifaceted impairments in impulsivity and brain structural abnormalities in opioid dependence and abstinence. Psychol Med. 2016;46(13):2841–53.Google Scholar
  105. 105.
    Qiu Y, Jiang G, Su H, Lv X, Zhang X, Tian J, et al. Progressive white matter microstructure damage in male chronic heroin dependent individuals: a DTI and TBSS study. PLoS One. 2013;8(5):e63212.Google Scholar
  106. 106.
    Bora E, Yücel M, Fornito A, Pantelis C, Harrison BJ, Cocchi L, et al. White matter microstructure in opiate addiction. Addict Biol. 2012;17(1):141–8.Google Scholar
  107. 107.
    Liu H, Li L, Hao Y, Cao D, Xu L, Rohrbaugh R, et al. Disrupted white matter integrity in heroin dependence: a controlled study utilizing diffusion tensor imaging. Am J Drug Alcohol Abuse. 2008;34(5):562–75.Google Scholar
  108. 108.
    Ivers JH, Fitzgerald J, Whelan C, Sweeney B, Keenan E, Fagan A, et al. Progressive white matter impairment as a predictor of outcome in a cohort of opioid-dependent patient's post-detoxification. Addict Biol. 2018;23(1):304–12.Google Scholar
  109. 109.
    Wollman SC, Alhassoon OM, Stern MJ, Hall MG, Rompogren J, Kimmel CL, et al. White matter abnormalities in long-term heroin users: a preliminary neuroimaging meta-analysis. Am J Drug Alcohol Abuse. 2015;41(2):133–8.Google Scholar
  110. 110.
    Wang Y, Li W, Li Q, Yang W, Zhu J, Wang W. White matter impairment in heroin addicts undergoing methadone maintenance treatment and prolonged abstinence: a preliminary DTI study. Neurosci Lett. 2011;494(1):49–53.Google Scholar
  111. 111.
    Li W, Zhu J, Li Q, Ye J, Chen J, Liu J, et al. Brain white matter integrity in heroin addicts during methadone maintenance treatment is related to relapse propensity. Brain and Behavior. 2016;6(2):e00436.Google Scholar
  112. 112.
    Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex. 2009;19(1):72–8.Google Scholar
  113. 113.
    Dart RC, Surratt HL, Cicero TJ, Parrino MW, Severtson SG, Bucher-Bartelson B, et al. Trends in opioid analgesic abuse and mortality in the United States. N Engl J Med. 2015;372(3):241–8.Google Scholar
  114. 114.
    Pariyadath V, Gowin JL, Stein EA. Resting state functional connectivity analysis for addiction medicine: from individual loci to complex networks. Prog Brain Res. 2016;224:155–73.Google Scholar
  115. 115.
    Heilig M, Epstein DH, Nader MA, Shaham Y. Time to connect: bringing social context into addiction neuroscience. Nat Rev Neurosci. 2016;17(9):592–9.Google Scholar
  116. 116.
    Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014;512(7513):185–9.Google Scholar
  117. 117.
    Yip SW, Scheinost D, Potenza MN, Carroll KM. Connectome-based prediction of cocaine abstinence. Am J Psychiatr. 2019.  https://doi.org/10.1176/appi.ajp.2018.17101147.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hestia Moningka
    • 1
    • 2
  • Sarah Lichenstein
    • 1
  • Sarah W. Yip
    • 1
    Email author
  1. 1.Department of PsychiatryYale School of MedicineNew HavenUSA
  2. 2.Division of Psychology and Language SciencesUniversity College LondonLondonUK

Personalised recommendations