Cited by 150Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology StudiesSubstance Abuse Treatment and Outcomes

The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex

Open access
Ceceli, Ahmet O., Bradberry, Charles W., Goldstein, Rita Z.·2022·Neuropsychopharmacology

Abstract

A growing preclinical and clinical body of work on the effects of chronic drug use and drug addiction has extended the scope of inquiry from the putative reward-related subcortical mechanisms to higher-order executive functions as regulated by the prefrontal cortex. Here we review the neuroimaging evidence in humans and non-human primates to demonstrate the involvement of the prefrontal cortex in emotional, cognitive, and behavioral alterations in drug addiction, with particular attention to the impaired response inhibition and salience attribution (iRISA) framework. In support of iRISA, functional and structural neuroimaging studies document a role for the prefrontal cortex in assigning excessive salience to drug over non-drug-related processes with concomitant lapses in self-control, and deficits in reward-related decision-making and insight into illness. Importantly, converging insights from human and non-human primate studies suggest a causal relationship between drug addiction and prefrontal insult, indicating that chronic drug use causes the prefrontal cortex damage that underlies iRISA while changes with abstinence and recovery with treatment suggest plasticity of these same brain regions and functions. We further dissect the overlapping and distinct characteristics of drug classes, potential biomarkers that inform vulnerability and resilience, and advancements in cutting-edge psychological and neuromodulatory treatment strategies, providing a comprehensive landscape of the human and non-human primate drug addiction literature as it relates to the prefrontal cortex.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41386-021-01153-9

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Connectivity between the prefrontal cortex and striatal regions has been consistently shown to be disrupted in individuals with SUDs.

    “The connectivity between the prefrontal cortex and striatal regions has been consistently shown to be disrupted in individuals with SUDs 59,63,64 .”

    From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [59]

    The Non-drug context section reports reduced white-matter integrity in pathways from the nucleus accumbens to the PFC in tobacco addiction, and the Conclusions section describes striatum–OFC hypoconnectivity as a predictor of addiction vulnerability. These support altered prefrontal–striatal connectivity in specific contexts, but do not establish that it is consistently disrupted across individuals with SUDs. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured
  • In humans with SUD, loss of striatal D2 receptors is associated with impaired activity of the prefrontal cortex.

    “In humans with SUD, the loss of striatal D2 receptors is associated with impaired activity of the prefrontal cortex 58,59 .”

    From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [59]

    The Function and Conclusions sections report impaired prefrontal activity in people with drug addiction. But the passages do not link that impairment to a loss of striatal D2 receptors; searches for D2 receptors and striatal receptor availability found no such association in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured
  • Neurobiological similarities between substance addiction and compulsive social media usage are striking, particularly PFC dysfunction critical for impulse control and decision-making.

    “The neurobiological similarities between substance addiction and compulsive social media usage are striking, particularly in terms of prefrontal cortex (PFC) dysfunction, which is critical for impulse control and decision-making [32, 33].”

    From The Impact of Social Media & Technology on Child and Adolescent Mental Health (2025) · cites this paper as [33]

    The Conclusions section links PFC dysfunction in drug addiction to deficits in inhibitory control and reward-related decision-making. Our copy does not establish a neurobiological similarity with compulsive social media use; searches for social media and behavioral addiction did not find a passage addressing that comparison. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 290 · Cited by 150

Part of this paper’s reference list could not be split into single entries.

Research works printed but not linked: 85

  1. O'Brien CP, Volkow N, Li T-K. What's in a word? Addiction versus dependence in DSM-V. Am J Psychiatry. 2006;163:764-5.
  2. Wise RA. Neurobiology of addiction. Curr Opin Neurobiol. 1996;6:243-51.
  3. Robinson TE, Gorny G, Mitton E, Kolb B. Cocaine self-administration alters the morphology of dendrites and dendritic spines in the nucleus accumbens and neocortex. Synapse. 2001;39:257-66.
  4. Balleine BW, O'Doherty JP. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neu- ropsychopharmacology. 2009;35:48-69.
  5. Volkow ND, Fowler JS. Addiction, a disease of compulsion and drive: involve- ment of the orbitofrontal cortex. Cereb Cortex. 2000;10:318-25.
  6. Gläscher J, Hampton AN, O'Doherty JP. Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making. Cereb Cortex. 2009;19:483-95.
  7. Pujara MS, Philippi CL, Motzkin JC, Baskaya MK, Koenigs M. Ventromedial pre- frontal cortex damage is associated with decreased ventral striatum volume and response to reward. J Neurosci. 2016;36:5047-54.
  8. Daw ND, O'Doherty JP, Dayan P, Seymour B, Dolan RJ. Cortical substrates for exploratory decisions in humans. Nature. 2006;441:876-9.
  9. Brockett AT, Roesch MR. The ever-changing OFC landscape: what neural signals in OFC can tell us about inhibitory control. Behav Neurosci. 2021;135:129-37.
  10. Rolls ET, Everitt BJ, Roberts A, Roberts AC, Robbins TW, Weiskrantz L. The orbitofrontal cortex. Philos Trans R Soc Lond Ser B. 1996;351:1433-44.
  11. O'Doherty J, Kringelbach ML, Rolls ET, Hornak J, Andrews C. Abstract reward and punishment representations in the human orbitofrontal cortex. Nat Neurosci. 2001;4:95-102.
  12. Bechara A, Tranel D, Damasio H. Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain. 2000;123:2189-202.
  13. Reber J, Feinstein JS, O'Doherty JP, Liljeholm M, Adolphs R, Tranel D. Selective impairment of goal-directed decision-making following lesions to the human ventromedial prefrontal cortex. Brain. 2017;140:1743-56.
  14. Petrides M, Tomaiuolo F, Yeterian EH, Pandya DN. The prefrontal cortex: com- parative architectonic organization in the human and the macaque monkey brains. Cortex. 2012;48:46-57.
  15. Aron AR, Behrens TE, Smith S, Frank MJ, Poldrack RA. Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. J Neurosci. 2007;27:3743-52.
  16. Dick AS, Garic D, Graziano P, Tremblay P. The frontal aslant tract (FAT) and its role in speech, language and executive function. Cortex. 2019;111:148-63.
  17. Sallet J, Mars RB, Noonan MP, Neubert F-X, Jbabdi S, O'Reilly JX, et al. The organization of dorsal frontal cortex in humans and macaques. J Neurosci. 2013;33:12255-74.
  18. Kane MJ, Engle RW. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychonomic Bull Rev. 2002;9:637-71.
  19. Goldin PR, McRae K, Ramel W, Gross JJ. The neural bases of emotion regulation: reappraisal and suppression of negative emotion. Biol Psychiatry. 2008;63:577-86.
  20. Barbey AK, Koenigs M, Grafman J. Dorsolateral prefrontal contributions to human working memory. Cortex. 2013;49:1195-205.
  21. Goldstein RZ, Volkow ND. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry. 2002;159:1642-52.
  22. Zilverstand A, Huang AS, Alia-Klein N, Goldstein RZ. Neuroimaging impaired response inhibition and salience attribution in human drug addiction: a sys- tematic review. Neuron. 2018;98:886-903.
  23. Barbas H, Pandya DN. Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey. J Comp Neurol. 1989;286:353-75.
  24. Öngür D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex. 2000;10:206-19.
  25. Yeterian EH, Pandya DN, Tomaiuolo F, Petrides M. The cortical connectivity of the prefrontal cortex in the monkey brain. Cortex. 2012;48:58-81.
  26. Mackey S, Allgaier N, Chaarani B, Spechler P, Orr C, Bunn J, et al. Mega-analysis of gray matter volume in substance dependence: general and substance- specific regional effects. Am J Psychiatry. 2019;176:119-28.
  27. Tanabe J, Tregellas JR, Dalwani M, Thompson L, Owens E, Crowley T, et al. Medial orbitofrontal cortex gray matter is reduced in abstinent substance- dependent individuals. Biol Psychiatry. 2009;65:160-4.
  28. Levy BJ, Wagner AD. Cognitive control and right ventrolateral prefrontal cortex: reflexive reorienting, motor inhibition, and action updating. Ann N Y Acad Sci. 2011;1224:40-62.
  29. Kovach CK, Daw ND, Rudrauf D, Tranel D, O'Doherty JP, Adolphs R. Anterior prefrontal cortex contributes to action selection through tracking of recent reward trends. J Neurosci. 2012;32:8434-42.
  30. Kaag AM, Schulte MHJ, Jansen JM, van Wingen G, Homberg J, van den Brink W, et al. The relation between gray matter volume and the use of alcohol, tobacco, cocaine and cannabis in male polysubstance users. Drug Alcohol Depend. 2018;187:186-94.
  31. Sutherland MT, Riedel MC, Flannery JS, Yanes JA, Fox PT, Stein EA, et al. Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations. Behav Brain Funct. 2016;12:16.
  32. Stoeckel LE, Chai XJ, Zhang J, Whitfield-Gabrieli S, Evins AE. Lower gray matter density and functional connectivity in the anterior insula in smokers compared with never smokers. Addict Biol. 2016;21:972-81.
  33. Rosenthal A, Beck A, Zois E, Vollstädt-Klein S, Walter H, Kiefer F, et al. Volumetric prefrontal cortex alterations in patients with alcohol dependence and the involvement of self-control. Alcohol Clin Exp Res. 2019;43:2514-24.
  34. Fortier CB, Leritz EC, Salat DH, Venne JR, Maksimovskiy AL, Williams V, et al. Reduced cortical thickness in abstinent alcoholics and association with alcoholic behavior. Alcohol Clin Exp Res. 2011;35:2193-201.
  35. Wiers CE, Gawron CK, Gröpper S, Spengler S, Stuke H, Lindenmeyer J, et al. Decreased gray matter volume in inferior frontal gyrus is related to stop-signal task performance in alcohol-dependent patients. Psychiatry Res. 2015;233:125-30.
  36. Wang J, Fan Y, Dong Y, Ma M, Ma Y, Dong Y, et al. Alterations in brain structure and functional connectivity in alcohol dependent patients and possible asso- ciation with impulsivity. PLoS One. 2016;11:e0161956.
  37. Alia-Klein N, Parvaz MA, Woicik PA, Konova AB, Maloney T, Shumay E, et al. Gene x disease interaction on orbitofrontal gray matter in cocaine addiction. Arch Gen Psychiatry. 2011;68:283-94.
  38. Hall MG, Alhassoon OM, Stern MJ, Wollman SC, Kimmel CL, Perez-Figueroa A, et al. Gray matter abnormalities in cocaine versus methamphetamine- dependent patients: a neuroimaging meta-analysis. Am J Drug Alcohol Abus. 2015;41:290-9.
  39. Franklin TR, Acton PD, Maldjian JA, Gray JD, Croft JR, Dackis CA, et al. Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biol Psychiatry. 2002;51:134-42.
  40. Yip SW, Worhunsky PD, Xu J, Morie KP, Constable RT, Malison RT, et al. Gray- matter relationships to diagnostic and transdiagnostic features of drug and behavioral addictions. Addict Biol. 2018;23:394-402.
  41. Hirsiger S, Hänggi J, Germann J, Vonmoos M, Preller KH, Engeli EJE, et al. Longitudinal changes in cocaine intake and cognition are linked to cortical thickness adaptations in cocaine users. Neuroimage Clin. 2019;21:101652.
  42. Smith DG, Jones PS, Williams GB, Bullmore ET, Robbins TW, Ersche KD. Over- lapping decline in orbitofrontal gray matter volume related to cocaine use and body mass index. Addict Biol. 2015;20:194-6.
  43. Matuskey D, Bhagwagar Z, Planeta B, Pittman B, Gallezot J-D, Chen J, et al. Reductions in brain 5-HT1B receptor availability in primarily cocaine-dependent humans. Biol Psychiatry. 2014;76:816-22.
  44. Konova AB, Moeller SJ, Tomasi D, Parvaz MA, Alia-Klein N, Volkow ND, et al. Structural and behavioral correlates of abnormal encoding of money value in the sensorimotor striatum in cocaine addiction. Eur J Neurosci. 2012;36:2979-88.
  45. 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. Am J Drug Alcohol Abus. 2017;43:505-17.
  46. Liu H, Hao Y, Kaneko Y, Ouyang X, Zhang Y, Xu L, et al. Frontal and cingulate gray matter volume reduction in heroin dependence: optimized voxel-based morphometry. Psychiatry Clin Neurosci. 2009;63:563-8.
  47. 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;71:223-8.
  48. Shi H, Liang Z, Chen J, Li W, Zhu J, Li Y, et al. Gray matter alteration in heroin- dependent men: an atlas-based magnetic resonance imaging study. Psychiatry Res Neuroimaging. 2020;304:111150.
  49. 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;126:304-8.
  50. Lin H-C, Wang P-W, Wu H-C, Ko C-H, Yang Y-H, Yen C-F. Altered gray matter volume and disrupted functional connectivity of dorsolateral prefrontal cortex in men with heroin dependence. Psychiatry Clin Neurosci. 2018;72:435-44.
  51. Li M, Tian J, Zhang R, Qiu Y, Wen X, Ma X, et al. Abnormal cortical thickness in heroin-dependent individuals. Neuroimage. 2014;88:295-307.
  52. Hanlon CA, Owens MM, Joseph JE, Zhu X, George MS, Brady KT, et al. Lower subcortical gray matter volume in both younger smokers and established smokers relative to non-smokers. Addict Biol. 2016;21:185-95.
  53. Fein G, Di Sclafani V, Cardenas VA, Goldmann H, Tolou-Shams M, Meyerhoff DJ. Cortical gray matter loss in treatment-naïve alcohol dependent individuals. Alcohol Clin Exp Res. 2002;26:558-64.
  54. Barrós-Loscertales A, Garavan H, Bustamante JC, Ventura-Campos N, Llopis JJ, Belloch V, et al. Reduced striatal volume in cocaine-dependent patients. Neu- roimage. 2011;56:1021-6.
  55. Connolly CG, Bell RP, Foxe JJ, Garavan H. Dissociated grey matter changes with prolonged addiction and extended abstinence in cocaine users. PLoS One. 2013;8:e59645.
  56. Durazzo TC, Mon A, Gazdzinski S, Yeh P-H, Meyerhoff DJ. Serial longitudinal magnetic resonance imaging data indicate non-linear regional gray matter volume recovery in abstinent alcohol-dependent individuals. Addict Biol. 2015;20:956-67.
  57. Parvaz MA, Moeller SJ, d'Oleire Uquillas F, Pflumm A, Maloney T, Alia-Klein N, et al. Prefrontal gray matter volume recovery in treatment-seeking cocaine- addicted individuals: a longitudinal study. Addict Biol. 2017;22:1391-401.
  58. Durazzo TC, Mon A, Gazdzinski S, Meyerhoff DJ. Regional brain volume changes in alcohol-dependent individuals during early abstinence: associations with relapse following treatment. Addict Biol. 2017;22:1416-25.
  59. Ersche KD, Jones PS, Williams GB, Turton AJ, Robbins TW, Bullmore ET. Abnormal brain structure implicated in stimulant drug addiction. Science. 2012;335:601-4.
  60. Kühn S, Witt C, Banaschewski T, Barbot A, Barker GJ, Büchel C, et al. From mother to child: orbitofrontal cortex gyrification and changes of drinking behaviour during adolescence. Addiction Biol. 2016;21:700-8.
  61. Ersche KD, Jones PS, Williams GB, Smith DG, Bullmore ET, Robbins TW. Dis- tinctive personality traits and neural correlates associated with stimulant drug use versus familial risk of stimulant dependence. Biol Psychiatry. 2013;74:137-44.
  62. Song X, García-Saldivar P, Kindred N, Wang Y, Merchant H, Meguerditchian A, et al. Strengths and challenges of longitudinal non-human primate neuroima- ging. NeuroImage. 2021;236:118009.
  63. Kroenke CD, Rohlfing T, Park B, Sullivan EV, Pfefferbaum A, Grant KA. Monkeys that voluntarily and chronically drink alcohol damage their brains: a longitudinal MRI study. Neuropsychopharmacology. 2014;39:823-30.
  64. Jedema HP, Song X, Aizenstein HJ, Bonner AR, Stein EA, Yang Y, et al. Long-term cocaine self-administration produces structural brain changes that correlate with altered cognition. Biol Psychiatry. 2021;89:376-85.
  65. Carvalho M, Carmo H, Costa VM, Capela JP, Pontes H, Remião F, et al. Toxicity of amphetamines: an update. Arch Toxicol. 2012;86:1167-231.
  66. Kousik SM, Napier TC, Carvey PM. The effects of psychostimulant drugs on blood brain barrier function and neuroinflammation. Front Pharmacol. 2012;3:121.
  67. De Erausquin GA, Alba-Ferrara L. What does anisotropy measure? Insights from increased and decreased anisotropy in selective fiber tracts in schizophrenia. Front Integr Neurosci. 2013;7:9.
  68. Soares J, Marques P, Alves V, Sousa N. A hitchhiker's guide to diffusion tensor imaging. Front Neurosci. 2013;7:31.
  69. Song S-K, Yoshino J, Le TQ, Lin S-J, Sun S-W, Cross AH, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage. 2005;26:132-40.
  70. Loy DN, Kim JH, Xie M, Schmidt RE, Trinkaus K, Song S-K. Diffusion tensor imaging predicts hyperacute spinal cord injury severity. J Neurotrauma. 2007;24:979-90.
  71. Della Nave R, Ginestroni A, Diciotti S, Salvatore E, Soricelli A, Mascalchi M. Axial diffusivity is increased in the degenerating superior cerebellar peduncles of Friedreich's ataxia. Neuroradiology. 2011;53:367-72.
  72. Hampton WH, Hanik IM, Olson IR. Substance abuse and white matter: findings, limitations, and future of diffusion tensor imaging research. Drug Alcohol Depend. 2019;197:288-98.
  73. Baeza-Loya S, Velasquez KM, Molfese DL, Viswanath H, Curtis KN, Thompson- Lake DGY, et al. Anterior cingulum white matter is altered in tobacco smokers. Am J Addict. 2016;25:210-4.
  74. Savjani RR, Velasquez KM, Thompson-Lake DGY, Baldwin PR, Eagleman DM, De La Garza R, et al. Characterizing white matter changes in cigarette smokers via diffusion tensor imaging. Drug Alcohol Depend. 2014;145:134-42.
  75. Fortier CB, Leritz EC, Salat DH, Lindemer E, Maksimovskiy AL, Shepel J, et al. Widespread effects of alcohol on white matter microstructure. Alcohol Clin Exp Res. 2014;38:2925-33.
  76. Jansen JM, van Holst RJ, van den Brink W, Veltman DJ, Caan MWA, Goudriaan AE. Brain function during cognitive flexibility and white matter integrity in alcohol-dependent patients, problematic drinkers and healthy controls. Addict Biol. 2015;20:979-89.
  77. Ashtari M, Cervellione K, Cottone J, Ardekani BA, Kumra S. Diffusion abnorm- alities in adolescents and young adults with a history of heavy cannabis use. J Psychiatr Res. 2009;43:189-204.
  78. Gruber SA, Silveri MM, Dahlgren MK, Yurgelun-Todd D. Why so impulsive? White matter alterations are associated with impulsivity in chronic marijuana smokers. Exp Clin Psychopharmacol. 2011;19:231-42.
  79. Suchting R, Beard CL, Schmitz JM, Soder HE, Yoon JH, Hasan KM, et al. A meta- analysis of tract-based spatial statistics studies examining white matter integrity in cocaine use disorder. Addiction Biol. 2021;26:e12902.
  80. He Q, Li D, Turel O, Bechara A, Hser Y-I. White matter integrity alternations associated with cocaine dependence and long-term abstinence: preliminary findings. Behavioural Brain Res. 2020;379:112388.
  81. Moeller SJ, Platt JM, Wu M, Goodwin RD. Perception of treatment need among adults with substance use disorders: longitudinal data from a repre- sentative sample of adults in the United States. Drug Alcohol Depend. 2020;209:107895. 169. Moeller SJ, Goodwin RD, Sullivan RM, Verdejo-Garcia A. Chapter 26 -Drug use and self-awareness of treatment need: an exemplar of how population-based survey studies can address questions relevant to the neuroscience of insight. In: Verdejo-Garcia A, editor. Cognition and Addiction, London: Academic Press; 2020. p. 351-64. 170. McLellan AT, Meyers K. Contemporary addiction treatment: A review of systems problems for adults and adolescents. Biol Psychiatry. 2004;56:764-70. 171. Gopalkrishnan N, Babacan H. Cultural diversity and mental health. Australas Psychiatry. 2015;23:6-8. 172.
  82. Hester R, Nestor L, Garavan H. Impaired error awareness and anterior cingulate cortex hypoactivity in chronic cannabis users. Neuropsychopharmacology. 2009;34:2450-8. 173. Hester R, Simões-Franklin C, Garavan H. Post-error behavior in active cocaine users: poor awareness of errors in the presence of intact performance adjust- ments. Neuropsychopharmacology. 2007;32:1974-84. 174. Moeller SJ, Maloney T, Parvaz MA, Dunning JP, Alia-Klein N, Woicik PA, et al. Enhanced choice for viewing cocaine pictures in cocaine addiction. Biol Psy- chiatry. 2009;66:169-76. 175. Moeller SJ, Konova AB, Parvaz MA, Tomasi D, Lane RD, Fort C, et al. Functional, structural, and emotional correlates of impaired insight in cocaine addiction. JAMA Psychiatry. 2014;71:61-70. 176. Moeller SJ, Maloney T, Parvaz MA, Alia-Klein N, Woicik PA, Telang F, et al. Impaired insight in cocaine addiction: laboratory evidence and effects on cocaine-seeking behaviour. Brain. 2010;133:1484-93. 177. Fleming SM, Weil RS, Nagy Z, Dolan RJ, Rees G. Relating introspective accuracy to individual differences in brain structure. Science. 2010;329:1541-3. 178. Moeller SJ, Fleming SM, Gan G, Zilverstand A, Malaker P, d'Oleire Uquillas F, et al. Metacognitive impairment in active cocaine use disorder is associated with individual differences in brain structure. Eur Neuropsychopharmacol. 2016;26:653-62. 179. Lucantonio F, Takahashi Y, Hoffman AF, Chang CY, Chaudhary S, Shaham Y, et al. Orbitofrontal activation restores insight lost after cocaine use. Nat Neurosci. 2014;17:1092-9. 180. Steptoe A, Wardle J, Smith H, Kopp M, Skrabski A, Vinck J, et al. Tobacco smoking in young adults from 21 European countries: association with attitudes and risk awareness. Addiction. 1995;90:571-82. 181. Kim JS, Park BK, Kim GJ, Kim SS, Jung JG, Oh MK, et al. The role of alcoholics' insight in abstinence from alcohol in male Korean alcohol dependents. J Korean Med Sci. 2007;22:132-7. 182. 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. 183.
  83. Schmahmann JD, Pandya D. Fiber pathways of the brain. Oxford University Press, New York, USA; 2009. 184. Wardlaw JM, Valdés Hernández MC, Muñoz-Maniega S. What are white matter hyperintensities made of? J Am Heart Assoc. 2015;4:e001140. 185. Lyoo IK, Streeter CC, Ahn KH, Lee HK, Pollack MH, Silveri MM, et al. White matter hyperintensities in subjects with cocaine and opiate dependence and healthy comparison subjects. Psychiatry Res. 2004;131:135-45. 186. Kaufman MJ. Cocaine-induced cerebral vasoconstriction detected in humans with magnetic resonance angiography. JAMA. 1998;279:376. 187. Bartzokis G, Goldstein IB, Hance DB, Beckson M, Shapiro D, Lu PH, et al. The incidence of T2-weighted MR imaging signal abnormalities in the brain of cocaine-dependent patients is age-related and region-specific. AJNR Am J Neuroradiol. 1999;20:1628-35. 188. Bachi K, Mani V, Kaufman AE, Alie N, Goldstein RZ, Fayad ZA, et al. Imaging plaque inflammation in asymptomatic cocaine addicted individuals with simultaneous positron emission tomography/magnetic resonance imaging. World J Radiol. 2019;11:62-73. 189.
  84. Canterberry M, Hanlon CA, Hartwell KJ, Li X, Owens M, LeMatty T, et al. Sus- tained reduction of nicotine craving with real-time neurofeedback: exploring the role of severity of dependence. Nicotine Tob Res. 2013;15:2120-4. 255. Hanlon CA, Hartwell KJ, Canterberry M, Li X, Owens M, LeMatty T, et al. Reduction of cue-induced craving through realtime neurofeedback in nicotine users: the role of region of interest selection and multiple visits. Psychiatry Res. 2013;213:79-81. 256. Karch S, Keeser D, Hümmer S, Paolini M, Kirsch V, Karali T, et al. Modulation of craving related brain responses using real-time fMRI in patients with alcohol use disorder. PLOS One. 2015;10:e0133034. 257.
  85. Martz ME, Hart T, Heitzeg MM, Peltier SJ. Neuromodulation of brain activation associated with addiction: a review of real-time fMRI neurofeedback studies. NeuroImage Clin. 2020;27:102350. 258. Corominas-Roso M, Ibern I, Capdevila M, Ramon R, Roncero C, Ramos-Quiroga JA. Benefits of EEG-neurofeedback on the modulation of impulsivity in a sample of cocaine and heroin long-term abstinent inmates: a pilot study. Int J Offender Ther Comp Criminol. 2020;64:1275-98. 259. Stewart JL, May AC, Paulus MP. Bouncing back: brain rehabilitation amid opioid and stimulant epidemics. NeuroImage Clin. 2019;24:102068. 260. Sokhadze TM, Cannon RL, Trudeau DL. EEG biofeedback as a treatment for substance use disorders: review, rating of efficacy, and recommendations for further research. Appl Psychophysiol Biofeedback. 2008;33:1-28. 261. Kirschner M, Sladky R, Haugg A, Stämpfli P, Jehli E, Hodel M, et al. Self-regulation of the dopaminergic reward circuit in cocaine users with mental imagery and neurofeedback. EBioMedicine. 2018;37:489-98. 262. Mayberg HS, Lozano AM, Voon V, McNeely HE, Seminowicz D, Hamani C, et al. Deep brain stimulation for treatment-resistant depression. Neuron. 2005;45:651-60. 263. Schlaepfer TE, Bewernick BH. Deep brain stimulation for major depression. Handb Clin Neurol. 2013;116:235-43. 264. Benabid AL. Deep brain stimulation for Parkinson's disease. Curr Opin Neurobiol. 2003;13:696-706. 265. Wang TR, Moosa S, Dallapiazza RF, Elias WJ, Lynch WJ. Deep brain stimulation for the treatment of drug addiction. Neurosurg Focus. 2018;45:E11. 266. Heinze H-J, Heldmann M, Voges J, Hinrichs H, Marco-Pallares J, Hopf J-M, et al. Counteracting incentive sensitization in severe alcohol dependence using deep brain stimulation of the nucleus accumbens: clinical and basic science aspects. Front Hum Neurosci. 2009;3:22. 267.

Authors
Ceceli, A. O., Bradberry, C. W., & Goldstein, R. Z.
Affiliations
  • Icahn School of Medicine at Mount Sinai (US)
  • National Institute on Drug Abuse (US)
Corresponding author
Goldstein, Rita Z.
Venue
Neuropsychopharmacology
Published
2021-08-18
Volume
47
Issue
1
Pages
276-291
ISSN
  • 0893-133X (ISSN-L)
  • 1740-634X
OpenAlex
W3194450287
Cited by
150
References
290
Citations by year
2026: 29 · 2025: 31 · 2024: 45 · 2023: 17 · 2022: 25 · 2021: 3
FWCI
3.40
Type
journal article
Language
en
Open access
Locations
  • Neuropsychopharmacology (publishedVersion, other-oa)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (R01DA048301, R01DA041528, 01DA047851, 271201800035C)
  • Intramural Research Program
  • U.S. Department of Health & Human Services | NIH | National Center for Complementary and Integrative Health (R01AT010627)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
Keywords
drug addiction, prefrontal cortex, response inhibition, salience attribution, self-control, insight into illness, chronic drug use, abstinence, recovery, neuroplasticity, non-human primates, executive function
MeSH terms
Animals, Brain, Prefrontal Cortex, Substance-Related Disorders, Executive Function, Neuroimaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review