Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications

Article metrics


The loss of control over drug intake that occurs in addiction was initially believed to result from disruption of subcortical reward circuits. However, imaging studies in addictive behaviours have identified a key involvement of the prefrontal cortex (PFC) both through its regulation of limbic reward regions and its involvement in higher-order executive function (for example, self-control, salience attribution and awareness). This Review focuses on functional neuroimaging studies conducted in the past decade that have expanded our understanding of the involvement of the PFC in drug addiction. Disruption of the PFC in addiction underlies not only compulsive drug taking but also accounts for the disadvantageous behaviours that are associated with addiction and the erosion of free will.

Key Points

  • Neuroimaging studies have revealed an emerging pattern of generalized prefrontal cortex (PFC) dysfunction in drug-addicted individuals that is associated with worse outcome — more drug use, worse PFC-related task performance and greater likelihood of relapse.

  • Widespread PFC activation in drug-addicted individuals upon taking cocaine or other drugs and upon presentation of drug-related cues is replaced by widespread PFC hypoactivity during exposure to higher-order emotional and cognitive challenges and/or during protracted withdrawal when not stimulated.

  • The nature of the findings in PFC is different when abusers are studied during intoxication or craving and when they are studied during acute or protracted withdrawal, and this is to be expected considering the distinct role of the PFC in these processes.

  • PFC regions may be implicated in what seem to be opposite processes. This may reflect the limited temporal resolution of imaging technologies or methodological variability. Processes also reflect the function of networks rather than isolated regions, so that the output of a region will differ as it connects with different networks.

  • Although activity among PFC regions is highly integrated and flexible, such that any one region is involved in multiple functions, the dorsal PFC (including the dorsal anterior cingulate cortex, dorsolateral PFC and inferior frontal gyrus) has been predominantly implicated in top-down control and meta-cognitive functions (including awareness), the ventromedial PFC (including subgenual ACC and medial orbitofrontal cortex) in emotion regulation (including conditioning and assigning incentive salience to drugs and drug-related cues), and the ventrolateral PFC and lateral OFC in automatic response tendencies (for example, drug-related attention bias) and impulsivity.

  • Dysfunction of these PFC regions may contribute to impaired response inhibition and salience attribution (iRISA) in addiction, neuropsychological mechanisms that underlie the development of craving, compulsive use and impaired self-awareness (previously labelled 'denial' of illness and/or need for treatment), which are characteristic symptoms of drug addiction.

  • PFC dysfunction may in some instances precede drug use and confer vulnerability for (or protection against) developing substance use disorders.

  • Specific biomarkers could be targeted for intervention purposes. For example, PFC abnormalities could be used to identify the children and adolescents who would benefit most from intensive drug abuse prevention efforts.

  • The combination of targeted pharmacological interventions (for example, to enhance dopaminergic neurotransmission) with cognitive–behavioural exercises (for example, to enhance inhibitory control and non drug-related motivation, and reduce drug-related attention bias) could normalize select PFC functions. Ameliorating these deficits could help addicted subjects to engage in rehabilitation treatment.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Behavioural manifestations of the iRISA syndrome of drug addiction.
Figure 2: Recent neuroimaging studies of PFC activity in drug-addicted individuals.
Figure 3: A model of PFC involvement in iRISA in addiction.
Figure 4: The effect of oral methylphenidate on anterior cingulate cortex activity and function in cocaine addiction.


  1. 1

    Wise, R. A. Neurobiology of addiction. Curr. Opin. Neurobiol. 6, 243–251 (1996).

  2. 2

    Everitt, B. J., Dickinson, A. & Robbins, T. W. The neuropsychological basis of addictive behaviour. Brain Res. Brain Res. Rev. 36, 129–138 (2001).

  3. 3

    Di Chiara, G. & Imperato, A. Drugs abused by humans preferentially increase synaptic dopamine concentrations in the mesolimbic system of freely moving rats. Proc. Natl Acad. Sci. USA 85, 5274–5278 (1988).

  4. 4

    Volkow, N. D. & Fowler, J. S. Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cereb. Cortex 10, 318–325 (2000).

  5. 5

    Robinson, T. E., Gorny, G., Mitton, E. & Kolb, B. Cocaine self-administration alters the morphology of dendrites and dendritic spines in the nucleus accumbens and neocortex. Synapse 39, 257–266 (2001).

  6. 6

    Robinson, T. E. & Kolb, B. Alterations in the morphology of dendrites and dendritic spines in the nucleus accumbens and prefrontal cortex following repeated treatment with amphetamine or cocaine. Eur. J. Neurosci. 11, 1598–1604 (1999).

  7. 7

    Goldstein, R. Z. & Volkow, N. D. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 159, 1642–1652 (2002).

  8. 8

    Volkow, N. D., Fowler, J. S. & Wang, G. J. The addicted human brain: insights from imaging studies. J. Clin. Invest. 111, 1444–1451 (2003).

  9. 9

    Volkow, N. D. & Li, T. K. Drug addiction: the neurobiology of behaviour gone awry. Nature Rev. Neurosci. 5, 963–970 (2004).

  10. 10

    Schoenbaum, G., Roesch, M. R., Stalnaker, T. A. & Takahashi, Y. K. A new perspective on the role of the orbitofrontal cortex in adaptive behaviour. Nature Rev. Neurosci. 10, 885–892 (2009).

  11. 11

    Mansouri, F. A., Tanaka, K. & Buckley, M. J. Conflict-induced behavioural adjustment: a clue to the executive functions of the prefrontal cortex. Nature Rev. Neurosci. 10, 141–152 (2009).

  12. 12

    Kufahl, P. R. et al. Neural responses to acute cocaine administration in the human brain detected by fMRI. Neuroimage 28, 904–914 (2005).

  13. 13

    Kufahl, P. et al. Expectation modulates human brain responses to acute cocaine: a functional magnetic resonance imaging study. Biol. Psychiatry 63, 222–230 (2008).

  14. 14

    Volkow, N. D. et al. Expectation enhances the regional brain metabolic and the reinforcing effects of stimulants in cocaine abusers. J. Neurosci. 23, 11461–11468 (2003). This study shows that the regional brain activation induced by intravenous MPH is influenced by the expectation that the subjects have when the drug is given, indicating that drug effects in an addicted individual are not just a function of the pharmacological characteristics of the drug but of past experiences and the expectations that these generate.

  15. 15

    Howell, L. L., Votaw, J. R., Goodman, M. M. & Lindsey, K. P. Cortical activation during cocaine use and extinction in rhesus monkeys. Psychopharmacology 208, 191–199 (2010).

  16. 16

    Howell, L. L. et al. Cocaine-induced brain activation determined by positron emission tomography neuroimaging in conscious rhesus monkeys. Psychopharmacology 159, 154–160 (2002).

  17. 17

    Henry, P. K., Murnane, K. S., Votaw, J. R. & Howell, L. L. Acute brain metabolic effects of cocaine in rhesus monkeys with a history of cocaine use. Brain Imaging Behav. 4, 212–219 (2010).

  18. 18

    Ahmed, S. H. & Koob, G. F. Transition from moderate to excessive drug intake: change in hedonic set point. Science 282, 298–300 (1998).

  19. 19

    Febo, M. et al. Imaging cocaine-induced changes in the mesocorticolimbic dopaminergic system of conscious rats. J. Neurosci. Methods 139, 167–176 (2004).

  20. 20

    Mandeville, J. B. et al. FMRI of cocaine self-administration in macaques reveals functional inhibition of basal ganglia. Neuropsychopharmacology 36, 1187–1198 (2011).

  21. 21

    Zubieta, J. K. et al. Regional cerebral blood flow responses to smoking in tobacco smokers after overnight abstinence. Am. J. Psychiatry 162, 567–577 (2005).

  22. 22

    Sell, L. A. et al. Neural responses associated with cue evoked emotional states and heroin in opiate addicts. Drug Alcohol Depend. 60, 207–216 (2000).

  23. 23

    Domino, E. F. et al. Effects of nicotine on regional cerebral glucose metabolism in awake resting tobacco smokers. Neuroscience 101, 277–282 (2000).

  24. 24

    Myrick, H. et al. Differential brain activity in alcoholics and social drinkers to alcohol cues: relationship to craving. Neuropsychopharmacology 29, 393–402 (2004).

  25. 25

    de Greck, M. et al. Decreased neural activity in reward circuitry during personal reference in abstinent alcoholics-a fMRI study. Hum. Brain Mapp. 30, 1691–1704 (2009).

  26. 26

    Zijlstra, F., Veltman, D. J., Booij, J., van den Brink, W. & Franken, I. H. Neurobiological substrates of cue-elicited craving and anhedonia in recently abstinent opioid-dependent males. Drug Alcohol Depend. 99, 183–192 (2009).

  27. 27

    Yalachkov, Y., Kaiser, J. & Naumer, M. J. Brain regions related to tool use and action knowledge reflect nicotine dependence. J. Neurosci. 29, 4922–4929 (2009).

  28. 28

    Heinz, A. et al. Brain activation elicited by affectively positive stimuli is associated with a lower risk of relapse in detoxified alcoholic subjects. Alcohol. Clin. Exp. Res. 31, 1138–1147 (2007).

  29. 29

    Grusser, S. M. et al. Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacology 175, 296–302 (2004).

  30. 30

    Garavan, H. et al. Cue-induced cocaine craving: neuroanatomical specificity for drug users and drug stimuli. Am. J. Psychiatry 157, 1789–1798 (2000). In cocaine users, watching a cocaine-related film induced greater ACC activation than watching a sexually explicit film. This study suggests that drug-related cues in drug-addicted individuals activate similar neuroanatomical substrates as naturally evocative stimuli in healthy controls.

  31. 31

    Brody, A. L. et al. Brain metabolic changes during cigarette craving. Arch. Gen. Psychiatry 59, 1162–1172 (2002).

  32. 32

    Artiges, E. et al. Exposure to smoking cues during an emotion recognition task can modulate limbic fMRI activation in cigarette smokers. Addict. Biol. 14, 469–477 (2009).

  33. 33

    Zhang, X. et al. Masked smoking-related images modulate brain activity in smokers. Hum. Brain Mapp. 30, 896–907 (2009).

  34. 34

    Childress, A. R. et al. Prelude to passion: limbic activation by “unseen” drug and sexual cues. PLoS ONE 3, e1506 (2008).

  35. 35

    Filbey, F. M. et al. Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology 33, 1391–1401 (2008).

  36. 36

    Urban, N. B. et al. Sex differences in striatal dopamine release in young adults after oral alcohol challenge: a positron emission tomography imaging study with [11C]raclopride. Biol. Psychiatry 68, 689–696 (2010).

  37. 37

    King, A., McNamara, P., Angstadt, M. & Phan, K. L. Neural substrates of alcohol-induced smoking urge in heavy drinking nondaily smokers. Neuropsychopharmacology 35, 692–701 (2010).

  38. 38

    Volkow, N. D. et al. Activation of orbital and medial prefrontal cortex by methylphenidate in cocaine-addicted subjects but not in controls: relevance to addiction. J. Neurosci. 25, 3932–3939 (2005).

  39. 39

    Ko, C. H. et al. Brain activities associated with gaming urge of online gaming addiction. J. Psychiatr. Res. 43, 739–747 (2009).

  40. 40

    Crockford, D. N., Goodyear, B., Edwards, J., Quickfall, J. & el-Guebaly, N. Cue-induced brain activity in pathological gamblers. Biol. Psychiatry 58, 787–795 (2005).

  41. 41

    Goudriaan, A. E., De Ruiter, M. B., Van Den Brink, W., Oosterlaan, J. & Veltman, D. J. Brain activation patterns associated with cue reactivity and craving in abstinent problem gamblers, heavy smokers and healthy controls: an fMRI study. Addict. Biol. 15, 491–503 (2010).

  42. 42

    Reuter, J. et al. Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nature Neurosci. 8, 147–148 (2005).

  43. 43

    Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).

  44. 44

    Volkow, N. D., Wang, G. J., Fowler, J. S. & Telang, F. Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Phil. Trans. R. Soc. Lond. B Biol. Sci. 363, 3191–3200 (2008).

  45. 45

    Wang, G. J. et al. Brain dopamine and obesity. Lancet. 357, 354–357 (2001).

  46. 46

    Uher, R. et al. Medial prefrontal cortex activity associated with symptom provocation in eating disorders. Am. J. Psychiatry 161, 1238–1246 (2004).

  47. 47

    Miyake, Y. et al. Neural processing of negative word stimuli concerning body image in patients with eating disorders: an fMRI study. Neuroimage 50, 1333–1339 (2010).

  48. 48

    Culbertson, C. S. et al. Effect of bupropion treatment on brain activation induced by cigarette-related cues in smokers. Arch. Gen. Psychiatry 68, 505–515.

  49. 49

    Franklin, T. et al. Effects of varenicline on smoking cue-triggered neural and craving responses. Arch. Gen. Psychiatry 68, 516–526.

  50. 50

    Wang, Z. et al. Neural substrates of abstinence-induced cigarette cravings in chronic smokers. J. Neurosci. 27, 14035–14040 (2007).

  51. 51

    Janes, A. C. et al. Brain fMRI reactivity to smoking-related images before and during extended smoking abstinence. Exp. Clin. Psychopharmacol. 17, 365–373 (2009).

  52. 52

    McClernon, F. J., Kozink, R. V., Lutz, A. M. & Rose, J. E. 24-h smoking abstinence potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum. Psychopharmacology 204, 25–35 (2009).

  53. 53

    McBride, D., Barrett, S. P., Kelly, J. T., Aw, A. & Dagher, A. Effects of expectancy and abstinence on the neural response to smoking cues in cigarette smokers: an fMRI study. Neuropsychopharmacology 31, 2728–2738 (2006).

  54. 54

    Wilson, S. J., Sayette, M. A., Delgado, M. R. & Fiez, J. A. Instructed smoking expectancy modulates cue-elicited neural activity: a preliminary study. Nicotine Tob. Res. 7, 637–645 (2005).

  55. 55

    Volkow, N. D. et al. Cognitive control of drug craving inhibits brain reward regions in cocaine abusers. Neuroimage 49, 2536–2543 (2010). This study shows that when cocaine abusers try to suppress craving, this results in inhibition of limbic brain regions that is inversely associated with activation of the right inferior frontal cortex (Brodmann area 44), which is a key region for inhibitory control.

  56. 56

    Brody, A. L. et al. Neural substrates of resisting craving during cigarette cue exposure. Biol. Psychiatry 62, 642–651 (2007).

  57. 57

    Kober, H. et al. Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc. Natl Acad. Sci. USA 107, 14811–14816 (2010). Considering the long-term consequences of consuming cigarettes was associated with decreased craving and decreased activity in PFC regions associated with craving, and with increased activity in PFC regions associated with cognitive control. This study offers a specific cognitive–behavioural intervention to reduce cue-induced craving.

  58. 58

    Pelchat, M. L., Johnson, A., Chan, R., Valdez, J. & Ragland, J. D. Images of desire: food-craving activation during fMRI. Neuroimage 23, 1486–1493 (2004).

  59. 59

    Volkow, N. D., Fowler, J. S., Wang, G. J. & Swanson, J. M. Dopamine in drug abuse and addiction: results from imaging studies and treatment implications. Mol. Psychiatry 9, 557–569 (2004).

  60. 60

    Koob, G. F. & Le Moal, M. Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 24, 97–129 (2001).

  61. 61

    Solomon, R. L. & Corbit, J. D. An opponent-process theory of motivation. I. Temporal dynamics of affect. Psychol. Rev. 81, 119–145 (1974).

  62. 62

    Solomon, R. L. & Corbit, J. D. An opponent-process theory of motivation. II. Cigarette addiction. J. Abnorm. Psychol. 81, 158–171 (1973).

  63. 63

    Rolls, E. T. Precis of The brain and emotion. Behav. Brain Sci. 23, 177–191; discussion 192–233 (2000).

  64. 64

    Russell, M. in Drugs and Drug Dependence (ed. Edwards, G.) 182–187 (Lexington Books, 1976).

  65. 65

    Gold, M. S. in Substance Abuse: A Comprehensive Textbook (eds Lowinson, J. H., Ruiz, P., Millman, R. B. & Langrod, J. G.) 181–199 (Williams & Wilkins, 1997).

  66. 66

    Cheetham, A., Allen, N. B., Yucel, M. & Lubman, D. I. The role of affective dysregulation in drug addiction. Clin. Psychol. Rev. 30, 621–634 (2010).

  67. 67

    Sinha, R. The role of stress in addiction relapse. Curr. Psychiatry Rep. 9, 388–395 (2007).

  68. 68

    Aguilar de Arcos, F., Verdejo-Garcia, A., Peralta-Ramirez, M. I., Sanchez-Barrera, M. & Perez-Garcia, M. Experience of emotions in substance abusers exposed to images containing neutral, positive, and negative affective stimuli. Drug Alcohol Depend. 78, 159–167 (2005).

  69. 69

    Verdejo-Garcia, A., Bechara, A., Recknor, E. C. & Perez-Garcia, M. Executive dysfunction in substance dependent individuals during drug use and abstinence: an examination of the behavioral, cognitive, and emotional correlates of addiction. J. Int. Neuropsychol. Soc. 12, 405–415 (2006).

  70. 70

    Goldstein, R. Z. et al. Is decreased prefrontal cortical sensitivity to monetary reward associated with impaired motivation and self-control in cocaine addiction? Am. J. Psychiatry 164, 43–51 (2007). Sustained monetary reward was associated with a robust neuronal activation pattern in healthy control subjects but not in cocaine-addicted subjects. In addition, this study reported results that are consistent with impaired self-awareness in cocaine addiction.

  71. 71

    Tremblay, L. & Schultz, W. Relative reward preference in primate orbitofrontal cortex. Nature 398, 704–708 (1999).

  72. 72

    Elliott, R., Newman, J. L., Longe, O. A. & Deakin, J. F. Differential response patterns in the striatum and orbitofrontal cortex to financial reward in humans: a parametric functional magnetic resonance imaging study. J. Neurosci. 23, 303–307 (2003).

  73. 73

    Breiter, H. C., Aharon, I., Kahneman, D., Dale, A. & Shizgal, P. Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 30, 619–639 (2001).

  74. 74

    Kringelbach, M. L., O'Doherty, J., Rolls, E. T. & Andrews, C. Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its subjective pleasantness. Cereb. Cortex 13, 1064–1071 (2003).

  75. 75

    Knutson, B., Westdorp, A., Kaiser, E. & Hommer, D. FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 12, 20–27 (2000).

  76. 76

    O'Doherty, J., Kringelbach, M. L., Rolls, E. T., Hornak, J. & Andrews, C. Abstract reward and punishment representations in the human orbitofrontal cortex. Nature Neurosci. 4, 95–102 (2001).

  77. 77

    Hornak, J. et al. Reward-related reversal learning after surgical excisions in orbito-frontal or dorsolateral prefrontal cortex in humans. J. Cogn. Neurosci. 16, 463–478 (2004).

  78. 78

    Goldstein, R. Z. et al. Subjective sensitivity to monetary gradients is associated with frontolimbic activation to reward in cocaine abusers. Drug Alcohol Depend. 87, 233–240 (2007).

  79. 79

    Roesch, M. R., Taylor, A. R. & Schoenbaum, G. Encoding of time-discounted rewards in orbitofrontal cortex is independent of value representation. Neuron 51, 509–520 (2006).

  80. 80

    Kirby, K. N. & Petry, N. M. Heroin and cocaine abusers have higher discount rates for delayed rewards than alcoholics or non-drug-using controls. Addiction 99, 461–471 (2004).

  81. 81

    Monterosso, J. R. et al. Frontoparietal cortical activity of methamphetamine-dependent and comparison subjects performing a delay discounting task. Hum. Brain Mapp. 28, 383–393 (2007).

  82. 82

    Kampman, K. M. What's new in the treatment of cocaine addiction? Curr. Psychiatry Rep. 12, 441–447 (2010).

  83. 83

    Goldstein, R. Z. et al. Anterior cingulate cortex hypoactivations to an emotionally salient task in cocaine addiction. Proc. Natl Acad. Sci. USA 106, 9453–9458 (2009).

  84. 84

    Goldstein, R. Z. et al. Dopaminergic response to drug words in cocaine addiction. J. Neurosci. 29, 6001–6006 (2009).

  85. 85

    Reichel, C. M. & Bevins, R. A. Competition between the conditioned rewarding effects of cocaine and novelty. Behav. Neurosci. 122, 140–150 (2008).

  86. 86

    Mattson, B. J., Williams, S., Rosenblatt, J. S. & Morrell, J. I. Comparison of two positive reinforcing stimuli: pups and cocaine throughout the postpartum period. Behav. Neurosci. 115, 683–694 (2001).

  87. 87

    Zombeck, J. A. et al. Neuroanatomical specificity of conditioned responses to cocaine versus food in mice. Physiol. Behav. 93, 637–650 (2008).

  88. 88

    Aigner, T. G. & Balster, R. L. Choice behavior in rhesus monkeys: cocaine versus food. Science 201, 534–535 (1978).

  89. 89

    Woolverton, W. L. & Anderson, K. G. Effects of delay to reinforcement on the choice between cocaine and food in rhesus monkeys. Psychopharmacolog. 186, 99–106 (2006).

  90. 90

    Buhler, M. et al. Nicotine dependence is characterized by disordered reward processing in a network driving motivation. Biol. Psychiatry 67, 745–752 (2010). Occasional smokers showed greater behavioural responses and mesocorticolimbic reactivity to stimuli predicting monetary versus cigarette rewards, whereas in dependent smokers these responses were equal for both reward types. This suggests an imbalance in the incentive salience attributed to drug-reward-predicting versus non-drug reward predicting cues in drug addiction.

  91. 91

    Moeller, S. J. et al. Enhanced choice for viewing cocaine pictures in cocaine addiction. Biol. Psychiatry 66, 169–176 (2009).

  92. 92

    Moeller, S. J. et al. Impaired insight in cocaine addiction: laboratory evidence and effects on cocaine-seeking behaviour. Brain. 133, 1484–1493 (2010).

  93. 93

    Kim, Y. T. et al. Alterations in cortical activity of male methamphetamine abusers performing an empathy task: fMRI study. Hum. Psychopharmacol. 25, 63–70 (2010).

  94. 94

    Wang, Z. X. et al. Alterations in the processing of non-drug-related affective stimuli in abstinent heroin addicts. Neuroimage 49, 971–976 (2010).

  95. 95

    Salloum, J. B. et al. Blunted rostral anterior cingulate response during a simplified decoding task of negative emotional facial expressions in alcoholic patients. Alcohol. Clin. Exp. Res. 31, 1490–1504 (2007).

  96. 96

    Asensio, S. et al. Altered neural response of the appetitive emotional system in cocaine addiction: an fMRI Study. Addict. Biol. 15, 504–516 (2010).

  97. 97

    Gruber, S. A., Rogowska, J. & Yurgelun-Todd, D. A. Altered affective response in marijuana smokers: an FMRI study. Drug Alcohol Depend. 105, 139–153 (2009).

  98. 98

    Payer, D. E. et al. Differences in cortical activity between methamphetamine-dependent and healthy individuals performing a facial affect matching task. Drug Alcohol Depend. 93, 93–102 (2008).

  99. 99

    Deroche-Gamonet, V., Belin, D. & Piazza, P. V. Evidence for addiction-like behavior in the rat. Science 305, 1014–1017 (2004).

  100. 100

    de Ruiter, M. B. et al. Response perseveration and ventral prefrontal sensitivity to reward and punishment in male problem gamblers and smokers. Neuropsychopharmacology 34, 1027–1038 (2009).

  101. 101

    Goldstein, R. Z. et al. The effect of practice on a sustained attention task in cocaine abusers. Neuroimage 35, 194–206 (2007).

  102. 102

    Goldstein, R. Z. et al. Severity of neuropsychological impairment in cocaine and alcohol addiction: association with metabolism in the prefrontal cortex. Neuropsychologia 42, 1447–1458 (2004).

  103. 103

    Garavan, H. & Hester, R. The role of cognitive control in cocaine dependence. Neuropsychol. Rev. 17, 337–345 (2007).

  104. 104

    Aharonovich, E., Nunes, E. & Hasin, D. Cognitive impairment, retention and abstinence among cocaine abusers in cognitive-behavioral treatment. Drug Alcohol Depend. 71, 207–211 (2003).

  105. 105

    Aharonovich, E. et al. Cognitive deficits predict low treatment retention in cocaine dependent patients. Drug Alcohol Depend. 81, 313–322 (2006).

  106. 106

    Goldstein, R. Z., Moeller, S. J. & Volkow, N. D. . in Neuroimaging in the Addictions (eds Adinoff, B. & Stein, E. A.) (Weily, 2011).

  107. 107

    Tarter, R. E. et al. Neurobehavioral disinhibition in childhood predicts early age at onset of substance use disorder. Am. J. Psychiatry 160, 1078–1085 (2003).

  108. 108

    Moffitt, T. E. et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc. Natl Acad. Sci. USA 108, 2693–2698 (2011).

  109. 109

    Kaufman, J. N., Ross, T. J., Stein, E. A. & Garavan, H. Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. J. Neurosci. 23, 7839–7843 (2003).

  110. 110

    Hester, R. & Garavan, H. Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. J. Neurosci. 24, 11017–11022 (2004).

  111. 111

    Fu, L. P. et al. Impaired response inhibition function in abstinent heroin dependents: an fMRI study. Neurosci. Lett. 438, 322–326 (2008).

  112. 112

    Li, C. S. et al. Neural correlates of impulse control during stop signal inhibition in cocaine-dependent men. Neuropsychopharmacology 33, 1798–1806 (2008).

  113. 113

    Li, C. S., Luo, X., Yan, P., Bergquist, K. & Sinha, R. Altered impulse control in alcohol dependence: neural measures of stop signal performance. Alcohol. Clin. Exp. Res. 33, 740–750 (2009).

  114. 114

    Kozink, R. V., Kollins, S. H. & McClernon, F. J. Smoking withdrawal modulates right inferior frontal cortex but not presupplementary motor area activation during inhibitory control. Neuropsychopharmacology 35, 2600–2606 (2010).

  115. 115

    Leland, D. S., Arce, E., Miller, D. A. & Paulus, M. P. Anterior cingulate cortex and benefit of predictive cueing on response inhibition in stimulant dependent individuals. Biol. Psychiatry 63, 184–190 (2008). Informative cueing enhanced inhibitory control in a go/no-go task, and this was correlated with enhanced ACC activation in methamphetamine-addicted individuals. This study offers a specific cognitive–behavioural intervention that could be used to enhance inhibitory control in addiction.

  116. 116

    Stroop, J. R. Studies of interference in serial verbal reactions. J. Exp. Psychol. 18, 643–662 (1935).

  117. 117

    Leung, H. C., Skudlarski, P., Gatenby, J. C., Peterson, B. S. & Gore, J. C. An event-related functional MRI study of the stroop color word interference task. Cereb. Cortex. 10, 552–560 (2000).

  118. 118

    Pardo, J. V., Pardo, P. J., Janer, K. W. & Raichle, M. E. The anterior cingulate cortex mediates processing selection in the Stroop attentional conflict paradigm. Proc. Natl Acad. Sci. USA 87, 256–259 (1990).

  119. 119

    Bench, C. J. et al. Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia 31, 907–922 (1993).

  120. 120

    Carter, C. S. & van Veen, V. Anterior cingulate cortex and conflict detection: an update of theory and data. Cogn. Affect. Behav. Neurosci. 7, 367–379 (2007).

  121. 121

    Bolla, K. et al. Prefrontal cortical dysfunction in abstinent cocaine abusers. J. Neuropsychiatry Clin. Neurosci. 16, 456–464 (2004).

  122. 122

    Eldreth, D. A., Matochik, J. A., Cadet, J. L. & Bolla, K. I. Abnormal brain activity in prefrontal brain regions in abstinent marijuana users. Neuroimage 23, 914–920 (2004).

  123. 123

    Salo, R., Ursu, S., Buonocore, M. H., Leamon, M. H. & Carter, C. Impaired prefrontal cortical function and disrupted adaptive cognitive control in methamphetamine abusers: a functional magnetic resonance imaging study. Biol. Psychiatry 65, 706–709 (2009).

  124. 124

    Azizian, A. et al. Smoking reduces conflict-related anterior cingulate activity in abstinent cigarette smokers performing a stroop task. Neuropsychopharmacology 35, 775–782 (2010).

  125. 125

    Brewer, J. A., Worhunsky, P. D., Carroll, K. M., Rounsaville, B. J. & Potenza, M. N. Pretreatment brain activation during stroop task is associated with outcomes in cocaine-dependent patients. Biol. Psychiatry 64, 998–1004 (2008).

  126. 126

    Ersche, K. D. et al. Influence of compulsivity of drug abuse on dopaminergic modulation of attentional bias in stimulant dependence. Arch. Gen. Psychiatry 67, 632–644 (2010). Stimulant-dependent individuals demonstrated an attentional bias for drug-related words, which was correlated with greater cue-related activation of the left prefrontal cortex; attentional bias was greater in people with highly compulsive patterns of stimulant abuse. This study also suggests that the effects of dopaminergic challenges on attentional interference and related brain activation depend on an individual's baseline compulsivity level.

  127. 127

    Luijten, M. et al. Neurobiological substrate of smoking related attentional bias. Neuroimage 54, 2374–2381 (2010).

  128. 128

    Janes, A. C. et al. Neural substrates of attentional bias for smoking-related cues: an fMRI study. Neuropsychopharmacology 35, 2339–2345 (2010).

  129. 129

    Goldstein, R. Z. et al. Role of the anterior cingulate and medial orbitofrontal cortex in processing drug cues in cocaine addiction. Neuroscience 144, 1153–1159 (2007).

  130. 130

    Nestor, L., McCabe, E., Jones, J., Clancy, L. & Garavan, H. Differences in “bottom-up” and “top-down” neural activity in current and former cigarette smokers: evidence for neural substrates which may promote nicotine abstinence through increased cognitive control. Neuroimage 56, 2258–2275.

  131. 131

    Khantzian, E. J. The self-medication hypothesis of addictive disorders: focus on heroin and cocaine dependence. Am. J. Psychiatry 142, 1259–1264 (1985).

  132. 132

    Khantzian, E. J. The self-medication hypothesis of substance use disorders: a reconsideration and recent applications. Harv. Rev. Psychiatry 4, 231–244 (1997).

  133. 133

    Langleben, D. D. et al. Acute effect of methadone maintenance dose on brain FMRI response to heroin-related cues. Am. J. Psychiatry. 165, 390–394 (2008).

  134. 134

    Garavan, H., Kaufman, J. N. & Hester, R. Acute effects of cocaine on the neurobiology of cognitive control. Phil. Trans. R. Soc. Lond. B Biol. Sci. 363, 3267–3276 (2008).

  135. 135

    Li, C. S. et al. Biological markers of the effects of intravenous methylphenidate on improving inhibitory control in cocaine-dependent patients. Proc. Natl Acad. Sci. USA 107, 14455–14459 (2010).

  136. 136

    Volkow, N. D. et al. Methylphenidate attenuates limbic brain inhibition after cocaine-cues exposure in cocaine abusers. PLoS ONE 5, e11509 (2010).

  137. 137

    Goldstein, R. Z. et al. Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task. Proc. Natl Acad. Sci. USA 107, 16667–16672 (2010). Oral MPH decreased impulsivity in a drug-relevant emotional Stroop task, and this decrease was associated with normalization of activation in the rostroventral ACC (extending to the mOFC) and dACC in cocaine-addicted individuals. These results suggest that oral MPH may have therapeutic benefits in improving cognitive–behavioural functions in cocaine-addicted individuals.

  138. 138

    Adinoff, B. et al. Altered neural cholinergic receptor systems in cocaine-addicted subjects. Neuropsychopharmacology 35, 1485–1499 (2010).

  139. 139

    Goldstein, R. Z. et al. The neurocircuitry of impaired insight in drug addiction. Trends Cogn. Sci. 13, 372–380 (2009).

  140. 140

    Reekie, Y. L., Braesicke, K., Man, M. S. & Roberts, A. C. Uncoupling of behavioral and autonomic responses after lesions of the primate orbitofrontal cortex. Proc. Natl Acad. Sci. USA 105, 9787–9792 (2008).

  141. 141

    Goldstein, R. Z. et al. Compromised sensitivity to monetary reward in current cocaine users: an ERP study. Psychophysiology 45, 705–713 (2008).

  142. 142

    Chiu, P. H., Lohrenz, T. M. & Montague, P. R. Smokers' brains compute, but ignore, a fictive error signal in a sequential investment task. Nature Neurosci. 11, 514–520 (2008).

  143. 143

    Rinn, W., Desai, N., Rosenblatt, H. & Gastfriend, D. R. Addiction denial and cognitive dysfunction: a preliminary investigation. J. Neuropsychiatry Clin. Neurosci. 14, 52–57 (2002).

  144. 144

    Hester, R., Nestor, L. & Garavan, H. Impaired error awareness and anterior cingulate cortex hypoactivity in chronic cannabis users. Neuropsychopharmacology 34, 2450–2458 (2009). Cannabis users showed a deficit in awareness of commission errors, and this was associated with hypoactivity in the ACC and right insula in the go/no-go task. This study points to deficits in the role of the ACC and insula in monitoring interoceptive awareness in drug addiction.

  145. 145

    Payer, D. E., Lieberman, M. D. & London, E. D. Neural correlates of affect processing and aggression in methamphetamine dependence. Arch. Gen. Psychiatry. 68, 271–282 (2010). The ventrolateral PFC was hypoactive during affect matching in methamphetamine-dependent subjects, and this was associated with more self-reported alexithymia, pointing to a mechanism that limits emotional insight and possibly contributes to heightened aggression in addiction.

  146. 146

    Kim, J. S. et al. The role of alcoholics' insight in abstinence from alcohol in male Korean alcohol dependents. J. Korean Med. Sci. 22, 132–137 (2007).

  147. 147

    Dosenbach, N. U., Fair, D. A., Cohen, A. L., Schlaggar, B. L. & Petersen, S. E. A dual-networks architecture of top-down control. Trends Cogn. Sci. 12, 99–105 (2008).

  148. 148

    Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neurosci. 12, 535–540 (2009).

  149. 149

    Poldrack, R. A. & Mumford, J. A. Independence in ROI analysis: where is the voodoo? Soc. Cogn. Affect. Neurosci. 4, 208–213 (2009).

  150. 150

    Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl Acad. Sci. USA. 107, 4734–4739 (2010).

  151. 151

    Hanlon, C. A., Wesley, M. J., Roth, A. J., Miller, M. D. & Porrino, L. J. Loss of laterality in chronic cocaine users: an fMRI investigation of sensorimotor control. Psychiatry Res. 181, 15–23 (2009).

  152. 152

    Kushnir, V. et al. Enhanced smoking cue salience associated with depression severity in nicotine-dependent individuals: a preliminary fMRI study. Int. J. Neuropsychopharmacol. 7 July 2010 (doi:10.1017/51461145710000696).

  153. 153

    Woicik, P. A. et al. The neuropsychology of cocaine addiction: recent cocaine use masks impairment. Neuropsychopharmacology 34, 1112–1122 (2009).

  154. 154

    Dunning, J. P. et al. Motivated attention to cocaine and emotional cues in abstinent and current cocaine users--an ERP study. Eur. J. Neurosci. 33, 1716–1723 (2011).

  155. 155

    Raichle, M. E. & Snyder, A. Z. A default mode of brain function: a brief history of an evolving idea. Neuroimage 37, 1083–1090; discussion 1097–1089 (2007).

  156. 156

    Greicius, M. D., Krasnow, B., Reiss, A. L. & Menon, V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl Acad. Sci. USA 100, 253–258 (2003).

  157. 157

    Hong, L. E. et al. Association of nicotine addiction and nicotine's actions with separate cingulate cortex functional circuits. Arch. Gen. Psychiatry 66, 431–441 (2009).

  158. 158

    Cole, D. M. et al. Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics. Neuroimage 52, 590–599 (2010).

  159. 159

    Zhang, X. et al. Anatomical differences and network characteristics underlying smoking cue reactivity. Neuroimage 54, 131–141 (2011).

  160. 160

    Zhang, X. et al. Factors underlying prefrontal and insula structural alterations in smokers. Neuroimage 54, 42–48 (2011).

  161. 161

    Tomasi, D. et al. Disrupted functional connectivity with dopaminergic midbrain in cocaine abusers. PLoS ONE 5, e10815 (2010).

  162. 162

    Gu, H. et al. Mesocorticolimbic circuits are impaired in chronic cocaine users as demonstrated by resting-state functional connectivity. Neuroimage 53, 593–601 (2010).

  163. 163

    Wang, W. et al. Changes in functional connectivity of ventral anterior cingulate cortex in heroin abusers. Chin. Med. J. 123, 1582–1588 (2010).

  164. 164

    Daglish, M. R. et al. Functional connectivity analysis of the neural circuits of opiate craving: “more” rather than “different”? Neuroimage 20, 1964–1970 (2003).

  165. 165

    Yuan, K. et al. Combining spatial and temporal information to explore resting-state networks changes in abstinent heroin-dependent individuals. Neurosci. Lett. 475, 20–24 (2010).

  166. 166

    Fein, G. et al. Cortical gray matter loss in treatment-naive alcohol dependent individuals. Alcohol. Clin. Exp. Res. 26, 558–564 (2002).

  167. 167

    Chanraud, S. et al. Brain morphometry and cognitive performance in detoxified alcohol-dependents with preserved psychosocial functioning. Neuropsychopharmacology 32, 429–438 (2007).

  168. 168

    Chanraud, S., Pitel, A. L., Rohlfing, T., Pfefferbaum, A. & Sullivan, E. V. Dual tasking and working memory in alcoholism: relation to frontocerebellar circuitry. Neuropsychopharmacology 35, 1868–1878 (2010).

  169. 169

    Makris, N. et al. Decreased volume of the brain reward system in alcoholism. Biol. Psychiatry. 64, 192–202 (2008).

  170. 170

    Wobrock, T. et al. Effects of abstinence on brain morphology in alcoholism: a MRI study. Eur. Arch. Psychiatry Clin. Neurosci. 259, 143–150 (2009).

  171. 171

    Narayana, P. A., Datta, S., Tao, G., Steinberg, J. L. & Moeller, F. G. Effect of cocaine on structural changes in brain: MRI volumetry using tensor-based morphometry. Drug Alcohol Depend. 111, 191–199 (2010).

  172. 172

    Franklin, T. R. et al. Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biol. Psychiatry 51, 134–142 (2002).

  173. 173

    Matochik, J. A., London, E. D., Eldreth, D. A., Cadet, J. L. & Bolla, K. I. Frontal cortical tissue composition in abstinent cocaine abusers: a magnetic resonance imaging study. Neuroimage 19, 1095–1102 (2003).

  174. 174

    Sim, M. E. et al. Cerebellar gray matter volume correlates with duration of cocaine use in cocaine-dependent subjects. Neuropsychopharmacology 32, 2229–2237 (2007).

  175. 175

    Schwartz, D. L. et al. Global and local morphometric differences in recently abstinent methamphetamine-dependent individuals. Neuroimage 50, 1392–1401 (2010).

  176. 176

    Yuan, Y. et al. Gray matter density negatively correlates with duration of heroin use in young lifetime heroin-dependent individuals. Brain Cogn. 71, 223–228 (2009).

  177. 177

    Lyoo, I. K. et al. Prefrontal and temporal gray matter density decreases in opiate dependence. Psychopharmacology 184, 139–144 (2006).

  178. 178

    Liu, H. et al. Frontal and cingulate gray matter volume reduction in heroin dependence: optimized voxel-based morphometry. Psychiatry Clin. Neurosci. 63, 563–568 (2009).

  179. 179

    Brody, A. L. et al. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol. Psychiatry 55, 77–84 (2004).

  180. 180

    Kuhn, S., Schubert, F. & Gallinat, J. Reduced thickness of medial orbitofrontal cortex in smokers. Biol. Psychiatry 68, 1061–1065 (2010).

  181. 181

    Medina, K. L. et al. Prefrontal cortex volumes in adolescents with alcohol use disorders: unique gender effects. Alcohol. Clin. Exp. Res. 32, 386–394 (2008).

  182. 182

    Medina, K. L. et al. Prefrontal cortex morphometry in abstinent adolescent marijuana users: subtle gender effects. Addict. Biol. 14, 457–468 (2009).

  183. 183

    Tanabe, J. et al. Medial orbitofrontal cortex gray matter is reduced in abstinent substance-dependent individuals. Biol. Psychiatry 65, 160–164 (2009).

  184. 184

    Volkow, N. D. et al. Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. Am. J. Psychiatry 158, 2015–2021 (2001).

  185. 185

    Volkow, N. D. et al. Profound decreases in dopamine release in striatum in detoxified alcoholics: possible orbitofrontal involvement. J. Neurosci. 27, 12700–12706 (2007).

  186. 186

    Volkow, N. D. et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage 42, 1537–1543 (2008).

  187. 187

    Asensio, S. et al. Striatal dopamine D2 receptor availability predicts the thalamic and medial prefrontal responses to reward in cocaine abusers three years later. Synapse 64, 397–402 (2009).

  188. 188

    Fehr, C. et al. Association of low striatal dopamine d2 receptor availability with nicotine dependence similar to that seen with other drugs of abuse. Am. J. Psychiatry 165, 507–514 (2008).

  189. 189

    Narendran, R. et al. Altered prefrontal dopaminergic function in chronic recreational ketamine users. Am. J. Psychiatry 162, 2352–2359 (2005).

  190. 190

    Martinez, D. et al. Amphetamine-induced dopamine release: markedly blunted in cocaine dependence and predictive of the choice to self-administer cocaine. Am. J. Psychiatry 164, 622–629 (2007).

  191. 191

    Gorelick, D. A. et al. Imaging brain mu-opioid receptors in abstinent cocaine users: time course and relation to cocaine craving. Biol. Psychiatry 57, 1573–1582 (2005).

  192. 192

    Ghitza, U. E. et al. Brain mu-opioid receptor binding predicts treatment outcome in cocaine-abusing outpatients. Biol. Psychiatry 68, 697–703 (2010).

  193. 193

    Williams, T. M. et al. Brain opioid receptor binding in early abstinence from alcohol dependence and relationship to craving: an [11C]diprenorphine PET study. Eur. Neuropsychopharmacol. 19, 740–748 (2009).

  194. 194

    Kling, M. A. et al. Opioid receptor imaging with positron emission tomography and [18F]cyclofoxy in long-term, methadone-treated former heroin addicts. J. Pharmacol. Exp. Ther. 295, 1070–1076 (2000).

  195. 195

    Sekine, Y. et al. Brain serotonin transporter density and aggression in abstinent methamphetamine abusers. Arch. Gen. Psychiatry 63, 90–100 (2006).

  196. 196

    McCann, U. D. et al. Positron emission tomographic studies of brain dopamine and serotonin transporters in abstinent (±)3,4-methylenedioxymethamphetamine (“ecstasy”) users: relationship to cognitive performance. Psychopharmacology 200, 439–450 (2008).

  197. 197

    Szabo, Z. et al. Positron emission tomography imaging of the serotonin transporter in subjects with a history of alcoholism. Biol. Psychiatry 55, 766–771 (2004).

  198. 198

    Kalivas, P. W. The glutamate homeostasis hypothesis of addiction. Nature Rev. Neurosci. 10, 561–572 (2009).

  199. 199

    Laviolette, S. R. & Grace, A. A. The roles of cannabinoid and dopamine receptor systems in neural emotional learning circuits: implications for schizophrenia and addiction. Cell. Mol. Life Sci. 63, 1597–1613 (2006).

  200. 200

    Lopez-Moreno, J. A., Gonzalez-Cuevas, G., Moreno, G. & Navarro, M. The pharmacology of the endocannabinoid system: functional and structural interactions with other neurotransmitter systems and their repercussions in behavioral addiction. Addict. Biol. 13, 160–187 (2008).

  201. 201

    Rao, H. et al. Altered resting cerebral blood flow in adolescents with in utero cocaine exposure revealed by perfusion functional MRI. Pediatrics 120, e1245–e1254 (2007).

  202. 202

    Roberts, G. M. & Garavan, H. Evidence of increased activation underlying cognitive control in ecstasy and cannabis users. Neuroimage 52, 429–435 (2010).

  203. 203

    Tapert, S. F. et al. Functional MRI of inhibitory processing in abstinent adolescent marijuana users. Psychopharmacology 194, 173–183 (2007).

  204. 204

    Heitzeg, M. M., Nigg, J. T., Yau, W. Y., Zucker, R. A. & Zubieta, J. K. Striatal dysfunction marks preexisting risk and medial prefrontal dysfunction is related to problem drinking in children of alcoholics. Biol. Psychiatry 68, 287–295 (2010).

  205. 205

    Heitzeg, M. M., Nigg, J. T., Yau, W. Y., Zubieta, J. K. & Zucker, R. A. Affective circuitry and risk for alcoholism in late adolescence: differences in frontostriatal responses between vulnerable and resilient children of alcoholic parents. Alcohol. Clin. Exp. Res. 32, 414–426 (2008).

  206. 206

    Volkow, N. D. et al. High levels of dopamine D2 receptors in unaffected members of alcoholic families: possible protective factors. Arch. Gen. Psychiatry 63, 999–1008 (2006).

  207. 207

    Sowell, E. R. et al. Abnormal cortical thickness and brain-behavior correlation patterns in individuals with heavy prenatal alcohol exposure. Cereb. Cortex 18, 136–144 (2008).

  208. 208

    Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. Individual and additive effects of the CNR1 and FAAH genes on brain response to marijuana cues. Neuropsychopharmacology 35, 967–975 (2010).

  209. 209

    Stice, E., Yokum, S., Bohon, C., Marti, N. & Smolen, A. Reward circuitry responsivity to food predicts future increases in body mass: moderating effects of DRD2 and DRD4. Neuroimage 50, 1618–1625 (2010).

  210. 210

    Lotfipour, S. et al. Orbitofrontal cortex and drug use during adolescence: role of prenatal exposure to maternal smoking and BDNF genotype. Arch. Gen. Psychiatry 66, 1244–1252 (2009).

  211. 211

    Hill, S. Y. et al. Disruption of orbitofrontal cortex laterality in offspring from multiplex alcohol dependence families. Biol. Psychiatry 65, 129–136 (2009).

  212. 212

    Alia-Klein, N. et al. Gene x disease interaction on orbitofrontal gray matter in cocaine addiction. Arch. Gen. Psychiatry 68, 283–294 (2011).

  213. 213

    Wager, T. D., Lindquist, M. & Kaplan, L. Meta-analysis of functional neuroimaging data: current and future directions. Soc. Cogn. Affect. Neurosci. 2, 150–158 (2007).

  214. 214

    Wager, T. D., Lindquist, M. A., Nichols, T. E., Kober, H. & Van Snellenberg, J. X. Evaluating the consistency and specificity of neuroimaging data using meta-analysis. Neuroimage 45, S210–S221 (2009).

  215. 215

    Goldstein, R. Z. & Volkow, N. D. Oral methylphenidate normalizes cingulate activity and decreases impulsivity in cocaine addiction during an emotionally salient cognitive task. Neuropsychopharmacology 36, 366–367 (2011).

  216. 216

    Kringelbach, M. L. & Rolls, E. T. The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog. Neurobiol. 72, 341–372 (2004).

  217. 217

    Blair, R. J. The amygdala and ventromedial prefrontal cortex: functional contributions and dysfunction in psychopathy. Phil. Trans. R. Soc. Lond. B Biol. Sci. 363, 2557–2565 (2008).

  218. 218

    Ridderinkhof, K. R. et al. Alcohol consumption impairs detection of performance errors in mediofrontal cortex. Science 298, 2209–2211 (2002).

  219. 219

    Rajkowska, G. & Goldman-Rakic, P. S. Cytoarchitectonic definition of prefrontal areas in the normal human cortex: II. Variability in locations of areas 9 and 46 and relationship to the Talairach Coordinate System. Cereb. Cortex 5, 323–337 (1995).

  220. 220

    Petrides, M. Lateral prefrontal cortex: architectonic and functional organization. Phil. Trans. R. Soc. Lond. B Biol. Sci. 360, 781–795 (2005).

Download references


This study was supported by grants from the US National Institute on Drug Abuse (R01DA023579 to R.Z.G.), the Intramural NIAAA program and the Department of Energy, Office of Biological and Environmental Research (for infrastructure support). We are grateful for A. B. Konova's contribution to the design of figure 2. We are indebted to our reviewers whose comments were greatly appreciated and guided our revision of the original manuscript.

Author information

Correspondence to Rita Z. Goldstein.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (Table).

This table is a summary of the fMRI and PET studies in drug-addicted individuals vs. healthy control subjects conducted during 2000–2010 (articles published in 2011 have not been methodically reviewed), which were used to create (PDF 286 kb)

Supplementary information S2 (Table)

Functional neuroimaging studies (conducted during 2000–2010) comparing PFC activity during direct drug administration in addicted individuals and healthy controls. (PDF 218 kb)

Supplementary information S3 (Table)

Functional neuroimaging studies (conducted during 2000–2010) comparing brain activity during cue exposure in addicted individuals (S) and healthy controls (C) (PDF 235 kb)

Supplementary information S4 (Table)

Functional neuroimaging studies (conducted during 2000–2010) studying effects of abstinence and self-regulation on PFC activity. (PDF 215 kb)

Supplementary information S5 (Table)

Functional neuroimaging studies (conducted during 2000–2010) comparing PFC activity in addicted individuals (S) and healthy controls (C) during performance of selected emotion tasks. (PDF 247 kb)

Supplementary information S6 (Table)

Functional neuroimaging studies (conducted during 2000–2010) comparing PFC activity in addicted individuals (S) and healthy controls (C) during performance of inhibitory control tasks. (PDF 242 kb)

Supplementary information S7 (Table)

Functional neuroimaging studies (conducted during 2000–2010) comparing neurotransmitter systems in individuals with addiction (S) and healthy controls (C). (PDF 238 kb)

Supplementary information S8 (figure).

Recent neuroimaging studies of PFC activity in drug-addicted individuals. (PDF 3543 kb)

Related links

Related links


Rita Z. Goldstein's homepage

The Brookhaven National Laboratory Neuropsychoimaging Group homepage

National Institute on Drug Abuse homepage

University of Colorado CANLab Software website


18Fluorodyoxyglucose PET

(18F-PET). Positron emission tomography (PET) with a radioligand to image regional glucose uptake, a measure of metabolic activity that can also be used to assess global brain function.


(MPH). A mild stimulant (approved for treatment of attention deficit hyperactivity disorder) with similar pharmacological effects to cocaine (it blocks the dopamine transporter) but with lower abuse potential owing to slower rates of clearance from the synapse.

Non-contingent administration

Administration of a certain drug that is not dependent on the subject's behaviour.

Fixed-rate self-administration

Self-administration of a certain drug on a ratio between drug delivery and behaviour that is fixed by an experimenter (for example, after emission of a certain number of responses or after a certain time has elapsed following the previous response).

Arterial spin labelling

(Also known as arterial spin tagging). An MRI technique that is capable of measuring cerebral blood flow in vivo. It provides cerebral perfusion maps without requiring the administration of a contrast agent or the use of ionizing radiation, as it uses magnetically labelled endogenous blood water as a freely diffusible tracer.

Masked cue

A cue that is presented below conscious processing level (that is, outside of conscious awareness). This is usually achieved with a very short duration of cue presentation followed by presentation of another cue that is consciously perceived (longer duration).


An NMDA receptor antagonist primarily used for the induction and maintenance of general anaesthesia. In addition, it can induce analgesia, elevated blood pressure and hallucinations, and it has been used as a recreational drug.


A positron emission tomography (PET) receptor radioligand that competes with endogenous opiates for binding to the mu opiate receptor.

Affect matching

A neuropsychological test in which images of faces are matched based on their emotional facial expressions. This task can be used to assess impairments in emotional (or social) processing.

Go/no-go task

A neuropsychological task that is commonly used to assess inhibitory control. Subjects are required to press a button when one stimulus type appears and withhold a response when another stimulus type appears.

Stop signal reaction time task

(SSRT). A neuropsychological test that measures the ability to stop a response that has already been initiated. It is used clinically as an index of inhibitory control. Slower SSRT is associated with disruption of executive functions.

Errors of omission and commission

Errors on a go/no-go task: a subject had to go but they did not go (omission of a response) or had to withhold a response but pressed a button instead (commission of an unnecessary response). The former is an index of inattentiveness while the latter is an index of impulsive (premature) responding.

Stroop task

A neuropsychological task in which conflict is created between an automatic response (for example, reading) and a slower response (for example, colour naming), with both competing for the same processing resources. Impaired performance on Stroop tasks is associated with prefrontal cortex dysfunction.


A state of deficiency in understanding, processing or describing emotions, including the difficulty in identifying and/or describing one's own feelings and externally oriented thinking.

Rights and permissions

Reprints and Permissions

About this article

Further reading