Dissociated Grey Matter Changes with Prolonged Addiction and Extended Abstinence in Cocaine Users (2013)

Comments; Not only did grey matter in the frontal cortex return to normal in abstinent cocaine addicts – it eventually bypassed the levels of those who had never been addicted. Amazing.

Colm G. Connolly, Ryan P. Bell,, John J. Foxe, Hugh Garavan


Extensive evidence indicates that current and recently abstinent cocaine abusers compared to drug-naïve controls have decreased grey matter in regions such as the anterior cingulate, lateral prefrontal and insular cortex. Relatively little is known, however, about the persistence of these deficits in long-term abstinence despite the implications this has for recovery and relapse. Optimized voxel based morphometry was used to assess how local grey matter volume varies with years of drug use and length of abstinence in a cross-sectional study of cocaine users with various durations of abstinence (1–102 weeks) and years of use (0.3–24 years).

Lower grey matter volume associated with years of use was observed for several regions including anterior cingulate, inferior frontal gyrus and insular cortex. Conversely, higher grey matter volumes associated with abstinence duration were seen in non-overlapping regions that included the anterior and posterior cingulate, insular, right ventral and left dorsal prefrontal cortex. Grey matter volumes in cocaine dependent individuals crossed those of drug-naïve controls after 35 weeks of abstinence, with greater than normal volumes in users with longer abstinence.

The brains of abstinent users are characterized by regional grey matter volumes, which on average, exceed drug-naïve volumes in those users who have maintained abstinence for more than 35 weeks.

The asymmetry between the regions showing alterations with extended years of use and prolonged abstinence suggest that recovery involves distinct neurobiological processes rather than being a reversal of disease-related changes. Specifically, the results suggest that regions critical to behavioral control may be important to prolonged, successful, abstinence.


Citation: Connolly CG, Bell RP, Foxe JJ, Garavan H (2013) Dissociated Grey Matter Changes with Prolonged Addiction and Extended Abstinence in Cocaine Users. PLoS ONE 8(3): e59645. doi:10.1371/journal.pone.0059645

Editor: Fei Wang, Yale University School of Medicine, United States of America

Received: October 28, 2012; Accepted: February 16, 2013; Published: March 18, 2013

Copyright: © 2013 Connolly et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by NIMH grant number R01-DA014100 awarded to HG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Cocaine is a major worldwide public health issue for which current treatments are unsatisfactory [1], [2]. Understanding the differences between the brains of cocaine users and nonusers is a critical step in identifying neurobiological characteristics of addiction that may guide the development of therapeutic interventions. Also of considerable importance, but much less well researched, is understanding what differentiates users who abstain and successfully avoid relapse from those who fail to maintain abstinence and repeatedly relapse. As treatment programs typically have very high dropout rates [3], [4] reflecting the relapsing nature of the disease, an understanding of the neurobiology of successful abstinence may identify key targets for therapeutic interventions. However, one consequence of high dropout rates is that little is known about the neurobiology of successful long-term abstinence as high levels of relapse and attrition from treatment makes prospective studies of long-term abstinence effects difficult.

Voxel based morphometry [5] is a technique that can examine local tissue volume differences. Using this method, relative to healthy drug-naïve controls, grey matter changes have been observed in multiple regions of the brain of cocaine addicts. Widespread decreased GM concentration has been reported in lateral and medial aspects of the orbitofrontal cortex (OFC), anterior cingulate (ACC), anteroventral insular cortices, lateral prefrontal cortex (LPFC), temporal cortices [6][11], cerebellum [12] and subcortical regions [13][15]. Cocaine use has been linked to accelerated age-related decreases in grey matter in the temporal lobes [16]. Fein et al. [17] using a related method observed significant reduction in prefrontal grey matter volume for cocaine dependent (CD) and combined cocaine and alcohol dependent individuals. It has been suggested that these focal decreases in GM may underlie the functional hypoactivity and cognitive deficits observed in cocaine users [8]. These regions have been variously implicated in the executive functions of conflict monitoring [18], performance monitoring [19], interoception [20], decision-making [21] and reward processing [22], all of which have been demonstrated to be compromised in cocaine addicts. However, the literature is not consistent as others have failed to observe differences in GM between CD and control participants [23].

Our prior report characterizing long-term abstinence probed the functional neuroanatomy of cognitive control using a GO/NOGO task [24]. The short- and long-term abstinent CD groups in this study displayed greater activation levels for correct inhibitions and errors relative to drug-naïve controls. More specifically, the results suggested that early abstinence (1–5 weeks) may be characterized by heightened activity in regions subserving inhibitory control with heightened activity underlying behavioral monitoring processes playing a more prominent role later in abstinence (40–102 weeks). Our previous investigation of white matter using diffusion tensor imaging revealed one set of structural changes that differentiated long-term abstinent (44–102 weeks) from more recently abstinent users (1–5 weeks) and another set that differentiated all abstinent individuals from healthy controls [25]. One interpretation is that the first set of white matter changes may arise during abstinence or may have preceded and facilitated abstinence while the second set may reflect changes that arose from or preceded cocaine use. An implication arising from this interpretation is that abstinence and recovery may have neurobiological underpinnings that are distinct from those associated with the disease.

A recent study compared grey and white matter densities in abstinent (1–16 weeks) and current CD individuals and healthy control participants and observed that the current users, compared to controls and abstainers, had lower tissue density in frontal, temporal, cerebellar and subcortical regions. The abstinent group had much less pronounced deficits with lower grey matter density in caudate/putamen and bilateral cerebellum compared to controls [13]. It would appear that GM deficits are reduced in abstinent users but it remains unclear whether these differences would persist with prolonged abstinence, due in part to the high rates of relapse making such prospective studies difficult.

The aim of the present study, using a cross-sectional design, was to examine volume differences in cortical grey matter in a sample of former cocaine addicts who varied in length of abstinence and duration of use. We hypothesized that abstinence duration would be associated with a set of GM volume changes in regions critical to executive function, specifically anterior cingulate and lateral prefrontal cortex. We further hypothesized that any GM volume changes that may be attributable to length of use would be distinct from those related to abstinence duration. Comparison to a non-drug using control group allowed us to assess how changes of GM with abstinence duration relate to volumes typical of drug naïve controls. The cross-sectional design employed here suffers by being unable to resolve whether effects related to abstinence duration arose from abstinence or preceded abstinence. However, it is nonetheless valuable in that it can characterize individuals with a demonstrated ability to remain abstinent over various durations. This characterization may be of therapeutic importance in that observed neurobiological differences might serve as targets for therapy. Additionally, they may be useful biomarkers for possible investigation in future longitudinal studies of abstinence.

Materials and Methods

Ethics Statement

This study was approved by the Institutional Review Board of the Nathan S. Klein Institute for Psychiatric Research (NKI).


Eighty-six volunteers (9 female; mean age 38.1, range 20–55) (see Table 1) participated in this study. Written informed consent was obtained in accordance with the Declaration of Helsinki and participants were compensated for their time. Participants were divided into two groups: one group of 43 abstinent cocaine users (2 female) and a second of 43 age-matched controls (7 female). Control participants were recruited from the volunteer recruitment pool at the NKI. CD participants were recruited from in-patient and out-patient treatment centers in New York State. All CD participants received an initial diagnosis of cocaine dependence as assessed by Structural Clinical Interview for the DSM-IV (SCID) [26]. Participants early in treatment were in an in-patient facility that was monitored on a 24-hour basis. They were subject to periodic Breathalyzer tests for alcohol and random urine toxicology screens for multiple substances. Additionally, subjects were not permitted to leave the facility without an escort. Those later in treatment were allowed to leave the facility on their own recognizance but were evaluated by clinical staff (including urine toxicology and Breathalyzer tests) upon their return. Continued enrollment in the in-patient and out-patient treatment programs was predicated on negative toxicology screenings. CD participants met at least weekly with a personal counselor certified by the state of New York in the treatment of alcoholism and drug abuse. Length of abstinence was verified with the counselor at the addiction treatment centers. Exclusion criteria for both CD and control participants were: (1) Any DSM IV, Axis 1 diagnosis excluding dependence or a past diagnosis of depression caused by CD based on the SCID; (2) Head trauma resulting in loss of consciousness for longer than 30 minutes; (3) Presence of any past or current brain pathology; (4) A HIV diagnosis; (5) Contraindications for MRI; (6) Under 19 or over 55 year of age; (7) The presence of white matter (WM) hyperintensity (only one patient was excluded due to clinically significant WM hyperintensity). Given the high rates of co-morbid alcohol and drug abuse in the target patient population [27], participants were not excluded for abuse of other drugs or alcohol prior to the onset of CD (3 participants had co-morbid alcohol dependence and 7 had co-morbid heroin dependence.) Thus the CD group may be thought of as polydrug abusers with a primary dependence on cocaine. None were currently using any amount of alcohol or drugs. Years of drug use prior to abstinence was obtained during the initial SCID interview.


Table 1. Demographic characteristics for the control and abstinent cocaine groups.


MR Data Acquisition

All scanning was conducted on a 1.5T Siemens VISION scanner (Erlangen, Germany) at NKI that was equipped with a 30.5-cm i.d. three-axis local gradient coil and an end-capped quadrature birdcage radio-frequency head coil. High-resolution T1-weighted MPRAGE anatomical images were acquired with the following parameters: TE = 4.9 ms, TR = 11.6 ms, flip angle 8°, FOV 300 mm, 1.2 mm isotropic voxels, matrix 256×256, and 172 sagittal slices.

MR Data Analysis

The high-resolution T1-weighted images were subjected to a voxel-based morphometry (VBM) analysis [5], [28] carried out with FSL tools [29]. The data were median filtered (3×3 voxels), brain-extracted using AFNI’s 3dSkullStrip [30], and then segmented into grey and white matter and cerebrospinal fluid [31]. The grey matter images were then affinely aligned to MNI152 standard space [32], [33] followed by non-linear registration [34], [35] to further refine the alignment. The resulting data were averaged to create a study-specific template, to which the native grey matter images were then non-linearly re-registered. The registered partial volume images were then modulated by multiplying by the Jacobian of the warp field [28]. This step compensates for the contraction/enlargement due to the non-linear component of the transformation (http://dbm.neuro.uni-jena.de/vbm/segment​ation/modulation/), making correcting for total intra-cranial volume of the individual unnecessary [36]. Removal of global brain volume effects in this manner permitted inference on the local GM volume differences. The modulated segmented images were then smoothed with an isotropic Gaussian kernel (σ = 2 mm ~ 4.7 mm FWHM).

The resultant grey matter images of the abstinent CD group were then subjected to voxelwise Huber robust regression [37], [38] in the R statistical analysis package [39]. The two variables of interest, weeks of abstinence and years of use prior to abstinence were included in a single voxelwise whole-brain regression model. Since years of use could be a proxy for age and given the well-established relationship between age and GM volume [28], [40], age was also included as a nuisance covariate in the regression model. The voxelwise regression coefficients and associated T statistics for each regression term were then split into maps of positive and negative coefficients. Significant voxels passed a voxelwise statistical threshold (t(39) = 2.97, p = 0.005, uncorrected) and, to control for multiple comparisons, were required to be part of a cluster of no less than 360 µl. The volume threshold was determined by a Monte-Carlo simulation that together with the voxelwise threshold resulted in a 5% probability of a cluster surviving due to chance. Regions of interest (ROI) were identified in this manner and the grey matter volume for each region was extracted for each of the CD and, for comparison, the control participants. To determine at what point the GM volume in each region of interest crosses that of the controls, a robust regression line against duration of abstinence and years of use for the CD individuals was fit to these values for each region of interest and the intersection of this line with that of the mean of the controls computed. However, this approach tends to inflate correlation values [41] so care in interpreting the results is warranted.



The CD participants did not differ from controls in age (Welch t(77.5) = −0.6, p>0.05, or gender (χ2 = 1.98, p = 0.15), but did differ on years of education (Welch t(82.6) = −5.1, p<0.001; see Table 1 for demographic information). Years of education correlated negatively with abstinence duration (Pearson’s ρ = −0.43, t(41) = −3.1, p<0.005) but not with years of use (Pearson’s ρ = −0.02, t(41) = −0.12, p>0.1) for the CD group. Years of use did not correlate with length of abstinence (Pearson’s ρ = −0.17, t(41) = −1.2, p>0.05).

VBM Regression Results

Years of use.

Four regions (Table 2) showed positive correlations with years of use, that is grey matter volume increased in these regions with longer terms of use. These regions were located bilaterally in the precentral gyrus, and one region in each of the left medial frontal gyrus and right nodule of the cerebellum. Several regions (Table 2) displayed negative correlations with years of use. These were located in the right cerebellar tonsil, bilaterally in the superior temporal and inferior frontal gyri, in the right anterior insula, and one in each of the right subcollasal gyrus and right anterior cingulate gyrus shown in Figure 1 (left).


Figure 1. Regions in the left and right anterior cingulate showing, respectively, increases in GM with weeks of abstinence and decreases in GM with years of use.

The solid line is the robust regression line for CD individuals. The dashed line is the mean GM in the same ROI for the control participants.



Table 2. Regions identified in the regression analysis.


Weeks of abstinence.

A number of regions (Table 2) were observed to show positive correlations with weeks of abstinence, that is grey matter volume in these regions increased with abstinence. These included left insula, left and right cingulate gyri, the left cuneus, left and right superior frontal gyri, left culmen of the cerebellum, and the right middle temporal gyrus. As can be seen in Figures 1 and 2, in each of these regions, those CD users with shorter periods of abstinence show less GM than controls. Those who were abstinent longer show greater GM volumes than controls. The cross-over point from relatively smaller to relatively greater volumes was quite consistent across all regions, averaging 35.6 weeks of abstinence (range 26.4–44.9, sd 6.2). Three regions (see Table 2) were observed to display negative correlations with length of abstinence. These included regions in bilateral cuneus and one in the left precuneus. In these regions, on average 24.2 weeks of abstinence (range 18.5–27.6, sd 5.0) passed before the level of GM equaled that of controls and then declined further with increased periods of abstinence.


Figure 2. Regions in the right posterior cingulate, left insula and left and right superior frontal gyrii showing increased GM with weeks of abstinence.

The solid line is the robust regression line for CD individuals. The dashed line is the mean GM in the same ROI for the control participants.


As abstinence duration correlated with years of education, we conducted cluster-level correlations between GM volumes and weeks of abstinence with both age and years of education included as nuisance regressors. The effects reported above remained significant for all regions.

We conducted a series of Welch T-tests to determine if the GM volumes of users who were abstinent longer than the cross-over point were significantly greater than the volumes of the controls. These tests were performed separately for each ROI with the cross-over points of each ROI identified from the linear regressions. All of these tests were significantly different (all p<0.05).

Independence between use and abstinence effects.

We tested whether the areas shown to have altered volumes associated with years of use were also observed to change with abstinence. We performed correlations for abstinence effects in those areas that showed years of use effects (and vice versa). For all clusters, only two, the right precuneus and left cuneus clusters identified initially as showing positive correlations with abstinence (p<0.05) also showed significant negative correlations with years of use (p<0.05).


The present results are some of the first to examine grey matter volumes related to the length of cocaine use and abstinence in a population of former cocaine addicts. We observed several regions displaying decreased GM with increasing years of use. Although these results are necessarily correlational, they suggest a cumulative effect of cocaine use wherein the longer the period of substance use the lower the grey matter volume [22]. That these effects were observed in abstinent users is consistent with prior reports of GM deficits in alcoholism that last from 6–9 months to more than a year or, in some reports, up to at least 6 years following abstinence [42][44]. Similarly, decreased GM as a function of years of use of heroin [6], [45], [46] and cocaine [15] have previously been reported. Conversely, increased GM as a function of years of use was also observed in the cerebellum, bilateral precentral gyrus (both effects discussed below) and also in the perigenual region of the cingulate gyrus associated with affective processing [47]. This may be a consequence of repeated cocaine use blunting responses in regions important to emotional regulation [48]. Alternatively, given that emotional reactivity has been implicated as a factor modulating vulnerability to drug abuse [49], this may have been a preexisting factor that served to increase the likelihood of the development and prolongation of drug abuse.

If addiction can be characterized as a loss of self-directed volitional control [22], abstinence and its maintenance may be characterized by a reassertion of these aspects of executive function [24]. Current cocaine users demonstrate reduced GM in brain regions critical to executive function, such as the anterior cingulate, lateral prefrontal, orbitofrontal and insular cortices [6][11]. In contrast, the group of abstinent CD users reported here show elevations in GM as a function of abstinence duration that exceeds control levels after 36 weeks, on average, of abstinence. One possible explanation for this is that abstinence may require reassertion of cognitive control and behavior monitoring that is diminished during current cocaine dependence [11], [50], [51]. We, and others, have previously hypothesized that drug abusers may develop increased cerebellar activity to compensate for reduced prefrontal activity in tasks demanding elevated levels of cognitive control [52], [53] and that this may play a role in maintaining abstinence [24]. Reassertion of behavioral control may produce a practice-related expansion [54] in GM regions such as the anterior insula, anterior cingulate, cerebellum, and dorsolateral prefrontal cortex and is consistent with our previous reports of elevated activity levels, compared to controls, in long-term abstinent substance users [24], [55]. A viable alternative, given the cross-sectional nature of the data, is that the differences in GM volumes preceded abstinence and the relationship with abstinence duration indicates that those with greater volumes in these regions are more likely to maintain abstinence for longer. A small, but growing, body of literature has begun to examine this possibility in users of several substances as assessing baseline predictors, such as grey matter volume, may provide an indication of what might be different from the onset of abstinence in those who maintain abstinence. In the case of alcohol, gray matter volume in the parietal-occipital sulcus, medial and right lateral prefrontal cortex [56] and brain regions critical to behavioral control and reward processing [57], [58] have been shown to predict likelihood of relapse and successful abstinence. Similarly, grey matter volume in cortical and subcortical regions measured prior to cessation has been shown to be predictive of treatment outcome in smokers [59]. To our knowledge, no similar morphometric analyses of grey matter in users of stimulants, such as cocaine, have been performed. However, a variety of functional activation studies have shown that activation levels in brain regions associated with behavioral control, interoception and reward valuation show promise as predictors of treatment outcome in methamphetamine [60] and cocaine users [61][64]. We have previously investigated the integrity of white matter in the same cohort of CD users as reported here [25]. That study identified a dissociation of disease and abstinence effects that are consistent with the results reported herein. For example, the prefrontal changes reported here may complement white matter changes we previously observed in the longitudinal fasciculus [25]. It should be noted, however, that our previous DTI study did not include tractographic analyses so we cannot be certain that the grey matter changes reported here are linked to the white matter changes that we have previously reported. Future studies that investigate both grey matter and tractographic differences that may be related to duration of abstinence and length of use are required to resolve this ambiguity. Ultimately, adjudicating between these alternatives, namely that the volume differences reported herein arose as a consequence of abstinence or predated and facilitated abstinence, requires large-scale longitudinal studies. Nevertheless, both interpretations of the present data identify elevated levels of volume in regions that underlie cognitive control as characteristic of successful abstinence.

Impulsivity has been identified as a risk factor for the development of substance use disorders wherein individuals displaying higher levels of impulsivity are prone to both experimentation with and misuse of illicit drugs [65], [66]. Additionally, substance use may influence maladaptive behaviors through either acute effects (such as through action on the midbrain dopamine system [67], [68]), or as a consequence of prolonged drug use. For example, acutely, drugs may lead to impaired inhibition [50] and altered risky-choice behavior [51], [69][71]. Continued use may result in escalation of use and subsequent dependence, possibly by altering the neural substrate of performance monitoring [72] and stimulus-reward processing brain systems [73], amongst others. A common observation in trait impulsiveness is elevated motor activity [74]. The observation of elevated GM reported in bilateral precentral gyrus with years of use may be significant insofar as it may reflect elevated environmental exploration on the part of the addict to procure the abused substance [75]. Indeed, such an hypothesis is consistent with reports of increased GM in motor cortex with the acquisition of complex motor skills [76].

Left and right inferior frontal gyrus and right anterior cingulate have been identified as key loci underlying response inhibition [77][81] and are associated with impaired cognitive control in current addicts [82] and heavier, prolonged substance abuse [83]. As noted above, impaired behavioral inhibition is one of the defining characteristics of drug addiction. The observation of reduced GM with years of use in these regions may reflect the cumulative effect of damage caused by prolonged usage. Previous VBM studies of cocaine addicts have observed reduced GM in cerebellum [12] and have suggested that this may reflect the cumulative effect of cocaine-induced oxidative stress and vasoconstriction [12]. Furthermore, the region of reduced GM is located in a lobule of the cerebellum with many reciprocal connections to prefrontal cortex [84], [85]. This may contribute to an inability to moderate behavior notwithstanding any possible negative consequence it may have [22], [86], [87], and thus contributing to continued drug abuse. Alternatively, these effects may have been preexisting and constitute an endophenotype for impaired behavioral control that may have contributed to the development of drug abuse [11]. It should be noted that we also observed regions displaying increased GM with abstinence in bilateral cingulate gyri that did not overlap with those showing decreased GM with years of use. This suggests that the brain is capable of compensating in response to changes in demands, such as the maintenance of abstinence [54], [76].

The present results are tempered by some limitations. A fuller characterization of the subjects would be of value in order to assess the psychological consequences of the observed structural changes. In addition, the CD group reported here included individuals who were dependent on alcohol and heroin. While polydrug use of this sort is representative of the CD population, it raises the possibility that the effects reported here could be influenced by these other drug dependencies. Future studies might aim to resolve this ambiguity by recruiting a purely cocaine dependent cohort or a larger sample of polydrug abusers which would facilitate analyses to explore independent and interactive drug use effects. Additionally, future studies should aim to determine whether the number of attempts at abstinence has any bearing on GM change. Finally, consistent with most human clinical studies, it is not possible to address the etiology of the changes reported here. That is, we cannot say with certainty that they arose as a consequence of cocaine consumption or predated it. Notwithstanding this ambiguity, the present results demonstrate a dissociation between the effects of prolonged addiction and extended abstinence. The dissociation between regions showing alterations in grey matter with increased years of use and those altering with increased abstinence suggests that recovery is not simply a reversal of the process of disease. Rather it suggests an asymmetry between the two wherein cortical regions critical to behavioral control may serve as a biomarker of successful abstinence. Furthermore, these systems may be apt for targeting during treatment, such as with mindfulness-based approaches [88] that have been shown to modulate both function and structure of some of the regions reported here [89][91]. This may ultimately lead to decreased relapse and increase the likelihood of prolonged, successful abstinence.


Data analysis was supported by access to the IITAC high-performance computing cluster, funded by the Higher Education Authority, The National Development Plan and the Trinity Centre for High Performance Computing.

Author Contributions

Conceived and designed the experiments: HG JJF. Performed the experiments: RPB. Analyzed the data: CGC. Contributed reagents/materials/analysis tools: CGC RPB. Wrote the paper: CGC RPB JJF HG.


  1. 1. EMCDDA European Monitoring Centre for Drug Addiction Drugs (2009) 2009 Annual report on the state of the drugs problem in Europe. Luxembourg: Publications Office of the European Union. Available: http://www.emcdda.europa.eu/publications​/annual-report/2009 Accessed 2012 May 08.
  2. 2. Substance Abuse and Mental Health Services Administration (2010) Results from the 2009 National Survey on Drug Use and Health: Mental Health Findings. Rockville, MD: Office of Applied Studies, NSDUH Series H-39, HHS Publication No. SMA 10–4609.
  3. 3. Carroll KM, Rounsaville BJ, Gordon LT, Nich C, Jatlow P, et al. (1994) Psychotherapy and pharmacotherapy for ambulatory cocaine abusers. Arch Gen Psychiatry 51: 177–187. doi: 10.1001/archpsyc.1994.03950030013002.
  4. 4. Simpson DD, Joe GW, Fletcher BW, Hubbard RL, Anglin MD (1999) A national evaluation of treatment outcomes for cocaine dependence. Arch Gen Psychiatry 56: 507–514. doi: 10.1001/archpsyc.56.6.507.
  5. CrossRef
  6. PubMed/NCBI
  7. Google Scholar
  8. 5. Ashburner J, Friston KJ (2000) Voxel-based morphometry–the methods. NeuroImage 11: 805–821 . doi: 10.1006/nimg.2000.0582.
  9. 6. Liu X, Matochik JA, Cadet JL, London ED (1998) Smaller volume of prefrontal lobe in polysubstance abusers: a magnetic resonance imaging study. Neuropsychopharmacol 18: 243–252 . doi: 10.1016/s0893-133x(97)00143-7.
  10. CrossRef
  11. PubMed/NCBI
  12. Google Scholar
  13. CrossRef
  14. PubMed/NCBI
  15. Google Scholar
  16. CrossRef
  17. PubMed/NCBI
  18. Google Scholar
  19. 7. Bartzokis G, Beckson M, Lu P, Nuechterlein K, Edwards N, et al. (2001) Age-related changes in frontal and temporal lobe volumes in men – A magnetic resonance imaging study. Arch Gen Psychiatry 58: 461–465. doi: 10.1001/archpsyc.58.5.461.
  20. 8. Franklin TR, Acton PD, Maldjian JA, Gray JD, Croft JR, et al. (2002) Decreased gray matter concentration in the insular, orbitofrontal, cingulate, and temporal cortices of cocaine patients. Biol Psychiatry 51: 134–142. doi: 10.1016/S0006-3223(01)01269-0.
  21. 9. Matochik JA, London ED, Eldreth DA, Cadet J-L, Bolla KI (2003) Frontal cortical tissue composition in abstinent cocaine abusers: a magnetic resonance imaging study. NeuroImage 19: 1095–1102. doi: 10.1016/S1053-8119(03)00244-1.
  22. 10. Lim KO, Wozniak JR, Mueller BA, Franc DT, Specker SM, et al. (2008) Brain macrostructural and microstructural abnormalities in cocaine dependence. Drug Alcohol Depend 92: 164–172 . doi: 10.1016/j.drugalcdep.2007.07.019.
  23. CrossRef
  24. PubMed/NCBI
  25. Google Scholar
  26. 11. Ersche KD, Barnes A, Jones PS, Morein-Zamir S, Robbins TW, et al. (2011) Abnormal structure of frontostriatal brain systems is associated with aspects of impulsivity and compulsivity in cocaine dependence. Brain 134: 2013–2024 . doi: 10.1093/brain/awr138.
  27. CrossRef
  28. PubMed/NCBI
  29. Google Scholar
  30. CrossRef
  31. PubMed/NCBI
  32. Google Scholar
  33. 12. Sim ME, Lyoo IK, Streeter CC, Covell J, Sarid-Segal O, et al. (2007) Cerebellar gray matter volume correlates with duration of cocaine use in cocaine-dependent subjects. Neuropsychopharmacol 32: 2229–2237 . doi: 10.1038/sj.npp.1301346.
  34. CrossRef
  35. PubMed/NCBI
  36. Google Scholar
  37. 13. Hanlon CA, Dufault DL, Wesley MJ, Porrino LJ (2011) Elevated gray and white matter densities in cocaine abstainers compared to current users. Psychopharmacology. doi:10.1007/s00213-011-2360-y.
  38. CrossRef
  39. PubMed/NCBI
  40. Google Scholar
  41. CrossRef
  42. PubMed/NCBI
  43. Google Scholar
  44. 14. Jacobsen LK, Giedd JN, Gottschalk C, Kosten TR, Krystal JH (2001) Quantitative morphology of the caudate and putamen in patients with cocaine dependence. Am J Psychiatry 158: 486–489. doi: 10.1176/appi.ajp.158.3.486.
  45. 15. Barrós-Loscertales A, Garavan H, Bustamante JC, Ventura-Campos N, Llopis JJ, et al. (2011) Reduced striatal volume in cocaine-dependent patients. NeuroImage 56: 1021–1026 . doi: 10.1016/j.neuroimage.2011.02.035.
  46. 16. Bartzokis G, Beckson M, Lu PH, Edwards N, Rapoport R, et al. (2000) Age-related brain volume reductions in amphetamine and cocaine addicts and normal controls: implications for addiction research. Psychiatry Res 98: 93–102. doi: 10.1016/S0925-4927(99)00052-9.
  47. 17. Fein G, Di Sclafani V, Meyerhoff DJ (2002) Prefrontal cortical volume reduction associated with frontal cortex function deficit in 6-week abstinent crack-cocaine dependent men. Drug Alcohol Depend 68: 87–93. doi: 10.1016/S0376-8716(02)00110-2.
  48. CrossRef
  49. PubMed/NCBI
  50. Google Scholar
  51. 18. Ullsperger M, Cramon von DY (2001) Subprocesses of performance monitoring: a dissociation of error processing and response competition revealed by event-related fMRI and ERPs. NeuroImage 14: 1387–1401 . doi: 10.1006/nimg.2001.0935.
  52. 19. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD (2001) Conflict monitoring and cognitive control. Psychol Rev 108: 624–652. doi: 10.1037/0033-295X.108.3.624.
  53. CrossRef
  54. PubMed/NCBI
  55. Google Scholar
  56. 20. Goldstein RZ, Craig ADB, Bechara A, Garavan H, Childress AR, et al. (2009) The Neurocircuitry of Impaired Insight in Drug Addiction. Trends Cogn Sci 13: 372–380 . doi: 10.1016/j.tics.2009.06.004.
  57. CrossRef
  58. PubMed/NCBI
  59. Google Scholar
  60. 21. Bechara A, Damasio AR, Damasio H, Anderson SW (1994) Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50: 7–15. doi: 10.1016/0010-0277(94)90018-3.
  61. 22. Goldstein RZ, Volkow ND (2002) Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry 159: 1642–1652. doi: 10.1176/appi.ajp.159.10.1642.
  62. 23. Narayana PA, Datta S, Tao G, Steinberg JL, Moeller FG (2010) Effect of cocaine on structural changes in brain: MRI volumetry using tensor-based morphometry. Drug Alcohol Depend 111: 191–199 . doi: 10.1016/j.drugalcdep.2010.04.012.
  63. 24. Connolly CG, Foxe JJ, Nierenberg J, Shpaner M, Garavan H (2012) The neurobiology of cognitive control in successful cocaine abstinence. Drug Alcohol Depend 121: 45–53 . doi: 10.1016/j.drugalcdep.2011.08.007.
  64. CrossRef
  65. PubMed/NCBI
  66. Google Scholar
  67. 25. Bell RP, Foxe JJ, Nierenberg J, Hoptman MJ, Garavan H (2011) Assessing white matter integrity as a function of abstinence duration in former cocaine-dependent individuals. Drug Alcohol Depend 114: 159–168 . doi: 10.1016/j.drugalcdep.2010.10.001.
  68. 26. First M, Spitzer R, Gibbon M, Williams J (2002) Structured clinical interview for DSM-IV-TR Axis I disorders-patient edition (SCID-I/P, 11/2002). New York: Biometrics Research, New York State Psychiatric Institute.
  69. CrossRef
  70. PubMed/NCBI
  71. Google Scholar
  72. 27. Leri F, Bruneau J, Stewart J (2003) Understanding polydrug use: review of heroin and cocaine co-use. Addiction 98: 7–22. doi: 10.1046/j.1360-0443.2003.00236.x.
  73. 28. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, et al. (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14: 21–36 . doi: 10.1006/nimg.2001.0786.
  74. 29. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23 Suppl 1S208–S219 . doi: 10.1016/j.neuroimage.2004.07.051.
  75. 30. Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29: 162–173. doi: 10.1006/cbmr.1996.0014.
  76. 31. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE T Med Imaging 20: 45–57 . doi: 10.1109/42.906424.
  77. 32. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17: 825–841. doi: 10.1016/S1053-8119(02)91132-8.
  78. CrossRef
  79. PubMed/NCBI
  80. Google Scholar
  81. 33. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5: 143–156 . doi: 10.1016/S1361-8415(01)00036-6.
  82. CrossRef
  83. PubMed/NCBI
  84. Google Scholar
  85. CrossRef
  86. PubMed/NCBI
  87. Google Scholar
  88. CrossRef
  89. PubMed/NCBI
  90. Google Scholar
  91. 34. Andersson JLR, Jenkinson M, Smith S (2007) Non-linear optimisation. Oxford, UK: FMRIB, University of Oxford. Available: http://www.fmrib.ox.ac.uk/analysis/techr​ep/tr07ja1/tr07ja1.pdf Accessed 2012 Feb 07.
  92. CrossRef
  93. PubMed/NCBI
  94. Google Scholar
  95. 35. Andersson JLR, Jenkinson M, Smith S (2007) Non-linear registration, aka Spatial normalisation. Oxford, UK: FMRIB, University of Oxford. Available: http://www.fmrib.ox.ac.uk/analysis/techr​ep/tr07ja2/tr07ja2.pdf Accessed 2012 Feb 07.
  96. 36. Scorzin JE, Kaaden S, Quesada CM, Müller C-A, Fimmers R, et al. (2008) Volume determination of amygdala and hippocampus at 1.5 and 3.0T MRI in temporal lobe epilepsy. Epilepsy Res 82: 29–37 . doi: 10.1016/j.eplepsyres.2008.06.012.
  97. 37. Huber PJ (1964) Robust estimation of a location parameter. Ann Math Statist 35: 73–101. doi: 10.1214/aoms/1177703732.
  98. 38. Fox J (2002) An R and S-Plus companion to applied regression. Thousand Oaks, CA: Sage Publications, Inc.
  99. 39. R Development Core Team (2012) R: A Language and Environment for Statistical Computing. 2nd ed. Vienna, Austria: R Foundation for Statistical Computing. Available: http://cran.r-project.org/doc/manuals/fu​llrefman.pdf Accessed 2012 Mar 17.
  100. 40. Milton WJ, Atlas SW, Lexa FJ, Mozley PD, Gur RE (1991) Deep gray matter hypointensity patterns with aging in healthy adults: MR imaging at 1.5 T. Radiology. 181: 715–719.
  101. 41. Vul E, Harris C, Winkielman P, Pashler H (2009) Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition. Perspect Psychol Sci 4: 274–290 .
  102. 42. Chanraud S, Pitel A-L, Rohlfing T, Pfefferbaum A, Sullivan EV (2010) Dual Tasking and Working Memory in Alcoholism: Relation to Frontocerebellar Circuitry. Neuropsychopharmacol 35: 1868–1878 .
  103. 43. Wobrock T, Falkai P, Schneider-Axmann T, Frommann N, Woelwer W, et al. (2009) Effects of abstinence on brain morphology in alcoholism. Eur Arch Psy Clin N 259: 143–150 .
  104. 44. Makris N, Oscar-Berman M, Jaffin SK, Hodge SM, Kennedy DN, et al. (2008) Decreased volume of the brain reward system in alcoholism. Biol Psychiatry 64: 192–202 . doi: 10.1016/j.biopsych.2008.01.018.
  105. 45. Lyoo IK, Pollack MH, Silveri MM, Ahn KH, Diaz CI, et al. (2006) Prefrontal and temporal gray matter density decreases in opiate dependence. Psychopharmacology 184: 139–144 . doi: 10.1007/s00213-005-0198-x.
  106. 46. Yuan Y, Zhu Z, Shi J, Zou Z, Yuan F, et al. (2009) Gray matter density negatively correlates with duration of heroin use in young lifetime heroin-dependent individuals. Brain Cogn 71: 223–228 . doi: 10.1016/j.bandc.2009.08.014.
  107. 47. Bush G, Luu P, Posner M (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 4: 215–222. doi: 10.1016/s1364-6613(00)01483-2.
  108. 48. Bolla K, Ernst M, Kiehl K, Mouratidis M, Eldreth D, et al. (2004) Prefrontal cortical dysfunction in abstinent cocaine abusers. J Neuropsychiatry Clin Neurosci 16: 456–464 . doi: 10.1176/appi.neuropsych.16.4.456.
  109. 49. Piazza PV, Maccari S, Deminière JM, Le Moal M, Mormède P, et al. (1991) Corticosterone levels determine individual vulnerability to amphetamine self-administration. Proc Natl Acad Sci USA 88: 2088–2092. doi: 10.1073/pnas.88.6.2088.
  110. 50. Fillmore MT, Rush CR (2002) Impaired inhibitory control of behavior in chronic cocaine users. Drug Alcohol Depend 66: 265–273. doi: 10.1016/S0376-8716(01)00206-X.
  111. CrossRef
  112. PubMed/NCBI
  113. Google Scholar
  114. CrossRef
  115. PubMed/NCBI
  116. Google Scholar
  117. 51. Grant S, Contoreggi C, London ED (2000) Drug abusers show impaired performance in a laboratory test of decision making. Neuropsychologia 38: 1180–1187. doi: 10.1016/S0028-3932(99)00158-X.
  118. CrossRef
  119. PubMed/NCBI
  120. Google Scholar
  121. 52. Hester R, Garavan H (2004) Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. J Neurosci 24: 11017–11022 . doi: 10.1523/JNEUROSCI.3321-04.2004.
  122. 53. Desmond JE, Chen SHA, DeRosa E, Pryor MR, Pfefferbaum A, et al. (2003) Increased frontocerebellar activation in alcoholics during verbal working memory: an fMRI study. NeuroImage 19: 1510–1520. doi: 10.1016/S1053-8119(03)00102-2.
  123. 54. Ilg R, Wohlschlaeger AM, Gaser C, Liebau Y, Dauner R, et al. (2008) Gray matter increase induced by practice correlates with task-specific activation: A combined functional and morphometric magnetic resonance Imaging study. J Neurosci 28: 4210–4215 . doi: 10.1523/JNEUROSCI.5722-07.2008.
  124. 55. Nestor L, McCabe E, Jones J, Clancy L, Garavan H (2011) 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 . doi: 10.1016/j.neuroimage.2011.03.054.
  125. 56. Rando K, Hong K-I, Bhagwagar Z, Li C-SR, Bergquist K, et al. (2011) Association of frontal and posterior cortical gray matter volume with time to alcohol relapse: a prospective study. Am J Psychiatry 168: 183–192 . doi: 10.1176/appi.ajp.2010.10020233.
  126. CrossRef
  127. PubMed/NCBI
  128. Google Scholar
  129. 57. Cardenas VA, Durazzo TC, Gazdzinski S, Mon A, Studholme C, et al. (2011) Brain morphology at entry into treatment for alcohol dependence is related to relapse propensity. Biol Psychiatry 70: 561–567 . doi: 10.1016/j.biopsych.2011.04.003.
  130. CrossRef
  131. PubMed/NCBI
  132. Google Scholar
  133. CrossRef
  134. PubMed/NCBI
  135. Google Scholar
  136. 58. Durazzo TC, Tosun D, Buckley S, Gazdzinski S, Mon A, et al. (2011) Cortical thickness, surface area, and volume of the brain reward system in alcohol dependence: relationships to relapse and extended abstinence. Alcohol Clin Exp Res 35: 1187–1200 . doi: 10.1111/j.1530-0277.2011.01452.x.
  137. 59. Froeliger B, Kozink RV, Rose JE, Behm FM, Salley AN, et al. (2010) Hippocampal and striatal gray matter volume are associated with a smoking cessation treatment outcome: results of an exploratory voxel-based morphometric analysis. Psychopharmacology 210: 577–583 . doi: 10.1007/s00213-010-1862-3.
  138. 60. Paulus MP, Tapert SF, Schuckit MA (2005) Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse. Arch Gen Psychiatry 62: 761–768 . doi: 10.1001/archpsyc.62.7.761.
  139. CrossRef
  140. PubMed/NCBI
  141. Google Scholar
  142. 61. Brewer JA, Worhunsky PD, Carroll KM, Rounsaville BJ, Potenza MN (2008) Pretreatment brain activation during stroop task is associated with outcomes in cocaine-dependent patients. Biol Psychiatry 64: 998–1004 . doi: 10.1016/j.biopsych.2008.05.024.
  143. CrossRef
  144. PubMed/NCBI
  145. Google Scholar
  146. 62. Clark VP, Beatty GK, Anderson RE, Kodituwakku P, Phillips JP, et al.. (2012) Reduced fMRI activity predicts relapse in patients recovering from stimulant dependence. Human Brain Mapping. doi:10.1002/hbm.22184.
  147. 63. Kosten TR, Sinha R, Potenza MN, Skudlarski P, Wexler BE (2006) Cue-induced brain activity changes and relapse in cocaine-dependent patients. Neuropsychopharmacol 31: 644–650 . doi: 10.1038/sj.npp.1300851.
  148. 64. Jia Z, Worhunsky PD, Carroll KM, Rounsaville BJ, Stevens MC, et al. (2011) An initial study of neural responses to monetary incentives as related to treatment outcome in cocaine dependence. Biol Psychiatry 70: 553–560 . doi: 10.1016/j.biopsych.2011.05.008.
  149. 65. Verdejo-García A, Lawrence AJ, Clark L (2008) Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci Biobehav R 32: 777–810 . doi: 10.1016/j.neubiorev.2007.11.003.
  150. 66. de Wit H (2009) Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol 14: 22–31 . doi: 10.1111/j.1369-1600.2008.00129.x.
  151. 67. Franken IHA (2003) Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuro-Psychoph 27: 563–579 . doi: 10.1016/j.neubiorev.2007.11.003.
  152. 68. Franken IHA, Booij J, van den Brink W (2005) The role of dopamine in human addiction: from reward to motivated attention. Eur J Pharmacol 526: 199–206 . doi: 10.1016/j.ejphar.2005.09.025.
  153. 69. Rogers RD, Everitt BJ, Baldacchino A, Blackshaw AJ, Swainson R, et al. (1999) Dissociable deficits in the decision-making cognition of chronic amphetamine abusers, opiate abusers, patients with focal damage to prefrontal cortex, and tryptophan-depleted normal volunteers: evidence for monoaminergic mechanisms. Neuropsychopharmacol 20: 322–339 . doi: 10.1016/s0893-133x(98)00091-8.
  154. 70. Clark L, Robbins T (2002) Decision-making deficits in drug addiction. Trends Cogn Sci 6: 361–363. doi: 10.1016/s0893-133x(98)00091-8.
  155. 71. Bechara A (2003) Risky business: emotion, decision-making, and addiction. J Gambl Stud 19: 23–51. doi: 10.1023/A:1021223113233.
  156. 72. Garavan H, Hester R (2007) The role of cognitive control in cocaine dependence. Neuropsychol Rev 17: 337–345 . doi: 10.1007/s11065-007-9034-x.
  157. 73. Jentsch JD, Taylor JR (1999) Impulsivity resulting from frontostriatal dysfunction in drug abuse: implications for the control of behavior by reward-related stimuli. Psychopharmacology 146: 373–390. doi: 10.1007/PL00005483.
  158. 74. Congdon E, Canli T (2008) A neurogenetic approach to impulsivity. J Pers 76: 1447–1484 . doi: 10.1111/j.1467-6494.2008.00528.x.
  159. 75. Schilling C, Kühn S, Romanowski A, Banaschewski T, Barbot A, et al.. (2011) Common structural correlates of trait impulsiveness and perceptual reasoning in adolescence. Human Brain Mapping. doi:10.1002/hbm.21446.
  160. 76. Driemeyer J, Boyke J, Gaser C, Büchel C, May A (2008) Changes in gray matter induced by learning – revisited. PLoS ONE 3: e2669 . doi: 10.1371/journal.pone.0002669.
  161. 77. Aron AR, Fletcher PC, Sahakian BJ, Robbins TW (2003) Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat Neurosci 6: 115–116 . doi: 10.1038/nn1003.
  162. 78. Aron AR, Robbins TW, Poldrack RA (2004) Inhibition and the right inferior frontal cortex. Trends Cogn Sci 8: 170–177 . doi: 10.1016/j.tics.2004.02.010.
  163. 79. Rubia K, Smith AB, Brammer MJ, Taylor E (2003) Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection. NeuroImage 20: 351–358. doi: 10.1016/S1053-8119(03)00275-1.
  164. 80. Swick D, Ashley V, Turken AU (2008) Left inferior frontal gyrus is critical for response inhibition. BMC Neurosci 9: 102 . doi: 10.1186/1471-2202-9-102.
  165. 81. Garavan H, Ross TJ, Stein EA (1999) Right hemispheric dominance of inhibitory control: an event-related functional MRI study. Proc Natl Acad Sci USA 96: 8301–8306. doi: 10.1073/pnas.96.14.8301.
  166. 82. Kaufman JN, Ross TJ, Stein EA, Garavan H (2003) Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. J Neurosci 23: 7839–7843.
  167. 83. Whelan R, Conrod PJ, Poline J-B, Lourdusamy A, Banaschewski T, et al. (2012) Adolescent impulsivity phenotypes characterized by distinct brain networks. Nat Neurosci 15: 920–925 . doi: 10.1038/nn.3092.
  168. 84. Matano S (2001) Brief communication: Proportions of the ventral half of the cerebellar dentate nucleus in humans and great apes. Am J Phys Anthropol 114: : 163–165. doi:10.1002/1096-8644(200102)114:2<163::​AID-AJPA1016>3.0.CO;2-F.
  169. 85. Krienen FM, Buckner RL (2009) Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb cortex 19: 2485–2497 . doi: 10.1093/cercor/bhp135.
  170. 86. Everitt BJ, Dickinson A, Robbins TW (2001) The neuropsychological basis of addictive behaviour. Brain Res Brain Res Rev 36: 129–138. doi: 10.1016/S0165-0173(01)00088-1.
  171. 87. Garavan H, Stout JC (2005) Neurocognitive insights into substance abuse. Trends Cogn Sci 9: 195–201 . doi: 10.1016/j.tics.2005.02.008.
  172. 88. Witkiewitz K, Marlatt GA, Walker D (2005) Mindfulness-based relapse prevention for alcohol and substance use disorders. J Cog Psychother 19: 211–228.
  173. 89. Hölzel BK, Carmody J, Vangel M, Congleton C, Yerramsetti SM, et al. (2011) Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Res 191: 36–43 . doi: 10.1016/j.pscychresns.2010.08.006.
  174. 90. Farb NAS, Segal ZV, Mayberg H, Bean J, McKeon D, et al. (2007) Attending to the present: mindfulness meditation reveals distinct neural modes of self-reference. Soc Cogn Affect Neurosci 2: 313–322 . doi: 10.1093/scan/nsm030.
  175. 91. Baron Short E, Kose S, Mu Q, Borckardt J, Newberg A, et al. (2010) Regional Brain Activation during Meditation Shows Time and Practice Effects: An Exploratory FMRI Study. Evid Based Complement Alternat Med 7: 121–127 . doi: 10.1093/ecam/nem163.