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Readiness to change and brain damage in patients with chronic alcoholism

Editor's comment:
The main objective of this study was to gain a better understanding of the potential contribution of macrostructural brain abnormalities to why some alcoholic patients have difficulty changing their drinking behavior even when they are inpatients at clinical treatment entry. Taken as a group, the alcoholic inpatients participating in the study were found to have widespread gray matter abnormalities on MRI. The study results suggest that as a consequence of their brain volume deficits and associated impairment of critical abilities such as decision making, executive functions and social cognition skills, some alcohol dependent patients may not be able to attend a regular treatment in an addiction department. It may be relevant to favour brain recovery of patients with lower motivation by extending the period they spend without alcohol before being admitted to an Addiction department.

Psychiatry Research 2013, Volume 213, pages 202–209

Abstract

High motivation to change is a crucial triggering factor to patients’ engagement in clinical treatment. This study investigates whether the low readiness to change observed in some alcoholic inpatients at treatment entry could, at least partially, be linked with macrostructural gray matter abnormalities in critical brain regions. Participants comprised 31 alcoholic patients and 27 controls, who underwent 1.5-T magnetic resonance imaging. The Readiness to Change Questionnaire, designed to assess three stages of motivation to change (precontemplation, contemplation and action stages), was completed by all patients, who were then divided into “Action” (i.e. patients in action stage) and “PreAction” (i.e. patients in precontemplation or in contemplation stage) subgroups. The PreAction subgroup, but not the Action subgroup, had gray matter volume deficits compared with controls. Unlike the patients in the Action subgroup, the PreAction patients had gray matter abnormalities in the cerebellum (Crus I), fusiform gyri and frontal cortex. The low level of motivation to modify drinking behavior observed in some alcoholic patients at treatment entry may be related to macrostructural brain abnormalities in regions subtending cognitive, emotional and social abilities. These brain volume deficits may result in impairment of critical abilities such as decision making, executive functions and social cognition skills. Those abilities may be needed to resolve ambivalence toward alcohol addiction and to apply “processes of change”, which are essential for activating the desire to change problematic behavior.

Keywords: Alcoholism, Brain morphology, Motivation, Frontal cortex.

1. Introduction

The psychological treatment of alcohol abuse and dependence is generally based on two main clinical approaches, which may be jeopardized by cognitive deficits arising from alcoholism-related brain damage. The first approach is cognitive behavioral therapy (Berglund et al, 2003, Assanangkornchai and Srisurapanont, 2007, and Clay et al, 2008), which aims to help patients recognize, avert or cope with high-risk relapse situations. The second approach (which can be combined with the first one) is motivational interviewing (Hettema et al, 2005 and Miller and Rose, 2009), which is intended to initiate and develop an intrinsic motivation to change addictive behavior. Motivational interviewing has a patient-centered and directive therapeutic style, which helps patients explore and resolve their ambivalence towards changing their behavior ( Miller and Rollnick, 1991 ). High motivation to change is a crucial triggering factor for patients’ engagement in clinical treatment ( DiClemente et al., 1999 ).

Motivational interviewing is based on the idea that alcoholic patients go through different stages of readiness to change their drinking behavior. According to the Transtheoretical Model (TTM) of intentional behavior change ( Prochaska and DiClemente, 1983 ; DiClemente, 2007 ), change consists of a cycle of five stages of motivation. The first three “PreAction” stages preceding the effective implementation of actions in addiction change include “Precontemplation” (substance misuse and no intention of stopping drinking), “Contemplation” (strong intention of changing addictive habits but ambivalent attitude) and “Preparation” (initiation of planning for change, sometimes accompanied by initiatives to reduce or stop alcohol consumption). The two last stages correspond to “Action” (cessation of excessive alcohol consumption and adoption of previously envisaged healthier lifestyle) and finally “Maintenance” (integration of new behavioral habits in daily life over time and prevention of relapse). Ideally, patients in alcohol treatment should be willing and ready to change and therefore be in the “Preparation” stage ( Prochaska, 2008 ).

The shift from a low to a high level of motivation to change drinking behavior involves patients completing a “decisional balance” ( Janis and Mann, 1977 ). Addictive behavior is characterized by ambivalence, as patients endlessly weigh the advantages and disadvantages of changing. Patients hesitate between their “limbic” driven addictive behavior (i.e., impulsive decision making system) and more controlled behavior subtended by frontal lobe activity (i.e., reflective decision making system) (Crews and Boettiger, 2009 and Verdejo-Garcia and Bechara, 2009). High motivation to change drinking behavior implicates effective decision making skills and executive functioning, which both involve the frontal cortex.

According to the TTM, in order to increase their readiness to change, patients must also apply so-called “processes of change”, namely the overt and covert activities in which individuals engage when attempting to modify problematic behavior ( Prochaska and DiClemente, 1983 ). More particularly, to progress through the “PreAction” stages, patients need experiential (cognitive-affective) activities that enable them to change the way they think and feel about their alcohol abuse. Blume et al. (2005) have postulated that verbal memory performance contributes to the process of change known as “consciousness raising” (i.e., gaining knowledge and information about the problem behavior and the advantages of changing). “Dramatic relief” (experiencing and expressing feelings about the problem behavior and solutions) and “environmental reevaluation” (assessing how the problem behavior affects the physical and social environment) are two other processes that rely not only on cognitive functions but also on emotional and social skills, including facial affect perception, theory of mind ( Premack and Woodruff, 1978 ) and empathy ( Eslinger, 1998 ).

Several studies have shown that patients with alcohol or drug abuse who seek or participate in treatment are at different stages of change (DiClemente and Hughes, 1990, Carney and Kivlahan, 1995, and Edens and Willoughby, 2000). Sometimes, alcoholic patients are open to participating in treatment without being genuinely ready to abstain from alcohol. This clinical observation was confirmed in our previous behavioral study ( Le Berre et al., 2012 ) conducted with the same clinical population of alcoholic patients as in the present study, i.e. after detoxification and at alcohol treatment entry. The investigation of the stage of change reached by each patient using the “Readiness to Change Questionnaire" revealed that some patients were still in the earlier precontemplation and contemplation stages while others were in the action stage. Since this heterogeneity in motivational level at treatment entry could be explained by cognitive factors, the aim of this previous study was to determine the contribution of several cognitive processes in the different motivational stages. Stepwise regression analysis revealed links between verbal episodic memory impairment and high precontemplation scores, as well as relationships between weak executive performance and high contemplation scores, and between good decision making skills and high action scores. Those results suggest that a set of complementary cognitive abilities such as memory, executive functions and decision-making skills may be needed to achieve awareness and resolve ambivalence towards alcohol addiction, which are essential for activating the desire to change problematic behavior.

In the present study, we aimed at going further in the understanding of the factors that could explain the heterogeneity in the motivational level of alcoholics at treatment entry. Our main objective was therefore to investigate whether the low readiness to change observed in some alcoholic inpatients at treatment entry could, at least partially, be related to macrostructural gray matter (GM) abnormalities in critical brain regions involved in cognitive, emotional and social skills ( Le Berre et al., 2012 ), and affected by the harmful effects of chronic alcohol consumption (Jernigan et al, 1991, Kril et al, 1997, Moselhy et al, 2001, and Chanraud et al, 2007). More specifically, the compromised motivation to change problematic drinking behavior observed in some alcoholic patients may be related to macrostructural abnormalities in the frontostriatal, frontocerebellar and medial tem-poral limbic systems.

2. Methods

2.1. Participants

Two groups of participants were included in this study: 31 patients with chronic alcoholism who were early in abstinence at clinical alcohol treatment entry and 27 control participants recruited by advertisement and word of mouth and drawn from our imaging database matched for age,t(56)=−0.34,p=0.73 ( Table 1 ). All participants were volunteers to participate in the clinical treatment. None of the patients were court mandated or under family pressure in order to avoid any bias on their motivation level to change their alcoholic behavior. The neuroimaging data were collected in the same magnet and using the same imaging protocol for all participants (Kalpouzos et al, 2008 and Kalpouzos et al, 2009). All the control subjects were selected according to stringent prospective inclusion/exclusion criteria and their enrollment was based on the absence of abnormality in clinical, magnetic resonance imaging (MRI) and neuropsychological examinations (Kalpouzos et al, 2008 and Kalpouzos et al, 2009). None of the participants (alcoholic patients and controls) were taking psychotropic medication, had a history of drug misuse, displayed psychiatric problems or had any history of pathology (head injury, coma, epilepsy, Wernicke's encephalopathy, cirrhosis or depression) that might have affected their cognitive functions. Control participants were interviewed to check that they did not meet the criteria for alcohol abuse or dependence according to the DSM-IV criteria. All the participants gave their informed consent to the study, which was approved by the local ethics committee.

Table 1 Participants’ main demographic, clinical and alcoholic features.

  Controls Alcohol-dependent patients
Number 27 31
Women/men 12/15 5/26
Age 44.80 (11.35) 43.87 (6.97)
Range 29–60 31–56
Years of education 10.77 (2.14)
Range 6–15
Vocabulary subtest (WAIS) * 6.90 (2.52)
Range 3–13
MMSE 27.35 (1.68)
Range 24–30
Beck depression inventory 7.68 (3.49)
Range 0–14
State-Trait Anxiety Inventory “State anxiety” (STAI Y-A) 33.84 (9.89)
Range 20–54
State-Trait Anxiety Inventory    
“Trait anxiety” (STAI Y-B) 50.77 (14.41)
Range 22–74
Smoking status    
(number of cigarettes per day) <10 20.94 (15.28)
Range 0–60
Days of abstinence before inclusion N/A 12.64 (7.16)
Range 7–40
Years of alcohol use N/A 28.19 (8.45)
Range 14–51
Years of alcohol misuse N/A 15.42 (10.12)
Range 2–37
Years of alcoholism N/A 8.26 (8.26)
Range 0.5–33
Quantity per day (in units of alcohol) N/A 21.95 (11.70)
Range 3.5–53.57
Number of withdrawals N/A 2.42 (1.54)
Range 1–8

lowast Standard T scores.

Data are shown as means (standard deviations).

Alcoholic patients were recruited by clinicians on the basis of the DSM-IV criteria for alcohol dependence ( American Psychiatric Association, 1994 ) while they were receiving treatment for alcohol dependence as inpatients at Caen University Hospital. Even though patients were still early in abstinence from alcohol, we only selected those who no longer presented any physiological symptoms of alcohol withdrawal, as established by the Cushman score ( Cushman et al., 1985 ). The goal was to decrease the likelihood of acute alcohol withdrawal effects. Patients had no history of other types of substance abuse or dependence (except tobacco). They were interviewed to determine the age at which they had had their first alcoholic drink, their age at onset of alcoholism, the length of time they had drunk to excess, their usual daily alcohol intake and the number of withdrawals (i.e. number of previous medical detoxifications) ( Table 1 ). Age, education, Mini-Mental State Examination (MMSE) ( Folstein et al., 1975 ) results, verbal intelligence capacities (vocabulary subtest of the Wechsler Adult Intelligence Scale, WAIS) ( Weschler, 2001 ), depression level (Beck Depression Inventory, BDI) ( Beck et al., 1961 ), anxiety level scores (State-Trait Anxiety Inventory (STAI) for adults: Y-A for “state anxiety” and Y-B for “trait anxiety”) ( Spielberger et al., 1983 ) and smoking status are reported in Table 1 .

2.2. Materials and procedure

2.2.1. Readiness to Change Questionnaire

The current study was conducted in the same sample of 31 alcoholic patients as in our previous article ( Le Berre et al., 2012 ) and using the same French adaptation of the Readiness to Change Questionnaire (RTCQ) ( Rollnick et al., 1992 ). This questionnaire was filled in by all the alcoholic patients after their alcohol withdrawal, at the very beginning of their clinical treatment. The RTCQ is a self-report instrument that yields information about motivation and readiness to change alcohol misuse behavior; it has and demonstrated satisfactory reliability and predictive validity (Rollnick et al, 1992 and Heather et al, 1993). The psychometric properties of the French version (reliability and validity) are not known. Based on the TTM ( Prochaska and DiClemente, 1983 ), this questionnaire is made up of 12 questions divided into three subscales, each corresponding to a different stage of change (four questions per subscale): Precontemplation (e.g., “It's a waste of time thinking about my drinking”), Contemplation (e.g., “I enjoy my drinking but sometimes I drink too much”) and Action (e.g., “Anyone can talk about wanting to do something about their drinking but I am actually doing something about it”). Patients were asked to rate their agreement with the statements about their alcohol consumption on a 5-point Likert-type scale ranging from “strongly disagree” (−2) to “strongly agree” (+2). We took the patients’ scores on each subscale (ranging from −8 to 8) as our variables. The subscale with the highest score indicated which motivational stage the patient had reached at the time of testing.

In our previous study ( Le Berre et al., 2012 ), we showed that in the alcoholics at alcohol treatment entry, the mean score on the action stage subscale was significantly higher than those on the precontemplation and contemplation stage subscales ( Table 2 ). Nevertheless, when we adopted a more qualitative approach, investigating the stage of change reached by each individual patient, we found that some patients were still in the earlier precontemplation and contemplation stages. More precisely, 21 alcoholic patients scored highest on the Action subscale (“Action” subgroup, 17 men and four women), nine on the Contemplation subscale and one on the Precontemplation subscale (“PreAction” subgroup, nine men and one woman). This finding is in accordance with clinical observation that not every patient is highly motivated to change at treatment entry (e.g.,DiClemente and Hughes, 1990, Carney and Kivlahan, 1995, and Edens and Willoughby, 2000). The motivational features of “Action” and “PreAction” subgroups are presented in Table 2 .

Table 2 Patients’ readiness to change.

  Alcoholic group (N=31) “Action” subgroup (N=21) “PreAction” subgroup (N=10)
Subscales Scores Scores Scores
Precontemplation −2.13 (3.40) −1.38 (2.94) −3.7 (3.92)
Range −8–5 −8–5 −8–4
Contemplation 1.03 (5.28) −1.05 (4.52) 5.4 (4.00)
Range −8–8 −8–6 −5–8
Action 5.35 (2.83) 6.33 (1.96) 3.3 (3.37)
Range −4–8 3–8 −4–7

Data are shown as means (standard deviations).

2.2.2. Magnetic resonance imaging
2.2.2.1. Anatomical image acquisition

All the participants (alcoholic patients and controls) underwent a structural MRI examination. The MRI datasets were acquired from the same scanner (1.5 T Signa Advantage EchoSpeed; General Electric) using the same parameters for all participants. The T1-weighted volume MRI scan consisted of a set of 128 adjacent axial slices parallel to the anterior commissure–posterior commissure (AC–PC) line, with a slice thickness of 1.5 mm and a voxel size of 0.94 mm×0.94 mm×1.5 mm, using the spoiled gradient echo sequence (SPGR), with a repetition time (TR) of 10.3 ms; echo time (TE) of 2.1 ms; field of view (FOV) of 24 cm×18 cm and matrix of 256×192. Standard correction for field inhomogeneities was applied using Statistical Parametric Mapping 5.

2.2.2.2. Preprocessing of anatomical data

The preprocessing steps were performed using both the VBM5.1 and the DARTEL ( Ashburner, 2007 ) toolboxes implemented in Statistical Parametric Mapping software (SPM5; Wellcome Department of Cognitive Neurology, Institute of Neurology, London, England). The use of the DARTEL toolbox was motivated by the need for producing an accurate study-specific (or population-specific) template for subsequent spatial normalization. Klein et al. (2009) have shown that the DARTEL toolbox was among the top ranked in delivering such a customized template.

Briefly, we used the VBM5.1 toolbox and the subject-specific template in Montreal Neurological Institute (MNI) space to preprocess the images. This included segmentation of GM, normalization to MNI space, modulation for preserving total volume, smoothing using a 10-mm full-width at half-maximum (FWHM) isotropic Gaussian kernel.

We also obtained the individual total volume of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using the VBM5.1 toolbox and calculated the individual total intracranial volume (TIV) by summing the volume of the three compartments. The TIV was then used as a covariate in the two-samplet-test analyses described below.

The resulting preprocessed images were masked so as to include only voxels considered as GM in the statistical analyses.

2.3. Statistical analysis

First, in order to determine the pattern of GM abnormalities within the alcoholic group, we compared the preprocessed DARTEL GM data of the alcoholic group with those of the control participants using a two-samplet-test design in SPM and correcting for multiple comparisons atp<0.01, false discovery rate (FDR)-corrected, with a minimum cluster size ofk=150. We then compared the GM data for each patient subgroup (Action and PreAction) with those of the control group. As there is an imbalance in the sex ratio between participants, with a majority of men in the alcoholic group, gender was included as a covariate for all these analyses. TIV was also included as a covariate to account for head size differences between participants.

Second, clinical variables such as drinking history, smoking status or depression and anxiety are known to have an impact on cognitive functioning and brain macrostructure (Pfefferbaum et al, 1995, Meyerhoff et al, 2006, and Chanraud et al, 2007). We therefore identified potential confounding variables by computing Pearson correlation coefficients in the alcoholic group between each motivation subscale score on the one hand, and age, education, verbal intelligence capacities (vocabulary subtest, WAIS) and cognitive (MMSE), psychiatric (BDI and STAI Y-B), smoking and drinking variables on the other hand. Those demographic, clinical and alcoholic features that correlated with the motivational data (p<0.05) were used as covariates in the comparison of GM volume between the Action and PreAction subgroups. Moreover, since readiness to change drinking behavior could be explained by personal and contextual factors including demographic variables, drinking severity variables and psychiatric comorbidity ( DiClemente et al., 2009 ), we looked at whether the Action and PreAction subgroups differed on the demographic, cognitive, psychiatric, smoking and drinking variables, using a nonparametric Mann–Whitney test. The variables on which the subgroups differed (p<0.05) were used as covariates in subsequent comparisons of GM volume within the alcoholic group.

Finally, we compared the GM data of the Action and PreAction subgroups using the unpaired two-samplet-test SPM5 routine. For exploratory purposes, we used a less stringentpvalue (p<0.001) uncorrected for multiple comparisons, with a minimum cluster size ofk=150 to avoid false-negative results. Indeed, we expected the differences between the alcoholic subgroups to be more subtle than the differences between the patients and the control participants. TIV was introduced as a covariate to account for head size differences between participants. Significant peak coordinates were registered to the MNI template and anatomically labelled using the AAL toolbox implemented in SPM5 ( Tzourio-Mazoyer et al., 2002 ).

3. Results

3.1. GM volumes in the alcoholic group and Readiness to Change subgroups

3.1.1. Pattern of GM abnormalities in the alcoholic group

As shown in Fig. 1 a, compared with controls, the alcoholic patients had decreased GM volume in the dorsolateral, dorsomedial and ventromedial prefrontal cortex extending to the anterior part of the parietal lobes, the lateral and medial (hippocampal and parahippocampal gyri) temporal lobes, the cingulate and occipital cortices, the subcortical regions including the amygdala, insula, thalamus, putamen and caudate, and the cerebellum. The patients were found to have a global loss in volume of 8% compared to the controls.

gr1

Fig. 1 Pattern of gray matter abnormalities.lowast: SPM5-derived glass brain in MNI space. Gray-shaded areas mark regions with significant group volume difference for the contrast of interest. Darker gray shades correspond to higherTvalues.

3.1.2. Pattern of GM abnormalities in the Action subgroup

There was no significant volume deficit in the Action subgroup compared with controls ( Fig. 1 b).

3.1.3. Pattern of GM abnormalities in the PreAction subgroup

As shown in Fig. 1 c, compared with controls, the patients in the PreAction subgroup had decreased GM volume in the orbitofrontal cortex (OFC), the dorsolateral, dorsomedial and ventromedial prefrontal cortex extending to the anterior part of the parietal lobes, the lateral and medial (hippocampal and parahippocampal gyri) temporal lobes, the cingulate and occipital cortices, the subcortical regions including the amygdala, insula, thalamus, putamen and caudate, and the cerebellum. The patients in the PreAction subgroup were found to have a global loss in volume of 13% compared to the controls.

3.2. Identification of potential confounding variables

Daily alcohol consumption was entered in the analysis below since this drinking variable was correlated with the Action subscale score (r=−0.40,p=0.03).

The Action and PreAction subgroups differed only on the length of abstinence prior to inclusion, with a longer sobriety period for the Action subgroup. Therefore, the number of days of abstinence before inclusion was entered as variable covariate in the subsequent analysis ( Table 3 ).

Table 3 Main demographic and clinical features in the Action and the PreAction subgroups.

  PreAction subgroup (N=10) Action subgroup (N=21) p value
Age 45.15 (5.71) 43.85 (7.63) 0.75
Range 33–53 31–56  
Years of education 10.50 (2.12) 10.90 (2.19) 0.69
Range 6–14 7–15  
Vocabulary Subtest (WAIS) 7.24 (2.12) 6.20 (3.22) 0.17
MMSE 26.60 (1.71) 27.71 (1.59) 0.08
Beck Depression Inventory 7.80 (2.39) 7.62 (3.96) 1.00
State-Trait Anxiety Inventory “State anxiety” (STAI Y-A) 33.10 (6.71) 34.19 (11.23) 0.97
State-Trait Anxiety Inventory “Trait anxiety” (STAI Y-B) 56.20 (10.28) 48.19 (15.56) 0.13
Days of abstinence before inclusion 9.60 (5.30) 14.09 (7.58) 0.03 lowast
Range 7–24 7–40  
Years of alcohol use 29.20 (7.67) 27.71 (8.93) 0.42
Range 16–42 14–51  
Years of alcohol abuse 19.80 (12.20) 13.33 (8.52) 0.15
Range 3–37 2–33  
Years of alcoholism 10.40 (9.49) 7.29 (7.61) 0.54
Range 1–24 1–33  
Quantity per day (in units of alcohol) 27.58 (15.16) 19.27 (8.86) 0.33
Range 5–41.25 3.5–36  
Number of withdrawals 2.80 (1.48) 2.24 (1.56) 0.20
Range 1–6 1–8  
Smoking status (number of cigarettes per day) 22 (17.67) 20.43 (14.45) 1.00
Range 0–60 0–50  

lowast Significant difference between patients in Action subgroup and patients in PreAction subgroup (p<0.05) for nonparametric Mann–Whitney U test.

Data are shown as means (standard deviations). Number of withdrawals=number of prior medical alcohol detoxification episodes.

3.3. Comparison of GM volume between the Action and PreAction subgroups

As shown in Table 4 and illustrated in Fig. 1 d, compared with patients in the Action subgroup, patients in the PreAction subgroup presented decreased GM volume in the right cerebellum (Crus I), bilaterally in the fusiform gyri, the lateral OFC, the right ventromedial prefrontal cortex (vmPFC) and the rostral cingulate zone (including the supplementary motor area and midcingulate gyrus), the left dorsolateral and dorsomedial prefrontal cortex, and the left caudate nucleus. The patients in the PreAction subgroup were found to have a global loss in volume of 13% compared to the patients in the Action subgroup. Even though this analysis was exploratory, our results remained significant atp<0.05 corrected for false discovery rate (FDR) corrected for all brain regions and atp<0.05 corrected for family-wise error (FWE) for the right cerebellum (Crus I).

Table 4 Between-group comparisons: gray matter (GM) abnormalities in the PreAction subgroup compared with the Action subgroup.

Regional clusters Side Cluster size MNI coordinates of peak voxel Maximal z-value
X Y Z
Cerebellum Crus I lowast R 2709 45 −53 −27 4.70
Fusiform g lowastlowast L 3987 −42 −57 −23 4.49
Mid orbitofrontal g lowastlowast L 8690 −45 52 −4 4.40
Midfrontal g lowastlowast L 802 −46 10 37 4.32
Fusiform g lowastlowast L 3928 −31 −53 −5 4.16
Supplementary motor area (with peak extending to right midcingulate g) lowastlowast R 1112 8 18 43 4.08
Sup med frontal g lowastlowast L 632 −4 44 21 4.04
Mid orbitofrontal g lowastlowast R 3470 37 55 −12 4.01
Med orbitofrontal g lowastlowast R 5125 7 50 −19 3.90
Precuneus lowastlowast R 233 12 −66 40 3.69
Sup med frontal g lowastlowast L 958 −9 37 53 3.69
Precuneus lowastlowast R 152 14 −48 47 3.62
Postcentral lowastlowast L 286 −51 −2 40 3.60
Supplementary motor area lowastlowast L 186 −3 13 62 3.48
Caudate lowastlowast L 409 3 3 −18 3.46
Supplementary motor area lowastlowast L 197 −8 −10 61 3.45
Paracentral lobule lowastlowast L 516 −10 −18 74 3.37
Midcingulate g lowastlowast R 177 5 −1 43 3.36
Fusiform g lowastlowast R 315 24 −64 −13 3.36

lowast Significant peak at p<0.05 (FWE).

lowastlowast Significant peak at p<0.05 (FDR).

Results are reported atp<0.001 (uncorrected).

L=left;R=right;g=gyrus; mid=middle; sup: superior; med=medial.

4. Discussion

The main objective of our study was to gain a better understanding of the potential contribution of macrostructural brain abnormalities to why some alcoholic patients have difficulty changing their drinking behavior even when they are inpatients at clinical treatment entry. Taken as a group, the alcoholic patients had widespread GM abnormalities in accordance with previous studies (Rosenbloom et al, 2003, Sullivan and Pfefferbaum, 2005, and Chanraud et al, 2007). The PreAction subgroup – but not the Action subgroup – exhibited GM volume deficits compared with controls. The absence of GM volume deficits in the Action subgroup compared with the control group suggests that the lack of motivation in some alcoholic patients at the beginning of their clinical treatment could be a consequence of their brain damage. The neuropsychological screening of possible cognitive impairments strengthened, when necessary, by brain examination could be considered, in the context of alcohol treatment, as a relevant clinical tool in order to choose the most appropriate treatment according to the level of motivation exhibited by the patient. Finally, in agreement with our hypotheses, compared with patients with a high level of motivation, patients with low-level motivation had GM abnormalities in the frontostriatal and fronto-cerebellar systems. Our results especially revealed decreased GM volumes in the cerebellum (Crus I), fusiform gyri and caudate nucleus, and widespread structural damage in the frontal cortex, including the lateral OFC, the ventromedial, dorsomedial and dorsolateral prefrontal cortices, and the rostral cingulate zone. These results strengthen the conclusions of our previous study ( Le Berre et al., 2012 ). Indeed, we found GM abnormalities in patients with low-level motivation in brain regions involved in decision making such as the vmPFC, lateral OFC and rostral cingulate zone and in executive functions such as dorsolateral prefrontal cortex (DLPFC), caudate nucleus and cerebellum (Crus I). Contrary to what we had hypothesized, our results did not show any GM abnormalities in regions involved in episodic memory, such as the medial temporal limbic system. This observation could be explained by the fact that episodic memory abilities are mainly involved during the precontemplation stage (Blume et al, 2005 and Le Berre et al, 2012). However, in this present study, our “PreAction” group is more representative of the contemplation than precontemplation stage (one patient in precontemplation and nine patients in contemplation stage). Finally, GM volume deficits found in cerebral regions known to be involved in social cognition abilities emphasize the need to conduct further studies of the relationship between social cognition and readiness to change drinking behavior.

4.1. Motivation and decision making: The contribution of the vmPFC, lateral OFC and rostral cingulate zone

In the present study, alcoholic patients with low readiness to change showed brain damage in the vmPFC, a brain structure known to be involved in decision making, in particular through its critical role in the network underlying somatic marker activation ( Bechara and Damasio, 2005 ). VmPFC damage results in inappropriate social and emotional behavior, with a tendency to favour instant gratification and ignore the long-term negative consequences of actions in daily life. Similarities with the profile of poor decision making performance and somatic activity in patients with vmPFC abnormalities have been noticed in some individuals who abuse substances including alcohol (Bechara et al, 2001, Bechara et al, 2002, and Bechara and Damasio, 2002). Therefore, unmotivated alcoholic patients may suffer from “myopia” for the future (Bechara et al, 1996 and Bechara et al, 2000) and, as a consequence, be only interested in instant gratification (i.e., the immediate benefits of their alcohol consumption). This myopia may dim patients’ awareness of the problems arising from substance abuse, keep them in denial about their disease ( Verdejo-Garcia and Perez-Garcia, 2008 ) and thus might limit the enhancement of motivation.

Our results also revealed damage in the lateral OFC and rostral cingulate zone in the PreAction subgroup. Brain activation in the lateral OFC is observed during response inhibition, notably when a previously rewarded response has to be suppressed ( Elliott et al., 2000 ). Thus, alcoholic patients with a lower level of motivation could have difficulty inhibiting their previously rewarded drinking behavior. The rostral cingulate zone subtends “performance monitoring” processes (detection of errors, response conflict, assessment of unfavourable outcomes of an action (for review, see Ridderinkhof et al., 2004 ), which are for a review), which are essential for optimum decision making and adequate behavioral adaptation. As a result, unmotivated alcoholic patients may be unable to tip their decisional balance in favour of alcohol abstinence, just as they may be incapable of averting the negative consequences of their unhealthy behavior due to emotional and cognitive dysfunctioning.

4.2. Motivation and executive functions: The contribution of the dorsolateral prefrontal cortex (DLPFC), caudate nucleus and cerebellum (Crus I)

Executive functions such as planning, organization, problem solving, inhibition and flexibility would surely be very helpful to patients who are in the midst of establishing their decisional balance sheet. These cognitive processes are traditionally thought to engage a frontal-subcortical circuit including the DLPFC and caudate nucleus (Alexander et al, 1986 and Bonelli and Cummings, 2007). Moreover, brain lesions limited to the posterior lobe of the cerebellum and the vermis generate a cerebellar cognitive-affective syndrome including executive impairment ( Schmahmann and Sherman, 1998 ). The cerebellum maintains multiple connections with the cerebral cortex via feedforward (afferent) loops through the pons and feedback (efferent) loops through the thalamus (Fitzpatrick et al, 2008, Schmahmann and Pandya, 2008, and Habas et al, 2009). Notably, an executive loop has been isolated and would be made up of interactions between the cerebellar neocortex (including Crus I) and prefrontal regions such as the DLPFC ( Kelly and Strick, 2003 ). In the present study, the alcoholic patients with a lower level of motivation had brain volume deficits in the nodes of this executive brain circuitry, thus possibly compromising their ability to move towards a favourable balance, that is, towards the decision to implement healthier behavior.

4.3. Motivation and social cognition: The contribution of the OFC, ventromedial and dorsomedial prefrontal cortex, fusiform gyri and cerebellum (Crus I)

In order to modify their thoughts and feelings concerning their drinking behavior, patients have to be engaged in processes of change requiring emotional and social skills and experiences (i.e., social cognition abilities). “Social cognition” refers, among others, to facial affect perception, theory of mind (ability to attribute mental states to self and others ( Premack and Woodruff, 1978 ), and empathy (ability to feel and experience another person's emotion). According to clinical and imaging research, these mental processes are subtended by prefrontal regions that include the OFC, and the ventromedial and dorsomedial prefrontal cortex (Gallagher et al, 2000, Hornak et al, 2003, Shamay-Tsoory et al, 2004, Shamay-Tsoory et al, 2005a, Shamay-Tsoory et al, 2005b, Vollm et al, 2006, and Heberlein et al, 2008). The fusiform gyri are involved in facial affect processing (Critchley et al, 2000 and Haxby et al, 2000) and the cerebellar neocortex is involved in theory of mind ( Calarge et al., 2003 ). Lower brain volume in these brain regions in PreAction patients compared with Action patients may result in emotional and social disabilities such as misunderstandings and difficulties in interpersonal communication. As a consequence, patients’ inability to interpret feedback from their peers may reinforce their lack of awareness of the negative impact of their excessive alcohol consumption on their social environment and their resistant attitude.

The interpretation and generalization of the present results need to be considered with caution and in light of certain limitations. First, the present study was conducted in a relatively small sample of alcoholic patients, precluding definitive conclu-sions and suggesting considering the present results as preliminary. Indeed, the small sample size, especially for the PreAction subgroup, may impede the detection of GM volume difference due to a limited statistical power. Even though our results survived correction for multiple comparisons, further investigations are required. Second, most of the alcoholics were smokers. Tobacco use is a common comorbidity in alcoholism and must be considered as a potential confounding variable since brain volume differences have been found between smokers and non-smoker alcoholics or non-alcoholic subjects in several studies (Brody et al, 2004, Gazdzinski et al, 2005, and Meyerhoff et al, 2006). Another limitation concerns the use of a cross-sectional design, which cannot lead to causal inferences and does not allow specifying whether GM volume deficits were a premorbid risk factor of addiction, potentially exacerbated by chronic alcohol consumption, or resulted mainly from the harmful consequences of alcoholism. This question can only be settled by conducting a longitudinal study. Finally, several studies highlighted a relationship between motivation to change and drinking outcome (Penberthy et al, 2007, Project MATCH Research Group, 1997, and Project MATCH Research Group, 1998). Concerning our clinical sample, a follow-up assessment was conducted at 6 months and outcome data were available for 16 patients in the Action subgroup for nine patients in the PreAction group. Based on the definition of relapse as a patient's having drunk only one dring during the 6-month period of study, our data revealed 56% of relapse in patients with higher motivation against 78% of relapse in patients with lower motivation. Even if there is no significant difference in the percentage of relapse between the two groups, these are interesting findings with clinical implications which would be worth investigating further with a larger patients group.

To conclude, the low level of motivation to modify inappropriate drinking behavior observed in some alcoholic patients at treatment entry could be partially related to macrostructural brain abnormalities in the regions that subtend cognitive, emotional and social abilities. Especially, our results showed brain volume shrinkage in regions involved in decision making, executive functions and social cognition abilities needed to resolve ambivalence toward alcohol addiction and to apply “processes of change”, which are essential for activating the desire to change problematic behavior. As a consequence of their brain volume deficits and associated low motivation, some alcohol-dependent patients may not be able to attend a regular treatment in an addiction department. Partial recovery of cerebral gray matter volume is possible with sustained abstinence from alcohol ( Mon et al., 2011 ). Therefore, improvement of readiness to change and recovery of GM volume deficits might be a concurrent phenomenon with sustained abstinence. This assumption is supported by the absence of reduced GM volume in the Action subgroup, which shows longer sobriety compared with the PreAction subgroup. Thus, it may be relevant to favour brain recovery of patients with lower motivation by extending the period they spend without alcohol before being admitted to an Addiction department. Further studies are therefore needed to assess the benefits of abstinence in the motivation to change alcoholic behavior in relation to GM volume recovery.

Acknowledgments

This research was supported by Inserm and the Basse-Normandie Regional Council [grant no. R07012EE]. The authors would like to thank Julia Rivier and Nezha Bissara for their help in collecting the data.

References

  • Alexander et al., 1986 G.E. Alexander, M.R. DeLong, P.L. Strick. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience. 1986;9:357-381
  • American Psychiatric Association, 1994 American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders 4th ed. (APA, Washington, DC, 1994)
  • Ashburner, 2007 J. Ashburner. A fast diffeomorphic image registration algorithm. NeuroImage. 2007;38:95-113
  • Assanangkornchai and Srisurapanont, 2007 S. Assanangkornchai, M. Srisurapanont. The treatment of alcohol dependence. Current Opinion in Psychiatry. 2007;20:222-227
  • Bechara and Damasio, 2002 A. Bechara, H. Damasio. Decision-making and addiction (part I): impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia. 2002;40:1675-1689
  • Bechara and Damasio, 2005 A. Bechara, A.R. Damasio. The somatic marker hypothesis: a neural theory of economic decision. Games and Economic Behavior. 2005;52:336-372
  • Bechara et al., 2001 A. Bechara, S. Dolan, N. Denburg, A. Hindes, S.W. Anderson, P.E. Nathan. Decision-making deficits, linked to a dysfunctional ventromedial prefrontal cortex, revealed in alcohol and stimulant abusers. Neuropsychologia. 2001;39:376-389
  • Bechara et al., 2002 A. Bechara, S. Dolan, A. Hindes. Decision-making and addiction (part II): myopia for the future or hypersensitivity to reward?. Neuropsychologia. 2002;40:1690-1705
  • Bechara et al., 2000 A. Bechara, D. Tranel, H. Damasio. Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain. 2000;123:2189-2202
  • Bechara et al., 1996 A. Bechara, D. Tranel, H. Damasio, A.R. Damasio. Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex. 1996;6:215-225
  • Beck et al., 1961 A.T. Beck, C.H. Ward, M. Mendelson, J. Mock, J. Erbaugh. An inventory for measuring depression. Archives of General Psychiatry. 1961;4:561-571
  • Berglund et al., 2003 M. Berglund, S. Thelander, M. Salaspuro, J. Franck, S. Andreasson, A. Ojehagen. Treatment of alcohol abuse: an evidence-based review. Alcoholism: Clinical and Experimental Research. 2003;27:1645-1656
  • Blume et al., 2005 A.W. Blume, K.B. Schmaling, G.A. Marlatt. Memory, executive cognitive function, and readiness to change drinking behavior. Addictive Behaviors. 2005;30:301-314
  • Bonelli and Cummings, 2007 R.M. Bonelli, J.L. Cummings. Frontal-subcortical circuitry and behavior. Dialogues in Clinical Neuroscience. 2007;9:141-151
  • Brody et al., 2004 A.L. Brody, M.A. Mandelkern, M.E. Jarvik, G.S. Lee, E.C. Smith, J.C. Huang, R.G. Bota, G. Bartzokis, E.D. London. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biological Psychiatry. 2004;55:77-84
  • Calarge et al., 2003 C. Calarge, N.C. Andreasen, D.S. O'Leary. Visualizing how one brain understands another: a PET study of theory of mind. The American Journal of Psychiatry. 2003;160:1954-1964
  • Carney and Kivlahan, 1995 M.M. Carney, D.R. Kivlahan. Motivational subtypes among veterans seeking substance abuse treatment: profiles based on stages of change. Psychology of Addictive Behaviors. 1995;9:1135-1142
  • Chanraud et al., 2007 S. Chanraud, C. Martelli, F. Delain, N. Kostogianni, G. Douaud, H.J. Aubin, M. Reynaud, J.L. Martinot. Brain morphometry and cognitive performance in detoxified alcohol-dependents with preserved psychosocial functioning. Neuropsychopharmacology. 2007;32:429-438
  • Clay et al., 2008 S.W. Clay, J. Allen, T. Parran. A review of addiction. Postgraduate Medicine. 2008;120:E01-E07
  • Crews and Boettiger, 2009 F.T. Crews, C.A. Boettiger. Impulsivity, frontal lobes and risk for addiction. Pharmacology, Biochemistry and Behavior. 2009;93:237-247
  • Critchley et al., 2000 H. Critchley, E. Daly, M. Phillips, M. Brammer, E. Bullmore, S. Williams, T. Van Amelsvoort, D. Robertson, A. David, D. Murphy. Explicit and implicit neural mechanisms for processing of social information from facial expressions: a functional magnetic resonance imaging study. Human Brain Mapping. 2000;9:93-105
  • Cushman et al., 1985 P.Jr. Cushman, R. Forbes, W. Lerner, M. Stewart. Alcohol withdrawal syndromes: clinical management with lofexidine. Alcoholism: Clinical and Experimental Research. 1985;9:103-108
  • DiClemente, 2007 C.C. DiClemente. Mechanisms, determinants and processes of change in the modification of drinking behavior. Alcoholism: Clinical and Experimental Research. 2007;31:13s-20s
  • DiClemente et al., 1999 C.C. DiClemente, L.E. Bellino, T.M. Neavins. Motivation for change and alcoholism treatment. Alcohol Research & Health. 1999;23:86-92
  • DiClemente et al., 2009 C.C. DiClemente, S.R. Doyle, D. Donovan. Predicting treatment seekers Readiness to Change their drinking behavior in the COMBINE study. Alcoholism: Clinical and Experimental Research. 2009;33:879-892
  • DiClemente and Hughes, 1990 C.C. DiClemente, S.O. Hughes. Stages of change profiles in outpatient alcoholism treatment. Journal of Substance Abuse. 1990;2:217-235
  • Edens and Willoughby, 2000 J.F. Edens, F.W. Willoughby. Motivational patterns of alcohol dependent patients: a replication. Psychology of Addictive Behaviors. 2000;14:397-400
  • Elliott et al., 2000 R. Elliott, R.J. Dolan, C.D. Frith. Dissociable functions in the medial and lateral orbitofrontal cortex: evidence from human neuroimaging studies. Cerebral Cortex. 2000;10:308-317
  • Eslinger, 1998 P.J. Eslinger. Neurological and neuropsychological bases of empathy. European Neurology. 1998;39:193-199
  • Fitzpatrick et al., 2008 L.E. Fitzpatrick, M. Jackson, S.F. Crowe. The relationship between alcoholic cerebellar degeneration and cognitive and emotional functioning. Neuroscience and Biobehavioral Reviews. 2008;32:466-485
  • Folstein et al., 1975 M.F. Folstein, S.E. Folstein, P.R. McHugh. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research. 1975;12:189-198
  • Gallagher et al., 2000 H.L. Gallagher, F. Happé, N. Brunswick, P.C. Fletcher, U. Frith, C.D. Frith. Reading the mind in cartoons and stories: an fMRI study of ‘theory of mind’ in verbal and nonverbal tasks. Neuropsychologia. 2000;38:11-21
  • Gazdzinski et al., 2005 S. Gazdzinski, T.C. Durazzo, C. Studholme, E. Song, P. Banys, D.J. Meyerhoff. Quantitative brain MRI in alcohol dependence: preliminary evidence for effects of concurrent chronic cigarette smoking on regional brain volumes. Alcoholism: Clinical and Experimental Research. 2005;29:1484-1495
  • Habas et al., 2009 C. Habas, N. Kamdar, D. Nguyen, K. Prater, C.F. Beckmann, V. Menon, M.D. Greicius. Distinct cerebellar contributions to intrinsic connectivity networks. The Journal of Neuroscience. 2009;29:8586-8594
  • Haxby et al., 2000 J.V. Haxby, E.A. Hoffman, M.I. Gobbini. The distributed human neural system for face perception. Trends in Cognitive Sciences. 2000;4:223-233
  • Heather et al., 1993 N. Heather, S. Rollnick, A. Bell. Predictive validity of the Readiness to Change Questionnaire. Addiction. 1993;88:1667-1677
  • Heberlein et al., 2008 A.S. Heberlein, A.A. Padon, S.J. Gillihan, M.J. Farah, L.K. Fellows. Ventromedial frontal lobe plays a critical role in facial emotion recognition. Journal of Cognitive Neuroscience. 2008;20:721-733
  • Hettema et al., 2005 J. Hettema, J. Steele, W.R. Miller. Motivational interviewing. Annual Review of Clinical Psychology. 2005;1:91-111
  • Hornak et al., 2003 J. Hornak, J. Bramham, E.T. Rolls, R.G. Morris, J. O'Doherty, P.R. Bullock, C.E. Polkey. Changes in emotion after circumscribed surgical lesions of the orbitofrontal and cingulate cortices. Brain. 2003;126:1691-1712
  • Janis and Mann, 1977 I.L. Janis, L. Mann. Emergency decision making: a theoretical analysis of responses to disaster warnings. Journal of Human Stress. 1977;3:35-45
  • Jernigan et al., 1991 T.L. Jernigan, N. Butters, G. DiTraglia, K. Schafer, T. Smith, M. Irwin, I. Grant, M. Schuckit, L.S. Cermak. Reduced cerebral grey matter observed in alcoholics using magnetic resonance imaging. Alcoholism: Clinical and Experimental Research. 1991;15:418-427
  • Kalpouzos et al., 2008 G. Kalpouzos, G. Chételat, B. Landeau, P. Clochon, F. Viader, F. Eustache, B. Desgranges. Structural and metabolic correlates of episodic memory in relation to the depth of encoding in normal aging. Journal of Cognitive Neurosciences. 2008;21:372-389
  • Kalpouzos et al., 2009 G. Kalpouzos, G. Chételat, J.C. Baron, B. Landeau, K. Mevel, C. Godeau, L. Barré, J.M. Constans, F. Viader, F. Eustache, B. Desgranges. Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiology of Aging. 2009;30:112-124
  • Kelly and Strick, 2003 R.M. Kelly, P.L. Strick. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. The Journal of Neuroscience. 2003;23:8432-8444
  • Klein et al., 2009 A. Klein, J. Andersson, B.A. Ardekani, J. Ashburner, B. Avants, M.C. Chiang, G.E. Christensen, D.L. Collins, J. Gee, P. Hellier, J.H. Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren, R.P. Woods, J.J. Mann, R.V. Parsey. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage. 2009;46:786-802
  • Kril et al., 1997 J.J. Kril, G.M. Halliday, M.D. Svoboda, H. Cartwright. The cerebral cortex is damaged in chronic alcoholics. Neuroscience. 1997;79:983-998
  • Le Berre et al., 2012 A.P. Le Berre, F. Vabret, C. Cauvin, K. Pinon, P. Allain, A.L. Pitel, F. Eustache, H. Beaunieux. Cognitive barriers to readiness to change in alcohol-dependent patients. Alcoholism: Clinical and Experimental Research. 2012;36:1542-1549
  • Meyerhoff et al., 2006 D.J. Meyerhoff, Y. Tizabi, J.K. Staley, T.C. Durazzo, J.M. Glass, S.J. Nixon. Smoking comorbidity in alcoholism: neurobiological and neurocognitive consequences. Alcoholism: Clinical and Experimental Research. 2006;30:253-264
  • Miller and Rollnick, 1991 W.R. Miller, S. Rollnick. Motivational Interviewing: Preparing People to Change Addictive Behavior. (Guilford Press, New York, 1991)
  • Miller and Rose, 2009 W.R. Miller, G.S. Rose. Toward a theory of motivational interviewing. American Psychologist. 2009;64:527-537
  • Mon et al., 2011 A. Mon, K. Delucchi, T.C. Durazzo, S. Gazdzinski, D.J. Meyerhoff. A mathematical formula for prediction of gray and white matter volume recovery in abstinent alcohol dependent individuals. Psychiatry Research: Neuroimaging. 2011;194:198-204
  • Moselhy et al., 2001 H.F. Moselhy, G. Georgiou, A. Kahn. Frontal lobe changes in alcoholism: a review of the literature. Alcohol and Alcoholism. 2001;36:357-368
  • Penberthy et al., 2007 J.K. Penberthy, N. Ait-Daoud, M. Breton, B. Kovatchev, C.C. DiClemente, B.A. Johnson. Evaluating readiness and treatment seeking effects in a pharmacotherapy trial for alcohol dependence. Alcoholism: Clinical and Experimental Research. 2007;31:1538-1544
  • Pfefferbaum et al., 1995 A. Pfefferbaum, E.V. Sullivan, D.H. Mathalon, P.K. Shear, M.J. Rosenbloom, K.O. Lim. Longitudinal changes in magnetic resonance imaging brain volumes in abstinent and relapsed alcoholics. Alcoholism: Clinical and Experimental Research. 1995;19:1177-1191
  • Premack and Woodruff, 1978 D. Premack, G. Woodruff. Does the chimpanzee have a theory of mind?. Behavioral and Brain Sciences. 1978;1:515-526
  • Prochaska, 2008 J.O. Prochaska. Decision making in the Transtheoretical Model of behavior change. Medical Decision Making. 2008;28:845-849
  • Prochaska and DiClemente, 1983 J.O. Prochaska, C.C. DiClemente. Stages and processes of self-change of smoking: toward an integrative model of change. Journal of Consulting and Clinical Psychology. 1983;51:390-395
  • Project MATCH Research Group, 1997 Project MATCH Research Group. Project MATCH secondary a priori hypotheses. Addiction. 1997;92:1671-1698
  • Project MATCH Research Group, 1998 Project MATCH Research Group. Matching alcoholism treatments to client heterogeneity: project MATCH three-year drinking outcomes. Alcoholism: Clinical and Experimental Research. 1998;22:1300-1311
  • Ridderinkhof et al., 2004 K.R. Ridderinkhof, M. Ullsperger, E.A. Crone, S. Nieuwenhuis. The role of the medial frontal cortex in cognitive control. Science. 2004;306:443-447
  • Rollnick et al., 1992 S. Rollnick, N. Heather, R. Gold, W. Hall. Development of a short ‘Readiness to Change’ questionnaire for use in brief, opportunistic interventions among excessive drinkers. British Journal of Addiction. 1992;87:743-754
  • Rosenbloom et al., 2003 M. Rosenbloom, E.V. Sullivan, A. Pfefferbaum. Using magnetic resonance imaging and diffusion tensor imaging to assess brain damage in alcoholics. Alcohol Research & Health. 2003;27:146-152
  • Schmahmann and Pandya, 2008 J.D. Schmahmann, D.N. Pandya. Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems. Cortex. 2008;44:1037-1066
  • Schmahmann and Sherman, 1998 J.D. Schmahmann, J.C. Sherman. The cerebellar cognitive affective syndrome. Brain. 1998;121:561-579
  • Shamay-Tsoory et al., 2005a S.G. Shamay-Tsoory, H. Lester, R. Chisin, O. Israel, R. Bar-Shalom, A. Peretz, R. Tomer, Z. Tsitrinbaum, J. Aharon-Peretz. The neural correlates of understanding the other's distress: a positron emission tomography investigation of accurate empathy. NeuroImage. 2005;27:468-472
  • Shamay-Tsoory et al., 2005b S.G. Shamay-Tsoory, R. Tomer, B.D. Berger, D. Goldsher, J. Aharon-Peretz. Impaired “affective theory of mind” is associated with right ventromedial prefrontal damage. Cognitive and Behavioural Neurology. 2005;18:55-67
  • Shamay-Tsoory et al., 2004 S.G. Shamay-Tsoory, R. Tomer, D. Goldsher, B.D. Berger, J. Aharon-Peretz. Impairment in cognitive and affective empathy in patients with brain lesions: anatomical and cognitive correlates. Journal of Clinical and Experimental Neuropsychology. 2004;26:1113-1127
  • Spielberger et al., 1983 C.D. Spielberger, R.L. Gorsuch, R. Lushene, P.R. Vagg, G.A. Jacobs. Manual for the State-Trait Anxiety Inventory (form Y). (Consulting Psychologists Press, Palo Alto, 1983)
  • Sullivan and Pfefferbaum, 2005 E.V. Sullivan, A. Pfefferbaum. Neurocircuitry in alcoholism: a substrate of disruption and repair. Psychopharmacology. 2005;180:583-594
  • Tzourio-Mazoyer et al., 2002 N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, M. Joliot. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;15:273-289
  • Verdejo-Garcia and Bechara, 2009 A. Verdejo-Garcia, A. Bechara. A somatic marker theory of addiction. Neuropharmacology. 2009;56:48-62
  • Verdejo-Garcia and Perez-Garcia, 2008 A. Verdejo-Garcia, M. Perez-Garcia. Substance abusers’ self-awareness of the neurobehavioral consequences of addiction. Psychiatry Research. 2008;158:172-180
  • Vollm et al., 2006 B.A. Vollm, A.N. Taylor, P. Richardson, R. Corcoran, J. Stirling, S. MsKie, J.F. Deakin, R. Elliott. Neuronal correlates of theory of mind and empathy: a functional magnetic resonance imaging study in a nonverbal task. NeuroImage. 2006;29:90-98
  • Weschler, 2001 D. Wechsler. Wechsler Adult Intelligence Scale. 3rd ed. (EAP, Paris, 2001)

Footnotes

a INSERM, U1077, Caen, France

b Université de Caen Basse-Normandie, UMR-S1077, Caen, France

c Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France

d Centre Hospitalier Universitaire, U1077, Caen, France

e Centre Hospitalier Universitaire, Service d’addictologie, Caen, France

f Centre Hospitalier Universitaire, Service de neurologie, Caen, France

lowast Corresponding author at: Unité de Recherche U1077, Laboratoire de Neuropsychologie, CHU Côte de Nacre, 14033 Caen Cedex, France. Tel.: +33 2 31 06 51 97; fax: +33 2 31 06 51 98.

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