Abnormal white matter integrity and decision-making deficits in alcohol dependence

Abnormal white matter integrity and decision-making deficits in alcohol dependence

Psychiatry Research: Neuroimaging 214 (2013) 382–388 Contents lists available at ScienceDirect Psychiatry Research: Neuroimaging journal homepage: w...

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Psychiatry Research: Neuroimaging 214 (2013) 382–388

Contents lists available at ScienceDirect

Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns

Abnormal white matter integrity and decision-making deficits in alcohol dependence Nabi Zorlu a,n, Fazil Gelal b, Ali Kuserli c, Ebuzer Cenik d, Ercan Durmaz a, Aybala Saricicek a, Seref Gulseren a a

Izmir Katip Celebi University Ataturk Training and Research Hospital, Department of Psychiatry, İzmir, Turkey Izmir Katip Celebi University Ataturk Training and Research Hospital, Department of Radiodiagnostics, İzmir, Turkey c Yuksekova State Hospital, Department of Psychiatry, Hakkari, Turkey d Torbali State Hospital, Department of Psychiatry, İzmir, Turkey b

art ic l e i nf o

a b s t r a c t

Article history: Received 27 November 2012 Received in revised form 17 May 2013 Accepted 25 June 2013

To date, there is no study that explored the correlation of microstructural changes in the whole brain white matter (WM) and decision-making in alcohol dependent patients (ADP). In the present study, we applied Tract Based Spatial Statistics (TBSS) to study WM changes in ADP compared with healthy controls. We also tested whether there was any relationship between WM integrity and decision-making in ADP. The study included 17 inpatient ADP who had been abstinent for at least 2 weeks before testing and scanning and 16 healthy control subjects. The Iowa Gambling Task (IGT) was used to measure decision-making. Results for the IGT showed a significant group (ADP vs. control) by block interaction. Follow-up univariate analyses of variance showed that the groups were significantly different in the last 20 trails. Four significant clusters were found in which fractional anisotropy was significantly lower in ADP than in control subjects, including the corpus callosum and parietal, occipital and frontal regions. We found significant correlations between impaired IGT performance in the last 20 trials and WM integrity in these regions. Together, these results might help to explain observed decision making deficits in ADP. & 2013 Elsevier Ireland Ltd. All rights reserved.

Keywords: Alcohol Diffusion tensor imaging Tract Based Spatial Statistics (TBSS) Decision making Iowa Gambling Task

1. Introduction Human imaging studies of alcohol dependent patients (ADP) have shown metabolic and neurochemical changes consistent with damage in both gray and white matter (WM) tissue within the brain (Wang et al., 2009; McQueeny et al., 2009). Over the last decade, diffusion tensor imaging (DTI) has increasingly been applied to study WM microstructure characteristics in normal development as well as various neurological conditions and psychiatric disorders (Chanraud et al., 2009). DTI is a magnetic resonance imaging (MRI) technique that is suitable to quantitatively investigate WM axonal integrity in vivo. DTI is based on the measurement of water molecule motion. Water diffusion in the brain is influenced by the local microstructure of the tissue. Water diffuses more readily parallel rather than perpendicular to a tract, a property termed anisotropic diffusion. Water diffusion is restricted by axonal structure such that it is greater in the direction parallel to the main axis of axons. Fractional anisotropy (FA) is a measure of the degree to which

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0925-4927/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pscychresns.2013.06.014

water diffusion is constrained in the brain and is widely used as a general index of axonal integrity (Kubicki et al., 2002). Damage to WM or demyelination along neuronal axons results in more isotropic water movement and is manifested as relatively low FA values. FA has been shown to be reduced in the corpus callosum (CC) (Pfefferbaum and Sullivan, 2005), right frontal lobe (Harris et al., 2008), and global WM (Yeh et al., 2009) in ADP suggesting potential disruption in WM microstructure. A problem with DTI is the interpretation of changes of FA. Reduced FA has been associated with local edema, cerebrospinal fluid (Mukherjee et al., 2002), compromised myelin structure, changes in axonal morphologic structure, and altered interaxonal spacing of fiber bundles (Arfanakis et al., 2002; Concha et al., 2005; Thomalla et al., 2004). Therefore the component measures from which FA is derived, the so-called first (λ1) and second (λ2,λ3) eigenvalues, measuring diffusion parallel (λ∥) and perpendicular ⊥ (λ ) to the primary axis of the axon, can provide additional insights ⊥ regarding the nature of WM deficits. An increase of λ is thought to signify increased space between fibers suggesting demyelination or dysmyelination (Harsan et al., 2006), whereas a decrease in λ∥ suggests axonal injury (Lazar et al., 2003). To date only two studies have examined individual eigenvalues in ADP to index myelin and axonal integrity across the entire brain. Yeh et al. (2009) and Sorg

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et al. (2012) found that demyelination/dysmyelination is the main microstructural change in WM tracts. Recently, voxelwise analysis has been used extensively to study differences in WM integrity between subject groups. Registration algorithms used in voxelwise analysis suffer from the low spatial precision of cross-subject image alignment, the residual misalignment due to group differences in ventricular size and the arbitrariness of the choice of spatial smoothing. In the present study, we applied a different analysis technique, Tract Based Spatial Statistics (TBSS), to study WM changes in ADP compared with healthy controls. The skeleton-based approach of TBSS may overcome potential problems with the registration and alignment of WM between subjects in order to allow cross-subject statistical analysis (Smith et al. 2006). Decision-making involves the outcome of cognitive processes leading to a choice between alternative courses of action. Poor decision making has been described as “deciding against one's best interests and inability to learn from previous mistakes, with repeated decisions leading to negative consequences” (Bechara and Damasio, 2005). A commonly used measure of decisionmaking is the Iowa Gambling Task (IGT) (Bechara et al., 1994). The IGT simulates real-life decision-making with uncertainty concerning premises and outcome as well as reward and punishment. The IGT was specifically developed to measure decisionmaking in patients with lesions of the ventromedial prefrontal cortex. Such patients often take part in risky behaviors that are immediately gratifying while ignoring negative future outcomes. Similarly, drug and alcohol abusers persist in behaviors that have short-term benefits despite long-term major negative consequences. Studies have shown that alcohol (Mazas et al., 2000), cannabis (Grant et al., 2000), MDMA (Hanson et al., 2008), heroin and cocaine (Verdejo-Garcia and Perez-Garcia, 2007) users exhibit impaired decision-making on the IGT. To date, no study explored the correlation of microstructural changes in the whole brain WM and decision-making in ADP. In the present study, we applied TBSS to study WM changes in ADP compared with healthy controls. We also tested whether there was any relationship between WM integrity and decisionmaking in ADP.

2. Methods The study included 17 inpatients ADP who met Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM- IV) (American Psychiatric Association, 1994) criteria for alcohol dependence and who had been abstinent for at least 2 weeks before testing and scanning and 16 healthy control subjects. All subjects were male and right-handed. The groups were matched for age and years of education. Control participants were selected by means of local advertisements and word-of-mouth communication among adult people from the community. Exclusion criteria for the alcohol dependence group were as follows: (1) any lifetime substance use other than alcohol (except nicotine), (2) current or past history of any DSM-IV Axis I psychiatric disorders except for a past (but not current) history of major depressive disorder, (3) current or past history of any significant neurological disorders, (4) history of loss of consciousness for more than 30 min, (5) seropositive test for HIV, (6) any severe hepatic, endocrine, renal disease, and (7) any contraindications for MRI scanning (metal implants, pacemakers, etc.). Control subjects met the same criteria as patients, except for the history of alcohol dependence. All subjects were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders (First et al., 1997) to exclude participants with past or current comorbid Axis I diagnoses and to confirm the diagnosis of alcohol dependence in the clinical group. ADP were interviewed in order to determine the age at which they started drinking, the length of time they had been exposed to alcohol, the length of time they had alcohol dependence and their usual daily alcohol consumption in the last 30 days before treatment. A standard drink was considered to have approximately 12 g of pure ethanol and was equivalent to (1) one 12-ounce can of beer, (2) 4 ounces of wine, or (3) 1.5 ounces of distilled spirits. During the standard course of inpatient treatment, regular monitoring of blood and urine for the presence of alcohol, amphetamines, barbiturates, benzodiazepines, cocaine, cannabis, and opiates was performed to assure sobriety before the MRI.

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All subjects gave written informed consent to participate in the study. The study was approved by local research and ethics committees.

2.1. Decision-making The IGT was used to measure decision-making (Bechara et al., 1994). All subjects completed the IGT within 3 days of the MRI examination. Briefly, subjects sit in front of four decks (A, B, C and D) of cards equal in appearance and size; the goal is to win as much money as possible. The subjects are told that the game requires a long series of card selections, one card at a time, from any of the four decks, until they are told to stop. Each deck consisted of 40 cards but participants did not know that the amount and probability of punishment varied across decks. Two of the four decks give high rewards, but also high losses, and result in a net loss in the long run (disadvantageous decks A and B). The two other decks result in low rewards, but also render lower losses, and result in a net gain in the long run (advantageous decks C and D). The task ends when the participant has selected a total of 100 cards. In scoring for the IGT, 100 choices were divided into five blocks of 20 choices each. A net score is calculated within each block by subtracting the number cards selected from the two disadvantageous decks (A+B) from the number selected from the two advantageous decks (C+D). Higher scores reflect more advantageous decision-making performance on the task. Raw scores for the IGT variables were used in all analyses.

2.2. Imaging protocol MRI was performed using a 1.5 T MR system (GE Signa HDxt, General Electric Medical Systems, Milwaukee, WI, USA). Diffusion imaging data were acquired in 100 diffusion gradient directions plus one b¼ 0 reference image using a sequence optimized to collect diffusion-weighted images (TR¼ 6500 ms, TE ¼ 90 ms, voxel size¼ 1.1  1.1  5.5 mm3).

2.3. DTI data analysis DTI data were analyzed using FMRIB's (Oxford Centre for Functional MRI of the Brain) Diffusion Toolbox, which is part of FSL (FMRIB Software Library) (Smith et al., 2004). Motion and eddy current artifacts were corrected using FSL EDDY_ CORRECT. A brain mask of the non-diffusion-weighted image was created using FSL's Brain Extraction Tool (Smith, 2002). The diffusion tensor was then calculated with FSL DTIFIT for whole brain volumes and the resulting FA maps, together with the λ∥ (λ1) and λ⊥ ((λ2+λ3)/2) maps, were used in subsequent TBSS analysis. Voxel-wise statistical analysis of the data was performed by using TBSS (Smith et al., 2006). FA data were aligned into a common space using a non-linear registration algorithm (FSL FNIRT) to register the images to the standard space Montreal Neurological Institute 152 template and upsampled to 1  1  1 mm3. Thereafter, the registered FA images were averaged to generate a crosssubject mean FA image, and then the mean FA image was applied to create a mean FA skeleton, which represents the main fiber tracks and the center of all fiber tracts common to the group. The skeleton was then thresholded at an FA value of 0.2, which limits the effects of poor alignment across subjects and reduces the likelihood of inclusion of gray matter and cerebrospinal fluid voxels in the skeleton. The skeleton that is now created contains WM tracts that are common to all subjects. A “distance map” is then created which is used to project each FA image onto the mean FA skeleton that is common to all subjects. Skeletons for λ∥ and λ⊥ were generated with the same procedures that were used to create the FA skeleton.

2.4. Statistical analyses To identify FA differences between ADP subjects and controls, the skeletonized FA data were fed into the voxel-wise statistical analysis, which is based on nonparametric approach utilizing permutation test theory (randomize tool in FSL) (Nichols and Holmes, 2002). The Threshold-Free Cluster Enhancement (TFCE) method was used to define the clusters (Smith and Nichols, 2009). Results were considered significant for Po 0.05. The most probable anatomic localization of each significant cluster was determined using publicly accessible white matter atlases (http://www.dtiatlas.org). For clusters where significantly lower FA values in ADP group were observed, the mean λ∥ and λ⊥ values of each cluster mask for each subject were extracted. The function “fslmeants” from FSL was used to extract means of λ∥ and λ⊥ values from these masks. Between-group differences for λ∥ and λ⊥ values in these region of interests Mann–Whitney U-test. Effect sizes were calculated with Cohen's d. Spearman's correlation coefficients and p-values were calculated for FA, λ∥ and λ⊥ values and IGT scores for each cluster. Partial correlation analyses controlling for age were used to explore the associations between FA, λ∥ and λ⊥ values and alcohol use variables. Demographic measures were compared between groups using the Mann–Whitney U-test. We conducted a mixed-design analysis of variance (ANOVA)

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(two-group: ADP versus controls  5-IGT blocks) to examine possible differences between groups on their IGT performance. For all analyses, the P-value was set to 0.05. Statistical analysis was performed using SPSS version 16.

3. Results 3.1. Demographic and alcohol use variables The groups were matched for age and education level. Table 1 shows the demographic and alcohol use variables for ADP and control subjects. All subjects were active smokers. The duration from the last alcohol consumption to the time of MR scan was 17.1 71.8 days for ADP. 3.2. Decision-making There was a significant group (ADP vs. control) by block interaction in IGT performance (F¼3.25; d.f. ¼4.00; P ¼0.014). As shown in Fig. 1, controls shifted their choices from the disadvantageous decks of cards to the more advantageous decks especially over the last two blocks of the task while the ADP did not seem to shift their choices from the disadvantageous decks of cards to the advantageous decks. Follow-up univariate ANOVAs showed that the groups were significantly different in block 5 (F¼4.28; d.f. ¼1; P ¼0.047). There was no significant difference in total IGT net score between groups (U¼134.000; P¼ 0.942).

Within the ADP group, we performed additional correlation analyses of FA, λ⊥ and λ∥ values with alcohol use variables.

3.4.1. IGT net score in block 5 We found a significant positive correlation with FA measured in clusters 1, 2 and 4 for all subjects (Rho ¼0.496, P ¼0.003; Rho¼0.420, P ¼0.015; and Rho¼0.497, P ¼0.003 respectively. See Fig. 3). FA measured in cluster 3 showed a trend for positive correlation (Rho ¼0.337, P ¼0.056). There was a significant positive correlation with FA measured in cluster 4 within the control group (Rho¼ 0.623, P ¼0.01). FA measured in cluster 1 showed a trend for positive correlation (Rho ¼0.474, P ¼0.064) within the control group. None of the correlations reached statistical significance with FA measured in clusters 1, 2, 3 and 4 in the ADP group (Rho¼ 0.259, P ¼0.316; Rho¼0.267, P¼ 0.300; Rho ¼0.148, P ¼ 0.572; and Rho¼ 0.180, P¼ 0.490 respectively). λ∥ values in clusters 1, 2, and 4 were significantly positively correlated with IGT score in block 5 for all subjects (Rho¼ 0.413, P¼ 0.017; Rho ¼0.359, P¼ 0.04; and Rho ¼0.524, P ¼0.002 respectively). λ⊥ values in clusters 1, 2, 3, and 4 were significantly negatively correlated with IGT score in block 5 for all subjects (Rho¼  0.459, P ¼0.007; Rho¼  0.391, P ¼0.024; Rho¼  0.374, P¼ 0.032; and Rho¼  0.349, p¼ 0.047 respectively). There was a significant positive correlation between IGT score in block 5 and λ∥ values in clusters 1, 2, and 4 in the control group (Rho ¼0.648, P¼ 0.007; Rho¼ 0.669, P ¼0.005, and Rho ¼0.553, P ¼0.026 respectively) and λ∥ values in cluster 4 in the ADP group (Rho¼ 0.612, P ¼0.009).

3.3. TBSS analyses FA was significantly lower in the ADP group than in the control group in four clusters, including the corpus callosum (CC), parietal, occipital and frontal tracts (Fig. 2, Table 2). There were no WM regions where control group had significantly lower FA values than the ADP group. To determine whether these FA differences were due to changes in λ⊥ and λ∥ values, we generated separate masks of each significant cluster representing an FA difference between ADP and controls and calculated the mean λ⊥ and λ∥ values for each of them. As shown in Table 2, ADP had greater λ⊥ values than controls in all four clusters, whereas ADP had lower λ∥ values than controls in clusters 2 and 3.

3.4.2. Total IGT net score We found a significant positive correlation with FA values (Rho¼ 0.356, P¼ 0.042) and a negative correlation with λ⊥ values (Rho¼  0.396, P ¼0.022), measured in cluster 4 for all subjects. Total IGT net score was positively correlated with FA values in cluster 1 (Rho ¼0.537, P ¼0.032) and cluster 4 (Rho¼ 0.566, P ¼ 0.022) and with λ∥ values in cluster 1 (Rho ¼0.746, P¼ 0.001) and cluster 2 (Rho ¼0.566, P¼ 0.022) in the control group. There was also a significant negative correlation between total IGT net score and λ⊥ values in cluster 4 (Rho ¼ 0.537, P¼ 0.032) in the control group. None of the correlations was statistically significant in the ADP group.

3.4. Correlation analyses Correlation analyses of significant mean FA values, λ⊥ and λ∥ values extracted from the significant clusters, total IGT net score, and IGT block 5 net score were conducted. We focused on block 5 as this block showed significant between-group differences.

3.4.3. Alcohol use variables. There was no significant correlation of FA, λ⊥, and significant λ∥ values with the years of alcohol dependence, years of alcohol use, and daily alcohol consumption in the last 30 days before treatment. Age started drinking was positively correlated with λ∥ values in cluster 2 (r¼ 0.674; P¼ 0.06).

Table 1 Demographic and alcohol use variables for ADP and control subjects.

6 4

Age 47.0 7 7.0 Education year 7.6 7 2.8 Duration of alcohol use (years) 29.7 7 7.5 Duration of dependence (years) 12.2 7 7.3 Age at first use (years) 17.3 7 3.9 Alcohol consumptiona 18.2 7 3.7

46.7 77.5 8.0 72.2

P

1.320 0.857 1.310 0.854

2

IGT Score

ADP (n¼ 17) Controls (n ¼16) U

0 -2

ADP Controls

-4 -6 -8

ADP ¼Alcohol dependent patients; Data are expressed as mean (standard deviation). a Consumption was defined as drinks per day during 1 month preceding detoxification, where one drink was considered to contain approximately 12 g of ethanol (standardization of beer, wine, and spirits).

-10

Fig. 1. Changes in the number of risky cards chosen by alcohol dependent patients (ADP) and controls over the course of 100 Iowa Gambling Task (IGT) trials. Each block represents 20 trials. Error bars are the standard errors of the mean.

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Fig. 2. Clusters with fractional anisotropy reduction (red) in alcohol dependent patients compared to controls. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 2 Mean ( 7 S.D.) values of FA, λ⊥ and λ∥ in the clusters in which decreased FA was observed in TBSS analysis in ADP compared to controls. (p o 0.05, TFCE corrected). Cluster

White matter tract and location

Voxels

ADP (n¼ 17)

Controls (n¼ 16)

P

Cohen's d

1

Genu and body of CC Bilateral corona radiata anterior (bilateral frontal lobe) Bilateral corona radiata superior (bilateral frontal lobe)

4481

2

Right inferior fronto-occipital fasciculus (right occipital lobe) Right inferior longitidunal fasciculus (right occipital lobe) Forceps major (right parietal lob) Splenium and body of CC Right posterior cingulum

3009

3

Left inferior fronto-occipital fasciculus (left occipital lobe) Left inferior longitidunal fasciculus (left occipital lobe) Forceps major (left parietal lobe)

4

Body of CC Left posterior cingulum

x(mm)

y(mm)

z(mm)

FA λ⊥ λ∥

0.51 70.05 0.59 70.08 1.44 70.10

0.577 0.04 0.52 7 0.04 1.50 7 0.08

0.016 0.004 0.055

1.33 1.11 0.66

8

26

5

FA λ⊥

0.47 70.04 0.59 70.05

0.53 7 0.03 0.54 7 0.03

0.014 0.006

1.69 1.21

33

 63

3

λ∥

1.31 70.09

1.37 70.03

0.006

0.89

1404

FA λ⊥ λ∥

0.50 70.03 0.57 70.05 1.37 70.05

0.56 7 0.02 0.50 7 0.02 1.417 0.03

0.016 0.000 0.024

2.35 1.84 0.97

 25

 80

3

431

FA λ⊥ λ∥

0.42 70.05 0.6770.07 1.31 70.05

0.497 0.04 0.58 7 0.04 1.34 7 0.04

0.018 0.002 0.73

1.55 1.58 0.66

 10

 38

20

Significant P-values are in bold. CC: corpus callosum. x, y, z: the location of its peak value in the cluster (MNI coordinates). λ⊥, λ∥:  10  3 mm2/s.

4. Discussion Here, we examined ADP compared to healthy controls for WM integrity throughout the brain by TBSS. We also examined the relationship between WM integrity and decision-making in ADP.

To our knowledge, this is the first study to explore the relationship between whole brain WM integrity and decision-making in ADP. Four significant clusters were found in which FA was significantly lower in ADP than in control subjects, including CC, parietal, occipital, and frontal tracts. The perpendicular diffusivity findings

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Fig. 3. Graphical presentation of the significant correlations between Iowa Gambling Task (IGT) net score in block 5 and fractional anisotropy (FA) values in cluster 1 (Rho¼0.496, P¼ 0.003), cluster 2 (Rho¼0.420, P¼ 0.015) and cluster 4 (Rho¼ 0.497, P¼ 0.003).

in ADP indicate that demyelination may be a prominent microstructural change in this cohort, but the interpretation of perpendicular diffusivity remains controversial (Wheeler-Kingshott and Cercignani, 2009). Also there were lower λ∥ values in ADP than in controls in parietal and occipital tracts, suggesting axonal injury in these regions. ADP showed worse performance on the IGT, notably in IGT block 5, than healthy controls. FA values in significant clusters did not correlate with IGT net score in block 5 in the ADP group whereas frontal, parietal, occipital, and callosal WM integrity was significantly correlated with impaired decision-making

for all subjects. Statistical significance for the combined group may be driven mainly by the control group as the correlations between the IGT Block 5 score and FA values in cluster 4 reached statistical significance and the correlations between the IGT Block 5 score and FA values in cluster 1 show trend toward statistical significance in the control group whereas none of the correlations were significant in the ADP group. Although not significant, all correlations in the subgroups were in the same direction with the combined group. Correlation analyses for the subgroups may be underpowered due to small sample sizes. Disturbed WM integrity has been reported in a number of previous DTI studies in ADP (Yeh et al., 2009; Konrad et al., 2012; Sorg et al., 2012). We found that ADP had greater λ⊥ values than controls in all significant clusters. Consistent with our findings, studies in alcohol (Yeh et al., 2009; Sorg et al., 2012), heroin (Bora et al., 2012) and cocaine dependent subjects (Moeller et al., 2007a) have found that FA differences were mainly due to changes in λ⊥ values suggesting myelin abnormalities. There are several possible explanations for the observed myelin abnormalities in ADP. Alcohol-induced changes in the expression of myelin-related genes and their products might be one potential reason. It was reported that myelin-related genes, myelin basic protein, myelin proteolipid protein (PLP) and myelin oligodendrocytes glycoprotein were down-regulated in the frontal cortex of human alcoholics (Liu et al., 2004). Similarly, Lee et al. (2010) showed that PLP mRNA and protein were decreased in rat hippocampus with chronic ethanol exposure, suggesting that ethanol may affect the stabilization of the myelin sheath through the modulation of PLP expression and induce the pathophysiology of alcoholic brain. Another possible mechanism might be nutritional deficiency in ADP. Dror et al. (2010) showed FA decreases due to thiamine deficiency in rats. Studies with older patients found relationships between serum folate (Scott et al., 2004), serum vitamin B12 levels (de Lau et al., 2009) and WM integrity. The finding of impaired decision-making on the IGT in the ADP group is consistent with several earlier studies (Mazas et al., 2000; Dom et al., 2006). Our findings indicate that card selections in the ADP group were similar to those of the control group in the initial blocks. However, ADP group shifted their decisions to disadvantageous cards during the last trials, especially in the last block. These patterns of decision-making can be observed in subjects with impulsive traits. Miranda et al. (2009) found that ADP with antisocial personality disorder (ASPD) exhibited initial improvement followed by a significant decrease in advantageous decision-making during the last 20 trials. Another study found that highly impulsive individuals had difficulty sustaining advantageous IGT performance on the final block of trials (Sweitzer et al., 2008). Our study sample was an inpatient group characterized by high problem severity, multiple detoxifications and heavy drinking. Therefore, we may speculate that our ADP sample has high impulsivity traits. But the lack of impulsivity measurement makes it difficult to perform a detailed assessment. To date, there is only little evidence about the correlation of WM integrity and decision-making across the entire brain. Garibotto et al., (2010) showed low FA values in the inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus and CC correlated with the impairment in decision-making in obsessive–compulsive disorder patients. In cocaine dependent patients vs. healthy controls, Lane et al. (2010) found a relationship between impaired decision-making and WM integrity in right corticospinal tract and right superior corona radiata in the frontal and parietal lobes, anterior body of the CC and bilateral anterior corona radiata in frontal lobe for all subjects. A number of previous studies suggest that decision-making is not mediated by the orbitofrontal cortex alone but involves a wider network of brain areas including the anterior and posterior cingulate cortex, limbic pathways, and the parietal and occipital cortex (Ernst

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et al., 2002; Mathews et al., 2004; Bechara and Van der Linden, 2005; Doya, 2008; Lawrence et al., 2009). Our significant tracts may be connected directly or indirectly to the network that suberves decision making. The CC is the major interhemispheric commissure that connects most of the neocortical areas. Consistent with our findings, previous studies have reported a relationship between CC WM integrity and impaired decision-making. Studies in MDMA dependent patients (Moeller et al., 2007b) and in cocaine dependent patients vs. healthy controls (Lane et al., 2010) showed CC WM integrity was correlated with decision-making. Similarly, Moeller et al. (2005) and Liu et al. (2010) found a relationship between increased impulsivity and low FA values in the CC in cocaine dependent patients and in ADP. We found that low FA values in the IFOF and inferior longitudinal fasciculus (ILF) in the right occipital lobe were correlated with the impairment in decision-making. Low FA values in the IFOF and ILF in the left occipital lobe also showed a trend for a positive correlation, but this trend was not significant. The IFOF connects the ventrolateral prefrontal and medial orbitofrontal cortex to posterior parietal and occipital association cortices (Burgel et al., 2006). The IFOF in humans represents the only direct long range association tract connecting the frontal and occipital lobes (Catani, 2006). The ILF connects occipital and temporal lobe structures including the amygdala, hippocampus, and parahippocampus (Catani et al., 2003). Similar to our findings, in obsessive–compulsive disorder patients Garibotto et al. (2010) showed that lower FA values in the IFOF were correlated with the impairment in decision-making. In addition, correlation between these two fasciculus structures and impulsive traits has been reported in previous studies. Low FA values have been found in subjects with antisocial personality disorder compared to healthy controls (Sundram et al., 2012). Xu et al. (2012) found negative correlation between IFOF and ILF WM integrity and seeking fun and novel experiences. Together, these findings may suggest that WM integrity and anatomical connections among frontal, occipital and temporal lobes are associated with an impulsive decision-making. The anterior corona radiata connects the striatum with the anterior cingulate cortex. Similar to our findings, Lane et al. (2010) found a relationship between impaired decision-making and WM integrity in bilateral anterior corona radiata in cocaine dependent patients compared to healthy controls. Low FA values in left anterior corona radiata were also reported to be related to the conflict component of attention, in a broader sense, which can be considered necessary for decision-planning and decision-making (Niogi et al., 2010). Another region in which low FA values were found to be correlated with impaired decision-making was posterior cingulate cortex. A recent fMRI study showed the activation of posterior cingulate cortex during the decision-making process (Sripada et al., 2011). Previous studies of correlations between DTI measures of WM integrity and measures of alcohol consumption reported inconsistent findings. Some researchers found an association between FA and measures of alcohol consumption (Yeh et al., 2009; Pfefferbaum et al., 2009), but others failed to detect a considerable association between FA and measures of alcohol consumption (Konrad et al., 2012; Liu et al., 2010). We did not find any significant correlation of FA, λ⊥ and significant λ∥ values with the years of alcohol dependence, years of alcohol use and daily alcohol consumption in the last 30 days before treatment. In this study alcohol consumption was assessed by a self-reported questionnaire. One possible reason for the lack of a correlation might be the recall bias. Another possible reason is that we included patients with high alcohol consumption, which narrowed the range of values, thus attenuating to detect a statistically significant correlation. Age at which the patients started drinking was correlated with low λ∥ values in right parietal and occipital tracts, suggesting axonal injury in these

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regions. These regions may be more vulnerable to the effects of early-onset drinking, despite dependence occurring later. Thus, our results suggest that early alcohol use may interfere with neuromaturation. An important limitation of this study is its cross-sectional nature. Thus, it is unclear whether our findings resulted from alcohol use or pre-existed in individuals more prone to alcohol use. Longitudinal studies are needed to clarify this issue. Also, while the TBSS approach strives to avoid the problems of voxel based morphology related to partial voluming, some of these issues may still remain. TBSS still has limitations in analyzing small fiber tracts, data with within-scan head motion, and regions of crossing fibers or tract junctions (Smith et al., 2006). In addition, grouping of WM tracts into the clusters may be a potential limitation as the clusters include both the association and commissural fibers. It should also be noted that this study was not specifically designed to examine the individual tracts that make up clusters. Therefore, the relative contribution of the individual WM tracts to the findings is not known. Another limitation of the study is we studied only men, and therefore were unable to examine gender-specific effects on WM deficits. Finally, the sample size was small, although in the range of similar studies. In conclusion, we found a widespread disruption of WM integrity in ADP compared to control subjects. We found significant correlations between impaired IGT performance in the last 20 trials and impaired WM integrity. Together, these results might help to explain the observed decision-making deficits in ADP. References Arfanakis, K., Haughton, V.M., Carew, J.D., Rogers, B.P., Dempsey, R.J., Meyerand, M.E., 2002. Diffusion tensor MR imaging in diffuse axonal injury. American Journal of Neuroradiology 23 (5), 794–802. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. American Psychiatric Association APA, Washington, DC. Bechara, A., Damasio, A.R., Damasio, H., Anderson, S.W., 1994. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–15. Bechara, A., Damasio, A.R., 2005. The somatic marker hypothesis: a neural theory of economic decision. Games and Economic Behavior 52, 336–372. Bechara, A., Van der Linden, M., 2005. Decision-making and impulse control after frontal lobe injuries. Current Opinion in Neurology 18, 734–739. Bora, E., Yücel, M., Fornito, A., Pantelis, C., Harrison, B.J., Cocchi, L., Pell, G., Lubman, D.I., 2012. White matter microstructure in opiate addiction. Addiction Biology 17 (1), 141–148. Burgel, U., Amunts, K., Hoemke, L., Mohlberg, H., Gilsbach, J.M., Zilles, K., 2006. White matter fiber tracts of the human brain: three-dimensional mapping at microscopic resolution, topography and intersubject variability. Neuroimage 29, 1092–1105. Catani, M., Jones, D.K., Donato, R., Ffytche, D.H., 2003. Occipito-temporal connections in the human brain. Brain 126, 2093–2107. Catani, M., 2006. Diffusion tensor magnetic resonance imaging tractography in cognitive disorders. Current Opinion in Neurology 19, 599–606. Chanraud, S., Reynaud, M., Wessa, M., Penttilä, J., Kostogianni, N., Cachia, A., Artiges, E., Delain, F., Perrin, M., Aubin, H.J., Cointepas, Y., Martelli, C., Martinot, J.L., 2009. Diffusion tensor tractography in mesencephalic bundles: relation to mental flexibility in detoxified alcohol-dependent subjects. Neuropsychopharmacology 34 (5), 1223–1232. Concha, L., Beaulieu, C., Gross, DW., 2005. Bilateral limbic diffusion abnormalities in unilateral temporal lobe epilepsy. Annals of Neurology 57 (2), 188–196. de Lau, L.M., Smith, A.D., Refsum, H., Johnston, C., Breteler, MM., 2009. Plasma vitamin B12 status and cerebral white-matter lesions. Journal of Neurology, Neurosurgery and Psychiatry 80 (2), 149–157. Dom, G., De Wilde, B., Hulstijn, W., van den Brink, W., Sabbe, B., 2006. Decision-making deficits in alcohol-dependent patients with and without comorbid personality disorder. Alcoholism: Clinical and Experimental Research 30, 1670–1677. Doya, K., 2008. Modulators of decision making. Nature Neuroscience 11, 410–416. Dror, V., Eliash, S., Rehavi, M., 2010. Neurodegeneration in thiamine deficient rats— a longitudinal MRI study. Brain Research 1308, 176–184. Ernst, M., Bolla, K., Mouratidis, M., Contoreggi, C., Matochik, J.A., Kurian, V., Cadet, J.L., Kimes, A.S., London, E.D., 2002. Decision-making in a risk-taking task: a PET study. Neuropsychopharmacology 26 (5), 682–691. First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B.W., 1997. Structured Clinical Interview for DSM-IV Axis I Disorders. American Psychiatric Press, Washington, DC. Garibotto, V., Scifo, P., Gorini, A., Alonso, C.R., Brambati, S., Bellodi, L., Perani, D., 2010. Disorganization of anatomical connectivity in obsessive compulsive

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