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Effect of the TaqIA polymorphism on ethanol response in the brain

Editor's comment:
Positron emission tomography with [F-18] fluorodeoxyglucose (FDG) was used in this study to assess regional cerebral glucose metabolism as a measure of relative brain activity while the study participants performed a vigilance task. The study participants, six A1+ and six A1− men, drank ethanol (0.75 ml/kg) or placebo beverages on each of two days. The study results showed lower anxiety and fatigue after ethanol in A1+ men, compared with higher anxiety and fatigue in A1− men, which strongly supports the hypothesis that ethanol is more reinforcing in A1 carriers. Alcohol-induced negative reinforcement may explain the greater risk for alcoholism associated with the A1 allele.

Psychiatry Research: Neuroimaging 2009, Volume174, pages 163–170

Abstract

Acute ethanol administration increases striatal dopamine release and decreases cerebral glucose metabolism. The A1 allele of the ANKK1 TaqIa polymorphism is associated with lower dopaminergic tone and greater risk for alcoholism, but the mechanisms are unclear. We hypothesized that ethanol would be more reinforcing in men with the A1 allele (A1+) than in men without it (A1−), as indicated by decreased anxiety and fatigue and altered activity in associated brain regions. In a pilot study, A1+ and A1− men (6/group) drank ethanol (0.75 ml/kg) or placebo beverages on each of 2 days. Positron emission tomography with [F-18]fluorodeoxyglucose (FDG) was used to assess regional cerebral glucose metabolism as a measure of relative brain activity while participants performed a vigilance task. Significant findings were as follows: Ethanol decreased anxiety and fatigue in A1+ men but increased them in A1– men. Ethanol increased activity in the striatum and insula of A1+ men, but reduced activity in the anterior cingulate of A1– men. Reduced anxiety and fatigue in A1+ men were significantly associated with greater activity within a right orbitofrontal region previously implicated in cognitive control, and less activity in structures associated with anxiety (amygdala), fatigue (thalamus), and craving/reinforcement (striatum). In contrast, anxiety and fatigue changes were unrelated to brain activity in A1− men. Although these results require replication in a larger sample, alcohol-induced negative reinforcement may explain the greater risk for alcoholism associated with the A1 allele.

Keywords: Alcohol genetics, PET, Addiction vulnerability, Cerebral glucose metabolism, Dopamine, Negative reinforcement.

1. Introduction

A growing literature points to an important role of genotype in vulnerability to substance abuse, with much of this work centering on the role of central dopamine systems in motivation and reinforcement ( Volkow et al., 2007 ). Reduced density of striatal D2 dopamine receptors has been associated with reinforcer-induced impulsivity in rats ( Dalley et al., 2007 ), and with presence of the TaqIA A1 allele of theANKK1gene (previously reported as located in the D2 dopamine receptor gene) ( Neville et al., 2004 ) in humans, both throughin vitro(Noble et al, 1991 and Thompson et al, 1997) andin vivo(Jonsson et al, 1999 and Pohjalainen et al, 1998) studies. Although A1 prevalence differs between ethnic groups ( Barr and Kidd, 1993 ), raising the possibility of stratification bias, studies both across and within diverse ethnic groups have reported robust associations of the polymorphism first with misuse of alcohol ( Blum et al., 1990 ) and subsequently with other forms of substance abuse (Li et al, 2004a, Noble, 2003, and Young et al, 2004). In a meta-analysis of 3329 Caucasian adults, the prevalence of the Al allele (AlAl and A1A2 genotypes) was significantly higher (P = 1.54 × 10− 8) in alcoholics (38.9%) than nonalcoholics (29.4%) ( Noble, 2003 ). Effect sizes are often small, however ( Berggren et al., 2006 ), requiring large samples or meta-analyses to show associations in the absence of more strongly linked endophenotypes ( White et al., 2008 ).

These associations have led to the hypothesis that the lower dopaminergic tone of the mesocorticolimbic reinforcement circuits in individuals with the A1 allele produces reinforcement deficiencies or anhedonia (Blum et al, 2000 and Noble, 2000). Low D2 function is thought to 1) increase the reward value of direct D2 agonists, including all drugs of abuse, 2) decrease the reward value of less potent natural reinforcers, and 3) decrease the capacity for frontocortical inhibition, all of which contribute to compulsive drug taking ( Volkow et al., 2004 ).

A report consistent with this view indicated that individuals with the A1 allele are characterized by less activation of the mesocorticolimbic reinforcement system than those without the allele ( Cohen et al., 2005 ). In contrast, treatment with the D2 agonist bromocriptine enhanced activation of the reward system in individuals with, but not without, the A1 allele ( Kirsch et al., 2006 ).

We have previously shown that presence of the A1 allele in adolescents is associated with abnormal levels of, or abnormal relationships between, constructs related to hedonic tone, including family stress ( Berman and Noble, 1997 ), the personality characteristics of novelty seeking and harm avoidance ( Berman et al., 2002 ), and negative affect ( Berman et al., 2003 ). In PTSD-afflicted veterans, we found greater anxiety/insomnia, social dysfunction and depression in those with than without the A1 allele ( Lawford et al., 2006 ). In combination with a gene coding for reduced dopamine transporter density, presence of the A1 allele has recently been associated both with higher levels of anxiety ( Kulikova et al., 2008b ) and mental fatigue ( Kulikova et al., 2008a ), as compared with individuals who did not combine these genotypes. In sum, the studies to date suggest that individuals with the A1 allele may be more susceptible to stress-induced anxiety and other negative affective states, and that these individuals are more likely to self-medicate negative affective states with dopamine-releasing drugs, such as alcohol.

The role of the A1 allele in mediating acute effects of alcohol is unknown. Alcohol consumption increases arousal and reduces anxiety in some individuals, while others primarily report increased fatigue. It increases striatal dopamine release, which has been associated with drug-mediated reward (Koob and Le Moal, 2008 and Yoder et al, 2009), and decreases cerebral glucose metabolism (de Wit et al, 1990 and Volkow et al, 2006b). To test the hypothesis that alcohol is more reinforcing in men with the A1 allele, we assessed self-reported anxiety and fatigue and cerebral metabolic responses to acute ethanol administration. We hypothesized that in men with the A1 allele ethanol would show reduced anxiety, fatigue, and activity in brain structures associated with these unpleasant states compared with men without it.

2. Methods

2.1. Study design

Effects of ethanol were compared between social drinkers with no copies of the A1 allele (A1− group) and those with one or two copies (A1+ group). Cerebral glucose metabolism was assessed using positron emission tomography (PET) while participants were engaged in an auditory continuous performance test (CPT) in two sessions, counterbalanced for order, 1–21 days apart (mean ± SD = 10.4 ± 7.8 days). In one session, participants drank a 250-ml mixture of caffeine-free diet soda plus a moderate dose of ethanol (0.75 g/kg body weight) equivalent to 1.4 ml of ethanol/ml of body water ( Savoie et al., 1988 ). In the other session, a non-pharmacologically relevant amount of ethanol was floated on top of the soda to simulate the taste and smell of a mixed beverage. In each session, anxiety and fatigue were self-reported. [F-18]Fluorodeoxyglucose (FDG) was administered to assess cerebral glucose metabolism, a measure of regional brain function (Phelps et al, 1979 and Reivich et al, 1979), and raw counts from FDG, scaled to the global mean, were used as a surrogate index of regional cerebral glucose metabolism (rCMRglc). Relative activity, as used in this report, refers to this measure. Blood-alcohol content was assessed via a breathalyzer at least once per hour until recording a reading of 0.02% or lower. If all other study procedures were completed, the subject was then released.

2.2. Research participants

Participants were recruited through advertisements in local newspapers. Volunteers who met initial criteria in a telephone screening came to UCLA. After giving written informed consent, as approved by the UCLA Office for Protection of Research Subjects, they underwent a comprehensive evaluation, including medical history, physical examination, psychiatric interview (Structured Clinical Inventory for DSM-IV (SCID-I) ( First et al., 1996 ), and completion of the Beck Depression Inventory ( Beck et al., 1961 ), the Wender Utah Rating Scale for attention-deficit hyperactivity disorder (ADHD) ( Ward et al., 1993 ), and a drug use survey.

Symptoms of depression (scores > 18 on the Beck Depression Inventory) or ADHD (scores ≥ 46 on the Wender Utah Rating Scale) were exclusionary, as were history of neurological disease, head trauma with loss of consciousness > 5 min, claustrophobia, systemic disease, HIV-seropositive status, lifetime history of any DSM-IV Axis I or II psychiatric diagnosis, and history of dependence on alcohol or an illicit drug of abuse. Habitual use of caffeine or nicotine and light use of marijuana (≤ 1 cigarette per week) or alcohol (< 90 g absolute alcohol per week) were allowed. Twelve right-handed (score > 20 on a modified version of the Edinburgh Handedness Test [ Oldfield, 1971 ]) Caucasian men aged 21–39 (mean [SD] = 29.0 [5.2]) completed the study and were paid for their participation.

2.3. Genotype analysis

A 10-ml blood sample was drawn from each subject. Genomic DNA was extracted and used as a template for determination of TaqI A alleles by the polymerase chain reaction ( Grandy et al., 1993 ). The amplification of DNA was carried out using a Perkin-Elmer GeneAmp 9600 Thermocycler. Approximately 500 ng of amplified DNA was digested with 5 U of TaqI restriction enzyme (New England Biolabs) at 65 °C overnight. The resulting products were separated by electrophoresis in a 2.5% agarose gel containing ethidium bromide and visualized under ultraviolet light. Three genotypes are revealed: A1A2 (three fragments: 310, 180 and 130 bp), A2A2 (two fragments: 180 and 130 bp), and A1A1 (one uncleaved 310 bp fragment). A1+ allele subjects had either the A1A1 or A1A2 genotype; A1− allele subjects had the A2A2 genotype only.

2.4. Experimental sessions

Participants abstained from alcohol and marijuana for 48 h before each test session but used coffee and/or tobacco as usual. At each session, recent drug and alcohol use were evaluated by a urine drug screen and breathalyzer test. All participants received a standard non-ketogenic breakfast (egg, juice, toast) upon arriving at UCLA. After breakfast, a venous catheter was inserted for infusing FDG. PET images were acquired with a Siemens ECAT EXACT HR+ tomograph (CTI, Knoxville, TN) in 3D mode. A plastic facemask (Scrypton Systems, Annapolis, MD) was fitted to each subject to minimize head motion. A 3-min68Ge transmission scan verified the position of the brain, and a 20-min68Ge transmission scan was performed for attenuation correction.

Participants were removed from the gantry, and trained on the CPT, which requires a button press prompted by a tone of designated pitch within a sequence of nontarget tones (inter-stimulus interval = 2 s). This task was used to standardize the cognitive set during assessment of rCMRglc. After training, participants drank the experimental beverage over 5 min and then provided self-ratings of the tension–anxiety and fatigue-inertia subscales from the Profile of Mood States ( McNair et al., 1971 ). The 30-min CPT was then initiated. Five minutes later, FDG (≤ 5 mCi, ≤ 185 MBq) was injected. When the CPT ended, the subject was repositioned in the scanner gantry, and brain images were acquired for 30 min. Since the FDG was injected within the first half hour after alcohol consumption, and peak alcohol concentration is attained in 40–50 min ( Sammi et al., 2000 ), uptake of the radiotracer presumably occurred during the ascending limb of the blood-alcohol curve.

We reconstructed 128 × 128 pixel images using a Hann filter (cut-off frequency = 0.5 cycles/pixel). The average transverse resolutions at 1 and 10 cm from the center of the field-of-view (FOV), measured in 3D mode and determined using a18F line source, were 6.52 and 7.16 mm (full-width at half-maximum, FWHM), and the average axial resolutions (FWHM) were 3.72 mm and 5.64 mm at 0 cm and 10 cm from the center of the FOV, respectively.

2.5. Data analysis

Response accuracy (% Hits, % False Alarms) and reaction time (RT) quantified vigilance performance at 15 and 30 min. Two-tailedt-tests compared the groups on vigilance and demographic variables. Chi-square analyses were used for frequency data. The statistical threshold was set atP < 0.05.

PET images were converted into ANALYZE©format for analysis using statistical parametric mapping software (SPM5, http://www.fil.ion.ucl.ac.uk/spm ). Each image was normalized into the standard coordinate system developed at the Montreal Neurological Institute (MNI space) by linear and non-linear transformation to a template image and smoothed with an 8-mm isotropic Gaussian kernel (voxel size 2 × 2 × 2 mm3). Effects of global activity were removed by proportional scaling.

The images were analyzed on a voxel-by-voxel basis according to the general linear model by calculating statistical parametric maps ( Friston et al., 1995 ). A parametric statistical model was assumed at each voxel to describe the variability in terms of experimental effects, and residual variability. Directional differences in group means were assessed at each voxel with Student'st-tests, producing images with voxel values equalling thetstatistics. The multiple comparisons problem was addressed by modeling the image as a sample of a continuous Gaussian random field. For each voxel, the corrected voxelPvalue is the probability under the null hypothesis of finding at least 1 voxel with an equal or largertwithin the search volume. For each cluster of contiguous voxels with suprathresholdt-values, the cluster (spatial extent)Pvalue is the probability of finding at least one cluster that large or larger within the search volume.

The effect of ethanol on relative activity was assessed by comparing the PET scans during the ethanol session with the corresponding scans during the placebo session, using a 2-group (A1+, A1−), 2-session (ethanol, placebo) design. The interaction of session and group quantified differences between the allelic groups in the effects of ethanol. To interpret interactions, planned contrasts assessed ethanol effects separately within each group. Covariate analyses quantified between-session correlations between change in anxiety and fatigue and change in relative brain activity, and group differences in the slope of this relationship.

We selected six regions of interest (ROIs) in each hemisphere on the basis of previous association with alcohol-mediated change in affect or reinforcement. The thalamus has been linked with alcohol-mediated fatigue and sedation (Jia et al, 2007 and Volkow et al, 2006b), the amygdala with anxiety (Koob and Le Moal, 2008 and Silberman et al, 2008), the insula (BA 13) with interoceptive mood or “gut-feelings” (Craig, 2002 and Ingvar et al, 1998), the dorsal and ventral striatum with craving, reward and intoxication (Koob and Le Moal, 2008 and Yoder et al, 2009), and the rostral anterior cingulate cortex (BA 24, 32, 33) with executive-modulation of alcohol feelings (Ingvar et al, 1998 and Sinha and Li, 2007).

ROIs were drawn within each cerebral hemisphere on a structural MR template, using MEDx (Sensor Systems, Sterling, VA, USA), and aided by the use of a standardized stereotaxic atlas of the human brain ( Talairach and Tournoux, 1988 ). Whole-brain analyses provided exploratory data for other brain structures. The voxel height threshold for inclusion in clusters wast = 3.22, (P = 0.005 uncorrected). We considered as evidential, however, only clusters that also retained a spatial extentPvalue < 0.05 after correction for multiple comparisons within the whole-brain (> 400 voxels) or ROI search volume (required cluster size varied with the size of the ROI). Effects that attained aPvalue < 0.1, using family-wise error correction, were considered as suggestive trends. ROI results which retained statistical significance after Bonferroni-correction for the total number of one-tailed comparisons (i.e., 2 comparisons [A1+ > A1−, A1− > A1+] × 12 ROIs = 24; 05 / 24 = 0.0021), were also identified.

3. Results

3.1. Blood-alcohol content (BAC)

All placebo sessions generated breathalyzer values of 0.00% before and after beverage consumption. All alcohol sessions generated values of 0.00% before, and peak values that did not significantly differ between allelic groups after beverage consumption (0.03–0.07 in A1−; 0.03–0.11 in A1+). The precise peak values and timecourse of the BAC could not be calculated due to sparse temporal sampling, but it has been recently reported that consumption of two to three drinks produces BACs of 0.05–0.06% in 5–6 min (Biller et al, 2009 and Heidelberg, 2009).

3.2. Vigilance task performance

The participants performed the vigilance task well after drinking both the placebo beverage (% Hits [mean ± SD] 98.5 ± 3.1; % False Alarms [FA] 1.1 ± 3.5; Reaction time [RT] 899 ± 298 ms) and ethanol (% Hits 98.8 ± 2.4; % FA 0.8 ± 2.4; RT 938 ± 344 ms). RT increased from 624 ms at 15 min to 919 ms at the final 30-min assessment (F1, 10 = 87.46,P < 0.001), suggesting that vigilance decreased with time. There were no significant effects of beverage, allelic group, or their interaction on task performance.

3.3. Ethanol effects on rCMRglc

The effects of ethanol as compared to placebo on brain activity in the ROIs are given in Table 1 . There was evidence that ethanol increased relative rCMRglc in the dorsal striatum and amygdala across groups, and that presence of the A1 allele altered the effect of ethanol on rCMRglc (corrected Group × Condition interaction) in the insula and anterior cingulate ( Fig. 1 ). There also was a trend for an interaction in the right ventral striatum (spatial extentP = 0.08; 8, 12, − 14 voxelP = 0.08). Planned comparisons within each group indicated that ethanol produced a relative increase in activity within the insula and striatum only in the A1+ group, and a relative decrease in anterior cingulate activity only in the A1− group. The statistical significance of the A1+ effect in the right insula was maintained after Bonferroni correction for the number of ROIs.

Table 1 Effects of ethanol on relative regional glucose metabolism (rCMRglc).

  Cluster-level Voxel-level
Volume corrected P value Cluster size ROI size %ROI Volume corrected P value T
P < .005   Coordinates
(voxels)     x y z
Main effects: ethanol increased relative rCMRglc
Amygdala Left 0.034 15 144 10% 0.029 3.92 − 22 0 − 22
Right
Dorsal striatum Left 0.079 50 993 6% 0.026 5.80 − 10 16 4
Right 0.023 121 1015 12% 0.033 5.66 12 12 12
 
Interactions: ethanol increased relative rCMRglc in A1+ or decreased relative rCMRglc in A1−
Insula Left
Right 0.048 77 1446 5% 0.017 6.43 36 6 2
ACC Left 0.046 66 1067 6% 0.040 5.20 −10 46 16
Right
 
A1+ Group: ethanol increased relative rCMRglc
Insula Left
Right 0.017 136 1446 9% 0.001lowastlowast 9.93 36 6 2
Dorsal striatum Left
Right 0.007 195 1015 21% 0.005 7.70 12 10 10
Ventral striatum Left
Right 0.038 18 184 10% 0.011 4.99 8 14 −14
 
A1− Group: ethanol decreased relative rCMRglc
ACC Left 0.039 75 1067 7% 0.030 5.51 −10 46 16
Right

Results represent voxels within six regions of interest (ROIs: insula, amygdala, rostral anterior cingulate cortex [ACC], dorsal striatum, ventral striatum, thalamus) where effects of ethanol, as compared to placebo, passed a height threshold ofP < 0.005, and where cluster size attainedP < 0.05 for spatial extent after volume correction. Suggestive results are tabled for contralateral ROIs. All main effects represent higher relative rCMRglc after ethanol (i.e.−no clusters had significantly lower rCMRglc after ethanol). All interactions represent higher relative rCMRglc after ethanol in the A1+ group, and lower rCMRglc after ethanol in the A1− group (i.e.− there were no clusters with lower relative rCMRglc in the A1+ group and higher relative rCMRglc in the A1− group). Within group analyses were conducted to aid interpretation of the interactions. Bold data representsP < 0.05.

lowastlowastStatistically significant after applying a Bonferroni correction for 24 comparisons (0.05/24 = 0.002).

gr1

Fig. 1 Effects of ethanol on relative regional glucose metabolism (rCMRglc). Data are from 12 men who were either A1+ or A1− (n = 6 for each group). Figures are depicted in neurological orientation. The gray-scale image is a T1 structural MRI that is representative of MNI space, where positive values of thex,y, andzcoordinates approximately represent mm to the right, anterior and superior, relative to the midpoint of the anterior commissure. In pseudocolor brain images, values oft > 3.22 (P < 0.005) are indicated in the color bar. A. Colored voxels within the striatum (coronal slice) and amygdala (axial slice) indicate ethanol was associated with higher relative rCMRglc than placebo (n = 12). B. Effects of ethanol ingestion on rCMRglc varied with presence of the A1 allele in the insula (left panel) and anterior cingulate cortex (right panel). Colored voxels on sagittal slices depict greater relative rCMRglc after alcohol in the insula of A1+ men and less relative rCMRglc after alcohol in the anterior cingulate of A1− men. Mean cluster values for individual participants, plotted below as proportional change scores (Alc-Pla/Pla), indicate the interactions in Table 1 result from little or no overlap between the allelic groups. Bar height = mean ± S.D.

3.4. Anxiety and fatigue

Self-ratings of anxiety and fatigue during the placebo session were subtracted from those obtained during the alcohol session. The two measures were summed to yield a composite Anxiety/Fatigue score (see Fig. 2 A). When ethanol ingestion was associated with less anxiety and fatigue than placebo ingestion, the Alcohol minus Placebo subtraction score had a negative value. One-tailedt-tests of group differences in anxiety (A1−: [mean ± SD] 0.67 ± 1.75; A1+: − 1.50 ± 2.43;P = 0.05), fatigue (A1−: 1.83 ± 2.23; A1+: − 0.67 ± 2.50;P = 0.05) and the composite of the two measures (A1−: 2.50 ± 2.07; A1+: − 2.17 ± 3.37;P = 0.008) suggested that ethanol increases anxiety/fatigue in the A1− allelic group, but decreases anxiety/fatigue in the A1+ allelic group.

gr2

Fig. 2 Effects of ethanol on anxiety and fatigue, and their relationship to rCMRglc, differed between groups. A. Self-ratings of anxiety and fatigue after placebo were subtracted from corresponding ratings after ethanol. A composite of both ratings differed between allelic groups (P < 0.008). Individual values are plotted to the right. Bar height = mean + SD;n = 6 per group. B. Colored voxels indicate that effects of ethanol on rCMRglc covaried with effects on composite anxiety + fatigue (all voxelt > 3.22,P < 0.005, volume-corrected spatial extentP < 0.05). In A1+ men, there was a positive correlation between ethanol's effects on anxiety/fatigue and metabolism in the amygdala, striatum & thalamus (red/yellow voxels), with negative correlations in the left insula and right ventrolateral prefrontal cortex (blue voxels). A1− men showed only a borderline negative correlation in the left insula.

3.5. Covariation between ethanol effects on anxiety/fatigue and rCMRglc

The relationship of ethanol-related change in anxiety/fatigue and rCMRglc were assessed through covariate analyses of the composite Alcohol-Placebo Anxiety/Fatigue score (see Table 2 and Fig. 2 B). Across all subjects, there was a positive correlation between Anxiety/Fatigue and activity in left anterior cingulate cortex and right thalamus, but a negative correlation with activity in left insula. Slope tests indicated that presence, as compared with absence, of the A1 allele, was associated with more positive slope within the left insula and ventral striatum.

Table 2 Ethanol effect on rCMRglc covaried with composite Anxiety/Fatigue score.

  Cluster-level Voxel-level
Volume corrected P value Cluster size ROI size %ROI Volume corrected P value T
P < 0.005   Coordinates
(voxels)     x y z
Main effects: positive covariation
ACC Left 0.05 62 1067 6% 0.058 5.00 − 10 44 − 16
Right
Thalamus Left
Right 0.013 138 1073 13% 0.057 5.09 8 −28 8
 
Main effects: negative covariation
Insula Left 0.008 193 1519 15% 0.022 6.49 − 34 14 2
Right
 
Interaction: A1+ > A1−
Insula Left 0.038 93 1519 6% 0.034 5.98 −36 14 14
Right
Ventral striatum Left 0.044 16 208 9% 0.018 4.75 − 22 12 − 12
Right
 
A1+ Group: positive covariation
Amygdala Left 0.021 31 144 22% 0.001lowastlowast 8.20 − 18 0 − 14
Right 0.048 7 159 17% 0.006 5.62 26 − 6 − 14
Dorsal striatum Left 0.003 254 993 31% 0.012 6.96 − 12 − 4 18
Right 0.020 130 1015 17% 0.040 5.67 12 4 18
Ventral striatum Left 0.008 89 208 43% 0.003 6.55 −24 14 −10
Right
Thalamus Left 0.004 221 1260 27% 0.032 5.90 − 10 − 4 14
Right 0.001lowastlowast 309 1073 29% 0.010 7.20 12 − 28 0
 
A1+ Group: negative covariation
Insula Left 0.005 230 1519 15% 0.003 9.49 − 42 22 2
Right
 
A1− Group: negative covariation
Insula Left 0.027 111 1519 7% 0.032 6.05 − 36 14 14
Right

Clusters of voxels within the regions of interest (see Table 1 ) where effects of ethanol on rCMRglc covaried with effects on a composite score of self-rated Anxiety and Fatigue (all voxelP < 0.005, volume-corrected spatial extentP < 0.05). All ROI interactions represent more positive covariation in the A1+ group, or more negative covariation in the A1− group. Below the interactions, planned comparisons assessed all ROIs for each allelic group. Bold data representsP < 0.05.

lowastlowastStatistically significant after applying a Bonferroni correction for 24 comparisons (0.05/24 = 0.002).

In planned comparisons within each group, the A1− group produced no evidence for positive correlations between activity and Anxiety/Fatigue (see lower portion of Table 2 and Fig. 2 B), and one negative correlation in the left insula. The interaction between allelic group and slope in the left dorsal insula reflected the negative correlation exhibited only in the A1− group.

In the A1+ group, an anteroventral insula cluster (15% of left insula) was the only negative correlation in ana prioriROI (230 voxels, spatial-extentP = 0.005; − 42, 22, 2 voxelt = 9.49,P = 0.003). A significant positive correlation with activity in the A1+, but not A1− group produced the interaction in the left ventral striatum ( Table 2 ). There were additional positive correlations in bilateral thalamus, amygdala, and striatum ( Table 2 ). Effects retained significance after Bonferroni correction in the left amygdala (voxel criterion) and the right thalamus (spatial extent).

Although noa prioriROI exhibited significant findings for the A1− > A1+ interaction, a cluster of 405 voxels in right ventrolateral orbitofrontal cortex retained significance after correction for whole-brain volume (spatial extentP < 0.05, peak voxelt = 8.79, 46, 42, − 8). Whole-brain corrected analyses indicated that this interaction represented negative correlation in the A1+ allelic group (798 voxels, spatial extentP = 0.001; 44, 38, − 8 voxelt = 15.52,P = 0.006) with no positive correlation in the A1− group (bothP's > 0.9).

4. Discussion

Mood disorders constitute the greatest disease burden in the developed world. Alcohol use disorders are almost as costly, implicated in 20–40% of hospital admissions and frequently comorbid with mood disorders but having a later age of onset ( Li et al., 2004b ). The dominant psychosocial models developed to explain the relationship between anxiety disorders and alcohol use disorders posit that alcohol reduces anxiety and other uncomfortable responses to stress in predisposed individuals. This effect is thought to encourage chronic self-medication because of the temporary negative reinforcement alcohol can provide ( Morris et al., 2005 ). Inconsistent empirical support for these models has underlined the importance of individual differences in risk for alcoholism ( Sher and Levenson, 1982 ), including gender, family history of alcoholism, and personality characteristics such as high novelty seeking and low harm avoidance (Croissant et al, 2008 and Croissant et al, 2006).

Presence of the TaqIA A1 allele interacts with stress to predict cognitive functions in adolescents ( Berman and Noble, 1997 ) and alcoholism in adults ( Madrid et al., 2001 ). It interacts with negative affect and gender to predict alcoholic personality characteristics ( Berman et al., 2003 ) and severity of substance abuse in adolescents ( Conner et al., 2005 ). Furthermore, among hospitalized substance abusers or alcoholics ( Nixon and Parsons, 1990 ), and drug-naive adolescent boys carrying the A1 allele, but not among boys without it ( Berman et al., 2002 ), the negative correlation between novelty seeking and harm avoidance that generally characterizes personality assessment is reversed. This finding suggests that rather than providing positive reinforcement, novelty seeking may serve a self-medicating negative reinforcement function in both severe substance abusers and in boys with the A1 allele. We predicted that acute ethanol administration would be more reinforcing in A1+ than A1− social drinkers, as indicated by ameliorated anxiety and fatigue, and altered brain activity in structures associated with negative affect.

Previous studies that used blood-sampling during PET to determine absolute metabolic rate for glucose have demonstrated that acute doses of ethanol equivalent to 2–3 mixed drinks produce robust decreases in cerebral glucose metabolism (> 20% in the posterior cerebral cortex) (Volkow et al, 2008 and Volkow et al, 2006b). Fig. 2 of the 2008 report ( Volkow et al., 2008 ) also presents relative (whole-brain normalized) metabolic images like those employed in the current study. These images depict a more modest relative decrease in posterior cortical metabolism and a relative increase in subcortical and temporal cortices, particularly in the left hemisphere. Although the current study formally assessed alcohol effects only within regions previously associated with affect or reinforcement, visual examination of results in posterior cortex confirmed the decreased relative metabolism previously reported ( Volkow et al., 2008 ), just as the relative increases in striatal and amygdala metabolism in Table 1 are consistent with the prior results in these structures. These observations suggest that the relative increases in Table 1 and Fig. 1 represent relative preservation of glucose metabolism (i.e., less decrease than the global average). Individual values in the areas of interest are consistent with A1+ men but not A1− men having relatively preserved glucose metabolism in the insula and striatum, and with A1− men having a greater than whole brain decrease in glucose metabolism in the anterior cingulate cortex.

Although dopamine release in the nucleus accumbens has been associated with alcohol-related reinforcement ( Volkow et al., 2007 ), dopamine release in the dorsal striatum, where the current effects are largest, has also been associated with craving for alcohol ( Heinz et al., 2005 ), and for other drugs (Volkow et al, 2006c and Wong et al, 2006). We interpret relative preservation of activity in the striatum and amygdala as indicating affective responses to ethanol, and the greater preservation of activity in the striatum and insula in A1+ men as suggesting larger affective responses. In A1− men, reduction of activity by ethanol in the rostral anterior cingulate, an area associated with cognitive modulation of alcohol feelings (Ingvar et al, 1998 and Sinha and Li, 2007), may indicate fewer alcohol-related feelings, or less ability to modify them.

The lower anxiety and fatigue after ethanol in A1+ men, compared with the higher anxiety and fatigue in A1− men, strongly supports the hypothesis that ethanol is more reinforcing in A1 carriers. The increased negative affect in A1− men is consistent with a recent fMRI study where the dopamine agonist bromocriptine improved speeded motor performance and activation of the nucleus accumbens in A1 carriers, who performed worse than noncarriers under placebo and improved less in a high-incentive condition, but whose deficits were abolished by bromocriptine ( Kirsch et al., 2006 ). In contrast, bromocriptine lowered performance in A1− individuals, just as ethanol worsened negative affect in our A1− men. The authors suggested that an inverted ‘U’ function may characterize the relationship of dopamine activity to optimal functioning, and that A1 carriers are characterized by both a reward deficiency syndrome and increased delivery of reward by dopamine agonists.

The latter idea is further supported by the current relationship between ethanol-induced change in negative affect and regional brain activity ( Table 2 ). Positive covariation of anxiety/fatigue score with thalamic activity across groups is consistent with previous studies of alcohol-induced fatigue (Jia et al, 2007 and Volkow et al, 2006b). Positive covariation of these scores with ACC activity is consistent with executive-modulation of alcohol feelings (Ingvar et al, 1998 and Sinha and Li, 2007). Negative covariation of concurrent changes in anxiety/fatigue and activity in a cluster comprising 15% of the left insula in A1+ men suggests that ethanol-related reduction in anxiety/fatigue (see Fig. 2 ) may reflect the positive interoceptive feelings produced by alcohol, sometimes referred to as a “warm glow.” In A1− men, the more dorsal negative covariation in 7% of left insula is harder to interpret and may be spurious, since it is the only one of 24 comparisons to produce aP < 0.05 cluster (see Table 2 ).

The dearth of relationships between the effects of ethanol on negative affect and brain activity in A1− men is, however, consistent with a report that nonalcoholic members of alcoholic families exhibit higher D2 receptor availability than alcoholics from the same families ( Volkow et al., 2006a ). The study also found associations between D2 availability and glucose metabolism in frontal regions implicated in emotional reactivity and executive control. We speculate that higher levels of D2 receptors in A1− men (Jonsson et al, 1999 and Noble, 2003) could protect against alcoholism by improving regulation of emotional responses that might otherwise lead to drug-seeking.

In contrast to the lack of evidence that changed negative affect was related to change in brain activity in A1− men, the decreased anxiety and fatigue produced by ethanol in A1+ men accompanied decreased activity in the structures most consistently associated with both anxiety (amygdala) (Koob and Le Moal, 2008 and Silberman et al, 2008), and fatigue (thalamus) (Jia et al, 2007 and Volkow et al, 2006b). Both effects survived a conservative Bonferroni correction. Table 2 also shows decreases in three of four ROIs comprising a structure previously associated with craving (striatum) (Koob and Le Moal, 2008 and Yoder et al, 2009). In addition, reduced negative affect after ethanol in the A1+ group was accompanied by significant activation of right ventrolateral orbitofrontal cortex. Activity in this region has been associated with lower stress, inhibition of stress-related amygdala activity, and higher psychosocial resources for threat-regulation ( Taylor et al., 2008 ).

Limitations of this pilot study include a lack of detailed information on lifetime alcohol consumption, and the restriction of testing to Caucasian males. Although these findings require replication in larger and more diverse samples, they provide the first evidence that the heightened risk for alcoholism associated with the DRD2 A1 allele may be mediated by increased reward sensitivity and negative reinforcement when drinking alcohol.

Acknowledgment

This work was supported in part by the Smithers Foundation (New York), the Peter F. McManus Charitable Trust (Wayne, Pennsylvania), NIH M01RR00865 (UCLA GCRC), an endowment from the Katherine K. and Thomas P. Pike Chair in Addiction Studies, and by a generous gift from the Marjorie M. Greene Trust.

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Footnotes

a Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, United States

b Department of Molecular and Medical Pharmacology of the David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, United States

c Brain Research Institute, University of California Los Angeles, Los Angeles, CA 90095, United States

d Ahmanson-Lovelace Brain Mapping Center, University of California Los Angeles, Los Angeles, CA 90095, United States

e Department of Physics, University of California Irvine, Irvine, CA 92697, United States

f Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Campus Charité Mitte, Berlin, Germany

lowast Corresponding author. Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, 760 Westwood Plaza, Los Angeles, CA 90024-1759, United States. Tel.: +1 310 525 0606; fax: +1 310 825 0812.