Gene by Disease Interaction on Orbitofrontal Gray Matter in Cocaine Addiction

doi:10.1001/archgenpsychiatry.2011.10

Arch Gen Psychiatry. Author manuscript; available in PMC 2011 June 30.

Published in final edited form as:

Arch Gen Psychiatry. 2011 March; 68(3): 283–294.

doi: 10.1001/archgenpsychiatry.2011.10

PMCID: PMC3127452

NIHMSID: NIHMS303849

Gene by Disease Interaction on Orbitofrontal Gray Matter in Cocaine Addiction

Nelly Alia-Klein,^*¹ Muhammad A. Parvaz,^1,² Patricia A. Woicik,¹ Anna B. Konova,^1,³ Thomas Maloney,¹ Elena Shumay,¹ Ruiliang Wang,¹ Frank Telang,⁵ Anat Biegon,¹ Gene-Jack Wang,^1,⁴ Joanna S. Fowler,^1,⁴ Dardo Tomasi,⁵ Nora D. Volkow,⁶ and Rita Z. Goldstein¹

¹Medical Department, Brookhaven National Laboratory, Upton, New York, 11973

²Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794

³Psychology, Stony Brook University, Stony Brook, NY 11794

⁴Mount Sinai School of Medicine, New York, NY 10029

⁵National Institute on Alcohol & Alcoholism, Bethesda MD 20892

⁶National Institute on Drug Abuse, Bethesda MD 20892

^* Corresponding author for reprint requests: Nelly Alia-Klein, Brookhaven National Laboratory, Medical 490, Upton, NY 11973-5000, Tel: 631-344-8104, Email: nellyklein/at/bnl.gov

The publisher's final edited version of this article is available at Arch Gen Psychiatry

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Abstract

Context

Chronic cocaine use has been associated with structural deficits in brain regions having dopamine receptive neurons. However, the concomitant use of other drugs and common genetic variability in monoamine regulation present additional structural variability.

Objective

To examine variations in gray matter volume (GMV) as a function of lifetime drug use and the monoamine oxidase A (MAOA) genotype in men with cocaine use disorders (CUD) and healthy male controls.

Design

Cross-sectional comparison between 40 CUD and 42 controls scanned with magnetic resonance imaging (MRI) to assess GMV and genotyped for the MAOA polymorphism. The impact of cocaine addiction on GM was tested by 1) comparing CUD with controls, 2) testing diagnosis-by-MAOA interactions, and 3) correlating GMV with lifetime cocaine, alcohol, and cigarette smoking, and testing their unique contribution to GM beyond other factors.

Outcome Measures

GMV were derived from MRI with voxel-based-morphometry. Genotyping was performed for a functional polymorphism (a variable number tandem repeat or VNTR) in the promoter region of the MAOA gene with “high” and “low” alleles.

Results

1) Individuals with CUD had reductions in GMV in the orbitofrontal (OFC), dorsolateral prefrontal (DLPFC) and temporal cortex, and hippocampus, compared to controls. 2) The OFC reductions were uniquely driven by CUD with low MAOA genotype and by lifetime cocaine use. 3) GMV in the DLPFC and hippocampus, was driven by lifetime alcohol use beyond the genotype and other pertinent variables.

Conclusions

This study documents for the first time, the enhanced sensitivity of CUD low MAOA carriers to GM loss, specifically in the OFC, indicating that this genotype may exacerbate the deleterious effects of cocaine in the brain. In addition, chronic alcohol use was a major contributor to GM loss in the DLPFC and hippocampus, and is likely to further impair executive function and learning in cocaine addiction.

Introduction

Drug addiction is a chronic disease associated with deficits in brain dopamine¹ (DA) and brain function in regions underlying the I-RISA (Impaired response inhibition and salience attribution) syndrome (for review²). These regions encompass the reward and the inhibitory circuitry that contain DA receptive neurons where ventral prefrontal regions as the orbitofrontal cortex (OFC) have received much emphasis.^{2, 3} Multiple neuroimaging studies in the past decade demonstrated a reliable pattern of functional deficits during cognitive/emotional challenges that involve reward contingencies (salience attribution) and inhibitory control (response inhibition) in cocaine use disorders (CUD).^{4, 5} For example, positron emission tomography (PET) and functional magnetic resonance imaging (MRI) studies have demonstrated that DA-related functional deficits in the OFC may underlie disproportionate salience attribution to cocaine and compulsive drug intake.^{3, 6,5, 7}

Although relatively few, studies have tested structural alterations in the same circuitry where functional activations are compromised and have documented such deficits.⁸ Individuals with cocaine addiction have shown decreased gray matter volume (GMV) or thinner cortex, in the dorsolateral prefrontal cortex (DLPFC), OFC and anterior cingulate cortex (ACC);^{9, 10,11, 12} other regions included the insula, temporal cortex and amygdala, as compared to healthy controls (CON).^{13, 14, 11, 12} Since DA projections influence cerebral morphology during development and throughout adulthood, it is expected that chronic exposure to substances that trigger supraphysiological DA levels in the synapse, such as cocaine, might cause persistent cellular changes resulting in reduced neural volume as compared to non-exposed individuals.⁸ Moreover, PET studies have shown that the reduction in brain metabolism in DLPFC, OFC and ACC in cocaine abusers is associated with loss of postsynaptic DA markers.¹⁵

Addiction to crack-cocaine involves long-term concurrent use of other substances that are known to influence brain morphology.^16-19 More than 60% of CUD also had a comorbid alcohol use disorder and over 80% smoked cigarettes, further compounding gray matter loss throughout the brain.^16-20 These high comorbidity rates make the assessment of chronic drug use other than cocaine imperative for the generalizability of the results to community samples of individuals with CUD. Therefore, the present study used MRI and whole brain voxel-based-morphometry (VBM) analysis to test changes in cerebral GMV as a function of CUD and in correlation with the chronicity of lifetime drug use. By conducting this analysis, however, it is not known whether the predicted structural alterations result uniquely from years of chronic drug use. It is possible that CUD had reduced DA and reduced neural volume in the relevant brain circuits before disease onset, which could have predisposed them to drug use and addiction. The potential contribution of genetic differences to GMV may be present before disease onset and may interact with chronic drug use rendering some CUD individuals more sensitive to gray matter loss than others.

Genetic variations that interact with and affect brain development may contribute to behaviors that increase addiction liability.²¹ The product of the monoamine oxidase A (MAOA) gene is an enzyme that regulates the metabolism of monoamine neurotransmitters, thereby modulating brain function and structure.^{22, 23} During prenatal development, the MAOA enzyme is crucial for catabolic degradation of dopamine and norepinephrine²³ inducing changes with long term consequences during childhood.²⁴ The MAOA genotype (defined as MIM 309850), a variable number tandem repeat (uVNTR) region, is divergent in primates suggesting it plays a pivotal role in differential MAOA expression in both humans and monkeys.²⁵ The MAOA genotype is relevant to GMV in healthy controls.^26,27 In a large VBM study, healthy carriers of the MAOA-L (low repeat allele) had reduced GMV in the cingulate cortex and bilateral amygdala and increased GMV in the OFC, as compared to MAOA-H (high repeat allele) carriers.²⁸ Furthermore, in the presence of extreme environmental challenge (childhood abuse) MAOA-L genotype increases the risk for antisocial behaviors in adulthood, pointing to a gene-by-environment interaction.²⁹ Studies have also suggested association of MAOA-L with the risk for alcohol addiction.^{30, 31} We reason that for individuals with CUD, the disease onset and its progression could be viewed as environmental challege,³² possibly impacting GMV in affected members of the MAOA-L genotype (CUD-L).

Therefore in this study we predicted a main effect of addiction where CUD will have reductions in GMV as compared to CON. Next, we hypothesized a gene-by-disease interaction driven mostly by GMV loss in CUD-L. We hypothesize that a model containing both genetic and chronic drug use variables will better explain the predicted morphological deficits in CUD.

METHODS

PARTICIPANTS

Eighty-two right-handed male participants (40 CUD and 42 CON) were recruited using advertisement in local newspapers. All participants provided informed consent in accordance with the local institutional review board. Physical/neurological, psychiatric and neuropsychological examinations were conducted including tests of intellectual functioning (Wide Range Achievement Test-3^rd edition, WRAT-3 reading³³ and the Matrix Reasoning subset of the Wechsler Abbreviated Scale of Intelligence³⁴), Beck Depression Inventory (BDI)³⁵ to assess symptoms in the past two weeks, the Addiction Severity Index³⁶, and the Structured Clinical Interview for DSM-IV Axis I Disorders (research version)³⁷. All participants were healthy individuals, not taking any medications, further excluded for contraindications to the MRI environment (e.g., metal in the body or claustrophobia), history of head trauma or loss of consciousness (>30 minutes), other neurological disease, abnormal vital signs at time of screening, history of major medical conditions (cardiovascular, endocrinological, oncological or autoimmune diseases), major psychiatric disorders (other than cocaine dependence and alcohol abuse for CUD and/or nicotine dependence for both groups), urine positive (Biopsych™) for psychoactive drugs or their metabolites (phencyclidine, benzodiazepines, amphetamines, cannabis, opiates, barbiturates, and inhalants) except for cocaine in CUD.

All CUD were current users: urine was positive for cocaine in all but 6 of the 40 CUD, reporting use (mean±SD) 2.1±1.5 days prior to the study. Current use or dependence on other drugs was denied and corroborated by pre-scan urine tests in all subjects (urine was negative for all other drugs in all subjects). Table 1 in the Results, contains the demographic and clinical comparisons between CUD and CON with nested genotype comparisons.

Table 1

Demographic and Drug Exposure Factors

GENOTYPING

The DNA samples for MAOA genotyping were extracted from whole blood (PAXgene Blood DNA Kit, Qiagen) from each participant. The polymerase chain reactions were performed as previously described.²⁷ In humans and primates, categorization of common genetic variability is based on a functional polymorphism in the promoter region of the MAOA gene; uVNTR, 3.5 or 4 repeats (i.e. “high”) and 2, 3, or 5 (“low”) repeats is common in the population where 3 and 4 occur in a ~60:40 ratio in men. As compared to the “high” variant, the “low” variant has relatively lower transcriptional activity in human non-neural cell lines.^27,38 In this sample, alleles were observed in expected ranges using Genescan version 3.7 and Genotyper version 3.6 software. Genetic analyses resulted in 42 participants classified as having the low MAOA repeat alleles (22 CUD-L and 20 CON-L) and 40 as having the high repeat alleles (18 CUD-H and 22 CON-H). This genotypes ratio is in agreement with Hardy-Weinberg assertions.

MRI ACQUISITION AND VOXEL-BASED MORPHOMETRY

All subjects were scanned using T1-weighted anatomical MRI scans on a 4-Tesla Varian/Siemens scanner, with SONATA gradient set. The MRI parameters of the 3D-MDEFT (3 dimensional modified driven-equilibrium Fourier transform)^{39, 40} sequences were as follows: TE/TR = 7/15 ms, 0.94 × 0.94 × 1.00 mm3 spatial resolution, axial orientation, 256 readout and 192 × 96 phase-encoding steps, within a 16 minutes scan time. The MDEFT sequence is particularly effective for white matter (WM) - GM tissue differentiation.⁴¹

All structural data were analyzed using MATLAB 7.0 (Math Works, Incl., Natick, Massachusetts://www.mathworks.com) and statistical parametric mapping (SPM5; Wellcome Department of Cognitive Neurology, London; http://www.fil.ion.ucl.ac.uk/spm) with VBM5.1 toolbox (Gaser, C, University of Jena, Department of Psychiatry, Germany, http://www.dbm.neuro.uni-jena.de/vbm/). Preprocessing (spatial normalization, tissue segmentation, and bias correction) was conducted using a unified model. Images were normalized to standard proportional stereotaxic space [Montreal Neurological Institute (MNI)]. Tissue probability maps (International Consortium for Brain Mapping-European version) were subsequently applied segmenting the images of all 82 participants into GM, WM, and cerebrospinal fluid tissue classes for each individual following Bayesian rule.^{42, 43} A hidden Markov random field (HMRF)⁴⁴ was applied to minimize the noise level by “removing” isolated voxels of one tissue class which are unlikely to be members of this tissue class, thus increasing the accuracy of the individual subject tissue probability maps. Lastly, Jacobian modulation was applied to compensate for the expansion/contraction that occurs during nonlinear transformation and to restore the original absolute GMV in the segmented GM images. The voxel resolution after normalization was 1 X 1 X 1 mm. Statistical analysis of the regional GMV was performed after smoothing the normalized and modulated segments using an isotropic 12 mm³ full-width at half-maximum (FWHM) Gaussian kernel.

Total brain tissue was computed as a sum of the extracted GMV and WMV for each participant. Note that we do not analyze WMV in this study since other methods such as Diffusion Tensor Imaging are more sensitive for this purpose (VBM’s WM T1 signal intensities are not correlated with the WM integrity).⁴⁵ Similarly to other studies,^46-48 cerebral spinal fluid was not used in the calculation for total brain tissue as the value outputs by SPM are susceptible to artifacts (e.g., if voxels are not fully differentiated as GM or WM, they can be mislabeled as CSF). In addition, GM and WM tend to vary together; however CSF is variable from day to day and may increase as GM decreases, misleading the total brain calculation.⁴⁹

STATISTICAL ANALYSIS

Statistical analysis for the demographic and drug exposure factors was performed using GLM with a 2 (diagnosis: CUD vs. CON) X 2 (genotype: low vs. high) or t-tests or chi-square, as needed, in SPSS (SPSS Inc., Chicago, IL)⁵⁰, as documented in Table 1. In SPM5, GLM 2 X 2 was used for the GM maps, controlling for total brain tissue and age: for the diagnosis main effect (CUD < or > CON) and the genotype main effect (MAOA-L < or > MAOA-H). Then, we conducted planned comparisons between CUD and CON of the same allele variation: CUD-L<CON-L and CUD-H<CON-H. Separate whole–brain regression analyses, controlling for total brain tissue and age were then conducted to test associations between GMV and lifetime years of cocaine use (in the CUD sample, n-40, Small volume correction was used⁵¹). Lifetime years of alcohol and cigarette use was evaluated in the whole sample (N=82). All SPM5 analyses were performed controlling for age and total brain tissue, with extent threshold of 100 voxels, and a threshold set at P<0.05, corrected with false discovery rate (FDR) equivalent to a T threshold of 3.3. Labels for the resulting coordinates were inspected using the Anatomy Toolbox and a coplanar stereotaxic atlas of the human brain.⁵²

The VOI were extracted with SPM5 EasyROI toolbox (http://www.sbirc.ed.ac.uk/cyril/cp_download.html) with an isotropic volume of the whole cluster around the significant peak voxel coordinates of the main effect results (CUD<CON from Table 2). This approach resulted in raw GMV values for each participant in each of these regions, allowing the measures to be used for figures and in SPSS (SPSS Inc., Chicago, IL)⁵⁰ in order to conduct GLM analysis, covarying for total brain tissue, age, race, verbal intelligence, and BDI symptoms (as documented in the Results). These SPSS analyses were Bonferroni corrected for the 5 main effect regions making the CUD-by-MAOA interaction results significant at P<.01. To understand the contribution to variability in GMV of all the variables studied and the potentially unique effects of chronic drug use, we used the VOIs in SPSS to conduct multiple regression analysis on each of the main effect coordinates. The model consisted of two hierarchical blocks: in the first block we entered total brain tissue, age, race, verbal intelligence, BDI and MAOA. In the second block we entered the lifetime drug use variables.

Table 2

The SPM5 Results of GM Differences ^{^a}

RESULTS

CHARACTERISTICS OF COCAINE ADDICTION

Individuals with CUD were significantly older than CON (mean±standard error) (CUD, 45±1; CON, 39±1), with no genotype effects (P>.281). Additional differences included race (less Caucasians in the CUD group than CON) and higher depression symptoms in CUD (8±1) than CON (3±1) with no genotype effects (P>.533) and lower verbal intelligence (CUD, 90±2; CON, 97±2) and an interaction with the CUD-H having lower scores as compared to CON-H (P<.002). There were no differences between the groups on years of education and socioeconomic status⁵³ (Table 1).

In terms of drug exposure factors, all CUD used cocaine (smoked crack) in the past 0-7 days before imaging and met DSM-IV⁵⁴ criteria for current cocaine dependence. The CUD participants reported use of 1.7±0.8 grams of cocaine per occasion with no genotype effects (P>.823). The years of lifetime cocaine use was 19±1 with no genotype effects (P>.967). The age of CUD onset was 26±1 years and CUD-L tended toward a younger age of onset (by approximately 4 years, P=.086, two-tailed) than CUD-H. In addition to chronic cocaine use, the CUD sample also had a substantial lifetime use of cigarettes and alcohol. A larger proportion (74%) of individuals with CUD reported cigarette smoking than CON (20%) with no difference on the number of cigarettes smoked per day (CUD, 8±1; CON, 5±2) and with no genotype effects (P>.321). In addition, 75% of the CUD were also diagnosed with alcohol abuse, with age of onset of 16±1 years and 60±8 ounces per occasion, with no genotype effects (P>.211).

GRAY MATTER EFFECTS OF COCAINE ADDICTION

Total GMV was reduced with older age across all subjects (r= −.30, P<.01) with no diagnosis or genotype effects (P>.85) and there were no main effects and no interactions in total WMV (P>.214). Controlling for age and total brain tissue, individuals with CUD had GMV reductions in the left OFC [Brodmann Area (BA) 11] (F_1,72=6.5; P<.003), right DLPFC (BA 9) (F_1,72=27.5; P<.0001), temporal cortex (BA 37) (F_1,72=5.3; P<.024), hippocampus and parahippocampal gyrus (F_1,72=8.6; P<.002) as compared with CON (CUD<CON; Table 2, Figure 1). The F values in parentheses throughout the Results represent the main effects of addiction after controlling for the potential influences of total brain tissue, age, race, verbal intelligence and BDI symptoms.

Figure 1

Gray matter volume reductions as a function of cocaine addiction (CUD<CON, N=82). Each brain region is presented with a graph using the VOI to show that the main effects of addiction are contributed by both genotype groups (except for the OFC). (more ...)

At this SPM threshold (p<.05, FDR corrected), there were no regions of increased GMV in CUD as compared to CON and no main effects of genotype as assessed with MAOA-L> or <MAOA-H contrasts. However, there was a significant CUD-by-MAOA interaction effect exclusively in the OFC (F_1,68=5.2; P<.005).

GENE-BY-DISEASE INTERACTION

Following our planned contrasts, and to investigate the source of the gene-by-disease interaction effect in the OFC, we matched the CUD and CON participants on allele variation (Table 2). Comparing CUD-H with CON-H (Figure 2, blue) revealed a diagnosis effect of GMV reductions in the hippocampus; however, this contrast did not produce significant results in any of the other main effect regions including the OFC even at a reduced threshold. Comparing CUD-L with CON-L (Figure 2, red), revealed robust GMV reductions in the OFC, DLPFC, temporal cortex and hippocampus, similarly to the main effects of addiction. Here, however, the results included not only the OFC between the anterior branches of the medial and lateral orbital sulci (BA 11) but also encompassed the medial edge of the orbital surface, i.e. gyrus rectus (Table 2). The GLM SPSS analyses using our VOIs in these OFC coordinates in all participants, and controlling for the covariates as above, revealed that CUD-L had significantly less GMV than CUD-H and both CON groups in the left OFC (MAOA*CUD; F_1,68=4.2; P=.007), and bilateral gyrus rectus (MAOA*CUD; left, F_1,68=10.6; P=.002, right, F_1,68=14.8; P=.0001) (Figure 2). This interaction was unique to the OFC (all other VOIs in Table 2, MAOA*CUD, P>.103).

Figure 2

Gene-by-disease interaction in the orbitofrontal cortex. The GMV measures in CUD-L<CON-L (red) and CUD-H<CON-H (blue) are overlaid on the SPM5 canonical template. The respective interaction graphs show regional GMV differences between (more ...)

LIFETIME DRUG USE AND OTHER VARIABLES

In order to understand the contribution of drug use duration in this sample, we conducted multiple regressions in SPM5 of GMV with years of drug use, controlling for age and total brain volume. In the CUD group (n=40), with increasing years of cocaine exposure, there were more volume reductions in the OFC (r = −.44, P = .003), DLPFC (r = −.41, P = .008) and hippocampus (r = −.46, P = .003); a similar pattern of results was obtained in the CUD group with lifetime alcohol (all r from −.34 to −.65, P<.001) and with cigarette smoking (all r from −.31 to −.52, P<.0001) (Table 3, SPM results). In Figure 3, the whole-brain correlation results of all three drugs were overlaid, showing a visible overlap of the detrimental effects of all drugs on the hippocampus.

Table 3

Multiple Regression Analyses with GMV and Lifetime Drug Use^{^a}

Figure 3

Lifetime effects of drug use on GMV. The central image shows correlation of GMV with lifetime use of each drug (cocaine=red, alcohol=yellow, smoking=green) overlaid on the SPM5 canonical template. The respective scatterplots are also overlaid with the (more ...)

In order to understand the contribution of all the variables studied and the unique effects of chronic drug use, we conducted hierarchical regression analyses in SPSS. As documented in Table 4, total GM tissue was not significantly affected by any of the variables except for the known effect of reduced total GMV with older age. As for the OFC, the block 1 variables contributed 21% to the GMV variance (driven by the MAOA genotype, age and race). The drug use variables accounted for significantly additional 19% of unique variance to the OFC GMV. This effect was driven by lifetime cocaine use. In the DLPFC, lifetime alcohol and cocaine use contributed the most unique variability to GMV adding 24% to the 17% that was explained by the block 1 variables). In the temporal cortex race and alcohol use were most predictive of GM differences between the groups. Results for the hippocampus were the most striking, showing that lifetime alcohol use contributed 30% of unique variance. Notably, in the hippocampus and DLPFC alcohol and cocaine use contributed more variability than the block 1 variables.

Table 4

The Contribution of Demographic, Genetic, and Drug Use Variables to GMV

COMMENT

These findings demonstrate a distributed pattern of GMV loss in CUD as compared to CON in the OFC, DLPFC, temporal, and hippocampal regions. Exclusively in the OFC, GMV reductions were driven by increasing years of cocaine use and by CUD-L having smaller GMV, showing a gene-by-disease interaction. The pattern of GMV in other regions was not affected by the genotype; rather, GMV loss in the temporal and especially the DLPFC and hippocampus was driven primarily by drug use, especially by alcohol use.

REDUCED GMV IN COCAINE ADDICTION

Participants with CUD had reduced GMV in the right dorsolateral region of the prefrontal cortex, in BA 9, a region critical for monitoring information in working memory and in the controlled retrieval of information.⁵⁵ Specifically in CUD, these regions showed a deficit in functional activation during a go/no-go task and deficits in these regions were associated with poor inhibitory control.⁵⁶ Using measures of cortical thickness, this precise DLPFC region was found to be thinner in CUD versus well matched CON participants.⁸ Additional GMV reductions were found in this study in the inferior posterior temporal cortex, BA 37, associated with object naming and recognition memory, found to have reduced GMV in opiate dependent individuals.⁵⁷ This temporal region is particularly sensitive to age-dependent damage in Alzheimer disease.⁵⁸ This region is located immediately adjacent to the posterior parahippocampal gyrus and the hippocampus, also found to have reduced GMV in CUD as compared to CON in this study. The hippocampus plays a role in extinction of currently non-relevant but previously rewarding stimuli and in retrieval of information pertinent to these learning mechanisms; as such, the hippocampus is implicated in drug-context memory and in relapse to drug seeking behaviors.^{59, 60} Together with the hippocampus, the regions found to have reduced GMV in CUD in the current study are associated with drug craving⁶¹ and drug seeking behaviors.⁶⁰ Since the hippocampus, in concert with DLPFC regions, has an important executive role in inhibiting previously acquired drug reward mechanisms⁶², these GMV decrements may perpetuate the I-RISA syndrome in drug addiction.²

The neurochemistry of these affected brain regions is modulated by tonic and phasic DA action.^{1, 63, 64} In humans, the in-vivo concentration of DA receptors is related to neural volume, as demonstrated by a recent imaging study showing a voxel-wise relationship between DA D2 receptor availability (PET with [18F]Fallypride) and GMV in the DLPFC (BA 6 and 9) and temporal and parahippocampal gyrus⁶⁵, regions that were found to have reduced GMV in CUD in the current study. Medium spiny neurons are the principal targets of DA terminals and DA depletion in animal studies results in neurons with shorter and fewer spines as compared to non exposed neurons.⁶⁶ Because chronic drug use and addiction is associated with reduced DA D2 receptor availability^{67, 68}neuronal volume is predicted to be similarly reduced, as evident especially in prefrontal cortical DA projections from the ventral tegmental area.^{69, 70} Studies in humans found a reduction of N-acetylaspartate (NAA, suggested as a putative marker for neuronal cell loss or damage) concentrations in CUD, and increased myoinositol, (a marker of glial activation) in frontal cortical regions.⁷¹

The present results demonstrate reduced volume of the OFC in the left hemisphere whereas the rest of the main effect regions were right lateralized. These results may support the notion of a disrupted regional laterality in drug addiction⁷², which is posited to be inherited⁸ and may start developing before disease onset and may indeed contribute to its onset and progression together with the influence of particular traits, as impulsivity.⁷³

GENE BY DISEASE INTERACTION IN THE OFC

In this study, CUD-L had significantly smaller volume than CUD-H and both CON groups in the OFC and gyrus rectus (BA 11). The OFC has been implicated in a wide variety of externalizing behavior disorders, and patients with specific damage to the OFC demonstrate more impulsive behavior than patients with other prefrontal damage.^{74, 75} The anterior part of the OFC consists of eulaminate (six-layer) cortex, including granular layer IV.⁷⁶ Neurons in the OFC BA 11 of the macaque monkey code novelty, with rapid habituation⁷⁷ and BA 11 is strongly linked with DLPFC areas (also found in this study to have reduced GMV in CUD), which together may guide goal-directed motivation.⁷⁸ The projections from the OFC to the entorhinal cortex, which innervates the pyramidal cells of the hippocampus, may underlie the process through which information about the emotional significance of stimuli is remembered.⁷⁹ Limited GMV in the OFC may undermine its functional connections with dorsolateral and entorhinal regions thereby impairing the ability to make advantageous decisions.^{69, 80} Supporting poor connectivity is a study finding disruption in WM fiber tracts to the OFC in CUD, which may further impair the OFC connectivity to the DLPFC and hippocampus regions.⁸¹ The regional GM loss we documented herein may correspond to WM loss, which is more reliably documented in manual segmentation or DTI studies than VBM.⁴⁵

The selectivity of MAOA on DA degradation is not entirely known, as MAOA influences other neurotransmitters that may impact GM.⁸² Although there is pharmacological evidence that serotonin levels are enhanced following MAOA inhibition; however, immunohistochemical and autoradiographic studies have established that MAOA is predominantly localized in catecholaminergic neurons.⁸³ The selectivity of MAOA specifically on DA degradation may also be relevant during prenatal development when MAOA is crucial for catabolic degradation of DA, norepinephrine and perhaps also serotonin.⁸⁴ Indeed, recent studies have shown that MAO (A and B) regulates neural progenitor cells during brain development an effect mediated through serotonin.⁸⁵ Dopamine depletion in adults as reliably documented in CUD³ can trigger large-scale gene expression changes through multiple regulatory subunit changes in mRNA expression levels.⁸⁶ Although the MAOA uVNTR polymorphism analyzed in this study is not directly indicative of brain MAOA activity⁸⁷ this genetic variant was linked to the differences in levels of the DA metabolite homovanillic acid in cerebrospinal fluid.⁸⁸

The mechanisms by which decreased transcriptional activity of MAOA might increase GM in the OFC in healthy controls²⁸ but interact with cocaine use to selectively diminish OFC in the current study remains unknown. The modulating effect of the MAOA genotype on structural variability may have started during early brain development, clearly before disease onset, and possibly continuing its impact at adolescence at onset of the disease process. Interestingly, CUD-L in this study had a slightly younger age of onset for cocaine use. It is possible that these particular individuals who later developed CUD had reduced GMV in the OFC before disease onset, since developmental factors such as maternal smoking are associated with increased likelihood of drug experimentation and decreased thickness of the OFC in adolescence.⁸⁹ In this context, it is noteworthy that MAOA-L genotype was associated with risk for alcoholism and antisocial alcoholism.³¹ It is also noteworthy that other factors in addition to the MAOA polymorphism affect the enzyme’s expression. In a recent paper, we demonstrated that the MAOA gene is subjected to epigenetic modifications.⁹⁰ This finding, together with the well established evidence that the drugs of abuse cause epigenetic aberrations⁹¹, led us to propose that the MAOA methylation pattern in CUD might be influenced by drug use, causing dysregulation of its expression.

Gray matter in the OFC, showing deficit in CUD-L, was uniquely driven by increasing years of cocaine exposure. In fact, the OFC was the only region affected specifically by cocaine and not alcohol years of use. It is possible that the OFC of CUD-L is more sensitive to the neurotoxic effects of cocaine than CUD-H exposed to similar amounts of the drug. Supporting this specificity is evidence from studies in rats showing that chronic stimulants limit spine density in the OFC (while chronic opiate use may increase spine density)^{92, 93}, perhaps making the OFC sensitive to morphological changes depending on the drug of abuse.⁹⁴ Additional morphometric damage can be caused by smoking exposure since chronic smoking inhibits MAOA⁹⁵ and high-affinity nicotinic receptors in the human OFC increase after smoking.⁹⁶ A recent VBM study showed GMV is reduced in cigarette smokers in DLPFC and inferior frontal regions.⁹⁷ However, consistent with the current results, nicotine administration to adolescent rats elicited less severe region-dependent effects than alcohol.⁹⁸

REGION SPECIFIC EFFECTS OF LIFETIME ALCOHOL USE

Lifetime alcohol use was the major contributor of GMV deficit in the DLPFC, temporal cortex and hippocampus of CUD contributing unique variability to GMV above and beyond the MAOA polymorphism and any of the other factors tested and more so than cocaine and cigarette smoking. In this study, we measured severity as the number of lifetime years of use. Animal models of binge alcohol administration, controlling for severity in a dose dependent manner, support a direct link between high levels of alcohol consumption and neurotoxicity in the hippocampus and surrounding dentate gyrus and associated entorhinal-perirhinal cortex during adolscence.⁹⁸ Similarly, reduced hippocampal volume was found among adolescents with alcohol use disorders.^{99, 100} Gray matter loss in the hippocampus may lead to more drug seeking as demonstrated by animal studies showing that blocking neurogenesis in the adult rat hippocampus caused increased cocaine seeking and more self-administration¹⁰¹, further facilitating a vicious cycle of cocaine use.¹⁰¹ The observed GMV reductions in the hippocampus, perhaps due to chronic alcohol use, may increase cocaine use through strong resistance to extinction of drug seeking behavior.¹⁰¹

CAVIATES

Our groups differed in age, ethnicity, verbal intelligence and symptoms of depression. Demographic effects of difference in the lifetime trajectory of drug addiction are a source of variability and a contributor to the overall impact of the disease.¹⁰² Lower verbal intelligence could indicate compromised education due to drug use during adolescence (note that the differences due to genotype are partly supported by another study¹⁰³). The BDI measure (reflecting symptoms in the past 2 weeks) cannot be separated from drug effects (such as acute withdrawal).¹⁰⁴ Rather than excluding these effects, we studied their impact in explaining GMV differences between the groups, enhancing the generalizability of the current results.²⁰ On the subject of enhancing generalizability it is important to remember that our findings come from a male sample; and females are largely understudied in drug addiction, a limitation of generalizability that needs to be addressed in future studies. Our sample of CUD was also underrepresented by Caucasians as compared to African Americans and the latter show GM effects in the OFC and temporal cortex. This represents a confounding factor in this study but it also highlights the evidence for racial differences in GMV that need to be accounted for beyond the clinical variable of interest. Indeed in this study, we demonstrated through hierarchical regression analysis that the MAOA and cocaine use effects contribute unique variability to GMV beyond other effects.

Similarly, there are additional factors affecting GM reductions including, for example, chronic lack of sleep (affecting the OFC)¹⁰⁵, and acute depression affecting hippocampal volume.¹⁰⁴ Both are common problems in CUD and should be further investigated in future studies. The present study had active currently using CUD participants (90% had positive urine for cocaine) and a case could be made for OFC reductions during acute use that may recover with abstinence. However, even after prolonged abstinence of 2-4 years GM reductions were still found in comparison of substance dependent individuals and controls, pointing to persistent and enduring GM deficits in the OFC.^{46, 106}

In a VBM study in healthy control participants, MAOA-L have had increased lateral OFC volume (BA 47) as compared to MAOA-H.²⁸ Conversely, in this study, CUD-L had significantly less GMV than CUD-H and both CON groups in the medial OFC and gyrus rectus. In the same previous study, healthy individuals with MAOA-L had reduced GM encompassing the entire cingulate gyrus and particularly in the anterior cingulate, a region not evident in the current results. Note that while inspecting CON-L vs. CON-H in our data, we could find a similar pattern including the anterior cingulate, using P<.05, uncorrected (results not shown). Differences in findings may stem from the use of varied methods with varied populations of controls and CUD. Other morphology studies found deficit in regions we did not find reduced GMV (e.g., amygdala¹³, anterior cingulate and insula¹¹); conversely, none of the studies found hippocampus GMV deficits that we found in this sample, though we studied CUD with comorbid alcohol abuse which has been associated with hippocampal volume loss. Future studies should continue to assess genotype differences within CUD since this study suggests CUD-L to be with potentially more extensive deficits than CUD-H (e.g., younger age of onset is a major risk factor for a more severe course of illness).

CONCLUSION

The extensive GMV loss in CUD in the OFC, DLPFC, temporal, and hippocampal regions, underlie demographic, genetic and drug use factors. Exclusively in the OFC, GMV reductions were driven by increasing years of cocaine use and by CUD-L having smaller GMV, showing a gene-by-disease interaction. The population we studied already started using drugs, which constraints the ability to track causes and effects of the substance abuse.^{8, 21} Addiction liability can be characterized dimensionally among already affected individuals, insofar as indices of severity.³² These results suggest that loss of GMV among CUD is multidetermined and can be assessed with a model that includes genetic, behavioral and drug use factors that we speculate have interacted continuously throughout the lifespan. Studies are emerging in support of this notion, that gene-by-environment interactions take different forms at different ontogenetic stages of development during the lifespan^{32, 107}. Therefore, the next generation of neurogenetic studies will have to document complex interactions over protracted developmental trajectories to explain the effects contributing to multifaceted psychopathology as drug addiction.

Acknowledgements

This research was conducted at Brookhaven National Laboratory under contract DE-AC-298CH10886 with the U.S. Department of Energy with infrastructure support from its Office of Biological and Environmental Research and by the National Institute on Drug Abuse (R01DA023579, R21DA02062) and by the National Institute on Alcohol Abuse and Alcoholism (2R01AA09481); and the National Association for Research on Schizophrenia and Depression (NARSAD).

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