This suggests that brain areas implicated in processes such as reward and cognition show the most consistent gray matter atrophy in alcohol dependent individuals, but it is unclear whether overall amount of alcohol consumption or aspects of dependence severity explain these findings. Furthermore, some of the neuroimaging studies focusing on alcohol users have not mentioned whether the alcohol users also used nicotine , did not examine the effects of nicotine use on brain structure , did not control for nicotine use in their analyses , assessed nicotine use with a dichotomous questionnaire , or simply mentioned the number of smokers in the study . This makes it difficult to ascertain whether the observed neural effects were attributable to either alcohol and/or nicotine use and further illSimilar to studies of alcohol use effects on brain morphometry, several MR imaging studies have been conducted to specifically examine the effects of nicotine use on brain structure . As with studies of alcohol users, studies of cigarette smokers have attempted to quantify and incorporate a lifetime use variable, such as pack-year smoking history, which has been found to negatively correlate with PFC gray matter densities as well as gray matter volume in the middle frontal gyrus, temporal gyrus, and the cerebellum . Interestingly, Brody et al., found no significant association between pack-year smoking history and regions of interest determined as having significant between group differences, such as the left dorsolateral PFC, ventrolateral PFC, and left dorsal ACC. Given these conflicting findings, it is uncertain whether quantity variables, such as pack-year smoking history, account for many of the gray matter volume reductions observed in nicotine dependence. Dissimilar to studies of alcohol dependent individuals,indoor vertical garden systems some studies of nicotine dependent individuals have examined symptoms of dependence severity in relation to brain morphometry.
For example, the Fagerström Test for Nicotine Dependence , which was not associated with pack-year smoking history, was not correlated with PFC or insular gray matter density . The lack of a significant correlation between FTND scores and pack-year smoking history suggests that quantity of use and dependence severity symptoms may be unrelated in nicotine dependence, and thus have distinct relationships with brain structure. Overall, gray matter degradation has been observed in the thalamus, medial frontal cortex, ACC, cerebellum, and nucleus accumbens in nicotine dependent individuals . Due to widespread results, a meta-analysis was conducted, which found that only the left ACC showed significant gray matter reductions in nicotine dependent individuals compared to healthy controls . While studying primarily alcohol or nicotine using populations carries unique benefits, specific investigation is needed into heavy drinking smokers as past studies have shown compounded neurocognitive effects , as well as pronounced gray matter volume reductions in heavy drinking smokers when compared to nonsmoking light drinkers . Chronic cigarette smoking has been found to have negative consequences on neurocognition during early abstinence from alcohol and in one particular study, it was found that after 8 months of abstinence, actively smoking alcohol-dependent individuals performed worse on several neurocognitive measures, such as working memory and processing speed, when compared to never-smoking alcohol-dependent individuals . Additionally, formerly smoking alcohol users were found to perform more poorly than never-smoking alcohol users at this time point. These findings not only illustrate the contribution of smoking status on neurocognitive measures but establish the clinical relevance of nicotine use in heavy drinkers. This relevance paired with the compounded neurocognitive and morphometric effects further merit investigation into this unique sub-population of substance users.
The present work aimed to ascertain the effects of alcohol and nicotine dependence severity on gray matter density in a sample of 39 non-treatment seeking heavy drinking smokers using standard voxel-based morphometry . While some imaging studies have previously investigated the relationship of FTND scores with brain structure, to our knowledge, no imaging study to date has examined how alcohol dependence severity relates to gray matter density in heavy drinking smokers. Thus, the goal of this study was to examine if alcohol or nicotine dependence severity was correlated with gray matter density in heavy drinking smokers, while controlling for age, gender, and total intracranial volume . By examining dependence severity scores in addition to quantity of use variables, we may be able to capture how dependence is related to structural changes in the brain in a way that is not captured by variables that focus singularly on quantity of use. Based on previous findings, we hypothesized that gray matter density would be negatively related to quantity of both alcohol and nicotine use, in regions such as the middle frontal gyrus. We also hypothesized that dependence severity scores would uniquely relate to gray matter atrophy in several regions previously identified across the meta-analyses of voxel-based morphometry studies, such as the ACC, dorsal striatum, and insula.The subjects for the present study are a subset of participants from a medication development study of varenicline, naltrexone, and their combination in a sample of heavy drinking smokers. Subjects participated in the medication component of the study, details of which have been described in a previous publication , and a sub-sample was invited to complete a neuroimaging session.
Participants were recruited from the greater Los Angeles area through online and print advertisements with the following inclusion criteria: 1) between 21 and 55 years of age; 2) reported smoking at least 7 cigarettes per day; and 3) endorsed heavy drinking per the National Institute on Alcohol Abuse and Alcoholism guidelines: for men, >14 drinks per week or ≥5 drinks per occasion at least once per month over the last 12 months; for women, >7 drinks per week or ≥4 drinks per occasion at least once per month over the last 12 months. Participants were excluded from the study based on the following criteria: 1) had a period of smoking abstinence greater than 3 months within the past year; 2) reported use of illicit substances within the last 60 days, confirmed via positive urine toxicology screen at assessment visit ; 3) endorsed lifetime history of psychotic disorders, bipolar disorders, or major depression with suicidal ideation; 4) endorsed moderate or severe depression symptoms as measured by a score of 20 or higher on the Beck Depression Inventory-II ; 5) reported current use of psychotropic medications; 6) reported any MRI contraindications, such as any metal fragment in the body or pregnancy; and 7) reported MRI constraints, such as left-handedness or color blindness. As no Structured Clinical Interview for Diagnostic Statistical Manual 4th edition , or DSM 5th edition , Axis I Disorders was administered, drinking status for participants was determined solely via NIAAA heavy drinking guidelines . After a telephone screening to determine eligibility, participants came to the laboratory for a screening visit, during which informed,plant drying rack written consent was obtained. A urine cotinine test along with carbon monoxide levels verified self-reported smoking patterns and a breath alcohol concentration of 0.00 was required at the beginning of each visit. Eligible participants then came in for a physical examination and if eligible afterwards, began taking medication for nine days, previously described elsewhere . Participants received varenicline alone , naltrexone alone , their combination, or matched placebo. After the medication period, participants who were eligible for the MRI session were selected at random, given an additional three days of medication, and scanned within those three days. To our knowledge, no studies to date have tested the effects of varenicline and naltrexone on structural MRI measures; however, to ensure that there were no significant gray matter differences between the medication groups, we conducted a whole-brain one-way between-subjects ANOVA . A total of 40 subjects participated in the neuroimaging study. The Institutional Review Board of University of California, Los Angeles, approved all procedures for the study.Participants were administered the Alcohol Dependence Scale , the FTND, and the 30-day Timeline Follow-back . The ADS is a 25-item self-report measure that identifies elements of alcohol dependence severity over the past 12 months, such as withdrawal symptoms and impaired control over alcohol use on a scored scale with a range of zero to 47. The FTND is a six-item self-report measure that captures features of nicotine dependence severity on a scored scale of zero to 10, and questions on this measure are not confined to a specific time frame of substance use.
The TLFB assessed the daily amount of alcoholic drinks and cigarettes participants consumed in the past 30 days before the scan, from which mean drinks/drinking day and cigarettes/day were calculated.All images were obtained with a 3.0 Tesla Siemens Trio MRI Scanner at the Center for Cognitive Neuroscience at UCLA.As we expected no structural differences unrelated to gray and white matter volumes to be present in the sample, paired with past studies employing methodologies similar to ours, we chose to follow standard VBM protocols and spatially normalize the T1-weighted raw images to the same stereotactic space first . To do this, each image was registered to a standard template in Montreal Neurological Institute space using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra . After spatial normalization, the resulting DARTELwarped T1-weighted images were segmented into three classifications . The segmented images were then modulated, a process by which the images are multiplied by the Jacobian determinants produced for each image during spatial normalization. The advantage of modulation is that it corrects for individual brain size and brain matter expansion or contraction that occurs during normalization. The sample homogeneity of the resulting images was checked using a mean covariance boxplot, which assesses the covariance among the sample of images across participants. Higher covariance values are preferred, which indicate the image is more similar to other volumes in the sample, while a lower covariance value signals a potential outlier . The mean covariance value for the current sample was .74. One participant had a covariance value greater than 2 standard deviations from the mean. Upon inspection, the image appeared to have failed during segmentation due to motion artifact and was excluded from further analyses. This resulted in a total of 39 subjects. Finally, modulated images were smoothed using an 8-mm full width at half maximum Gaussian kernel. The smoothed, modulated images were used for subsequent analyses.Two separate multiple regression models were built, with the first analyzing the relationship between symptoms of dependence severity and gray matter density. This model included ADS scores and FTND scores as predictor variables. The second model examined the relationship between quantity of substance use and gray matter density. The variables DPDD and CPD were chosen for this model and entered as predictor variables. Age, gender, and ICV were entered as covariates in both models. The significance level was set at p < 0.001, uncorrected with an absolute threshold mask value of 0.1, and a spatial extent threshold of 78 voxels was empirically determined per standard VBM protocol and used for analyses. Additionally, post-hoc achieved power analyses were conducted using the effect sizes calculated with Cohen’s f 2 .Previous research has indicated that gray matter tissue can regenerate within 14 days of alcohol abstinence in alcohol dependent patients and that gray matter regeneration is most profound within the first week to month of abstinence . Given these findings, we examined whether days to last drinking day before the imaging session correlated with gray matter density at the whole-brain level. Days to last drinking day was computed for each participant based on the TLFB information collected at the time of image acquisition. The analysis conducted included days to last drinking day as a predictor variable and age, gender, ICV, and ADS scores as covariates of interest. Furthermore, to understand whether any of the effects were related to cannabis use within the current sample, we examined the relationship between frequency of cannabis use and drinking and nicotine variables using non-parametric Spearman’s correlations. Cannabis use was assessed using a single-item categorical question asking, “On average, how often do you smoke marijuana?”The purpose of the present study was to examine the relationship between quantity of alcohol/nicotine use and alcohol/nicotine dependence severity with gray matter density in heavy drinking smokers. Similarly, some prior studies that examined nicotine users did not establish exclusionary criteria based on alcohol use .