Elsevier

Drug and Alcohol Dependence

Volume 153, 1 August 2015, Pages 22-28
Drug and Alcohol Dependence

Regional gray matter deficits in alcohol dependence: A meta-analysis of voxel-based morphometry studies

https://doi.org/10.1016/j.drugalcdep.2015.05.030Get rights and content

Highlights

  • Meta-analyses revealed strong evidence for regional gray matter (GM) loss in alcohol dependence.

  • GM loss in the prefrontal cortex, dorsal striatum/insula, and posterior cingulate cortex (PCC) was identified.

  • Regions identified are involved in several neurofunctional networks.

  • Anatomical deficits could play an important role in modulating alcohol dependence.

Abstract

Background

Many studies have revealed a widespread pattern of gray matter (GM) atrophy by using voxel-based morphometry (VBM) studies involving the pathophysiology of alcohol dependence. However, the spatial localization of GM abnormalities reported in previous studies is heterogeneous. Here, we aimed to investigate the concurrence across VBM studies to help clarify the structural abnormalities underpinning this condition.

Methods

A systematic search from January, 2000 to November, 2014 was performed to identify VBM studies that compared alcohol dependent patients and healthy controls. A quantitative meta-analysis of whole-brain VBM studies to estimate regional GM abnormalities in alcohol dependent patients was performed using the Anisotropic Effect Size version of the Signed Differential Mapping (AES-SDM) software package.

Results

Nine studies consisting of 296 alcohol dependent patients and 359 healthy controls were included in the present meta-analyses. Regional GM atrophy in alcohol dependent patients was found in the prefrontal cortex (including the anterior cingulate cortex), the dorsal striatum/insula, and the posterior cingulate cortex consistently across studies. The results remained largely unchanged in the following jackknife sensitivity analyses.

Conclusions

By conducting the first comprehensive meta-analysis of VBM studies, we identified consistent regional GM atrophy particularly within several neurofunctional networks associated with alcohol dependence. Our study demonstrated a characteristic pattern of GM abnormalities and provided further insights into understanding the underlying nature of alcohol dependence.

Introduction

Alcohol dependence is an addictive disorder characterized by the persistent, excessive and uncontrollable use of alcohol, resulting in psychological and physical dependence (Schuckit, 2009). Alcohol dependence represents a significant health burden worldwide and substantially contributes to both direct and indirect preventable mortality and morbidity (Rehm et al., 2013). Multifaceted biological underpinnings including complex genetics, the environment, predisposing personality characteristics, and psychiatric comorbidities, contribute to the pathogenesis of alcohol abuse and dependence (Zahr et al., 2011). Animal experiments and postmortem studies revealed that alcoholism was associated with regional and global brain damage (Harper and Kril, 1994, Schulte et al., 2012, Zahr et al., 2011). Over the past three decades, noninvasive neuroimaging techniques have significantly contributed to our understanding of the effects of alcohol dependence on the brain structure, function, and metabolism and allowed us to identify the neural circuits involved in reward, motivation, memory, and inhibitory control, which mediated addictive behaviors (Buhler and Mann, 2011, Cui et al., 2013, Hill, 2010).

Numerous structural magnetic resonance imaging (MRI) studies have aimed to identify the key brain areas involved in the pathophysiology of alcoholism. Gray matter (GM) and white matter (WM) tissue loss and enlarged ventricles have been well documented in association with alcoholism (Buhler and Mann, 2011). Compared with the manual method of drawing regions of interest (ROI) to measure the volume of brain structures, the voxel-based morphometry (VBM) technique allows voxel-wise comparisons of the local density or volume of GM over the whole-brain between groups (Ashburner and Friston, 2000). VBM studies have reported that alcoholism is associated with widespread local atrophy in many cortical and subcortical regions (Chanraud et al., 2007, Charlet et al., 2014, Demirakca et al., 2011, Fein et al., 2013, Gizewski et al., 2013, Grodin et al., 2013, Jang et al., 2007, Li et al., 2011, Mechtcheriakov et al., 2007, Rando et al., 2011, van Holst et al., 2012). However, there was considerable variation in terms of the GM changes from these reports. Small and heterogeneous samples of participants as well as substantial methodological differences may have contributed to the inconsistency across studies. Thus, there has been increasing interest in using the meta-analysis approach to identify consistent results. Thus, selecting robust alterations of brain structures in alcoholism may open new avenues to target the neural substrates that may greatly advance our understanding of the alcohol-dependent pathophysiology and may serve as markers of therapeutic efficacy.

A newly developed meta-analytic tool for neuroimaging studies, namely, Signed Differential Mapping (SDM; Radua and Mataix-Cols, 2009), has been effectively applied in a variety of neurological and psychiatric diseases (Radua et al., 2014). SDM was built and improved to incorporate the positive features of existing peak-probability methods, such as Anatomical Likelihood Estimation (ALE) and Multilevel Kernel Density Analysis (MKDA; Radua and Mataix-Cols, 2009, Radua et al., 2014). SDM, which has recently been updated to the new version called Anisotropic Effect-Size SDM (AES-SDM), combines both peak coordinates and statistical parametric maps. In addition, it employs anisotropic kernels during the recreation of effect size maps to account for the anisotropy in the spatial covariance, thus allowing more exhaustive and accurate meta-analyses (Radua et al., 2014).

To date, no such quantitative meta-analysis of VBM studies on alcohol dependence has been performed. To obtain a better understanding of the neurobiological basis, the present work aims to meta-analyze the GM changes in alcohol dependence using AES-SDM in a voxel-wise fashion (Radua et al., 2014). In addition, we aim to explore the effects of available demographics and clinical characteristics on GM alternations in alcohol dependence if possible.

Section snippets

Data sources and study selection

Systematic and comprehensive searches were performed in the PubMed database from January, 2000 to November, 2014 using a combination of keywords (“voxel*” or “VBM” or “morphometry” or “gray matter”) and (“alcohol dependence” or “alcoholism” or “alcohol abuse” or “alcohol addiction”). In addition, we searched the CNKI (China National Knowledge Internet) database for reports published in Chinese. The reference lists of the included studies and relevant scholarly reviews as well as Google scholar

Results

We identified nine eligible studies for the meta-analysis involving 296 alcohol dependent patients and 359 healthy controls according to the previously described search criteria (Fig. 1; Chanraud et al., 2007, Charlet et al., 2014, Demirakca et al., 2011, Grodin et al., 2013, Jang et al., 2007, Li et al., 2011, Mechtcheriakov et al., 2007, Rando et al., 2011, van Holst et al., 2012). Table 1, Table 2 summarize some of the features of these investigations. These patients were diagnosed as having

Discussion

To the best of our knowledge, this is the first quantitative meta-analysis of VBM studies to determine brain GM alternations that predominantly occur in alcohol-dependent patients. The meta-analysis, which employed the updated AES-SDM method, pooled nine studies consisting of 296 alcohol dependent patients and 359 healthy controls and identified consistent regions of GM reductions in the prefrontal cortex (including the anterior cingulate cortex), dorsolateral striatum/insular cortex, and

Role of funding source

No funding supported this work.

Contributors

Study concept and design: PR Xiao, PL Pan, HC Shi, and JG Zhong. Acquisition of data: PL Pan, HC Shi, and PR Xiao. Analysis and interpretation of data: PL Pan, HC Shi, JG Zhong, and ZY Dai. Drafting the manuscript: PR Xiao and HC Shi. Critical revision of the manuscript for important intellectual content: PL Pan. Statistical analysis: PL Pan and HC Shi. Study supervision: YL Zhu.

Conflict of interest

The authors reported no biomedical financial interests or potential conflicts of interest.

Acknowledgments

We thank all the authors of the included studies. We especially thank Dr. Joaquim Radua for his kind help and suggestions. We are very grateful to the reviewers for their valuable comments to our manuscript.

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