Altered human brain anatomy in chronic smokers: a review of magnetic resonance imaging studies

Lifestyle risks associated with brain functional connectivity and structure

Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development

Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.

Association between cigarette smoking and Parkinson’s disease: a neuroimaging study

Background:

Mounting evidence has revealed an inverse association between cigarette smoking and the risk of Parkinson’s disease (PD). Meanwhile, cigarette smoking has been found to be associated with cognitive impairment in PD patients. However, the neural mechanisms of the association between cigarette smoking and PD are not fully understood.

Objective:

The aim of this study is to explore the neural mechanisms of the association between cigarette smoking and PD.

Methods:

A total of 129 PD patients and 69 controls were recruited from the Parkinson’s Progression Markers Initiative (PPMI) cohort, including 39 PD patients with regular smoking history (PD-S), 90 PD patients without regular smoking history (PD-NS), 26 healthy controls with regular smoking history (HC-S), and 43 healthy controls without regular smoking history (HC-NS). Striatal dopamine transporter (DAT) binding and gray matter (GM) volume of the whole brain were compared among the four groups.

Results:

PD patients showed significantly reduced striatal DAT binding compared with healthy controls, and HC-S showed significantly reduced striatal DAT binding compared with HC-NS. Moreover, smoking and PD showed a significant interaction effect in the left medial prefrontal cortex (mPFC). PD-S showed reduced GM volume in the left mPFC compared with PD-NS.

Conclusion:

The degeneration of dopaminergic neurons in PD results in a substantial reduction of the DAT and dopamine levels. Nicotine may act as a stimulant to inhibit the action of striatal DAT, increasing dopamine levels in the synaptic gap. The inverse alteration of dopamine levels between PD and nicotine addiction may be the reason for the inverse association between smoking and the risk of PD. In addition, the mPFC atrophy in PD-S may be associated with cognitive impairment.

Assessment of brain connectome alterations in male chronic smokers using structural and generalized q-sampling MRI

An association has been shown between chronic cigarette smoking and structural abnormalities in the brain areas related to several functions relevant to addictive behavior. However, few studies have focused on the structural alternations of chronic smoking by using magnetic resonance imaging (MRI). Also, it remains unclear how structural alternations are associated with tobacco-dependence severity and the positive/negative outcome expectances. The q-sampling imaging (GQI) is an advanced diffusion MRI technique that can reconstruct more precise and consistent images of complex oriented fibers than other methods. We aimed to use GQI to evaluate the impact of the neurological structure caused by chronic smoking. Sixty-seven chronic smokers and 43 nonsmokers underwent a MRI scan. The tobacco dependence severity and the positive/negative outcome expectancies were assessed via self-report. We used GQI with voxel-based statistical analysis (VBA) to evaluate structural brain and connectivity abnormalities. Graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also performed to identify the structural network differences among groups. Chronic smokers had smaller GM and WM volumes in the bilateral frontal lobe and bilateral frontal region. The GM/WM volumes correlated with dependence severity and outcome expectancies in the brain areas involving high-level functions. Chronic smokers had shape changes in the left hippocampal head and tail and the inferior brain stem. Poorer WM integrity in chronic smokers was found in the left middle frontal region, the right superior fronto-occipital fasciculus, the right temporal region, the left parahippocampus, the left anterior internal capsule, and the right inferior parietal region. WM integrity correlated with dependence severity and outcome expectancies in brain areas involving high-level functions. Chronic smokers had decreased local segregation and global integration among the brain regions and networks. Our results provide further evidence indicating that chronic smoking may be associated with brain structure and connectivity changes.

Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

Simple Summary

Addiction to tobacco is a serious health and economical problem because it is one of the most addictive and the most consumed substance in the world. Although well documented, and despite the desire of numerous smokers to quit, maintenance of abstinence is a daily challenge for most of them. The heterogeneity in achieving this maintenance raises the question of potential differences in brain reactivity. An emerging field of research has been interested in brain markers helping to identify individuals who are the most likely to relapse. Using brain imaging techniques such as Magnetic Resonance Imaging (MRI), one can hope it will be possible to offer tailored care for each patient.

Abstract

Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.

GABA concentrations in the anterior cingulate and dorsolateral prefrontal cortices: Associations with chronic cigarette smoking, neurocognition, and decision making

Chronic cigarette smoking is associated with regional metabolite abnormalities in choline-containing compounds, creatine-containing compounds, glutamate, and N-acetylaspartate. The effects of cigarette smoking on anterior frontal cortical gamma-aminobutyric acid (GABA) concentration are unknown. This study compared chronic smokers (n = 33) and nonsmokers (n = 31) on anterior cingulate cortex (ACC) and right dorsolateral prefrontal cortex (DLPFC) GABA+ (the sum of GABA and coedited macromolecules) concentrations and associations of GABA+ levels in these regions with seven neurocognitive domains of functioning, decision making, and impulsivity measures. Smokers had significantly lower right DLPFC GABA+ concentration than nonsmokers, but groups were equivalent on ACC GABA+ level. Across groups, greater number of days since end of menstrual cycle was related to higher GABA+ level in the ACC but not right DLPFC GABA+ concentration. In exploratory correlation analyses, higher ACC and right DLPFC GABA+ levels were associated with faster processing speed and better auditory-verbal memory, respectively, in the combined group of smokers and nonsmokers; in smokers only, higher ACC GABA+ was related to better decision making and auditory-verbal learning. This study contributes additional novel data on the adverse effects of chronic cigarette smoking on the adult human brain and demonstrated ACC and DLPFC GABA+ concentrations were associated with neurocognition and decision making/impulsivity in active cigarette smokers. Longitudinal studies on the effects of smoking cessation on regional brain GABA levels, with a greater number of female participants, are required to determine if the observed metabolite abnormalities are persistent or normalize with smoking cessation.

Abnormal white matter tracts of insula in smokers

Nicotine addiction is characterized as a neural circuit dysfunction, particularly with regard to the alterations in central reward pathways. The insula, a cortical region that is thought to play a central role in this reward circuitry, has been implicated in the maintenance of nicotine addiction. However, it remains largely unclear about the white matter (WM) microstructural alterations of insula in nicotine addiction and whether the WM alterations of insula could predict smoking cessation outcomes. In this study, 58 male nicotine-dependent smokers and 34 matched male nonsmoking controls were recruited. After a 12-week smoking cessation treatment with varenicline, 38 smokers relapsed, and 20 did not relapse. Diffusion tensor imaging and probabilistic tractography were used to investigate the differences of WM tracts of insula between smokers and nonsmokers. Relative to nonsmokers, in the left hemisphere, smokers showed lower fractional anisotropy (FA) in the fiber tracts of anterior insula cortex-to-nucleus accumbens and posterior insula cortex-to-nucleus accumbens; in the right hemisphere, smokers showed higher FA, and lower axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) in the fiber tracts of anterior insula cortex-to-medial orbitofrontal cortex, posterior insula cortex-to-medial orbitofrontal cortex, and posterior insula cortex-to-nucleus accumbens. However, there were no differences of WM diffusion properties between relapsers and nonrelapsers. This study is the first using probabilistic tractography to exclusively clarify the precise roles of insular WM tracts in smokers, which may provide new insights into the underlying neurobiology of nicotine addiction.

Associations of cigarette smoking with gray and white matter in the UK Biobank

Cigarette smoking is associated with increased risk for myriad health consequences including cognitive decline and dementia, but research on the link between smoking and brain structure is nascent. In the current study, we assessed the relationship of cigarette smoking with gray matter (GM) and white matter (WM) in the UK Biobank, controlling for numerous confounding demographic and health variables. We used negative-binomial regression to model the association of cigarette smoking (having ever smoked regularly, cigarettes per day, and duration smoked) with GM and WM (GM N = 19,615; WM N = 17,760), adjusting for confounders. Ever smoked and duration were associated with smaller total GM volume. Ever smoked was associated with reduced volume of the right VIIIa cerebellum and elevated WM hyperintensity volume. Smoking duration was associated with reduced total WM volume. Regarding specific tracts, ever smoked was associated with reduced fractional anisotropy in the left cingulate gyrus part of the cingulum, left posterior thalamic radiation, and bilateral superior thalamic radiation, and increased mean diffusivity in the middle cerebellar peduncle, right medial lemniscus, bilateral posterior thalamic radiation, and bilateral superior thalamic radiation. This study identified significant associations of cigarette exposure with global measures of GM and WM, and select associations of ever smoked, but not cigarettes per day or duration, with specific GM and WM regions. By controlling for important sociodemographic and health confounders, such as alcohol use, this study identifies distinct associations between smoking and brain structure, highlighting potential mechanisms of risk for common neurological sequelae (e.g., dementia).

Decreased brain connectivity in smoking contrasts with increased connectivity in drinking

In a group of 831 participants from the general population in the Human Connectome Project, smokers exhibited low overall functional connectivity, and more specifically of the lateral orbitofrontal cortex which is associated with non-reward mechanisms, the adjacent inferior frontal gyrus, and the precuneus. Participants who drank a high amount had overall increases in resting state functional connectivity, and specific increases in reward-related systems including the medial orbitofrontal cortex and the cingulate cortex. Increased impulsivity was found in smokers, associated with decreased functional connectivity of the non-reward-related lateral orbitofrontal cortex; and increased impulsivity was found in high amount drinkers, associated with increased functional connectivity of the reward-related medial orbitofrontal cortex. The main findings were cross-validated in an independent longitudinal dataset with 1176 participants, IMAGEN. Further, the functional connectivities in 14-year-old non-smokers (and also in female low-drinkers) were related to who would smoke or drink at age 19. An implication is that these differences in brain functional connectivities play a role in smoking and drinking, together with other factors.

Discrimination of smoking status by MRI based on deep learning method

Background

This study aimed to assess the feasibility of deep learning-based magnetic resonance imaging (MRI) in the prediction of smoking status.

Methods

The head MRI 3D-T1WI images of 127 subjects (61 smokers and 66 non-smokers) were collected, and 176 image slices obtained for each subject. These subjects were 23-45 years old, and the smokers had at least 5 years of smoking experience. Approximate 25% of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the remaining subjects as the training set. Two deep learning models were developed: deep 3D convolutional neural network (Conv3D) and convolution neural network plus a recurrent neural network (RNN) with long short-term memory architecture (ConvLSTM).

Results

In the prediction of smoking status, Conv3D model achieved an accuracy of 80.6% (25/31), a sensitivity of 80.0% and a specificity of 81.3%, and ConvLSTM model achieved an accuracy of 93.5% (29/31), a sensitivity of 93.33% and a specificity of 93.75%. The accuracy obtained by these methods was significantly higher than that (<70%) obtained with support vector machine (SVM) methods.

Conclusions

The deep learning-based MRI can accurately predict smoking status. Studies with large sample size are needed to improve the accuracy and to predict the level of nicotine dependence.

Brain Structure Alterations in Respect to Tobacco Consumption and Nicotine Dependence: A Comparative Voxel-Based Morphometry Study

The main purpose of this study is to examine the lifetime tobacco consumption and the degree of nicotine dependence related gray matter (GM) and white matter (WM) volume alterations in young adult-male smokers. Fifty-three long-term male smokers and 53 well-matched male healthy non-smokers participated in the study, and the smokers were respectively categorized into light and heavy tobacco consumption subgroups by pack-years and into moderate and severe nicotine dependence subgroups using the Fagerström Test for Nicotine Dependence (FTND). Voxel-based morphometry analysis was then performed, and ANCOVA analysis combined with subsequent post hoc test were used to explore the between-group brain volume abnormalities related to the smoking amount and nicotine dependence. Light and heavy smokers displayed smaller GM and WM volumes than non-smokers, while heavy smokers were found with more significant brain atrophy than light smokers in GM areas of precuneus, inferior and middle frontal gyrus, superior temporal gyrus, cerebellum anterior lobe and insula, and in WM areas of cerebellum anterior lobe. However, the contrary trend was observed regarding alterations associated with severity of nicotine dependence. Severe nicotine dependence smokers rather demonstrated less atrophy levels compared to moderate nicotine dependence smokers, especially in GM areas of precuneus, superior and middle temporal gyrus, middle occipital gyrus, posterior cingulate and insula, and in WM areas of precuneus, posterior cingulate, cerebellum anterior lobe and midbrain. The results reveal that the nicotine dependence displays a dissimilar effect on the brain volume in comparison to the cigarette consumption. Our study could provide new evidences to understand the adverse effects of smoking on the brain structure, which is helpful for further treatment of smokers.

Effect of tobacco smoking on frontal cortical thickness development: A longitudinal study in a mixed cohort of ADHD-affected and -unaffected youth

Smoking rates are particularly high during adolescence and young adulthood, when the brain is still undergoing significant developmental changes. Cross-sectional studies have revealed altered brain structure in smokers, such as thinner frontal cortical areas. Attention-deficit/hyperactivity disorder (ADHD) increases the risk of becoming nicotine-dependent, and has also been associated with abnormalities in frontal gray matter structure. The present study examines the relationships between smoking, cortical thickness and ADHD symptoms in a longitudinal design that compares adolescent and young adult smokers (n=44; 35 ADHD-affected) and non-smokers (n=45; 32 ADHD-affected) on frontal cortical thickness. Average frontal cortical thickness was estimated through structural magnetic resonance imaging (MRI) at two time points (mean ages 17.7 and 21.1 years), on average 3.4 years apart. Smokers had a 2.6% thinner frontal cortex than non-smokers and this difference was not explained by ADHD or other confounding factors. The rate of cortical thinning across the 3.4-year MRI measurement interval was similar in the total group of smokers compared to non-smokers. However, speeded thinning did occur in smokers who had started regular smoking more recently, in between the two measurements. These novel regular smokers did not differ significantly from the non-smokers at baseline. This suggests that the thinner frontal cortex was not a predisposing factor but rather a consequence of smoking. Although smokers had more ADHD symptoms overall, smoking did not influence the developmental course of ADHD symptoms.

A Study of the Feasibility of FDG-PET/CT to Systematically Detect and Quantify Differential Metabolic Effects of Chronic Tobacco Use in Organs of the Whole Body-A Prospective Pilot Study

RATIONALE AND OBJECTIVES

The aim of this study was to assess the feasibility of ¹⁸F-fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) to systematically detect and quantify differential effects of chronic tobacco use in organs of the whole body.

MATERIALS AND METHODS

Twenty healthy male subjects (10 nonsmokers and 10 chronic heavy smokers) were enrolled. Subjects underwent whole-body FDG-PET/CT, diagnostic unenhanced chest CT, mini-mental state examination, urine testing for oxidative stress, and serum testing. The organs of interest (thyroid, skin, skeletal muscle, aorta, heart, lung, adipose tissue, liver, spleen, brain, lumbar spinal bone marrow, and testis) were analyzed on FDG-PET/CT images to determine their metabolic activities using standardized uptake value (SUV) or metabolic volumetric product (MVP). Measurements were compared between subject groups using two-sample t tests or Wilcoxon rank-sum tests as determined by tests for normality. Correlational analyses were also performed.

RESULTS

FDG-PET/CT revealed significantly decreased metabolic activity of lumbar spinal bone marrow (MVPmean: 29.8 ± 9.7 cc vs 40.8 ± 11.6 cc, P = 0.03) and liver (SUVmean: 1.8 ± 0.2 vs 2.0 ± 0.2, P = 0.049) and increased metabolic activity of visceral adipose tissue (SUVmean: 0.35 ± 0.10 vs 0.26 ± 0.06, P = 0.02) in chronic smokers compared to nonsmokers. Normalized visceral adipose tissue volume was also significantly decreased (P = 0.04) in chronic smokers. There were no statistically significant differences in the metabolic activity of other assessed organs.

CONCLUSIONS

Subclinical organ effects of chronic tobacco use are detectable and quantifiable on FDG-PET/CT. FDG-PET/CT may, therefore, play a major role in the study of systemic toxic effects of tobacco use in organs of the whole body for clinical or research purposes.

Altered spontaneous brain activity in chronic smokers revealed by fractional ramplitude of low-frequency fluctuation analysis: a preliminary study

Although a substantial body of previous functional magnetic resonance imaging (fMRI) studies have revealed different brain responses to external stimuli in chronic cigarette smokers compared with nonsmokers, only a few studies assessed brain spontaneous activity in the resting state in chronic smokers. The aim of this study was to investigate alterations of brain activity during the resting state in chronic smokers using fractional amplitude of low-frequency fluctuation (fALFF). In the present study, 55 smokers and 49 healthy nonsmokers were included. All the subjects underwent resting-state fMRI scans and the data were analyzed by the fALFF approach. The smokers showed significantly decreased fALFF in the left precuneus, right inferior temporal and occipital gyrus(ITG/IOG), while significantly increased fALFF in the right caudate. Subsequent correlation analysis revealed that the fALFF values of the left precuneus and right ITG/IOG were positively correlated with years of smoking across the smokers. This resting-state fMRI study suggests that the changed spontaneous neuronal activity, as reflected by the fALFF, in these regions may be implicated in the underlying the pathophysiology of smoking.

Structural Neuroimaging in Polysubstance Users

The simultaneous and/or concurrent use of licit and illicit substances (polysubstance use, PSU) is most common today. Structural magnetic resonance imaging (MRI) has been applied extensively to study individuals ostensibly using a single substance. These studies have produced a picture of regional gray matter and white matter alterations with each substance or class of substances. Very few studies measured regional brain morphometry in today's polysubstance users. This limited data suggest morphometric alterations with PSU that are not simply additive but often different from those of monosubstance users. Specifically, subcortical volume enlargements are observed that may be tied to mechanisms that also oppose volume reductions in cortical brain regions, thereby underestimating actual cortical atrophy. The complex actions of polysubstance use on brain structure and function need greater scrutiny with strong methodological approaches to inform more efficient treatment of polysubstance users.

Comparison of color discrimination in chronic heavy smokers and healthy subjects

Background: Cigarette smoke is probably the most significant source of exposure to toxic chemicals for humans, involving health-damaging components, such as nicotine, hydrogen cyanide and formaldehyde. The aim of the present study was to assess the influence of chronic heavy smoking on color discrimination (CD). Methods: All subjects were free of any neuropsychiatric disorder, identifiable ocular disease and had normal acuity. No abnormalities were detected in the fundoscopic examination and in the optical coherence tomography exam. We assessed color vision for healthy heavy smokers ( n = 15; age range, 20-45 years), deprived smokers ( n = 15, age range 20-45 years) and healthy non-smokers ( n = 15; age range, 20-45 years), using the psychophysical forced-choice method. All groups were matched for gender and education level. In this test, the volunteers had to choose the pseudoisochromatic stimulus containing a test frequency at four directions (e.g., up, down, right and left) in the subtest of Cambridge Colour Test (CCT): Trivector. Results: Performance on CCT differed between groups, and the observed pattern was that smokers had lower discrimination compared to non-smokers. In addition, deprived smokers presented lower discrimination to smokers and non-smokers. Contrary to expectation, the largest differences were observed for medium and long wavelengths. Conclusions: These results suggests that cigarette smoking, chronic exposure to its compounds, and withdrawal from nicotine affect color discrimination. This highlights the importance of understanding the diverse effects of nicotine on attentional bias.

Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations

Background

Whereas acute nicotine administration alters brain function which may, in turn, contribute to enhanced attention and performance, chronic cigarette smoking is linked with regional brain atrophy and poorer cognition. However, results from structural magnetic resonance imaging (MRI) studies comparing smokers versus nonsmokers have been inconsistent and measures of gray matter possess limited ability to inform functional relations or behavioral implications. The purpose of this study was to address these interpretational challenges through meta-analytic techniques in the service of clarifying the impact of chronic smoking on gray matter integrity and more fully contextualizing such structural alterations.

Methods

We first conducted a coordinate-based meta-analysis of structural MRI studies to identify consistent structural alterations associated with chronic smoking. Subsequently, we conducted two additional meta-analytic assessments to enhance insight into potential functional and behavioral relations. Specifically, we performed a multimodal meta-analytic assessment to test the structural–functional hypothesis that smoking-related structural alterations overlapped those same regions showing acute nicotinic drug-induced functional modulations. Finally, we employed database driven tools to identify pairs of structurally impacted regions that were also functionally related via meta-analytic connectivity modeling, and then delineated behavioral phenomena associated with such functional interactions via behavioral decoding.

Results

Across studies, smoking was associated with convergent structural decreases in the left insula, right cerebellum, parahippocampus, multiple prefrontal cortex (PFC) regions, and the thalamus. Indicating a structural–functional relation, we observed that smoking-related gray matter decreases overlapped with the acute functional effects of nicotinic agonist administration in the left insula, ventromedial PFC, and mediodorsal thalamus. Suggesting structural-behavioral implications, we observed that the left insula’s task-based, functional interactions with multiple other structurally impacted regions were linked with pain perception, the right cerebellum’s interactions with other regions were associated with overt body movements, interactions between the parahippocampus and thalamus were linked with memory processes, and interactions between medial PFC regions were associated with face processing.

Conclusions

Collectively, these findings emphasize brain regions (e.g., ventromedial PFC, insula, thalamus) critically linked with cigarette smoking, suggest neuroimaging paradigms warranting additional consideration among smokers (e.g., pain processing), and highlight regions in need of further elucidation in addiction (e.g., cerebellum).

Electronic supplementary material

The online version of this article (doi:10.1186/s12993-016-0100-5) contains supplementary material, which is available to authorized users.

Voxelwise meta-analysis of gray matter anomalies in chronic cigarette smokers

BACKGROUND

Evidence from previous voxel-based morphometry (VBM) studies revealed that widespread brain regions are involved in chronic smoking. However, the spatial localization reported for gray matter (GM) abnormalities is heterogeneous. The aim of the present study was quantitatively to integrate studies on GM abnormalities observed in chronic smokers.

METHODS

A systematic search of the PubMed, Web of Knowledge and Science Direct databases from January 1, 2000 to July 31, 2015 was performed to identify eligible whole-brain VBM studies. Comprehensive meta-analyses to investigate regional GM abnormalities in chronic smokers were conducted with the Seed-based d Mapping software package.

RESULTS

Eleven studies comprising 686 chronic cigarette smokers and 1024 nonsmokers were included in the meta-analyses. Consistently across studies, the chronic smokers showed a robust GM decrease in the bilateral prefrontal cortex and a GM increase in the right lingual cortex. Moreover, meta-regression demonstrated that smoking years and cigarettes per day were partly correlated with GM anomalies in chronic cigarette smokers.

CONCLUSIONS

The convergent findings of this quantitative meta-analysis reveal a characteristic neuroanatomical pattern in chronic smokers. Future longitudinal studies should investigate whether this brain morphometric pattern can serve as a useful target and a prognostic marker for smoking intervention.