Cigarette smoke contains nicotine and toxic chemicals and may cause significant neurochemical and anatomical brain changes. Voxel-based morphometry studies have examined the effects of smoking on the brain by comparing gray matter volume (GMV) in nicotine dependent individuals (NDs) to nonsmoking individuals with inconsistent results. Although sex differences in neural and behavioral features of nicotine dependence are reported, sex differences in regional GMV remain unknown. The current study examined sex differences in GMV in a large sample of 80 NDs (41 males) and 80 healthy controls (41 males) using voxel-based morphometry. Within NDs, we explored whether GMV was correlated with measures of cigarette use and nicotine dependence. High-resolution T1 structural scans were obtained from all participants. Segmentation and registration were performed in SPM8 using the optimized DARTEL approach. Covariates included age and an estimate of total global GMV. Differences were considered significant at p≤0.001, with a whole brain FWE-corrected cluster probability of p<0.025. Among NDs compared to Controls less GMV was observed in the thalamus and bilateral cerebellum and greater GMV was observed in the bilateral putamen and right parahippocampus. Lower thalamic GMV was observed in both female and male NDs compared to Controls. Female NDs also had lower GMV in the left cerebellum and in the ventral medial and orbitofrontal cortices with no areas of greater GMV. Male NDs had lower GMV in bilateral cerebellum and greater GMV in bilateral parahippocampus and left putamen. Within male NDs, GMV in the left putamen was correlated with number of pack years. This study, conducted in a large cohort, contributes to our knowledge of brain morphology in nicotine addiction and provides additional evidence of sex-specific effects on GMV in NDs. Identifying brain vulnerabilities with respect to sex provides a methodological framework for personalized therapies to improve relapse rates for both sexes.

Background:Structural magnetic resonance imaging techniques are powerful tools for examining the effects of drug use on the brain. The nicotine and cannabis literature has demonstrated differences between nicotine cigarette smokers and cannabis users compared to controls in brain structure; however, less is known about the effects of co-occurring cannabis and tobacco use.Methods:We used voxel-based morphometry to examine gray matter volume differences between four groups: (1) cannabis-dependent individuals who do not smoke tobacco (Cs); (2) cannabis-dependent individuals who smoke tobacco (CTs); (3) cannabis-naïve, nicotine-dependent individuals who smoke tobacco (Ts); and (4) healthy controls (HCs). We also explored associations between gray matter volume and measures of cannabis and tobacco use.Results:A significant group effect was observed in the left putamen, thalamus, right precentral gyrus, and left cerebellum. Compared to HCs, the Cs, CTs, and Ts exhibited larger gray matter volumes in the left putamen. Cs also had larger gray matter volume than HCs in the right precentral gyrus. Cs and CTs exhibited smaller gray matter volume than HCs in the thalamus, and CTs and Ts had smaller left cerebellar gray matter volume than HCs.Conclusions:This study extends previous research that independently examined the effects of cannabis or tobacco use on brain structure by including an examination of co-occurring cannabis and tobacco use, and provides evidence that cannabis and tobacco exposure are associated with alterations in brain regions associated with addiction.

Background Biological sex influences cigarette smoking behavior. More men than women smoke, but women have a harder time quitting. Sex differences in smoking cue (SC) reactivity may underlie such behavioral differences. However, the influence of sex on brain reactivity to SCs has yielded inconsistent findings suggesting the need for continued study. Here, we investigated the effect of sex on SC reactivity across two sites using different imaging modalities and SC stimulus types. Methods Pseudo-continuous arterial spin-labeled (pCASL) perfusion fMRI was used to assess brain responses to SC versus non-SC videos in 40 smokers (23 females) at the University of Pennsylvania. BOLD fMRI was used to assess brain responses to SC versus non-SC still images in 32 smokers (18 females) at McLean Hospital. Brain reactivity to SCs was compared between men and women and was correlated with SC-induced craving. Results In both cohorts, males showed higher SC versus non-SC reactivity compared to females in reward-related brain regions (i.e., ventral striatum/ventral pallidum, ventral medial prefrontal cortex). Brain activation during SC versus non-SC exposure correlated positively with SC-induced subjective craving in males, but not females. Conclusions The current work provides much needed replication and validation of sex differences in SC-reactivity. These findings also add to a body of literature showing that men have greater reward-related brain activation to drug cues across drug classes. Such sex differences confirm the need to consider sex not only when evaluating SC-reactivity but when examining nicotine dependence etiology and treatment.

Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n = 108; 63 male) and matched nonsmoking controls (n = 108; 63 male) and multi-dimensional scaling to visualize the heterogeneity of NUD in individual smokers based on rsFC measures. Machine-based learning models identified five resting-state networks that played a role in distinguishing smokers from controls: the posterior and anterior default mode networks, the sensorimotor network, the salience network and the right executive control network. The classification method constructed classifiers with an average correct classification rate of 88.1 percent and an average area under the curve of 0.93. Compared with controls, individuals with NUD had weaker functional connectivity measures within these networks (P < 0.05, false discovery rate corrected). Further, multi-dimensional scaling visualization demonstrated that controls were similar to each other whereas individuals with NUD had less similarity to controls and to other individuals with NUD. Our findings build upon previous literature demonstrating that machine learning-based approaches to classifying rsFC data offer a valuable technique to understanding network-level differences in nicotine-related neurobiology and extend previous findings by improving classification accuracy and demonstrating the heterogeneity in resting-state networks of individuals with NUD.

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