Reduced Thalamus Volume May Reflect Nicotine Severity in Young Male Smokers

Reduced Thalamus Volume May Reflect Nicotine Severity in Young Male Smokers Abstract Introduction Nicotine acts as an agonist at presynaptic nicotinic acetylcholine receptors and to facilitate synaptic release of several neurotransmitters including dopamine and glutamate. The thalamus has the highest density of nicotinic acetylcholine receptors in the brain, which may make this area more vulnerable to the addictive effects of nicotine. However, the volume of thalamus abnormalities and the association with smoking behaviors in young smokers remains unknown. Methods Thirty-six young male smokers and 36 age-, gender- and education-matched nonsmokers participated in the current study. The nicotine dependence severity and cumulative effect were assessed with the Fagerström test for nicotine dependence (FTND) and pack-years. We used subcortical volume analyses method in FreeSurfer to investigate the thalamus volume differences between young smokers and nonsmokers. Correlation analysis was used to investigate the relationship between thalamus volume and smoking behaviors (pack-years and FTND) in young smokers. Results and Conclusions Relative to nonsmokers, the young smokers showed reduced volume of bilateral thalamus. In addition, the left thalamus volume was correlated with FTND in young smokers. It is hoped that our findings can shed new insights into the neurobiology of young smokers. Implications In this article, we investigated the changes of thalamus volume in young male smokers compared with nonsmokers. Reduced left thalamus volume was correlated with FTND in young smokers, which may reflect nicotine severity in young male smokers. Introduction As the leading preventable cause of disease and death,1 smoking was associated with the high risk of several serious diseases, such as cardiovascular diseases2 and chronic obstructive pulmonary diseases.3 Interestingly, approximately 80% of adult smokers have progressed to nicotine dependence by 18 years of age, and people who do not start smoking as teenagers are unlikely ever to do so.4 Developmental plasticity renders the brain of adolescents and young adults vulnerable to the effects of nicotine and smoking.5 During this special development period, the brain of adolescents continues to develop structurally and functionally,6 which has been implicated as a cause of maladaptive decision making associated with immature cognitive control.7 Nicotine exposure during adolescence has been linked to subsequent deficits in brain circuits of attention and memory.8 As the largest consumer of cigarette, China has 350 million smokers including 14 million young smokers (www.chinacdc.cn). The Chinese Center for Disease Control and Prevention announced the latest national survey of youth smoking in May 2014, which reported that the smoking rate of junior high school students was 10.6% for males and 1.8% for females in China (http://www.chinacdc.cn/). Based on global smoking patterns, 88% of adult smokers begin smoking by the age of 18 and 99% by the age of 26.9,10 Young smokers are more likely to become life-long smokers and are more susceptible to nicotine addiction than adults.11 Nicotine exposure during prenatal or adolescent period may disrupt brain development to manifest as disturbances in cognitive functioning and behavioral changes.12–14 In the period from late adolescence to young adult, important changes in brain maturation occur, which may be especially sensitive to environmental perturbations and to nicotine exposure through cigarettes.15 However, very little is known about alterations in brain structure that may occur in young smokers, especially for the volume of thalamus and its association with smoking behaviors. Therefore, it is of great significance to investigate the underlying neural mechanisms of young smokers. Nicotine plays a critical role in the highly addictive properties of tobacco use.16 Acting as an agonist at presynaptic nicotinic acetylcholine receptors (nAChRs), nicotine facilitates synaptic release of several neurotransmitters including dopamine and glutamate.17 It is noteworthy that the thalamus has the highest density of nAChRs in the brain, which may make this area more vulnerable to the addictive effects of nicotine.17,18 The gray matter density and volume of thalamus abnormalities of smokers were discussed in previous voxel-based morphometry (VBM) studies.19–21 Nevertheless, the study by Brody et al.22 showed no differences of gray matter in the thalamus between smokers and nonsmokers. VBM studies were sensitive to a combination of changes in gray matter thickness, intensity, cortical surface area, and cortical folding, and may suffer from several limitations, like the degree of smoothing, registration techniques, and choice of normalization templates.23–25 Measurements of cortical thickness and subcortical volumetric segmentation are other methods of detecting GM structural changes in the FreeSurfer (http://surfer.nmr.mgh.harvard.edu).26 However, few studies used subcortical volumetric segmentation method to investigate the volume changes of the thalamus in young smokers. Therefore, we investigated the thalamus volume differences between young adult smokers and nonsmokers by subcortical volumetric segmentation method in the current study, which has proved to be more sensitive to the changes of thalamus than VBM.27–31 Moreover, we assessed the relationship between structural changes in the thalamus and the characteristic of young smokers. Pack-years may reflect the cumulative effect of smoking and Fagerström test for nicotine dependence (FTND) may reflect the severity of smoking. We hypothesized that abnormal thalamus volume would be detected in young smokers and these changes would be associated with smoking behavior. Materials and methods The study was approved by the Ethical Committee of Xi’an Jiaotong University and was conducted in accordance with the Declaration of Helsinki. All subjects and their legal guardians gave written informed consent prior to participation in the study. Participants Thirty-six male, right-handed subjects and 36 age- and education-matched male nonsmokers were recruited from the local university. All young smokers were screened according to the DSM-V criteria for current nicotine dependence as described in our previous studies.28,31–34 The participants did not smoke other products besides cigarettes (eg, e-cigarettes, cigars). Nicotine dependence severity was assessed with FTND.35 All smokers consumed more than 10 cigarettes per day in the last 6 months with expired air carbon monoxide (CO)>6 parts per million (ppm). Additionally, none of smokers had attempted to quit smoking or undergo smoking abstinence for longer than 3 months in the past year. The nonsmokers had smoked less than 5 cigarettes in their lifetime with expired CO≤3 ppm. Exclusion criteria for both groups were as follows: (1) any physical illness such as a brain tumor, obstructive lung disease, hepatitis, or epilepsy as assessed according to clinical evaluations and medical records; (2) any current medications that may affect cognitive functioning; (3) alcohol or drug abuse;(4) existence of a neurological disease; (5) claustrophobia; and (6) any participants with psychiatric disorder as assessed by the structured clinical interview for DSM-V. To remove the possible effect of other addiction, the exclusion criteria for both groups included any drug abuse, and tobacco and alcohol intake. Detailed demographic data are shown in Table 1. Table 1. Clinical Details of Young Smokers and Nonsmokers Clinical details  Smokers (n = 36)  Nonsmokers (n = 36)  P-value  Age (years)  20.8 ± 1.8  20.1 ± 1.2  0.22  Age range (years)  16–24  16–23    Education (years)  13.5 ± 1.3  13.3 ± 0.6  0.21  Age at start of smoking  15.1 ± 2.6  –  –  Cigarettes smoked per day  15.4 ± 4.5  –  –  Pack-years  3.5 ± 2.3  –  –  FTND  5.0 ± 1.9  –  –  Clinical details  Smokers (n = 36)  Nonsmokers (n = 36)  P-value  Age (years)  20.8 ± 1.8  20.1 ± 1.2  0.22  Age range (years)  16–24  16–23    Education (years)  13.5 ± 1.3  13.3 ± 0.6  0.21  Age at start of smoking  15.1 ± 2.6  –  –  Cigarettes smoked per day  15.4 ± 4.5  –  –  Pack-years  3.5 ± 2.3  –  –  FTND  5.0 ± 1.9  –  –  Data are means ± standard deviations. Pack-years = Years of smoking × Cigarettes smoked per day/20; FTND = Fagerström test for nicotine dependence. View Large Data Acquisition All MRI images were acquired using a 3-T MRI system (EXCITE, General Electric, Milwaukee, WI) at the First Affiliated Hospital of the Medical College, Xi’an Jiaotong University, China. A standard birdcage head coil was used, along with restraining foam pads, to minimize head motion and to diminish scanner noise. High-resolution 3D T1-weighted images were acquired for subcortical volume measurements with the following parameters: repetition time = 8.5 ms; echo time = 3.4 ms; flip angle = 12°; field of view = 240 × 240 mm2; data matrix = 240 × 240; slices = 140; and voxel size = 1 × 1 × 1 mm3. To prevent the potential effect of brain damage on results, for each subject, two neurologists examined the T1-weighted 3D images for structural lesions in the brain and no subject was excluded. Data Analysis Subcortical volumetric segmentation of the whole brain was performed with the FreeSurfer software package (Version 5.1.0, http://surfer.nmr.mgh.harvard.edu), which had been used widely in our previous studies.27,29–31,36 In more detail, the preprocessing was as follows: (1) removal of nonbrain tissue; (2) automated Talairach transformation; (3) segmentation of the subcortical white matter and deep gray matter volumetric structures; (4) intensity normalization; (5) tessellation of the gray matter/white matter boundary; (6) automated topology correction; (7) surface deformation; and (8) registration of the subjects’ brains to a common spherical atlas. The results implemented in the Freesurfer analysis were checked by a neurologist and manual adjustments were carried out if necessary. The thalamus was extracted according to Harvard subcortical structural atlas (http://www.cma.mgh.harvard.edu/). After the preprocessing, the volumes of bilateral thalamus and intracranial volume (ICV) were extracted and imported into SPSS 20.0 software for statistical analyses (SPSS Statistics, IBM, Armonk, NY). For each structure, a general linear model was fit with volume as the dependent variable, diagnosis (young smokers and nonsmokers) as the categorical predictors, and ICV as covariates. To correct for multiple comparisons, the Bonferroni correction was used. All tests were two-tailed, and the level of significance was p < 0.025 (0.05/2). Pearson correlation was applied between the absolute volumes of bilateral thalamus and smoking variables (pack-years and FTND). To correct for multiple comparisons of correlation analysis, the Bonferroni correction was also used (p < 0.0125 (0.05/4)). Results No significant ICV difference (p > 0.05) between the young smokers (1149060 ± 209249 mm3) and nonsmokers (1106206 ± 149594 mm3) was shown. Relative to nonsmokers, young smokers showed reduced volume in the left thalamus (F = 17.277, p < 0.001, Bonferroni correction), after controlling ICV as the covariate. Similarly, decreased volumes of the right thalamus (F = 14.617, p < 0.001) in young smokers were also observed when compared with nonsmokers (Figure 1). Correlation between smoking and body weight has been documented, for example, in the short term, nicotine increases energy expenditure and smokers tend to have lower body weight than nonsmokers and gray matter volume of thalamus is also associated with body weight status.37,38 To exclude the possible effect of BMI on our results, we analyzed the thalamus volume differences controlling the BMI as covariates and found reduced volume of left thalamus (F = 16.4, p < 0.001, Bonferroni correction) and right thalamus (F = 14.1, p < 0.001, Bonferroni correction) in young male smokers. Moreover, correlation analysis results showed that the volume of left thalamus was negatively correlated with FTND in smokers (r = -0.4285, p = 0.0091) (Figure 2). The volume of left thalamus was also negatively correlated with pack-years in smokers (r = -0.4031, p = 0.0148). With regard to the right hemisphere, the volume of right thalamus was negatively correlated with pack-years (r = -0.3604, p = 0.0309) and FTND ( = -0.3482, p = 0.0374) in smokers, although the results did not survive the Bonferroni correction. Figure 1. View largeDownload slide Relative to nonsmokers, young smokers showed reduced volume in left thalamus (F = 17.277, p < 0.001, Bonferroni correction), after controlling intracranial volume (ICV) as the covariates. Similarly, decreased volumes of right thalamus (F = 14.617, p < 0.001) in young smokers were also observed when compared with nonsmokers. Figure 1. View largeDownload slide Relative to nonsmokers, young smokers showed reduced volume in left thalamus (F = 17.277, p < 0.001, Bonferroni correction), after controlling intracranial volume (ICV) as the covariates. Similarly, decreased volumes of right thalamus (F = 14.617, p < 0.001) in young smokers were also observed when compared with nonsmokers. Figure 2. View largeDownload slide Correlation analysis results showed that (a) the volume of left thalamus was also negatively correlated with pack-years in smokers (r = −0.4031, p = 0.0148). (b) The volume of left thalamus was negatively correlated with FTND in smokers (r = −0.4285, p = 0.0091). (c) The volume of right thalamus was negatively correlated with pack-years (r = −0.3604, p = 0.0309). (d) The volume of right thalamus was negatively correlated with FTND (r = −0.3482, p = 0.0374) in smokers. Although the results did not survive the Bonferroni correction. Figure 2. View largeDownload slide Correlation analysis results showed that (a) the volume of left thalamus was also negatively correlated with pack-years in smokers (r = −0.4031, p = 0.0148). (b) The volume of left thalamus was negatively correlated with FTND in smokers (r = −0.4285, p = 0.0091). (c) The volume of right thalamus was negatively correlated with pack-years (r = −0.3604, p = 0.0309). (d) The volume of right thalamus was negatively correlated with FTND (r = −0.3482, p = 0.0374) in smokers. Although the results did not survive the Bonferroni correction. Discussion Just as the adverse effects of smoking on general health are well known, there is a growing consensus that a long history of nicotine smoking is associated with alterations in neural tissue integrity throughout the frontal–striatal–thalamic circuits involved in cognitive control and reward processing.19,22,28,31,39 Recently, we detected the structural and functional abnormalities within the frontostriatal circuits in young smokers.28,31,33 For instance, reduced cortical thickness of the prefrontal cortex and increased caudate volume were observed in young adult smokers.28 The resting state functional connectivity between dorsolateral prefrontal cortex and striatum (caudate) was disrupted and correlated with the cognitive control deficits in young smokers.31 In contrast, few studies focused on the implication of the volume of thalamus with smoking behaviors in young smokers. In the current study, we contributed toward filling this gap by revealing the reduced volume of bilateral thalamus in young smokers compared with nonsmokers (Figure 1). Moreover, the volume of left thalamus was correlated with the FTND in young smokers, which suggested that reduced thalamus volume may be related to nicotine severity in young smokers (Figure 2). Although the structural deficits of thalamus in smokers had been revealed in previous VBM studies,19,20 there were some inconsistent results, that is, no differences of gray matter in the thalamus between smokers and nonsmokers.22 This may be due to the sample size, different age range of participants, and the choice of VBM. Therefore, in current study, we enrolled a relatively homogenous sample of young smokers and employed a more sensitive parameter (subcortical volume) to assess the thalamus volume changes. Our results demonstrated that the bilateral thalamus showed reduced volume in young smokers (Figure 1). As the highly addictive property of tobacco use,16 nicotine facilitates synaptic release of several neurotransmitters by acting as an agonist at nAChRs.17 The thalamus has the highest density of nAChRs, which may render this area more vulnerable to the addictive effects of nicotine.17,18 Previous studies had investigated functional changes in the thalamus and the association with the neurobiology of substance addiction including cigarette smoking.40,41 Nicotine can induce abnormal activation of the thalamus in smokers,18,42 which may suggest that nicotine is capable of influencing the function of thalamus. Evidence suggests that cigarette smoking plays a critical role in inducing changes in the thalamus. Thus, it is rational that the volume of thalamus would be different between the young smokers and nonsmokers. In current study, we also found that the volume of left thalamus was correlated with the FTND in young smokers (Figure 2). As the key nodes of frontal–striatal–thalamic circuits involved in cognitive control and reward processing43, the thalamus played several roles in smoking behaviors. There was a positive correlation between the increase of cerebral blood flow in the thalamus during abstinence and the abstinence-induced craving,41 and an inverse correlation between abstinence-induced global withdrawal symptoms and reductions in cerebral blood flow in the thalamus.44 Reactivity to smoking-related cues may be an important factor that precipitates relapse in smokers, which had been investigated in several fMRI studies.45 Smoking cues reliably evoke larger responses of thalamus in smokers.45 Recently, stronger resting state functional connectivity between thalamus and anterior cingulate cortex was associated with the increased risky decision making and severity of nicotine dependence in young smokers.46 Our results suggested that the volume of left thalamus can reflect the severity of nicotine dependence of young smokers. Evidently, more comprehensive experiment design is necessary to reveal the accurate role of the reduced volume of thalamus in young smokers. The cross-sectional design made it difficult to clarify any causal relationships. We could not be certain about whether the reduced volume of thalamus was a consequence of nicotine addiction or a cause of nicotine addiction. We cannot deny the possibility that the abnormalities in the thalamus may have existed even before they have begun smoking and potentially be a predisposing factor or marker of vulnerability to nicotine dependence. We only controlled the acute alcohol use and did not control the regular use. Moderate alcohol use can impact thalamic volume and that moderate alcohol use does not necessarily imply alcohol abuse. Given that smokers have higher rates of drinking, it seems reasonable to assume that the smoking group probably also drinks at higher levels/rates than the non-smoking group. The possible effect of alcohol use on the volume of thalamus in smokers should be investigated in the future. Previous study had demonstrated that adolescents with prenatal exposure to tobacco showed a trend toward smaller volumes in thalamus than their peers who were not exposed to prenatal tobacco.47 Moreover, adolescents with smaller volume of thalamus showed higher levels of impulsivity, a factor known to contribute to smoking initiation.19 Conclusion We validated the reduced volume of thalamus and its association with smoking behaviors in young smokers, that is the left thalamus volume may reflect the nicotine severity in young smokers. It is hoped that our findings can shed new insights into the neurobiology of young smokers. Funding This paper is supported by the National Natural Science Foundation of China under Grant nos. 81571751, 81571753, 81301281, 61502376, 81401478, 81401488, 81470816, 61431013, 81471737, 81271546, and 81271549; the Natural Science Basic Research Plan in Shaanxi Province of China under Grant no. 2014JQ4118; the Fundamental Research Funds for the Central Universities under the Grant nos. JBG151207, JB161201, JB151204, and JB121405; the Natural Science Foundation of Inner Mongolia under Grant no. 2014BS0610; the Innovation Fund Project of Inner Mongolia University of Science and Technology Grant Nos. 2015QNGG03 and 2014QDL002; and General Financial Grant of the China Post-doctoral Science Foundation under Grant no. 2014M552416. Declaration of Interests None. References 1. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet . 2014; 384( 9937): 45– 52. Google Scholar CrossRef Search ADS PubMed  2. Japuntich SJ, Eilers MA, Shenhav Set al.   Secondhand tobacco smoke exposure among hospitalized nonsmokers with coronary heart disease. JAMA Intern Med . 2015; 175( 1): 133– 136. Google Scholar CrossRef Search ADS PubMed  3. Caramori G, Kirkham P, Barczyk A, Di Stefano A, Adcock I. Molecular pathogenesis of cigarette smoking–induced stable COPD. Ann NY Acad Sci . 2015; 1340: 55– 64. Google Scholar CrossRef Search ADS PubMed  4. Sussman S. Effects of sixty six adolescent tobacco use cessation trials and seventeen prospective studies of self-initiated quitting. Tob Induc Dis . 2002; 1( 1): 1– 48. Google Scholar CrossRef Search ADS PubMed  5. Mathers M, Toumbourou J, Catalano R, Williams J, Patton G. Consequences of youth tobacco use: a review of prospective behavioural studies. Addiction . 2006; 101( 7): 948– 958. Google Scholar CrossRef Search ADS PubMed  6. Casey B, Tottenham N, Liston C, Durston S. Imaging the developing brain: what have we learned about cognitive development? Trends Cogn Sci . 2005; 9( 3): 104– 110. Google Scholar CrossRef Search ADS PubMed  7. Pine D, Cohen P, Brook J. Emotional reactivity and risk for psychopathology among adolescents. CNS Spectr . 2001; 6( 1): 27. Google Scholar CrossRef Search ADS PubMed  8. Jacobsen LK, Krystal JH, Mencl WE, Westerveld M, Frost SJ, Pugh KR. Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers. Biol Psychiatry . 2005; 57( 1): 56– 66. Google Scholar CrossRef Search ADS PubMed  9. Jha P, Peto R. Global effects of smoking, of quitting, and of taxing tobacco. N Engl J Med . 2014; 370( 1): 60– 68. Google Scholar CrossRef Search ADS PubMed  10. US Department of Health and Human Services. Preventing tobacco use among youth and young adults: a report of the Surgeon General . Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. 2012; 3. 11. White HR, Bray BC, Fleming CB, Catalano RF. Transitions into and out of light and intermittent smoking during emerging adulthood. Nicotin Tob Res . 2009; 11( 2): 211– 9. Google Scholar CrossRef Search ADS   12. Abreu-Villaça Y, Seidler FJ, Tate CA, Slotkin TA. Nicotine is a neurotoxin in the adolescent brain: critical periods, patterns of exposure, regional selectivity, and dose thresholds for macromolecular alterations. Brain Res . 2003; 979( 1): 114– 128. Google Scholar CrossRef Search ADS PubMed  13. Slotkin TA, MacKillop EA, Rudder CL, Ryde IT, Tate CA, Seidler FJ. Permanent, sex-selective effects of prenatal or adolescent nicotine exposure, separately or sequentially, in rat brain regions: indices of cholinergic and serotonergic synaptic function, cell signaling, and neural cell number and size at 6 months of age. Neuropsychopharmacology  2007; 32( 5): 1082– 1097. Google Scholar CrossRef Search ADS PubMed  14. Counotte DS, Spijker S, Van de Burgwal LHet al.   Long-lasting cognitive deficits resulting from adolescent nicotine exposure in rats. Neuropsychopharmacology . 2009; 34( 2): 299– 306. Google Scholar CrossRef Search ADS PubMed  15. Morales AM, Ghahremani D, Kohno M, Hellemann GS, London ED. Cigarette exposure, dependence, and craving are related to insula thickness in young adult smokers. Neuropsychopharmacology . 2014; 39( 8): 1816– 22. Google Scholar CrossRef Search ADS PubMed  16. Jasinska AJ, Zorick T, Brody AL, Stein EA. Dual role of nicotine in addiction and cognition: a review of neuroimaging studies in humans. Neuropharmacology . 2014; 84: 111– 122. Google Scholar CrossRef Search ADS PubMed  17. Wonnacott S. Presynaptic nicotinic ACh receptors. Trends Neurosci . 1997; 20( 2): 92– 98. Google Scholar CrossRef Search ADS PubMed  18. Zubieta J-K, Lombardi U, Minoshima Set al.   Regional cerebral blood flow effects of nicotine in overnight abstinent smokers. Biol Psychiatry . 2001; 49( 11): 906– 913. Google Scholar CrossRef Search ADS PubMed  19. Hanlon CA, Owens MM, Joseph JEet al.   Lower subcortical gray matter volume in both younger smokers and established smokers relative to non‐smokers. Addict Biol . 2016; 21( 1): 185– 95. Google Scholar CrossRef Search ADS PubMed  20. Liao Y, Tang J, Liu T, Chen X, Hao W. Differences between smokers and non‐smokers in regional gray matter volumes: a voxel‐based morphometry study. Addict Biol . 2012; 17( 6): 977– 980. Google Scholar CrossRef Search ADS PubMed  21. Peng P, Wang Z, Jiang T, Chu S, Wang S, Xiao D. Brain-volume changes in young and middle‐aged smokers: a DARTEL‐based voxel‐based morphometry study. Clin Respir J . 2015; 25:doi: 10.1111/crj.12393. [Epub ahead of print]. 22. Brody AL, Mandelkern MA, Jarvik MEet al.   Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol Psychiatry . 2004; 55( 1): 77– 84. Google Scholar CrossRef Search ADS PubMed  23. Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DT-MRI data. Neuroimage . 2005; 26( 2): 546– 554. Google Scholar CrossRef Search ADS PubMed  24. Pereira JM, Xiong L, Acosta-Cabronero J, Pengas G, Williams GB, Nestor PJ. Registration accuracy for VBM studies varies according to region and degenerative disease grouping. Neuroimage . 2010; 49( 3): 2205– 2215. Google Scholar CrossRef Search ADS PubMed  25. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA . 2000; 97( 20): 11050– 11055. Google Scholar CrossRef Search ADS PubMed  26. Hutton C, Draganski B, Ashburner J, Weiskopf N. A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. Neuroimage . 2009; 48( 2): 371– 380. Google Scholar CrossRef Search ADS PubMed  27. Cai C, Yuan K, Yin Jet al.   Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imaging Behav . 2016; 10( 1): 12– 20. Google Scholar CrossRef Search ADS PubMed  28. Li Y, Yuan K, Cai Cet al.   Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers. Drug Alcohol Depend . 2015; 151: 211– 9. Google Scholar CrossRef Search ADS PubMed  29. Yuan K, Yu D, Cai Cet al.   Frontostriatal circuits, resting state functional connectivity and cognitive control in internet gaming disorder. Addict Biol . 2017; 22( 3): 813– 822. Google Scholar CrossRef Search ADS PubMed  30. Yuan K, Zhao L, Cheng Pet al.   Altered structure and resting-state functional connectivity of the basal ganglia in migraine patients without aura. J Pain . 2013; 14( 8): 836– 44. Google Scholar CrossRef Search ADS PubMed  31. Yuan K, Yu D, Bi Yet al.   The implication of frontostriatal circuits in young smokers: A resting‐state study. Hum Brain Mapp . 2016; 37( 6): 2013– 26. Google Scholar CrossRef Search ADS PubMed  32. Bi Y, Yuan K, Guan Yet al.   Altered resting state functional connectivity of anterior insula in young smokers. Brain Imaging Behav . 2017; 11( 1): 155– 65. Google Scholar CrossRef Search ADS PubMed  33. Feng D, Yuan K, Li Yet al.   Intra-regional and inter-regional abnormalities and cognitive control deficits in young adult smokers. Brain Imaging Behav . 2016; 10( 2): 506– 16. Google Scholar CrossRef Search ADS PubMed  34. Yu D, Yuan K, Zhang Bet al.   White matter integrity in young smokers: a tract‐based spatial statistics study. Addict Biol . 2016; 21( 3): 679– 87. Google Scholar CrossRef Search ADS PubMed  35. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerström test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict . 1991; 86( 9): 1119– 1127. Google Scholar CrossRef Search ADS PubMed  36. Li Y, Yuan K, Cai Cet al.   Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers. Drug Alcohol Depend . 2015; 151: 211– 219. Google Scholar CrossRef Search ADS PubMed  37. Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr . 2008; 87( 4): 801– 809. Google Scholar CrossRef Search ADS PubMed  38. Taki Y, Kinomura S, Sato Ket al.   Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obesity . 2008; 16( 1): 119– 124. Google Scholar CrossRef Search ADS PubMed  39. Kühn S, Schubert F, Gallinat J. Reduced thickness of medial orbitofrontal cortex in smokers. Biol Psychiatry . 2010; 68( 11): 1061– 1065. Google Scholar CrossRef Search ADS PubMed  40. Volkow ND, Wang G-J, Tomasi D, Baler RD. Unbalanced neuronal circuits in addiction. Curr Opin Neurobiol . 2013; 23( 4): 639– 648. Google Scholar CrossRef Search ADS PubMed  41. Wang Z, Faith M, Patterson Fet al.   Neural substrates of abstinence-induced cigarette cravings in chronic smokers. J Neurosci . 2007; 27( 51): 14035– 14040. Google Scholar CrossRef Search ADS PubMed  42. Stein EA, Pankiewicz J, Harsch HHet al.   Nicotine-induced limbic cortical activation in the human brain: a functional MRI study. Am J Psychiatry . 1998; 155( 8): 1009– 15. Google Scholar CrossRef Search ADS PubMed  43. Yuan K, Yu D, Bi Yet al.   The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue-induced craving of smokers. Hum Brain Mapp . 2017: doi: 10.1002/hbm.23690. [Epub ahead of print]” 44. Tanabe J, Crowley T, Hutchison Ket al.   Ventral striatal blood flow is altered by acute nicotine but not withdrawal from nicotine. Neuropsychopharmacology . 2008; 33( 3): 627– 633. Google Scholar CrossRef Search ADS PubMed  45. Engelmann JM, Versace F, Robinson JDet al.   Neural substrates of smoking cue reactivity: a meta-analysis of fMRI studies. Neuroimage . 2012; 60( 1): 252– 262. Google Scholar CrossRef Search ADS PubMed  46. Wei Z, Yang N, Liu Yet al.   Resting-state functional connectivity between the dorsal anterior cingulate cortex and thalamus is associated with risky decision-making in nicotine addicts. Sci Rep UK . 2016; 6: 21778. Google Scholar CrossRef Search ADS   47. Liu J, Lester BM, Neyzi Net al.   Regional brain morphometry and impulsivity in adolescents following prenatal exposure to cocaine and tobacco. JAMA Pediatr . 2013; 167( 4): 348– 354. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nicotine and Tobacco Research Oxford University Press

Loading next page...
 
/lp/ou_press/reduced-thalamus-volume-may-reflect-nicotine-severity-in-young-male-0tepFEvf0X
Publisher
Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
ISSN
1462-2203
eISSN
1469-994X
D.O.I.
10.1093/ntr/ntx146
Publisher site
See Article on Publisher Site

Abstract

Abstract Introduction Nicotine acts as an agonist at presynaptic nicotinic acetylcholine receptors and to facilitate synaptic release of several neurotransmitters including dopamine and glutamate. The thalamus has the highest density of nicotinic acetylcholine receptors in the brain, which may make this area more vulnerable to the addictive effects of nicotine. However, the volume of thalamus abnormalities and the association with smoking behaviors in young smokers remains unknown. Methods Thirty-six young male smokers and 36 age-, gender- and education-matched nonsmokers participated in the current study. The nicotine dependence severity and cumulative effect were assessed with the Fagerström test for nicotine dependence (FTND) and pack-years. We used subcortical volume analyses method in FreeSurfer to investigate the thalamus volume differences between young smokers and nonsmokers. Correlation analysis was used to investigate the relationship between thalamus volume and smoking behaviors (pack-years and FTND) in young smokers. Results and Conclusions Relative to nonsmokers, the young smokers showed reduced volume of bilateral thalamus. In addition, the left thalamus volume was correlated with FTND in young smokers. It is hoped that our findings can shed new insights into the neurobiology of young smokers. Implications In this article, we investigated the changes of thalamus volume in young male smokers compared with nonsmokers. Reduced left thalamus volume was correlated with FTND in young smokers, which may reflect nicotine severity in young male smokers. Introduction As the leading preventable cause of disease and death,1 smoking was associated with the high risk of several serious diseases, such as cardiovascular diseases2 and chronic obstructive pulmonary diseases.3 Interestingly, approximately 80% of adult smokers have progressed to nicotine dependence by 18 years of age, and people who do not start smoking as teenagers are unlikely ever to do so.4 Developmental plasticity renders the brain of adolescents and young adults vulnerable to the effects of nicotine and smoking.5 During this special development period, the brain of adolescents continues to develop structurally and functionally,6 which has been implicated as a cause of maladaptive decision making associated with immature cognitive control.7 Nicotine exposure during adolescence has been linked to subsequent deficits in brain circuits of attention and memory.8 As the largest consumer of cigarette, China has 350 million smokers including 14 million young smokers (www.chinacdc.cn). The Chinese Center for Disease Control and Prevention announced the latest national survey of youth smoking in May 2014, which reported that the smoking rate of junior high school students was 10.6% for males and 1.8% for females in China (http://www.chinacdc.cn/). Based on global smoking patterns, 88% of adult smokers begin smoking by the age of 18 and 99% by the age of 26.9,10 Young smokers are more likely to become life-long smokers and are more susceptible to nicotine addiction than adults.11 Nicotine exposure during prenatal or adolescent period may disrupt brain development to manifest as disturbances in cognitive functioning and behavioral changes.12–14 In the period from late adolescence to young adult, important changes in brain maturation occur, which may be especially sensitive to environmental perturbations and to nicotine exposure through cigarettes.15 However, very little is known about alterations in brain structure that may occur in young smokers, especially for the volume of thalamus and its association with smoking behaviors. Therefore, it is of great significance to investigate the underlying neural mechanisms of young smokers. Nicotine plays a critical role in the highly addictive properties of tobacco use.16 Acting as an agonist at presynaptic nicotinic acetylcholine receptors (nAChRs), nicotine facilitates synaptic release of several neurotransmitters including dopamine and glutamate.17 It is noteworthy that the thalamus has the highest density of nAChRs in the brain, which may make this area more vulnerable to the addictive effects of nicotine.17,18 The gray matter density and volume of thalamus abnormalities of smokers were discussed in previous voxel-based morphometry (VBM) studies.19–21 Nevertheless, the study by Brody et al.22 showed no differences of gray matter in the thalamus between smokers and nonsmokers. VBM studies were sensitive to a combination of changes in gray matter thickness, intensity, cortical surface area, and cortical folding, and may suffer from several limitations, like the degree of smoothing, registration techniques, and choice of normalization templates.23–25 Measurements of cortical thickness and subcortical volumetric segmentation are other methods of detecting GM structural changes in the FreeSurfer (http://surfer.nmr.mgh.harvard.edu).26 However, few studies used subcortical volumetric segmentation method to investigate the volume changes of the thalamus in young smokers. Therefore, we investigated the thalamus volume differences between young adult smokers and nonsmokers by subcortical volumetric segmentation method in the current study, which has proved to be more sensitive to the changes of thalamus than VBM.27–31 Moreover, we assessed the relationship between structural changes in the thalamus and the characteristic of young smokers. Pack-years may reflect the cumulative effect of smoking and Fagerström test for nicotine dependence (FTND) may reflect the severity of smoking. We hypothesized that abnormal thalamus volume would be detected in young smokers and these changes would be associated with smoking behavior. Materials and methods The study was approved by the Ethical Committee of Xi’an Jiaotong University and was conducted in accordance with the Declaration of Helsinki. All subjects and their legal guardians gave written informed consent prior to participation in the study. Participants Thirty-six male, right-handed subjects and 36 age- and education-matched male nonsmokers were recruited from the local university. All young smokers were screened according to the DSM-V criteria for current nicotine dependence as described in our previous studies.28,31–34 The participants did not smoke other products besides cigarettes (eg, e-cigarettes, cigars). Nicotine dependence severity was assessed with FTND.35 All smokers consumed more than 10 cigarettes per day in the last 6 months with expired air carbon monoxide (CO)>6 parts per million (ppm). Additionally, none of smokers had attempted to quit smoking or undergo smoking abstinence for longer than 3 months in the past year. The nonsmokers had smoked less than 5 cigarettes in their lifetime with expired CO≤3 ppm. Exclusion criteria for both groups were as follows: (1) any physical illness such as a brain tumor, obstructive lung disease, hepatitis, or epilepsy as assessed according to clinical evaluations and medical records; (2) any current medications that may affect cognitive functioning; (3) alcohol or drug abuse;(4) existence of a neurological disease; (5) claustrophobia; and (6) any participants with psychiatric disorder as assessed by the structured clinical interview for DSM-V. To remove the possible effect of other addiction, the exclusion criteria for both groups included any drug abuse, and tobacco and alcohol intake. Detailed demographic data are shown in Table 1. Table 1. Clinical Details of Young Smokers and Nonsmokers Clinical details  Smokers (n = 36)  Nonsmokers (n = 36)  P-value  Age (years)  20.8 ± 1.8  20.1 ± 1.2  0.22  Age range (years)  16–24  16–23    Education (years)  13.5 ± 1.3  13.3 ± 0.6  0.21  Age at start of smoking  15.1 ± 2.6  –  –  Cigarettes smoked per day  15.4 ± 4.5  –  –  Pack-years  3.5 ± 2.3  –  –  FTND  5.0 ± 1.9  –  –  Clinical details  Smokers (n = 36)  Nonsmokers (n = 36)  P-value  Age (years)  20.8 ± 1.8  20.1 ± 1.2  0.22  Age range (years)  16–24  16–23    Education (years)  13.5 ± 1.3  13.3 ± 0.6  0.21  Age at start of smoking  15.1 ± 2.6  –  –  Cigarettes smoked per day  15.4 ± 4.5  –  –  Pack-years  3.5 ± 2.3  –  –  FTND  5.0 ± 1.9  –  –  Data are means ± standard deviations. Pack-years = Years of smoking × Cigarettes smoked per day/20; FTND = Fagerström test for nicotine dependence. View Large Data Acquisition All MRI images were acquired using a 3-T MRI system (EXCITE, General Electric, Milwaukee, WI) at the First Affiliated Hospital of the Medical College, Xi’an Jiaotong University, China. A standard birdcage head coil was used, along with restraining foam pads, to minimize head motion and to diminish scanner noise. High-resolution 3D T1-weighted images were acquired for subcortical volume measurements with the following parameters: repetition time = 8.5 ms; echo time = 3.4 ms; flip angle = 12°; field of view = 240 × 240 mm2; data matrix = 240 × 240; slices = 140; and voxel size = 1 × 1 × 1 mm3. To prevent the potential effect of brain damage on results, for each subject, two neurologists examined the T1-weighted 3D images for structural lesions in the brain and no subject was excluded. Data Analysis Subcortical volumetric segmentation of the whole brain was performed with the FreeSurfer software package (Version 5.1.0, http://surfer.nmr.mgh.harvard.edu), which had been used widely in our previous studies.27,29–31,36 In more detail, the preprocessing was as follows: (1) removal of nonbrain tissue; (2) automated Talairach transformation; (3) segmentation of the subcortical white matter and deep gray matter volumetric structures; (4) intensity normalization; (5) tessellation of the gray matter/white matter boundary; (6) automated topology correction; (7) surface deformation; and (8) registration of the subjects’ brains to a common spherical atlas. The results implemented in the Freesurfer analysis were checked by a neurologist and manual adjustments were carried out if necessary. The thalamus was extracted according to Harvard subcortical structural atlas (http://www.cma.mgh.harvard.edu/). After the preprocessing, the volumes of bilateral thalamus and intracranial volume (ICV) were extracted and imported into SPSS 20.0 software for statistical analyses (SPSS Statistics, IBM, Armonk, NY). For each structure, a general linear model was fit with volume as the dependent variable, diagnosis (young smokers and nonsmokers) as the categorical predictors, and ICV as covariates. To correct for multiple comparisons, the Bonferroni correction was used. All tests were two-tailed, and the level of significance was p < 0.025 (0.05/2). Pearson correlation was applied between the absolute volumes of bilateral thalamus and smoking variables (pack-years and FTND). To correct for multiple comparisons of correlation analysis, the Bonferroni correction was also used (p < 0.0125 (0.05/4)). Results No significant ICV difference (p > 0.05) between the young smokers (1149060 ± 209249 mm3) and nonsmokers (1106206 ± 149594 mm3) was shown. Relative to nonsmokers, young smokers showed reduced volume in the left thalamus (F = 17.277, p < 0.001, Bonferroni correction), after controlling ICV as the covariate. Similarly, decreased volumes of the right thalamus (F = 14.617, p < 0.001) in young smokers were also observed when compared with nonsmokers (Figure 1). Correlation between smoking and body weight has been documented, for example, in the short term, nicotine increases energy expenditure and smokers tend to have lower body weight than nonsmokers and gray matter volume of thalamus is also associated with body weight status.37,38 To exclude the possible effect of BMI on our results, we analyzed the thalamus volume differences controlling the BMI as covariates and found reduced volume of left thalamus (F = 16.4, p < 0.001, Bonferroni correction) and right thalamus (F = 14.1, p < 0.001, Bonferroni correction) in young male smokers. Moreover, correlation analysis results showed that the volume of left thalamus was negatively correlated with FTND in smokers (r = -0.4285, p = 0.0091) (Figure 2). The volume of left thalamus was also negatively correlated with pack-years in smokers (r = -0.4031, p = 0.0148). With regard to the right hemisphere, the volume of right thalamus was negatively correlated with pack-years (r = -0.3604, p = 0.0309) and FTND ( = -0.3482, p = 0.0374) in smokers, although the results did not survive the Bonferroni correction. Figure 1. View largeDownload slide Relative to nonsmokers, young smokers showed reduced volume in left thalamus (F = 17.277, p < 0.001, Bonferroni correction), after controlling intracranial volume (ICV) as the covariates. Similarly, decreased volumes of right thalamus (F = 14.617, p < 0.001) in young smokers were also observed when compared with nonsmokers. Figure 1. View largeDownload slide Relative to nonsmokers, young smokers showed reduced volume in left thalamus (F = 17.277, p < 0.001, Bonferroni correction), after controlling intracranial volume (ICV) as the covariates. Similarly, decreased volumes of right thalamus (F = 14.617, p < 0.001) in young smokers were also observed when compared with nonsmokers. Figure 2. View largeDownload slide Correlation analysis results showed that (a) the volume of left thalamus was also negatively correlated with pack-years in smokers (r = −0.4031, p = 0.0148). (b) The volume of left thalamus was negatively correlated with FTND in smokers (r = −0.4285, p = 0.0091). (c) The volume of right thalamus was negatively correlated with pack-years (r = −0.3604, p = 0.0309). (d) The volume of right thalamus was negatively correlated with FTND (r = −0.3482, p = 0.0374) in smokers. Although the results did not survive the Bonferroni correction. Figure 2. View largeDownload slide Correlation analysis results showed that (a) the volume of left thalamus was also negatively correlated with pack-years in smokers (r = −0.4031, p = 0.0148). (b) The volume of left thalamus was negatively correlated with FTND in smokers (r = −0.4285, p = 0.0091). (c) The volume of right thalamus was negatively correlated with pack-years (r = −0.3604, p = 0.0309). (d) The volume of right thalamus was negatively correlated with FTND (r = −0.3482, p = 0.0374) in smokers. Although the results did not survive the Bonferroni correction. Discussion Just as the adverse effects of smoking on general health are well known, there is a growing consensus that a long history of nicotine smoking is associated with alterations in neural tissue integrity throughout the frontal–striatal–thalamic circuits involved in cognitive control and reward processing.19,22,28,31,39 Recently, we detected the structural and functional abnormalities within the frontostriatal circuits in young smokers.28,31,33 For instance, reduced cortical thickness of the prefrontal cortex and increased caudate volume were observed in young adult smokers.28 The resting state functional connectivity between dorsolateral prefrontal cortex and striatum (caudate) was disrupted and correlated with the cognitive control deficits in young smokers.31 In contrast, few studies focused on the implication of the volume of thalamus with smoking behaviors in young smokers. In the current study, we contributed toward filling this gap by revealing the reduced volume of bilateral thalamus in young smokers compared with nonsmokers (Figure 1). Moreover, the volume of left thalamus was correlated with the FTND in young smokers, which suggested that reduced thalamus volume may be related to nicotine severity in young smokers (Figure 2). Although the structural deficits of thalamus in smokers had been revealed in previous VBM studies,19,20 there were some inconsistent results, that is, no differences of gray matter in the thalamus between smokers and nonsmokers.22 This may be due to the sample size, different age range of participants, and the choice of VBM. Therefore, in current study, we enrolled a relatively homogenous sample of young smokers and employed a more sensitive parameter (subcortical volume) to assess the thalamus volume changes. Our results demonstrated that the bilateral thalamus showed reduced volume in young smokers (Figure 1). As the highly addictive property of tobacco use,16 nicotine facilitates synaptic release of several neurotransmitters by acting as an agonist at nAChRs.17 The thalamus has the highest density of nAChRs, which may render this area more vulnerable to the addictive effects of nicotine.17,18 Previous studies had investigated functional changes in the thalamus and the association with the neurobiology of substance addiction including cigarette smoking.40,41 Nicotine can induce abnormal activation of the thalamus in smokers,18,42 which may suggest that nicotine is capable of influencing the function of thalamus. Evidence suggests that cigarette smoking plays a critical role in inducing changes in the thalamus. Thus, it is rational that the volume of thalamus would be different between the young smokers and nonsmokers. In current study, we also found that the volume of left thalamus was correlated with the FTND in young smokers (Figure 2). As the key nodes of frontal–striatal–thalamic circuits involved in cognitive control and reward processing43, the thalamus played several roles in smoking behaviors. There was a positive correlation between the increase of cerebral blood flow in the thalamus during abstinence and the abstinence-induced craving,41 and an inverse correlation between abstinence-induced global withdrawal symptoms and reductions in cerebral blood flow in the thalamus.44 Reactivity to smoking-related cues may be an important factor that precipitates relapse in smokers, which had been investigated in several fMRI studies.45 Smoking cues reliably evoke larger responses of thalamus in smokers.45 Recently, stronger resting state functional connectivity between thalamus and anterior cingulate cortex was associated with the increased risky decision making and severity of nicotine dependence in young smokers.46 Our results suggested that the volume of left thalamus can reflect the severity of nicotine dependence of young smokers. Evidently, more comprehensive experiment design is necessary to reveal the accurate role of the reduced volume of thalamus in young smokers. The cross-sectional design made it difficult to clarify any causal relationships. We could not be certain about whether the reduced volume of thalamus was a consequence of nicotine addiction or a cause of nicotine addiction. We cannot deny the possibility that the abnormalities in the thalamus may have existed even before they have begun smoking and potentially be a predisposing factor or marker of vulnerability to nicotine dependence. We only controlled the acute alcohol use and did not control the regular use. Moderate alcohol use can impact thalamic volume and that moderate alcohol use does not necessarily imply alcohol abuse. Given that smokers have higher rates of drinking, it seems reasonable to assume that the smoking group probably also drinks at higher levels/rates than the non-smoking group. The possible effect of alcohol use on the volume of thalamus in smokers should be investigated in the future. Previous study had demonstrated that adolescents with prenatal exposure to tobacco showed a trend toward smaller volumes in thalamus than their peers who were not exposed to prenatal tobacco.47 Moreover, adolescents with smaller volume of thalamus showed higher levels of impulsivity, a factor known to contribute to smoking initiation.19 Conclusion We validated the reduced volume of thalamus and its association with smoking behaviors in young smokers, that is the left thalamus volume may reflect the nicotine severity in young smokers. It is hoped that our findings can shed new insights into the neurobiology of young smokers. Funding This paper is supported by the National Natural Science Foundation of China under Grant nos. 81571751, 81571753, 81301281, 61502376, 81401478, 81401488, 81470816, 61431013, 81471737, 81271546, and 81271549; the Natural Science Basic Research Plan in Shaanxi Province of China under Grant no. 2014JQ4118; the Fundamental Research Funds for the Central Universities under the Grant nos. JBG151207, JB161201, JB151204, and JB121405; the Natural Science Foundation of Inner Mongolia under Grant no. 2014BS0610; the Innovation Fund Project of Inner Mongolia University of Science and Technology Grant Nos. 2015QNGG03 and 2014QDL002; and General Financial Grant of the China Post-doctoral Science Foundation under Grant no. 2014M552416. Declaration of Interests None. References 1. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet . 2014; 384( 9937): 45– 52. Google Scholar CrossRef Search ADS PubMed  2. Japuntich SJ, Eilers MA, Shenhav Set al.   Secondhand tobacco smoke exposure among hospitalized nonsmokers with coronary heart disease. JAMA Intern Med . 2015; 175( 1): 133– 136. Google Scholar CrossRef Search ADS PubMed  3. Caramori G, Kirkham P, Barczyk A, Di Stefano A, Adcock I. Molecular pathogenesis of cigarette smoking–induced stable COPD. Ann NY Acad Sci . 2015; 1340: 55– 64. Google Scholar CrossRef Search ADS PubMed  4. Sussman S. Effects of sixty six adolescent tobacco use cessation trials and seventeen prospective studies of self-initiated quitting. Tob Induc Dis . 2002; 1( 1): 1– 48. Google Scholar CrossRef Search ADS PubMed  5. Mathers M, Toumbourou J, Catalano R, Williams J, Patton G. Consequences of youth tobacco use: a review of prospective behavioural studies. Addiction . 2006; 101( 7): 948– 958. Google Scholar CrossRef Search ADS PubMed  6. Casey B, Tottenham N, Liston C, Durston S. Imaging the developing brain: what have we learned about cognitive development? Trends Cogn Sci . 2005; 9( 3): 104– 110. Google Scholar CrossRef Search ADS PubMed  7. Pine D, Cohen P, Brook J. Emotional reactivity and risk for psychopathology among adolescents. CNS Spectr . 2001; 6( 1): 27. Google Scholar CrossRef Search ADS PubMed  8. Jacobsen LK, Krystal JH, Mencl WE, Westerveld M, Frost SJ, Pugh KR. Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers. Biol Psychiatry . 2005; 57( 1): 56– 66. Google Scholar CrossRef Search ADS PubMed  9. Jha P, Peto R. Global effects of smoking, of quitting, and of taxing tobacco. N Engl J Med . 2014; 370( 1): 60– 68. Google Scholar CrossRef Search ADS PubMed  10. US Department of Health and Human Services. Preventing tobacco use among youth and young adults: a report of the Surgeon General . Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. 2012; 3. 11. White HR, Bray BC, Fleming CB, Catalano RF. Transitions into and out of light and intermittent smoking during emerging adulthood. Nicotin Tob Res . 2009; 11( 2): 211– 9. Google Scholar CrossRef Search ADS   12. Abreu-Villaça Y, Seidler FJ, Tate CA, Slotkin TA. Nicotine is a neurotoxin in the adolescent brain: critical periods, patterns of exposure, regional selectivity, and dose thresholds for macromolecular alterations. Brain Res . 2003; 979( 1): 114– 128. Google Scholar CrossRef Search ADS PubMed  13. Slotkin TA, MacKillop EA, Rudder CL, Ryde IT, Tate CA, Seidler FJ. Permanent, sex-selective effects of prenatal or adolescent nicotine exposure, separately or sequentially, in rat brain regions: indices of cholinergic and serotonergic synaptic function, cell signaling, and neural cell number and size at 6 months of age. Neuropsychopharmacology  2007; 32( 5): 1082– 1097. Google Scholar CrossRef Search ADS PubMed  14. Counotte DS, Spijker S, Van de Burgwal LHet al.   Long-lasting cognitive deficits resulting from adolescent nicotine exposure in rats. Neuropsychopharmacology . 2009; 34( 2): 299– 306. Google Scholar CrossRef Search ADS PubMed  15. Morales AM, Ghahremani D, Kohno M, Hellemann GS, London ED. Cigarette exposure, dependence, and craving are related to insula thickness in young adult smokers. Neuropsychopharmacology . 2014; 39( 8): 1816– 22. Google Scholar CrossRef Search ADS PubMed  16. Jasinska AJ, Zorick T, Brody AL, Stein EA. Dual role of nicotine in addiction and cognition: a review of neuroimaging studies in humans. Neuropharmacology . 2014; 84: 111– 122. Google Scholar CrossRef Search ADS PubMed  17. Wonnacott S. Presynaptic nicotinic ACh receptors. Trends Neurosci . 1997; 20( 2): 92– 98. Google Scholar CrossRef Search ADS PubMed  18. Zubieta J-K, Lombardi U, Minoshima Set al.   Regional cerebral blood flow effects of nicotine in overnight abstinent smokers. Biol Psychiatry . 2001; 49( 11): 906– 913. Google Scholar CrossRef Search ADS PubMed  19. Hanlon CA, Owens MM, Joseph JEet al.   Lower subcortical gray matter volume in both younger smokers and established smokers relative to non‐smokers. Addict Biol . 2016; 21( 1): 185– 95. Google Scholar CrossRef Search ADS PubMed  20. Liao Y, Tang J, Liu T, Chen X, Hao W. Differences between smokers and non‐smokers in regional gray matter volumes: a voxel‐based morphometry study. Addict Biol . 2012; 17( 6): 977– 980. Google Scholar CrossRef Search ADS PubMed  21. Peng P, Wang Z, Jiang T, Chu S, Wang S, Xiao D. Brain-volume changes in young and middle‐aged smokers: a DARTEL‐based voxel‐based morphometry study. Clin Respir J . 2015; 25:doi: 10.1111/crj.12393. [Epub ahead of print]. 22. Brody AL, Mandelkern MA, Jarvik MEet al.   Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol Psychiatry . 2004; 55( 1): 77– 84. Google Scholar CrossRef Search ADS PubMed  23. Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DT-MRI data. Neuroimage . 2005; 26( 2): 546– 554. Google Scholar CrossRef Search ADS PubMed  24. Pereira JM, Xiong L, Acosta-Cabronero J, Pengas G, Williams GB, Nestor PJ. Registration accuracy for VBM studies varies according to region and degenerative disease grouping. Neuroimage . 2010; 49( 3): 2205– 2215. Google Scholar CrossRef Search ADS PubMed  25. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA . 2000; 97( 20): 11050– 11055. Google Scholar CrossRef Search ADS PubMed  26. Hutton C, Draganski B, Ashburner J, Weiskopf N. A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. Neuroimage . 2009; 48( 2): 371– 380. Google Scholar CrossRef Search ADS PubMed  27. Cai C, Yuan K, Yin Jet al.   Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imaging Behav . 2016; 10( 1): 12– 20. Google Scholar CrossRef Search ADS PubMed  28. Li Y, Yuan K, Cai Cet al.   Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers. Drug Alcohol Depend . 2015; 151: 211– 9. Google Scholar CrossRef Search ADS PubMed  29. Yuan K, Yu D, Cai Cet al.   Frontostriatal circuits, resting state functional connectivity and cognitive control in internet gaming disorder. Addict Biol . 2017; 22( 3): 813– 822. Google Scholar CrossRef Search ADS PubMed  30. Yuan K, Zhao L, Cheng Pet al.   Altered structure and resting-state functional connectivity of the basal ganglia in migraine patients without aura. J Pain . 2013; 14( 8): 836– 44. Google Scholar CrossRef Search ADS PubMed  31. Yuan K, Yu D, Bi Yet al.   The implication of frontostriatal circuits in young smokers: A resting‐state study. Hum Brain Mapp . 2016; 37( 6): 2013– 26. Google Scholar CrossRef Search ADS PubMed  32. Bi Y, Yuan K, Guan Yet al.   Altered resting state functional connectivity of anterior insula in young smokers. Brain Imaging Behav . 2017; 11( 1): 155– 65. Google Scholar CrossRef Search ADS PubMed  33. Feng D, Yuan K, Li Yet al.   Intra-regional and inter-regional abnormalities and cognitive control deficits in young adult smokers. Brain Imaging Behav . 2016; 10( 2): 506– 16. Google Scholar CrossRef Search ADS PubMed  34. Yu D, Yuan K, Zhang Bet al.   White matter integrity in young smokers: a tract‐based spatial statistics study. Addict Biol . 2016; 21( 3): 679– 87. Google Scholar CrossRef Search ADS PubMed  35. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerström test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict . 1991; 86( 9): 1119– 1127. Google Scholar CrossRef Search ADS PubMed  36. Li Y, Yuan K, Cai Cet al.   Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers. Drug Alcohol Depend . 2015; 151: 211– 219. Google Scholar CrossRef Search ADS PubMed  37. Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr . 2008; 87( 4): 801– 809. Google Scholar CrossRef Search ADS PubMed  38. Taki Y, Kinomura S, Sato Ket al.   Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obesity . 2008; 16( 1): 119– 124. Google Scholar CrossRef Search ADS PubMed  39. Kühn S, Schubert F, Gallinat J. Reduced thickness of medial orbitofrontal cortex in smokers. Biol Psychiatry . 2010; 68( 11): 1061– 1065. Google Scholar CrossRef Search ADS PubMed  40. Volkow ND, Wang G-J, Tomasi D, Baler RD. Unbalanced neuronal circuits in addiction. Curr Opin Neurobiol . 2013; 23( 4): 639– 648. Google Scholar CrossRef Search ADS PubMed  41. Wang Z, Faith M, Patterson Fet al.   Neural substrates of abstinence-induced cigarette cravings in chronic smokers. J Neurosci . 2007; 27( 51): 14035– 14040. Google Scholar CrossRef Search ADS PubMed  42. Stein EA, Pankiewicz J, Harsch HHet al.   Nicotine-induced limbic cortical activation in the human brain: a functional MRI study. Am J Psychiatry . 1998; 155( 8): 1009– 15. Google Scholar CrossRef Search ADS PubMed  43. Yuan K, Yu D, Bi Yet al.   The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue-induced craving of smokers. Hum Brain Mapp . 2017: doi: 10.1002/hbm.23690. [Epub ahead of print]” 44. Tanabe J, Crowley T, Hutchison Ket al.   Ventral striatal blood flow is altered by acute nicotine but not withdrawal from nicotine. Neuropsychopharmacology . 2008; 33( 3): 627– 633. Google Scholar CrossRef Search ADS PubMed  45. Engelmann JM, Versace F, Robinson JDet al.   Neural substrates of smoking cue reactivity: a meta-analysis of fMRI studies. Neuroimage . 2012; 60( 1): 252– 262. Google Scholar CrossRef Search ADS PubMed  46. Wei Z, Yang N, Liu Yet al.   Resting-state functional connectivity between the dorsal anterior cingulate cortex and thalamus is associated with risky decision-making in nicotine addicts. Sci Rep UK . 2016; 6: 21778. Google Scholar CrossRef Search ADS   47. Liu J, Lester BM, Neyzi Net al.   Regional brain morphometry and impulsivity in adolescents following prenatal exposure to cocaine and tobacco. JAMA Pediatr . 2013; 167( 4): 348– 354. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Journal

Nicotine and Tobacco ResearchOxford University Press

Published: Apr 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off