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Lv Q, Wang M, Bu C, Liao J, Wang K, Xu H, Liang X, Zheng N, Lin L, Ma L, Wang W, Ma Z, Cheng M, Zhao X, Lu L, Zhang Y. Smoking and High-Altitude Exposure Affect Intrinsic Neural Activity: A fMRI Study of Interactive Effects. Addict Biol 2025; 30:e70042. [PMID: 40272188 PMCID: PMC12020023 DOI: 10.1111/adb.70042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2025] [Revised: 03/23/2025] [Accepted: 04/10/2025] [Indexed: 04/25/2025]
Abstract
Smoking and high-altitude (HA) exposure both adversely affect human health, with smoking linked to various cancers and high-altitude environments causing physiological and neurological changes. Although the effects of smoking and HA exposure on brain structure and function have been studied separately, their combined impact is still rarely explored. This study aims to investigate the interactive effects of smoking and HA exposure on intrinsic brain activity using the resting-state functional magnetic resonance imaging (rs-fMRI) analysed by the amplitude of low-frequency fluctuations (ALFF) method. We used a mixed sample design, including four groups: (i) HA smokers (n = 22); (ii) HA nonsmokers (n = 22); (iii) sea-level (SL) smokers (n = 26); and (iv) SL nonsmokers (n = 26), for a total of 96 male participants. All subjects underwent resting-state functional magnetic resonance imaging. ALFF was used to assess differences in brain activity among the four groups. Two-way analysis of variance (ANOVA) was conducted to analyse the effects of smoking, high-altitude exposure and their interaction on ALFF. As for the main effect of smoking, elevated ALFF was found in the right superior frontal gyrus, right middle frontal gyrus, right inferior frontal gyrus, right middle cingulate cortex and right precentral gyrus. As for the main effect of HA exposure, elevated ALFF was found in the right putamen, right insula, right inferior frontal gyrus, right middle temporal gyrus, right precentral gyrus, right inferior temporal gyrus and right fusiform. A significant interaction effect between smoking and HA exposure was observed in the right precentral gyrus. Post hoc analysis for the right precentral gyrus showed significantly increased ALFF in groups including HA versus SL smokers; HA versus SL nonsmokers; and HA smokers versus HA nonsmokers. Our findings demonstrate that both smoking and HA exposure independently influence spontaneous brain activity, with a significant interaction between the two factors in modulating brain function. These results offer a neuroimaging-based perspective on substance addiction in high-altitude populations and contribute to a deeper understanding of high-altitude adaptation.
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Affiliation(s)
- Qingqing Lv
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Minghe Wang
- ZhengZhou Health Vocational CollegeZhengzhouChina
| | - Chunxiao Bu
- Department of Magnetic Resonance ImagingFirst Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Junjie Liao
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Kefan Wang
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Hui Xu
- Department of RadiologyQinghai Provincial People's HospitalXiningChina
| | - Xijuan Liang
- Department of RadiologyQinghai Provincial People's HospitalXiningChina
| | - Ning Zheng
- Clinical and Technical SupportPhilips HealthcareBeijingChina
| | - Liangjie Lin
- Clinical and Technical SupportPhilips HealthcareBeijingChina
| | - Longyao Ma
- Department of Magnetic Resonance ImagingFirst Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Weijian Wang
- Department of Magnetic Resonance ImagingFirst Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Zhen Ma
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Meiying Cheng
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Xin Zhao
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Lin Lu
- Department of RadiologyThird Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yong Zhang
- Department of Magnetic Resonance ImagingFirst Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
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Schwefel MK, Kaufmann C, Gutmann G, Henze R, Fydrich T, Rapp MA, Ströhle A, Heissel A, Heinzel S. Effect of physical exercise training on neural activity during working memory in major depressive disorder. J Affect Disord 2025; 372:269-278. [PMID: 39638060 DOI: 10.1016/j.jad.2024.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 11/28/2024] [Accepted: 12/02/2024] [Indexed: 12/07/2024]
Abstract
BACKGROUND Deficits in working memory (WM) are common in patients with Major Depression Disorder (MDD). Previous research mainly in healthy adults indicated that physical exercise training may improve cognitive functions by stimulating neuronal plasticity particularly in hippocampal structures. Thus, the goal of this functional Magnetic Resonance Imaging (fMRI) study was to examine alterations in neuronal activity during a WM task and to investigate changes in brain volume and functioning following a physical exercise training in patients with MDD with a specific focus on hippocampal structures. METHODS 86 (39 female) MDD outpatients (average age 37.3), diagnosed by clinical psychologists, were randomly assigned to one of three groups for a 12-week intervention: High intensity exercise training (HEX), low intensity exercise training (LEX) or waiting list control group (WL). An n-back task (with WM loads of 0, 1, 2, and 3) during fMRI was conducted before and after interventions/waiting period. RESULTS Both exercise groups showed better performance and shorter reaction times at higher WM loads after 12-weeks of physical exercise training. Specifically in the HEX, we found an improvement in physical fitness and an increase in neural activation in the left hippocampus as compared to the WL following the exercise training. Training-related structural volume changes in gray matter or hippocampus were not detected. CONCLUSIONS Our results partly support the hypothesis that physical exercise training positively affects WM functions by improving neuronal plasticity in hippocampal regions. Exercise training seems to be a promising intervention to improve deficient WM performance in patients with MDD. CLINICAL TRIALS REGISTRATION NAME Neurobiological correlates and mechanisms of the augmentation of psychotherapy with endurance exercise in mild to moderate depression - SPeED, http://apps.who.int/trialsearch/Trial2.aspx?TrialID=DRKS00008869, DRKS00008869.
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Affiliation(s)
- M K Schwefel
- Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
| | - C Kaufmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - G Gutmann
- Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - R Henze
- Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - T Fydrich
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - M A Rapp
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - A Ströhle
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - A Heissel
- Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | - S Heinzel
- Clinical Psychology and Psychotherapy, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Institute of Psychology, Department of Educational Sciences and Psychology, TU Dortmund University, Dortmund, Germany
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Deiana G, He J, Cabrera-Mendoza B, Ciccocioppo R, Napolioni V, Polimanti R. Brain-wide pleiotropy investigation of alcohol drinking and tobacco smoking behaviors. Transl Psychiatry 2025; 15:61. [PMID: 39979292 PMCID: PMC11842717 DOI: 10.1038/s41398-025-03288-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 01/13/2025] [Accepted: 02/12/2025] [Indexed: 02/22/2025] Open
Abstract
To investigate the pleiotropic mechanisms linking brain structure and function to alcohol drinking and tobacco smoking, we integrated genome-wide data generated by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN; up to 805,431 participants) with information related to 3935 brain imaging-derived phenotypes (IDPs) available from UK Biobank (N = 33,224). We observed global genetic correlation of smoking behaviors with white matter hyperintensities, the morphology of the superior longitudinal fasciculus, and the mean thickness of pole-occipital. With respect to the latter brain IDP, we identified a local genetic correlation with age at which the individual began smoking regularly (hg38 chr2:35,895,678-36,640,246: rho = 1, p = 1.01 × 10-5). This region has been previously associated with smoking initiation, educational attainment, chronotype, and cortical thickness. Our genetically informed causal inference analysis using both latent causal variable approach and Mendelian randomization linked the activity of prefrontal and premotor cortex and that of superior and inferior precentral sulci, and cingulate sulci to the number of alcoholic drinks per week (genetic causality proportion, gcp = 0.38, p = 8.9 × 10-4, rho = -0.18 ± 0.07; inverse variance weighting, IVW beta = -0.04, 95%CI = -0.07--0.01). This relationship could be related to the role of these brain regions in the modulation of reward-seeking motivation and the processing of social cues. Overall, our brain-wide investigation highlighted that different pleiotropic mechanisms likely contribute to the relationship of brain structure and function with alcohol drinking and tobacco smoking, suggesting decision-making activities and chemosensory processing as modulators of propensity towards alcohol and tobacco consumption.
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Affiliation(s)
- Giovanni Deiana
- Center for Neuroscience, Pharmacology Unit, School of Pharmacy, University of Camerino, Camerino, Italy
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jun He
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- VA CT Healthcare System, West Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- VA CT Healthcare System, West Haven, CT, USA
| | - Roberto Ciccocioppo
- Center for Neuroscience, Pharmacology Unit, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Valerio Napolioni
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- VA CT Healthcare System, West Haven, CT, USA.
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, 06511, USA.
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Jiang L, Han X, Wang Y, Ding W, Sun Y, Zhou Y, Lin F. Anterior and posterior cerebral white matter show different patterns of microstructural alterations in young adult smokers. Brain Imaging Behav 2025; 19:195-203. [PMID: 39715889 DOI: 10.1007/s11682-024-00963-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2024] [Indexed: 12/25/2024]
Abstract
Neuroimaging studies revealed that smoking is associated with abnormal white matter (WM) microstructure. However, results are controversial, and the impact of smoking on the WM integrity in young smokers is still unclear. In this study, we used diffusion tensor imaging to investigate the smoking-related WM alterations in young adult smokers. One hundred and twenty-six subjects (60 current smokers and 66 nonsmokers) aged 18-29 years participated in the study. The tract-based spatial statistics with multiple diffusion indices was applied to explore diffusivity patterns associated with smoking. Correlation analysis was performed to evaluate relationships between fractional anisotropy (FA) and smoking-related variables in young adult smokers. Compared with nonsmokers, young adult smokers showed higher FA dominantly in the anterior cerebral WM regions, while lower FA mainly in the posterior cerebral WM areas. The dominant diffusivity pattern for regions with larger FA was characterized by lower radial and axial diffusion (Dr and Da), while in areas with smaller FA, higher Dr without significant difference in Da was the main diffusivity pattern. Moreover, diffusion indices in the genu and body of the corpus callosum were related with smoking-related variables. Our findings indicate that smoking may have differential effects on the WM integrity in the anterior and posterior parts of the brain, and may also accelerate brain aging in young adult smokers.
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Affiliation(s)
- Lei Jiang
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Xu Han
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200127, P.R. China
| | - Yao Wang
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200127, P.R. China
| | - Weina Ding
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200127, P.R. China
| | - Yawen Sun
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200127, P.R. China
| | - Yan Zhou
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200127, P.R. China.
| | - Fuchun Lin
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, P.R. China.
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China.
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Zhang M, Niu X, Dang J, Sun J, Tao Q, Wang W, Han S, Cheng J, Zhang Y. Neuroanatomical subtypes of tobacco use disorder and relationship with clinical and molecular features. Prog Neuropsychopharmacol Biol Psychiatry 2025; 136:111235. [PMID: 39732318 DOI: 10.1016/j.pnpbp.2024.111235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Revised: 12/05/2024] [Accepted: 12/21/2024] [Indexed: 12/30/2024]
Abstract
BACKGROUND Individual neurobiological heterogeneity among patients with tobacco use disorder (TUD) hampers the identification of neuroimaging phenotypes. METHODS The current study recruited 122 TUD individuals and 57 healthy controls, and obtained their 3D-T1 images. Heterogeneity through discriminative analysis (HYDRA) was applied to uncover the potential subtype of TUD where regional gray matter volume (GMV) was treated as the feature. Then we examined the clinical, neuroimaging and molecular characteristics of subtypes. RESULTS Two distinct neuroanatomical subtypes were found. In subtype 1, TUD individuals showed decreased GMV in right orbitofrontal cortex (OFC), while subtype 2 exhibited distributed pattern of widely GMV increase. Moreover, subtype 1 showed older initial smoking age, longer duration of smoking than Subtype 2. Persistent smoking behavior in subtype 1 is more likely caused by substance dependence/addiction rather than psychosocial factors. GMV correlated negatively with cumulative tobacco exposure in Subtype 1 but not in Subtype 2. Besides, neuroanatomical aberrance in subtype 1 was mainly associated with dopamine system, while neuroanatomical abnormalities in subtype 2 were primarily associated with GABAa. CONCLUSIONS Overall, our results revealed two opposite neuroanatomical subtypes of TUD, which largely overlapped with their clinical and molecular features respectively. TUD subtypes taxonomy based on objective anatomy could help to facilitate the development of individualized treatment for TUD.
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Affiliation(s)
- Mengzhe Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China
| | - Xiaoyu Niu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China
| | - Jinghan Dang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China
| | - Jieping Sun
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China
| | - Qiuying Tao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.
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Miller AP, Baranger DAA, Paul SE, Garavan H, Mackey S, Tapert SF, LeBlanc KH, Agrawal A, Bogdan R. Neuroanatomical Variability and Substance Use Initiation in Late Childhood and Early Adolescence. JAMA Netw Open 2024; 7:e2452027. [PMID: 39786408 PMCID: PMC11686416 DOI: 10.1001/jamanetworkopen.2024.52027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 10/19/2024] [Indexed: 01/12/2025] Open
Abstract
Importance The extent to which neuroanatomical variability associated with early substance involvement, which is associated with subsequent risk for substance use disorder development, reflects preexisting risk and/or consequences of substance exposure remains poorly understood. Objective To examine neuroanatomical features associated with early substance use initiation and to what extent associations may reflect preexisting vulnerability. Design, Setting, and Participants Cohort study using data from baseline through 3-year follow-up assessments of the ongoing longitudinal Adolescent Brain Cognitive Development Study. Children aged 9 to 11 years at baseline were recruited from 22 sites across the US between June 1, 2016, and October 15, 2018. Data were analyzed from February to September 2024. Exposures Substance use initiation through 3-year follow-up (ie, age <15 years). Main Outcomes and Measures Self-reported alcohol, nicotine, cannabis, and other substance use initiation and baseline magnetic resonance imaging (MRI)-derived estimates of brain structure (ie, global and regional cortical volume, thickness, surface area, sulcal depth, and subcortical volume). Covariates included family (eg, familial relationships), pregnancy (eg, prenatal exposure to substances), child (eg, sex and pubertal status), and MRI (eg, scanner model) variables. Results Among 9804 children (mean [SD] baseline age, 9.9 [0.6] years; 5160 boys [52.6%]; 213 Asian [2.2%], 1474 Black [15.0%], 514 Hispanic/Latino [5.2%], 29 American Indian [0.3%], 10 Pacific Islander [0.1%], 7463 White [76.1%], and 75 other [0.7%]) with nonmissing baseline neuroimaging and covariate data, 3460 (35.3%) reported substance use initiation before age 15. Initiation of any substance or alcohol use was associated with thinner cortex in prefrontal regions (eg, rostral middle frontal gyrus, β = -0.03; 95% CI, -0.02 to -0.05; P = 6.99 × 10-6) but thicker cortex in all other lobes, larger globus pallidus and hippocampal volumes, as well as greater global indices of brain structure (eg, larger whole brain volume, β = 0.05; 95% CI, 0.03 to 0.06; P = 2.80 × 10-8) following Bonferroni or false discovery rate multiple testing correction. Cannabis use initiation was associated with lower right caudate volume (β = -0.03; 95% CI, -0.01 to -0.05; P = .002). Post hoc examinations restricting to postbaseline initiation suggested that the majority of associations, including thinner prefrontal cortex and greater whole brain volume, preceded initiation. Conclusions and Relevance In this cohort study of children, preexisting neuroanatomical variability was associated with substance use initiation. In addition to putative neurotoxic effects of substance exposure, brain structure variability may reflect predispositional risk for initiating substance use earlier in life with potential cascading implications for development of later problems.
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Affiliation(s)
- Alex P. Miller
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis
| | - David A. A. Baranger
- Department of Psychological and Brain Sciences, Washington University in St Louis, Missouri
| | - Sarah E. Paul
- Department of Psychological and Brain Sciences, Washington University in St Louis, Missouri
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington
| | - Scott Mackey
- Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington
| | - Susan F. Tapert
- Department of Psychiatry, University of California, San Diego
| | - Kimberly H. LeBlanc
- Division of Extramural Research, National Institute on Drug Abuse, Bethesda, Maryland
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in St Louis, Missouri
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Lv Q, Bu C, Xu H, Liang X, Ma L, Wang W, Ma Z, Cheng M, Tan S, Zheng N, Zhao X, Lu L, Zhang Y. Exploring spontaneous brain activity changes in high-altitude smokers: Insights from ALFF/fALFF analysis. Brain Cogn 2024; 181:106223. [PMID: 39383675 DOI: 10.1016/j.bandc.2024.106223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 09/25/2024] [Accepted: 09/27/2024] [Indexed: 10/11/2024]
Abstract
INTRODUCTION This study aims to explore the impact of smoking on intrinsic brain activity among high-altitude (HA) populations. Smoking is associated with various neural alterations, but it remains unclear whether smokers in HA environments exhibit specific neural characteristics. METHODS We employed ALFF and fALFF methods across different frequency bands to investigate differences in brain functional activity between high-altitude smokers and non-smokers. 31 smokers and 31 non-smokers from HA regions participated, undergoing resting-state functional magnetic resonance imaging (rs-fMRI) scans. ALFF/fALFF values were compared between the two groups. Correlation analyses explored relationships between brain activity and clinical data. RESULTS Smokers showed increased ALFF values in the right superior frontal gyrus (R-SFG), right middle frontal gyrus (R-MFG), right anterior cingulate cortex (R-ACC), right inferior frontal gyrus (R-IFG), right superior/medial frontal gyrus (R-MSFG), and left SFG compared to non-smokers in HA. In sub-frequency bands (0.01-0.027 Hz and 0.027-0.073 Hz), smokers showed increased ALFF values in R-SFG, R-MFG, right middle cingulate cortex (R-MCC), R-MSFG, Right precentral gyrus and L-SFG while decreased fALFF values were noted in the right postcentral and precentral gyrus in the 0.01-0.027 Hz band. Negative correlations were found between ALFF values in the R-SFG and smoking years. CONCLUSION Our study reveals the neural characteristics of smokers in high-altitude environments, highlighting the potential impact of smoking on brain function. These results provide new insights into the neural mechanisms of high-altitude smoking addiction and may inform the development of relevant intervention measures.
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Affiliation(s)
- Qingqing Lv
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chunxiao Bu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Xu
- Department of Magnetic Resonance Imaging, Qinghai Provincial People's Hospital, Xining, China
| | - Xijuan Liang
- Department of Magnetic Resonance Imaging, Qinghai Provincial People's Hospital, Xining, China
| | - Longyao Ma
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhen Ma
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meiying Cheng
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shifang Tan
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ning Zheng
- Clinical & Technical Support, Philips Healthcare, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Lin Lu
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Orrù G, Baroni M, Conversano C, Gemignani A. Exploring the therapeutic potential of tDCS, TMS and DBS in overcoming tobacco use disorder: an umbrella review. AIMS Neurosci 2024; 11:449-467. [PMID: 39801797 PMCID: PMC11712234 DOI: 10.3934/neuroscience.2024027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 10/08/2024] [Accepted: 10/18/2024] [Indexed: 01/03/2025] Open
Abstract
The purpose of the present study was to investigate the effects of neuromodulation techniques, including transcranial direct current stimulation, transcranial magnetic stimulation, and deep brain stimulation, on the treatments of nicotine dependence. Specifically, our objective was to assess the existing evidence by conducting an umbrella review of systematic reviews. The quality of the included studies was evaluated using the standardized tools designed to evaluate systematic reviews. The PubMed/MEDLINE database was queried for systematic reviews, and yielded 7 systematic reviews with a substantial sample size (N = 4,252), some of which included meta-analyses. A significant finding across these studies was the effectiveness of neuromodulation techniques to reduce nicotine cravings and consumption, through the evidence remains not yet conclusive. A significant efficacy of transcranial direct current stimulation and repetitive transcranial magnetic stimulation that targeted the dorsolateral prefrontal cortex was found, as well as the lateral prefrontal cortex and insula bilaterally, on smoking frequency and craving. Moreover, smoking behaviors may also be positively affected by the use of deep brain stimulation (DBS) targeting the nucleus accumbens. In conclusion, neuromodulation approaches hold promise as effective treatments for tobacco use disorder. Nonetheless, further research is required to comprehensively understand their effectiveness and to determine if combining them with other treatments can aid individuals to successfully quit smoking.
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Affiliation(s)
- Graziella Orrù
- Department of Surgical, Medical, Molecular & Critical Area Pathology, University of Pisa, via Savi, 10, 56126, Pisa, Italy
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Serra M, Simola N, Pollack AE, Costa G. Brain dysfunctions and neurotoxicity induced by psychostimulants in experimental models and humans: an overview of recent findings. Neural Regen Res 2024; 19:1908-1918. [PMID: 38227515 DOI: 10.4103/1673-5374.390971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/10/2023] [Indexed: 01/17/2024] Open
Abstract
Preclinical and clinical studies indicate that psychostimulants, in addition to having abuse potential, may elicit brain dysfunctions and/or neurotoxic effects. Central toxicity induced by psychostimulants may pose serious health risks since the recreational use of these substances is on the rise among young people and adults. The present review provides an overview of recent research, conducted between 2018 and 2023, focusing on brain dysfunctions and neurotoxic effects elicited in experimental models and humans by amphetamine, cocaine, methamphetamine, 3,4-methylenedioxymethamphetamine, methylphenidate, caffeine, and nicotine. Detailed elucidation of factors and mechanisms that underlie psychostimulant-induced brain dysfunction and neurotoxicity is crucial for understanding the acute and enduring noxious brain effects that may occur in individuals who use psychostimulants for recreational and/or therapeutic purposes.
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Affiliation(s)
- Marcello Serra
- Department of Biomedical Sciences, Section of Neuroscience, University of Cagliari, Cagliari, Italy
| | - Nicola Simola
- Department of Biomedical Sciences, Section of Neuroscience, University of Cagliari, Cagliari, Italy
| | - Alexia E Pollack
- Department of Biology, University of Massachusetts-Boston, Boston, MA, USA
| | - Giulia Costa
- Department of Biomedical Sciences, Section of Neuroscience, University of Cagliari, Cagliari, Italy
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Deiana G, He J, Cabrera-Mendoza B, Ciccocioppo R, Napolioni V, Polimanti R. Brain-wide pleiotropy investigation of alcohol drinking and tobacco smoking behaviors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.27.24307989. [PMID: 38854122 PMCID: PMC11160805 DOI: 10.1101/2024.05.27.24307989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
To investigate the pleiotropic mechanisms linking brain structure and function to alcohol drinking and tobacco smoking, we integrated genome-wide data generated by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN; up to 805,431 participants) with information related to 3,935 brain imaging-derived phenotypes (IDPs) available from UK Biobank (N=33,224). We observed global genetic correlation of smoking behaviors with white matter hyperintensities, the morphology of the superior longitudinal fasciculus, and the mean thickness of pole-occipital. With respect to the latter brain IDP, we identified a local genetic correlation with age at which the individual began smoking regularly (hg38 chr2:35,895,678-36,640,246: rho=1, p=1.01×10 -5 ). This region has been previously associated with smoking initiation, educational attainment, chronotype, and cortical thickness. Our genetically informed causal inference analysis using both latent causal variable approach and Mendelian randomization linked the activity of prefrontal and premotor cortex and that of superior and inferior precentral sulci, and cingulate sulci to the number of alcoholic drinks per week (genetic causality proportion, gcp=0.38, p=8.9×10 -4 , rho=-0.18±0.07; inverse variance weighting, IVW beta=-0.04, 95%CI=-0.07 - -0.01). This relationship could be related to the role of these brain regions in the modulation of reward-seeking motivation and the processing of social cues. Overall, our brain-wide investigation highlighted that different pleiotropic mechanisms likely contribute to the relationship of brain structure and function with alcohol drinking and tobacco smoking, suggesting decision-making activities and chemosensory processing as modulators of propensity towards alcohol and tobacco consumption.
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Weidler C, Gramegna C, Müller D, Schrickel M, Habel U. Resting-state functional connectivity and structural differences between smokers and healthy non-smokers. Sci Rep 2024; 14:6878. [PMID: 38519565 PMCID: PMC10960011 DOI: 10.1038/s41598-024-57510-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/19/2024] [Indexed: 03/25/2024] Open
Abstract
Previous studies have shown an association between cigarette use and altered resting-state functional connectivity (rsFC) in many large-scale networks, sometimes complemented by measures of cortical atrophy. In this study, we aimed to further explore the neural differences between smokers and healthy non-smokers through the integration of functional and structural analyses. Imaging data of fifty-two smokers and forty-five non-smokers were analyzed through an independent component analysis for group differences in rsFC. Smokers showed lower rsFC within the dorsal attention network (DAN) in the left superior and middle frontal gyrus and left superior division of the lateral occipital cortex compared to non-smokers; moreover, cigarette use was found to be associated with reduced grey matter volume in the left superior and middle frontal gyrus and right orbitofrontal cortex, partly overlapping with functional findings. Within smokers, daily cigarette consumption was positively associated with increased rsFC within the cerebellar network and the default mode network and decreased rsFC within the visual network and the salience network, while carbon monoxide level showed a positive association with increased rsFC within the sensorimotor network. Our results suggest that smoking negatively impacts rsFC within the DAN and that changes within this network might serve as a circuit-based biomarker for structural deficits.
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Affiliation(s)
- Carmen Weidler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Chiara Gramegna
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
- PhD Program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
- Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy.
| | - Dario Müller
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Maike Schrickel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
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Komninou MA, Egli S, Rossi A, Ernst J, Krauthammer M, Schuepbach RA, Delgado M, Bartussek J. Former smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study. Front Psychiatry 2024; 15:1347071. [PMID: 38559401 PMCID: PMC10979642 DOI: 10.3389/fpsyt.2024.1347071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To examine the relationship between current and former smoking and the occurrence of delirium in surgical Intensive Care Unit (ICU) patients. Methods We conducted a single center, case-control study involving 244 delirious and 251 non-delirious patients that were admitted to our ICU between 2018 and 2022. Using propensity score analysis, we obtained 115 pairs of delirious and non-delirious patients matched for age and Simplified Acute Physiology Score II (SAPS II). Both groups of patients were further stratified into non-smokers, active smokers and former smokers, and logistic regression was performed to further investigate potential confounders. Results Our study revealed a significant association between former smoking and the incidence of delirium in ICU patients, both in unmatched (adjusted odds ratio (OR): 1.82, 95% confidence interval (CI): 1.17-2.83) and matched cohorts (OR: 3.0, CI: 1.53-5.89). Active smoking did not demonstrate a significant difference in delirium incidence compared to non-smokers (unmatched OR = 0.98, CI: 0.62-1.53, matched OR = 1.05, CI: 0.55-2.0). Logistic regression analysis of the matched group confirmed former smoking as an independent risk factor for delirium, irrespective of other variables like surgical history (p = 0.010). Notably, also respiratory and vascular surgeries were associated with increased odds of delirium (respiratory: OR: 4.13, CI: 1.73-9.83; vascular: OR: 2.18, CI: 1.03-4.59). Medication analysis showed that while Ketamine and Midazolam usage did not significantly correlate with delirium, Morphine use was linked to a decreased likelihood (OR: 0.27, 95% CI: 0.13-0.55). Discussion Nicotine's complex neuropharmacological impact on the brain is still not fully understood, especially its short-term and long-term implications for critically ill patients. Although our retrospective study cannot establish causality, our findings suggest that smoking may induce structural changes in the brain, potentially heightening the risk of postoperative delirium. Intriguingly, this effect seems to be obscured in active smokers, potentially due to the recognized neuroprotective properties of nicotine. Our results motivate future prospective studies, the results of which hold the potential to substantially impact risk assessment procedures for surgeries.
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Affiliation(s)
- Maria Angeliki Komninou
- Institute of Intensive Care Medicine, University Hospital Zurich & University of Zurich, Zurich, Switzerland
| | - Simon Egli
- Institute of Intensive Care Medicine, University Hospital Zurich & University of Zurich, Zurich, Switzerland
| | - Aurelio Rossi
- Institute of Intensive Care Medicine, University Hospital Zurich & University of Zurich, Zurich, Switzerland
| | - Jutta Ernst
- Center of Clinical Nursing Sciences, University Hospital Zurich, Zurich, Switzerland
| | - Michael Krauthammer
- Department for Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Reto A. Schuepbach
- Institute of Intensive Care Medicine, University Hospital Zurich & University of Zurich, Zurich, Switzerland
| | - Marcos Delgado
- Institute of Intensive Care Medicine, University Hospital Zurich & University of Zurich, Zurich, Switzerland
- Department of Anesthesia and Intensive Care Medicine, Tiefenau Hospital, Insel Group. University of Bern, Bern, Switzerland
| | - Jan Bartussek
- Institute of Intensive Care Medicine, University Hospital Zurich & University of Zurich, Zurich, Switzerland
- Department for Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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van de Weijer MP, Vermeulen J, Schrantee A, Munafò MR, Verweij KJH, Treur JL. The potential role of gray matter volume differences in the association between smoking and depression: A narrative review. Neurosci Biobehav Rev 2024; 156:105497. [PMID: 38100958 DOI: 10.1016/j.neubiorev.2023.105497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/14/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023]
Abstract
Tobacco use and major depression are both leading contributors to the global burden of disease and are also highly comorbid. Previous research indicates bi-directional causality between tobacco use and depression, but the mechanisms that underlie this causality are unclear, especially for the causality from tobacco use to depression. Here we narratively review the available evidence for a potential causal role of gray matter volume in the association. We summarize the findings of large existing neuroimaging meta-analyses, studies in UK Biobank, and the Enhancing NeuroImaging Genetics through MetaAnalysis (ENIGMA) consortium and assess the overlap in implicated brain areas. In addition, we review two types of methods that allow us more insight into the causal nature of associations between brain volume and depression/smoking: longitudinal studies and Mendelian Randomization studies. While the available evidence suggests overlap in the alterations in brain volumes implicated in tobacco use and depression, there is a lack of research examining the underlying pathophysiology. We conclude with recommendations on (genetically-informed) causal inference methods useful for studying these associations.
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Affiliation(s)
- Margot P van de Weijer
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands.
| | - Jentien Vermeulen
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Marcus R Munafò
- School of Psychological Science, University of Bristol, Bristol, the United Kingdom
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
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Yang Z, Zhao W, Linli Z, Guo S, Feng J. Associations between polygenic risk scores and accelerated brain ageing in smokers. Psychol Med 2023; 53:7785-7794. [PMID: 37555321 PMCID: PMC10755245 DOI: 10.1017/s0033291723001812] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. METHODS Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking. RESULTS The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders. CONCLUSION By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.
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Affiliation(s)
- Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Zeqiang Linli
- School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, P.R.China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Jianfeng Feng
- Centre for Computational Systems Biology, Fudan University, Shanghai 200433, P.R.China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, England
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Niu X, Gao X, Lv Q, Zhang M, Dang J, Sun J, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Increased spontaneous activity of the superior frontal gyrus with reduced functional connectivity to visual attention areas and cerebellum in male smokers. Front Hum Neurosci 2023; 17:1153976. [PMID: 37007679 PMCID: PMC10063805 DOI: 10.3389/fnhum.2023.1153976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/03/2023] [Indexed: 03/19/2023] Open
Abstract
BackgroundChronic smokers have abnormal spontaneous regional activity and disrupted functional connectivity as revealed by previous neuroimaging studies. Combining different dimensions of resting-state functional indicators may help us learn more about the neuropathological mechanisms of smoking.MethodsThe amplitude of low frequency fluctuations (ALFF) of 86 male smokers and 56 male non-smokers were first calculated. Brain regions that displayed significant differences in ALFF between two groups were selected as seeds for further functional connectivity analysis. Besides, we examined correlations between brain areas with abnormal activity and smoking measurements.ResultsIncreased ALFF in left superior frontal gyrus (SFG), left medial superior frontal gyrus (mSFG) and middle frontal gyrus (MFG) as well as decreased ALFF in right calcarine sulcus were observed in smokers compared with non-smokers. In the seed-based functional connectivity analysis, smokers showed attenuated functional connectivity with left SFG in left precuneus, left fusiform gyrus, left lingual gyrus, left cerebellum 4 5 and cerebellum 6 as well as lower functional connectivity with left mSGF in left fusiform gyrus, left lingual gyrus, left parahippocampal gyrus (PHG), left calcarine sulcus, left cerebellum 4 5, cerebellum 6 and cerebellum 8 (GRF corrected, Pvoxel < 0.005, Pcluster<0.05). Furthermore, attenuated functional connectivity with left mSGF in left lingual gyrus and PHG displayed a negative correlation with FTND scores (r = −0.308, p = 0.004; r = −0.326, p = 0.002 Bonferroni corrected).ConclusionOur findings of increased ALFF in SFG with reduced functional connectivity to visual attention areas and cerebellum subregions may shed new light on the pathophysiology of smoking.
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Affiliation(s)
- Xiaoyu Niu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Qingqing Lv
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jinghan Dang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jieping Sun
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- Jingliang Cheng,
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- Shaoqiang Han,
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- *Correspondence: Yong Zhang,
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Shi Z, Li X, Byanyima J, O’Brien CP, Childress AR, Lynch KG, Loughead J, Wiers CE, Langleben DD. Effects of current smoking severity on brain gray matter volume in opioid use disorder - a voxel-based morphometry study. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2023; 49:180-189. [PMID: 36787540 PMCID: PMC10164057 DOI: 10.1080/00952990.2023.2169616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 02/16/2023]
Abstract
Background: Cigarette smoking (CS) and opioid use disorder (OUD) significantly alter brain structure. Although OUD and cigarette smoking are highly comorbid, most prior neuroimaging research in OUD did not control for smoking severity. Specifically, the combined effect of smoking and OUD on the brain gray matter volume (GMV) remains unknown.Objectives: We used structural magnetic resonance imaging (sMRI) to examine: (1) the GMV differences between OUD and non-OUD individuals with comparable smoking severity; and (2) the differential effect of smoking severity on the brain GMV between individuals with and without OUD.Methods: We performed a secondary analysis of existing sMRI datasets of 116 individuals who smoked cigarettes daily, among whom 60 had OUD (CS-OUD; 37 male, 23 female) and 56 did not (CS; 31 male, 25 female). Brain GMV was estimated by voxel-based morphometry analysis.Results: Compared to the CS group, the CS-OUD group had a higher GMV in the occipital cortex and lower GMV in the prefrontal and temporal cortex, striatum, and pre/postcentral gyrus (whole-brain corrected-p < .05). There was a significant interaction between group and smoking severity on GMV in the medial orbitofrontal cortex (whole-brain corrected-p < .05), such that heavier smoking was associated with lower medial orbitofrontal GMV in the CS-OUD but not CS participants (r=-0.32 vs. 0.12).Conclusions: Our findings suggest a combination of independent and interactive effects of cigarette smoking and OUD on the brain gray matter. Elucidating the neuroanatomical correlates of comorbid opioid and tobacco use may shed the light on the development of novel interventions for affected individuals.
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Affiliation(s)
- Zhenhao Shi
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Xinyi Li
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Juliana Byanyima
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Charles P. O’Brien
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Anna Rose Childress
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Kevin G. Lynch
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - James Loughead
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
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Gao X, Zhang M, Yang Z, Niu X, Zhou B, Chen J, Wang W, Wei Y, Han S, Cheng J, Zhang Y. Nicotine addiction and overweight affect intrinsic neural activity and neurotransmitter activity: A fMRI study of interaction effects. Psychiatry Clin Neurosci 2023; 77:178-185. [PMID: 36468828 DOI: 10.1111/pcn.13516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/11/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Nicotine addiction and overweight often co-exist, but the neurobiological mechanism of their co-morbidity remains to be clarified. In this study, we explore how nicotine addiction and overweight affect intrinsic neural activity and neurotransmitter activity. METHODS This study included 54 overweight people and 54 age-, sex-, and handedness-matched normal-weight individuals, who were further divided into four groups based on nicotine addiction. We used a two-way factorial design to compare intrinsic neural activity (calculated by the fALFF method) in four groups based on resting-state functional magnetic resonance images (rs-fMRI). Furthermore, the correlation between fALFF values and PET- and SPECT-derived maps to examine specific neurotransmitter system changes underlying nicotine addiction and overweight. RESULTS Nicotine addiction and overweight affect intrinsic neural activity by themselves. In combination, they showed antagonistic effects in the interactive brain regions (left insula and right precuneus). Cross-modal correlations displayed that intrinsic neural activity changes in the interactive brain regions were related to the noradrenaline system (NAT). CONCLUSION Due to the existence of interaction, nicotine partially restored the changes of spontaneous activity in the interactive brain regions of overweight people. Therefore, when studying one factor alone, the other should be used as a control variable. Besides, this work links the noradrenaline system with intrinsic neural activity in overweight nicotine addicts. By examining the interactions between nicotine addiction and overweight from neuroimaging and molecular perspectives, this study provides some ideas for the treatment of both co-morbidities.
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Affiliation(s)
- Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Xiaoyu Niu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular imaging of Henan Province, Henan, China.,Engineering Technology Research Center for detection and application of brain function of Henan Province, Henan, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan, China.,Engineering Research Center of Brain Function Development and Application of Henan Province, Henan, China
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18
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Zhang M, Gao X, Yang Z, Niu X, Wang W, Han S, Wei Y, Cheng J, Zhang Y. Integrative brain structural and molecular analyses of interaction between tobacco use disorder and overweight among male adults. J Neurosci Res 2023; 101:232-244. [PMID: 36333937 DOI: 10.1002/jnr.25141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/29/2022] [Accepted: 10/23/2022] [Indexed: 11/07/2022]
Abstract
Tobacco smoking and overweight lead to adverse health effects, which remain an important public health problem worldwide. Researches indicate overlapping pathophysiology may contribute to tobacco use disorder (TUD) and overweight, but the neurobiological interaction mechanism between the two factors is still unclear. This study used a mixed sample design, including the following four groups: (i) overweight long-term smokers (n = 24, age = 31.80 ± 5.70, cigarettes/day = 20.50 ± 7.89); (ii) normal weight smokers (n = 28, age = 31.29 ± 5.56, cigarettes/day = 16.11 ± 8.35); (iii) overweight nonsmokers (n = 19, age = 33.05 ± 5.60), and (iv) normal weight nonsmokers (n = 28, age = 31.68 ± 6.57), a total of 99 male subjects. All subjects underwent T1-weighted high-resolution MRI. We used voxel-based morphometry to compare gray matter volume (GMV) among the four groups. Then, JuSpace toolbox was used for cross-modal correlations of MRI-based modalities with nuclear imaging derived estimates, to examine specific neurotransmitter system changes underlying the two factors. Our results illustrate a significant antagonistic interaction between TUD and weight status in left dorsolateral prefrontal cortex (DLPFC), and a quadratic effect of BMI on DLPFC GMV. For main effect of TUD, long-term smokers were associated with greater GMV in bilateral OFC compared with nonsmokers irrespective of weight status, and such alteration is negatively associated with pack-year and FTND scores. Furthermore, we also found GMV changes related to TUD and overweight are associated with μ-opioid receptor system and TUD-related GMV alterations are associated with noradrenaline transporter maps. This study sheds light on novel multimodal neuromechanistic about the relationship between TUD and overweight, which possibly provides hints into future treatment for the special population of comorbid TUD and overweight.
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Affiliation(s)
- Mengzhe Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinyu Gao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoyu Niu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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19
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Melo Van Lent D, Gokingco H, Short MI, Yuan C, Jacques PF, Romero JR, DeCarli CS, Beiser AS, Seshadri S, Himali JJ, Jacob ME. Higher Dietary Inflammatory Index scores are associated with brain MRI markers of brain aging: Results from the Framingham Heart Study Offspring cohort. Alzheimers Dement 2023; 19:621-631. [PMID: 35522830 PMCID: PMC9637238 DOI: 10.1002/alz.12685] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION We investigated cross-sectional associations between the Dietary Inflammatory Index (DII) and measures of brain volume and cerebral small vessel disease among participants of the Framingham Heart Study Offspring cohort. METHODS A total of 1897 participants (mean ± standard deviation, age 62±9) completed Food Frequency Questionnaires and brain magnetic resonance imaging (MRI). RESULTS Higher (pro-inflammatory) DII scores, averaged across a maximum of three time points, were associated with smaller total brain volume (beta ± standard error: -0.16 ± 0.03; P < .0001) after adjustment for demographic, clinical, and lifestyle covariates. In addition, higher DII scores were associated with smaller total gray matter volume (-0.08 ± 0.03; P = .003) and larger lateral ventricular volume (0.04 ± 0.02; P = .03). No associations were observed with other brain MRI measures. DISCUSSION Our findings showed associations between higher DII scores and global brain MRI measures. As we are one of the first groups to report on the associations between higher DII scores and brain volume, replication is needed to confirm our findings.
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Affiliation(s)
- Debora Melo Van Lent
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Hannah Gokingco
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Meghan I Short
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Changzheng Yuan
- School of Public Health, Zhejiang University Medical School, Hangzhou, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paul F Jacques
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts, USA
| | - José R Romero
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Charles S DeCarli
- Department of Neurology, School of Medicine & Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California Davis, Davis, California, USA
| | - Alexa S Beiser
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Jayandra J Himali
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, Texas, USA
| | - Mini E Jacob
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
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20
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Koevoets EW, Geerlings MI, Monninkhof EM, Mandl R, Witlox L, van der Wall E, Stuiver MM, Sonke GS, Velthuis MJ, Jobsen JJ, van der Palen J, Bos MEMM, Göker E, Menke-Pluijmers MBE, Sommeijer DW, May AM, de Ruiter MB, Schagen SB. Effect of physical exercise on the hippocampus and global grey matter volume in breast cancer patients: A randomized controlled trial (PAM study). Neuroimage Clin 2022; 37:103292. [PMID: 36565574 PMCID: PMC9800528 DOI: 10.1016/j.nicl.2022.103292] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Physical exercise in cancer patients is a promising intervention to improve cognition and increase brain volume, including hippocampal volume. We investigated whether a 6-month exercise intervention primarily impacts total hippocampal volume and additionally hippocampal subfield volumes, cortical thickness and grey matter volume in previously physically inactive breast cancer patients. Furthermore, we evaluated associations with verbal memory. METHODS Chemotherapy-exposed breast cancer patients (stage I-III, 2-4 years post diagnosis) with cognitive problems were included and randomized in an exercise intervention (n = 70, age = 52.5 ± 9.0 years) or control group (n = 72, age = 53.2 ± 8.6 years). The intervention consisted of 2x1 hours/week of supervised aerobic and strength training and 2x1 hours/week Nordic or power walking. At baseline and at 6-month follow-up, volumetric brain measures were derived from 3D T1-weighted 3T magnetic resonance imaging scans, including hippocampal (subfield) volume (FreeSurfer), cortical thickness (CAT12), and grey matter volume (voxel-based morphometry CAT12). Physical fitness was measured with a cardiopulmonary exercise test. Memory functioning was measured with the Hopkins Verbal Learning Test-Revised (HVLT-R total recall) and Wordlist Learning of an online cognitive test battery, the Amsterdam Cognition Scan (ACS Wordlist Learning). An explorative analysis was conducted in highly fatigued patients (score of ≥ 39 on the symptom scale 'fatigue' of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire), as previous research in this dataset has shown that the intervention improved cognition only in these patients. RESULTS Multiple regression analyses and voxel-based morphometry revealed no significant intervention effects on brain volume, although at baseline increased physical fitness was significantly related to larger brain volume (e.g., total hippocampal volume: R = 0.32, B = 21.7 mm3, 95 % CI = 3.0 - 40.4). Subgroup analyses showed an intervention effect in highly fatigued patients. Unexpectedly, these patients had significant reductions in hippocampal volume, compared to the control group (e.g., total hippocampal volume: B = -52.3 mm3, 95 % CI = -100.3 - -4.4)), which was related to improved memory functioning (HVLT-R total recall: B = -0.022, 95 % CI = -0.039 - -0.005; ACS Wordlist Learning: B = -0.039, 95 % CI = -0.062 - -0.015). CONCLUSIONS No exercise intervention effects were found on hippocampal volume, hippocampal subfield volumes, cortical thickness or grey matter volume for the entire intervention group. Contrary to what we expected, in highly fatigued patients a reduction in hippocampal volume was found after the intervention, which was related to improved memory functioning. These results suggest that physical fitness may benefit cognition in specific groups and stress the importance of further research into the biological basis of this finding.
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Affiliation(s)
- E W Koevoets
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - M I Geerlings
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Department of General Practice, Amsterdam UMC, Amsterdam, the Netherlands
| | - E M Monninkhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - R Mandl
- Department of Psychiatry, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - L Witlox
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - E van der Wall
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - M M Stuiver
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Center for Quality of Life, Netherlands Cancer Institute, Amsterdam, the Netherlands; Center of Expertise Urban Vitality, Faculty of Health, University of Applied Sciences, Amsterdam, the Netherlands
| | - G S Sonke
- Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - M J Velthuis
- Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands
| | - J J Jobsen
- Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands
| | - J van der Palen
- Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands; Department of Research Methodology, Measurement, Universiteit Twente, Enschede, the Netherlands
| | - M E M M Bos
- Department of Medical Oncology, ErasmusMC Cancer Institute, Rotterdam, the Netherlands
| | - E Göker
- Department of Medical Oncology, Alexander Monro Hospital, Bilthoven, the Netherlands
| | | | - D W Sommeijer
- Department of Internal Medicine, Flevohospital, Almere, the Netherlands; Department of Medical Oncology, Amsterdam UMC, Amsterdam, the Netherlands
| | - A M May
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - M B de Ruiter
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - S B Schagen
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands; Brain and Cognition Group, University of Amsterdam, Amsterdam, the Netherlands.
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21
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Chen Y, Dhingra I, Chaudhary S, Fucito L, Li CSR. Overnight Abstinence Is Associated With Smaller Secondary Somatosensory Cortical Volumes and Higher Somatosensory-Motor Cortical Functional Connectivity in Cigarette Smokers. Nicotine Tob Res 2022; 24:1889-1897. [PMID: 35796689 PMCID: PMC9653081 DOI: 10.1093/ntr/ntac168] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 05/31/2022] [Accepted: 07/05/2022] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Abstinence symptoms present challenges to successful cessation of cigarette smoking. Chronic exposure to nicotine and long-term nicotine abstinence are associated with alterations in cortical and subcortical gray matter volumes (GMVs). AIMS AND METHODS We aimed at examining changes in regional GMVs following overnight abstinence and how these regional functions relate to abstinence symptoms. Here, in a sample of 31 regular smokers scanned both in a satiety state and after overnight abstinence, we employed voxel-wise morphometry and resting-state functional connectivity (rsFC) to investigate these issues. We processed imaging data with published routines and evaluated the results with a corrected threshold. RESULTS Smokers showed smaller GMVs of the left ventral hippocampus and right secondary somatosensory cortex (SII) after overnight abstinence as compared to satiety. The GMV alterations in right SII were positively correlated with changes in withdrawal symptom severity between states. Furthermore, right SII rsFC with the precentral gyrus was stronger in abstinence as compared to satiety. The inter-regional rsFC was positively correlated with motor impulsivity and withdrawal symptom severity during abstinence and negatively with craving to smoke during satiety. CONCLUSIONS These findings highlight for the first time the effects of overnight abstinence on cerebral volumetrics and changes in functional connectivity of a higher-order sensory cortex. These changes may dispose smokers to impulsive behaviors and aggravate the urge to smoke at the earliest stage of withdrawal from nicotine. IMPLICATIONS Overnight abstinence leads to changes in gray matter volumes and functional connectivity of the second somatosensory cortex in cigarette smokers. Higher somatosensory and motor cortical connectivity in abstinence is significantly correlated with trait motor impulsivity and withdrawal symptom severity. The findings add to the literature of neural markers of nicotine addiction.
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Affiliation(s)
- Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Isha Dhingra
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Lisa Fucito
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
- Inter-department Neuroscience Program, Yale University, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
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22
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Gao X, Zhang M, Yang Z, Niu X, Chen J, Zhou B, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Explore the effects of overweight and smoking on spontaneous brain activity: Independent and reverse. Front Neurosci 2022; 16:944768. [PMCID: PMC9597461 DOI: 10.3389/fnins.2022.944768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Accumulating evidence suggested that overweight and smoking often co-exist. However, current neuroimaging researches have almost always studied smoking or overweight status separately. Here we sought to investigate the neurobiological mechanisms of this comorbid association, by detecting spontaneous brain activity changes associated with smoking and weight status separately and collectively. We used 2 × 2 factorial design and included the following four groups: overweight/normal-weight smokers (n = 34/n = 30) and overweight/normal-weight non-smokers (n = 22/n = 24). The spontaneous brain activity among the four groups was comparable using an amplitude of low-frequency fluctuation (ALFF) method based on resting-state fMRI (rs-fMRI). Furthermore, correlation analyses between brain activity changes, smoking severity and BMI values were performed. A main effect of smoking was discovered in the default mode network (DMN) and visual network related brain regions. Moreover, overweight people had high ALFF value in the brain regions associated with reward and executive control. More importantly, smoking and overweight both affected brain activity of the middle temporal gyrus (MTG), but the effect was opposite. And the brain activity of MTG was negatively correlated with smoking years, pack year and BMI value. These results suggest that smoking and overweight not only affect spontaneous brain activity alone, but also paradoxically affect spontaneous brain activity in the MTG. This suggests that we need to control for weight as a variable when studying spontaneous brain activity in smokers. Besides, this interaction may provide a neurological explanation for the comorbidity of overweight and smoking and a target for the treatment of comorbid populations.
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Affiliation(s)
- Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Xiaoyu Niu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Jingliang Cheng,
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Shaoqiang Han,
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- *Correspondence: Yong Zhang,
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23
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Otsuka R, Nishita Y, Nakamura A, Kato T, Ando F, Shimokata H, Arai H. Basic lifestyle habits and volume change in total gray matter among community dwelling middle-aged and older Japanese adults. Prev Med 2022; 161:107149. [PMID: 35803358 DOI: 10.1016/j.ypmed.2022.107149] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/10/2022] [Accepted: 07/03/2022] [Indexed: 11/25/2022]
Abstract
The brain controls human behavior, and the gray matter is the main resource of neuronal cells. We examined the longitudinal relationship between six basic lifestyle habits (diet, exercise, sleep, alcohol consumption, smoking, and social activity including employment) and total gray matter volume in community-dwelling adults in Japan. This two-year follow-up study with data derived from the National Institute for Longevity Sciences, Longitudinal Study of Aging, Aichi, Japan, included adults aged 40-87 years (n = 1665, men: 51%). Lifestyle habits were assessed at baseline (2008-2010) using self-reported questionnaires and three-day dietary records. Total gray matter volume at baseline and after two years was estimated using T1-weighted brain magnetic resonance imaging and FreeSurfer software. The association between each lifestyle factor, the total number of healthy lifestyle habits, and gray matter volume change was determined via a multiple linear regression analysis adjusting for baseline age, total gray matter volume, and other confounders. The mean ± standard deviation decrease in total gray matter volume during the two-year follow-up period was 0.94 ± 1.86% in men and 0.61 ± 2.27% in women. In the multiple regression analysis, volume loss in total gray matter positively correlated with male smoking, while it was negatively correlated with male social activity and employment, female dietary diversity, and the total number of healthy lifestyle habits (standardized beta coefficient; -0.061 in men [p = 0.07], -0.113 in women [p < 0.05]). Therefore, engaging in social activities, non-smoking, a diverse diet, or adopting one healthy lifestyle habit may help prevent gray matter volume loss.
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Affiliation(s)
- Rei Otsuka
- Department of Epidemiology of Aging, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan.
| | - Yukiko Nishita
- Department of Epidemiology of Aging, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan; Department of Biomarker Research, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan
| | - Takashi Kato
- Department of Clinical and Experimental Neuroimaging, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan
| | - Fujiko Ando
- Department of Epidemiology of Aging, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan; Faculty of Health and Medical Sciences, Aichi Shukutoku University, Aichi 480-1197, Japan
| | - Hiroshi Shimokata
- Department of Epidemiology of Aging, Research Institute, National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan; Graduate School of Nutritional Sciences, Nagoya University of Arts and Sciences, Aichi 470-0196, Japan
| | - Hidenori Arai
- National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan
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24
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Qi S, Fu Z, Wu L, Calhoun VD, Zhang D, Daughters SB, Hsu PC, Jiang R, Vergara VM, Sui J, Addicott MA. Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation. Front Neurosci 2022; 16:923065. [PMID: 35968362 PMCID: PMC9363622 DOI: 10.3389/fnins.2022.923065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/20/2022] [Indexed: 02/04/2023] Open
Abstract
Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Stacey B. Daughters
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ping-Ching Hsu
- Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Victor M. Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Merideth A. Addicott
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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25
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Wang C, Zhou C, Guo T, Huang P, Xu X, Zhang M. Association between cigarette smoking and Parkinson’s disease: a neuroimaging study. Ther Adv Neurol Disord 2022; 15:17562864221092566. [PMID: 35464739 PMCID: PMC9019319 DOI: 10.1177/17562864221092566] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/20/2022] [Indexed: 11/16/2022] Open
Abstract
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.
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Affiliation(s)
- Chao Wang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Hangzhou 310009, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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26
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Linli Z, Feng J, Zhao W, Guo S. Associations between smoking and accelerated brain ageing. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110471. [PMID: 34740709 DOI: 10.1016/j.pnpbp.2021.110471] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 12/31/2022]
Abstract
Smoking accelerates the ageing of multiple organs. However, few studies have quantified the association between smoking, especially smoking cessation, and brain ageing. Using structural magnetic resonance imaging data from the UK Biobank (n = 33,293), a brain age predictor was trained using a machine learning technique in the non-smoker group (n = 14,667) and then tested in the smoker group (n = 18,626) to determine the relationships between BrainAge Gap (predicted age - true age) and smoking parameters. Further, we examined whether smoking was associated with poorer cognition and whether this relationship was mediated by brain age. The predictor achieved an appreciable performance in training data (r = 0.712, mean-absolute-error [MAE] = 4.220) and test data (r = 0.725, MAE = 4.160). On average, smokers showed a larger BrainAge Gap (+0.304 years, Cohens'd = 0.083) than controls, more explicitly, the extents vary depending on their smoking characteristic that active regular smokers had the largest BrainAge Gap (+1.190 years, Cohens'd = 0.321), and light smokers had a moderate BrainAge Gap (+0.478, Cohens'd = 0.129). The increased smoking amount was associated with a larger BrainAge Gap (β = 0.035, p = 1.72 × 10-20) while a longer duration of quitting smoking in ex-smokers was associated with a smaller BrainAge Gap (β = -0.015, p = 2.14 × 10-05). Furthermore, smoking was associated with poorer cognition, and this relationship was partially mediated by BrainAge Gap. The study provides insight into the association between smoking, brain ageing, and cognition, which provide more publicly acceptable propaganda against smoking.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
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27
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Chen Y, Chaudhary S, Wang W, Li CSR. Gray matter volumes of the insula and anterior cingulate cortex and their dysfunctional roles in cigarette smoking. ADDICTION NEUROSCIENCE 2022; 1:100003. [PMID: 37220533 PMCID: PMC10201991 DOI: 10.1016/j.addicn.2021.100003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The salience network, including the insula and anterior cingulate cortex (ACC), has been implicated in nicotine addiction. Structural imaging studies have reported diminished insula and ACC gray matter volumes (GMVs) in smokers as compared to nonsmokers. However, it remains unclear how insula and ACC GMVs may relate to years of smoking, addiction severity, or behavioral traits known to dispose individuals to smoking. Here, with a dataset curated from the Human Connectome Project and voxel-based morphometry, we replicated the findings of smaller GMVs of the insula and medial prefrontal cortex, including the dorsal ACC and supplementary motor area (dACC/SMA), in (70 heavy < 209 light < 209 never) smokers matched in age, sex, and average daily num ber of drinks. The GMVs of the insula or dACC/SMA were not significantly correlated with years of smoking or Fagerstrom Test for Nicotine Dependence (FTND) scores. Heavy relative to never smokers demonstrated higher externalizing and internalizing scores, as evaluated by the NIH Emotion. In heavy smokers, the dACC/SMA but not insula GMV was positively correlated with both externalizing and internalizing scores. The findings together confirm volumetric changes in the salience network in heavy smokers and suggest potentially distinct dysfunctional roles of the insula and dACC/SMA in chronic smoking.
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Affiliation(s)
- Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Wuyi Wang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Chiang-Shan R. Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, United States
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
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28
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Conti AA, Baldacchino AM. Chronic tobacco smoking, impaired reward-based decision-making, and role of insular cortex: A comparison between early-onset smokers and late-onset smokers. Front Psychiatry 2022; 13:939707. [PMID: 36090372 PMCID: PMC9459116 DOI: 10.3389/fpsyt.2022.939707] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/01/2022] [Indexed: 11/21/2022] Open
Abstract
INTRODUCTION The literature suggests that tobacco smoking may have a neurotoxic effect on the developing adolescent brain. Particularly, it may impair the decision-making process of early-onset smokers (<16 years), by rendering them more prone to impulsive and risky choices toward rewards, and therefore more prone to smoking relapses, in comparison to late-onset smokers (≥16 years). However, no study has ever investigated reward-based decision-making and structural brain differences between early-onset smokers and late-onset smokers. METHODS Computerized measures of reward-based decision-making [Cambridge Gambling Task (CGT); 5-trials adjusting delay discounting task (ADT-5)] were administered to 11 early-onset smokers (mean age at regular smoking initiation = 13.2 years), 17 late-onset smokers (mean age at regular smoking initiation = 18.0 years), and 24 non-smoker controls. Voxel-based morphometry (VBM) was utilized to investigate the gray matter (GM) and white matter (WM) volume differences in fronto-cortical and striatal brain regions between early-onset smokers, late-onset smokers, and non-smokers. RESULTS Early-onset smokers displayed a riskier decision-making behavior in comparison to non-smokers as assessed by the CGT (p < 0.01, Cohen's f = 0.48). However, no significant differences (p > 0.05) in reward-based decision-making were detected between early-onset smokers and late-onset smokers. VBM results revealed early-onset smokers to present lower GM volume in the bilateral anterior insular cortex (AI) in comparison to late-onset smokers and lower WM volume in the right AI in comparison to late-onset smokers. CONCLUSION Impairments in reward-based decision-making may not be affected by tobacco smoking initiation during early adolescence. Instead, lower GM and WM volume in the AI of early-onset smokers may underline a vulnerability to develop compulsive tobacco seeking and smoking behavior during adulthood.
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Affiliation(s)
- Aldo Alberto Conti
- Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alexander Mario Baldacchino
- Division of Population and Behavioral Science, University of St Andrews School of Medicine, St Andrews, United Kingdom
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Yu M, Gao X, Niu X, Zhang M, Yang Z, Han S, Cheng J, Zhang Y. Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder. Front Psychiatry 2022; 13:1070142. [PMID: 36683981 PMCID: PMC9853532 DOI: 10.3389/fpsyt.2022.1070142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND A large and growing body of neuroimaging research has concentrated on patients with attention-deficit/hyperactivity disorder (ADHD), but with inconsistent conclusions. This article was intended to investigate the common and certain neural alterations in the structure and function of the brain in patients with ADHD and further explore the differences in brain alterations between adults and children with ADHD. METHODS We conducted an extensive literature search of whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies associated with ADHD. Two separate meta-analyses with the seed-based d mapping software package for functional neural activation and gray matter volume (GMV) were carried out, followed by a joint analysis and a subgroup analysis. RESULTS This analysis included 29 VBM studies and 36 fMRI studies. Structurally, VBM analysis showed that the largest GMV diminutions in patients with ADHD were in several frontal-parietal brain regions, the limbic system, and the corpus callosum. Functionally, fMRI analysis discovered significant hypoactivation in several frontal-temporal brain regions, the right postcentral gyrus, the left insula, and the corpus callosum. CONCLUSION This study showed that abnormal alterations in the structure and function of the left superior frontal gyrus and the corpus callosum may be the key brain regions involved in the pathogenesis of ADHD in patients and may be employed as an imaging metric for patients with ADHD pending future research. In addition, this meta-analysis discovered neuroanatomical or functional abnormalities in other brain regions in patients with ADHD as well as findings that can be utilized to guide future research.
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Affiliation(s)
- Miaomiao Yu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Xiaoyu Niu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
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Ottino-González J, Uhlmann A, Hahn S, Cao Z, Cupertino RB, Schwab N, Allgaier N, Alia-Klein N, Ekhtiari H, Fouche JP, Goldstein RZ, Li CSR, Lochner C, London ED, Luijten M, Masjoodi S, Momenan R, Oghabian MA, Roos A, Stein DJ, Stein EA, Veltman DJ, Verdejo-García A, Zhang S, Zhao M, Zhong N, Jahanshad N, Thompson PM, Conrod P, Mackey S, Garavan H. White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group. Drug Alcohol Depend 2022; 230:109185. [PMID: 34861493 PMCID: PMC8952409 DOI: 10.1016/j.drugalcdep.2021.109185] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/27/2021] [Accepted: 11/14/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. METHODS Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. RESULTS The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). CONCLUSIONS Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.
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Affiliation(s)
- Jonatan Ottino-González
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States.
| | - Anne Uhlmann
- Department of Child & Adolescent Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Sage Hahn
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Zhipeng Cao
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Renata B Cupertino
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Nathan Schwab
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Nicholas Allgaier
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Nelly Alia-Klein
- Department of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, New York, United States
| | - Hamed Ekhtiari
- Institute for Cognitive Sciences Studies, University of Tehran, Tehran, Iran; Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Jean-Paul Fouche
- SA MRC Genomics and Brain Disorders Unit, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Rita Z Goldstein
- Department of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, New York, United States
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States
| | - Christine Lochner
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Edythe D London
- Department of Psychiatry and Biobehavioural Sciences, University of California, Los Angeles, California, United States
| | - Maartje Luijten
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Sadegh Masjoodi
- Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Momenan
- Clinical Neuroimaging Research Core, National Institutes on Alcohol Abuse & Alcoholism, National Institutes of Health, Bethesda, Maryland, United States
| | - Mohammad Ali Oghabian
- Neuroimaging & Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Annerine Roos
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa; SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute of Drug Abuse, Baltimore, Maryland, United States
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC - location VUMC, Amsterdam, the Netherlands
| | - Antonio Verdejo-García
- School of Psychological Sciences & Turner Institute for Brain & Mental Health, Monash University, Melbourne, Australia
| | - Sheng Zhang
- Department of Psychiatry, Yale University, New Haven, Connecticut, United States
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Na Zhong
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Neda Jahanshad
- Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, San Diego, California, United States
| | - Paul M Thompson
- Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, San Diego, California, United States
| | - Patricia Conrod
- Department of Psychiatry, Université de Montreal, Montreal, Quebec, Canada
| | - Scott Mackey
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, United States
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31
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Niu X, Gao X, Zhang M, Yang Z, Yu M, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Meta-analysis of structural and functional brain alterations in internet gaming disorder. Front Psychiatry 2022; 13:1029344. [PMID: 37033880 PMCID: PMC10074425 DOI: 10.3389/fpsyt.2022.1029344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/03/2022] [Indexed: 04/11/2023] Open
Abstract
Background Many neuroimaging studies have reported abnormalities in brain structure and function in internet gaming disorder (IGD). However, the findings were divergent. We aimed to provide evidence-based evidence of structural and functional changes in IGD by conducting a meta-analysis integrating these studies quantitatively. Method A systematic search was conducted in PubMed, ScienceDirect, Web of Science, and Scopus from January 1, 2010 to October 31, 2021, to identify eligible voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies. Brain alternations between IGD subjects and healthy controls (HCs) were compared using the anisotropic seed-based d mapping (AES-SDM) meta-analytic method. Meta-regression analysis was used to investigate the relationship between gray matter volume (GMV) alterations and addiction-related clinical features. Results The meta-analysis contained 15 VBM studies (422 IGD patients and 354 HCs) and 30 task-state fMRI studies (617 IGD patients and 550 HCs). Compared with HCs, IGD subjects showed: (1) reduced GMV in the bilateral anterior/median cingulate cortex, superior/inferior frontal gyrus and supplementary motor area; (2) hyperactivation in the bilateral inferior frontal gyrus, precentral gyrus, left precuneus, right inferior temporal gyrus and right fusiform; (3) hypoactivation in the bilateral lingual and the left middle frontal gyrus; and (4) both decreased GMV and increased activation in the left anterior cingulate. Furthermore, Meta-regression revealed that GMV reduction in left anterior cingulate were positively correlated with BIS-11 score [r = 0.725, p = 0.012(uncorrected)] and IAT score [r = 0.761, p = 0.017(uncorrected)]. Conclusion This meta-analysis showed structural and functional impairments in brain regions related to executive control, cognitive function and reward-based decision making in IGD. Furthermore, multi-domain assessments captured different aspects of neuronal changes in IGD, which may help develop effective interventions as potential therapeutic targets.
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Affiliation(s)
- Xiaoyu Niu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Miaomiao Yu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- Jingliang Cheng,
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- Shaoqiang Han,
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Magnetic Resonance and Brain Function, Zhengzhou, China
- Henan Engineering Technology Research Center for Detection and Application of Brain Function, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, China
- Zhengzhou Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging, Zhengzhou, China
- Henan Key Laboratory of Imaging Intelligence Research, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- *Correspondence: Yong Zhang,
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Demichelis G, Pinardi C, Giani L, Medina JP, Gianeri R, Bruzzone MG, Becker B, Proietti A, Leone M, Chiapparini L, Ferraro S, Nigri A. Chronic cluster headache: A study of the telencephalic and cerebellar cortical thickness. Cephalalgia 2021; 42:444-454. [PMID: 34875879 DOI: 10.1177/03331024211058205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PURPOSE Previous studies on brain morphological alterations in chronic cluster headache revealed inconsistent findings. METHOD The present cross-sectional explorative study determined telencephalic and cerebellar cortex thickness alterations in a relatively wide sample of chronic cluster headache patients (n = 28) comparing them to matched healthy individuals. RESULTS The combination of two highly robust state-of-the-art approaches for thickness estimation (Freesurfer, CERES), strengthened by functional characterization of the identified abnormal regions, revealed four main results: chronic cluster headache patients show 1) cortical thinning in the right middle cingulate cortex, left posterior insula, and anterior cerebellar lobe, regions involved in nociception's sensory and sensory-motor aspects and possibly in autonomic functions; 2) cortical thinning in the left anterior superior temporal sulcus and the left collateral/lingual sulcus, suggesting neuroplastic maladaptation in areas possibly involved in social cognition, which may promote psychiatric comorbidity; 3) abnormal functional connectivity among some of these identified telencephalic areas; 4) the identified telencephalic areas of cortical thinning present robust interaction, as indicated by the functional connectivity results, with the left posterior insula possibly playing a pivotal role. CONCLUSION The reported results constitute a coherent and robust picture of the chronic cluster headache brain. Our study paves the way for hypothesis-driven studies that might impact our understanding of the pathophysiology of this condition.
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Affiliation(s)
- Greta Demichelis
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Chiara Pinardi
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Luca Giani
- Department of Neurology and Headache Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Jean Paul Medina
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ruben Gianeri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Benjiamin Becker
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Alberto Proietti
- Department of Neurology and Headache Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Massimo Leone
- Department of Neurology and Headache Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Luisa Chiapparini
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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Zhang M, Gao X, Yang Z, Wen M, Huang H, Zheng R, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Shared gray matter alterations in subtypes of addiction: a voxel-wise meta-analysis. Psychopharmacology (Berl) 2021; 238:2365-2379. [PMID: 34313804 DOI: 10.1007/s00213-021-05920-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 07/05/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Numerous studies based on voxel-based morphometry (VBM) have revealed gray matter (GM) alterations in multiple brain regions for addiction. However, findings are poorly replicated, and it remains elusive whether distinct diagnoses of addiction are underpinned by shared abnormalities. Our aim was to conduct a quantitative meta-analysis of structural neuroimaging studies investigating GM abnormalities in two main categories of addiction: substance use disorders (SUD) and behavioral addictions (BA). METHOD A systematic database search was conducted in several databases from Jan 1, 2010, to Oct 23, 2020, to identify eligible VBM studies. Meta-analysis was performed with the seed-based d mapping software package to compare alternations between individuals with addiction-related disorders and healthy controls (HC). RESULTS A total of 59 VBM studies including 2096 individuals with addiction-related disorders and 2637 HC met the inclusion criteria. Individuals with addiction-related disorders showed shared GM volume decrease in bilateral prefrontal cortex, bilateral insula, bilateral rolandic operculum, left superior temporal gyrus, and right Heschl gyrus and GM increase in right lingual gyrus and right fusiform gyrus comparing with HC (p < 0.005). Subgroup analysis found heterogeneity between SUD and BA mainly in left inferior occipital gyrus and right striatum (p < 0.005). Meta-regression revealed that GM atrophy in right anterior cingulate (r = 0.541, p = 0.03 (uncorrected)) and left inferior frontal gyrus (r = 0.595, p = 0.015) were positively correlated with higher impulsivity. CONCLUSIONS This meta-analysis identified a concordance across subtypes of addiction in terms of the brain structural changes in prefrontal and insula areas, which may relate to higher impulsivity observed across addiction diagnoses. This concordance provides an organizing model that emphasizes the importance of shared neural substrates in addiction.
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Affiliation(s)
- Mengzhe Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinyu Gao
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhengui Yang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengmeng Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huiyu Huang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ruiping Zheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yarui Wei
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Shaoqiang Han
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Grodin EN, Burnette E, Towns B, Venegas A, Ray LA. Effect of alcohol, tobacco, and cannabis co-use on gray matter volume in heavy drinkers. PSYCHOLOGY OF ADDICTIVE BEHAVIORS 2021; 35:760-768. [PMID: 34435833 PMCID: PMC8484037 DOI: 10.1037/adb0000743] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Alcohol, tobacco, and cannabis are the three most frequently used drugs in the United States and co-use is common. Alcohol, tobacco, and cannabis use has been separately associated with altered brain structure, and alcohol and tobacco co-use results in decreases in gray matter volume. Less is known about the effect of alcohol and cannabis co-use, and alcohol, tobacco, and cannabis tri-use. Therefore, this study examined the effect of co- and tri-use on gray matter volume, a measure of brain cell density, in heavy drinkers. METHOD Heavy drinkers (n = 237; 152m/85f; age = 32.52; white = 111; black = 28; Latino = 9; American Indian = 2; Pacific Islander = 4; Asian = 59; mixed = 15; other = 9) were classified into four groups based on their alcohol, tobacco, and cannabis use: alcohol only users (n = 70), alcohol and tobacco co-users (n = 90), alcohol and cannabis co-users (n = 35), and alcohol, tobacco, and cannabis tri-users (n = 42). All participants completed a structural MRI scan. Voxel-based morphometry was conducted to evaluate the effect of co-use on gray matter volume, with alcohol only users as the reference group. Age, sex, and scanner were included as covariates. RESULTS Alcohol and tobacco co-users had significantly decreased left orbitofrontal gray matter volume relative to alcohol only users (Cohen's d = .79). There were no differences in gray matter volume between the alcohol only and alcohol and cannabis co-users, or between the alcohol only and tri-user groups. CONCLUSION The additive effect of tobacco co-use on gray matter volumes in heavy drinkers was limited and localized. The effect of tri-use of alcohol, tobacco, and cannabis may have interacted, such that overlapping cannabis and tobacco use masked volume differences present in separate co-using groups. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Erica N. Grodin
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA
| | - Elizabeth Burnette
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA
- Neuroscience Interdepartmental Program, University of California at Los Angeles, Los Angeles, CA
| | - Brandon Towns
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA
| | - Alexandra Venegas
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA
| | - Lara A. Ray
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA
- Brain Research Institute, University of California at Los Angeles, Los Angeles, CA
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35
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Gao X, Zhang M, Yang Z, Wen M, Huang H, Zheng R, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Structural and Functional Brain Abnormalities in Internet Gaming Disorder and Attention-Deficit/Hyperactivity Disorder: A Comparative Meta-Analysis. Front Psychiatry 2021; 12:679437. [PMID: 34276447 PMCID: PMC8281314 DOI: 10.3389/fpsyt.2021.679437] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/21/2021] [Indexed: 12/20/2022] Open
Abstract
Background: Patients with Internet gaming disorder (IGD) and attention-deficit/hyperactivity disorder (ADHD) have high comorbidity but it is still unknown whether these disorders have shared and distinctive neuroimage alterations. Objective: The aim of this meta-analysis was to identify shared and disorder-specific structural, functional, and multimodal abnormalities between IGD and ADHD. Methods: A systematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies comparing people with IGD or ADHD with healthy controls. Regional gray matter volume (GMV) and fMRI differences were compared over the patient groups and then a quantitative comparison was performed to find abnormalities (relative to controls) between IGD and ADHD using seed-based d mapping meta-analytic methods. Result: The meta-analysis contained 14 IGD VBM studies (contrasts covering 333 IGDs and 335 HCs), 26 ADHD VBM studies (1,051 patients with ADHD and 887 controls), 30 IGD fMRI studies (603 patients with IGD and 564 controls), and 29 ADHD fMRI studies (878 patients with ADHD and 803 controls). Structurally, VBM analysis showed disorder-specific GMV abnormality in the putamen among IGD subjects and orbitofrontal cortex in ADHD and shared GMV in the prefrontal cortex. Functionally, fMRI analysis discovered that IGD-differentiating increased activation in the precuneus and shared abnormal activation in anterior cingulate cortex, insular, and striatum. Conclusion: IGD and ADHD have shared and special structural and functional alterations. IGD has disorder-differentiating structural alterations in the putamen and ADHD has alterations in the orbitofrontal cortex. Disorder-differentiating fMRI activations were predominantly observed in the precuneus among IGD subjects and shared impairing function connection was in the rewards circuit (including ACC, OFC, and striatum).
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Affiliation(s)
- Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Mengmeng Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Huiyu Huang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
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Conti AA, Baldacchino AM. Neuroanatomical Correlates of Impulsive Choices and Risky Decision Making in Young Chronic Tobacco Smokers: A Voxel-Based Morphometry Study. Front Psychiatry 2021; 12:708925. [PMID: 34526922 PMCID: PMC8435625 DOI: 10.3389/fpsyt.2021.708925] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/13/2021] [Indexed: 11/29/2022] Open
Abstract
Introduction: Impairments in the multifaceted neuropsychological construct of cognitive impulsivity are a main feature of chronic tobacco smokers. According to the literature, these cognitive impairments are relevant for the initiation and maintenance of the smoking behavior. However, the neuroanatomical correlates of cognitive impulsivity in chronic smokers remain under-investigated. Methods: A sample of 28 chronic smokers (mean age = 28 years) not affected by polysubstance dependence and 24 matched non-smoker controls was recruited. Voxel Based Morphometry (VBM) was employed to assess Gray Matter (GM) volume differences between smokers and non-smokers. The relationships between GM volume and behavioral manifestations of impulsive choices (5 trial adjusting delay discounting task, ADT-5) and risky decision making (Cambridge Gambling Task, CGT) were also investigated. Results: VBM results revealed GM volume reductions in cortical and striatal brain regions of chronic smokers compared to non-smokers. Additionally, smokers showed heightened impulsive choices (p < 0.01, Cohen's f = 0.50) and a riskier decision- making process (p < 0.01, Cohen's f = 0.40) compared to non-smokers. GM volume reductions in the left Anterior Cingulate Cortex (ACC) correlated with impaired impulsive and risky choices, while GM volume reductions in the left Ventrolateral Prefrontal Cortex (VLPFC) and Caudate correlated with heightened impulsive choices. Reduced GM volume in the left VLPFC correlated with younger age at smoking initiation (mean = 16 years). Conclusion: Smokers displayed significant GM volume reductions and related cognitive impulsivity impairments compared to non-smoker individuals. Longitudinal studies would be required to assess whether these impairments underline neurocognitive endophenotypes or if they are a consequence of tobacco exposure on the adolescent brain.
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Affiliation(s)
- Aldo Alberto Conti
- Division of Population and Behavioral Science, University of St. Andrews School of Medicine, St. Andrews, United Kingdom
| | - Alexander Mario Baldacchino
- Division of Population and Behavioral Science, University of St. Andrews School of Medicine, St. Andrews, United Kingdom
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