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Sun J, Zhang M, Dang J, Niu X, Tao Q, Kang Y, Ma L, Mei B, Wei Y, Wang W, Han S, Cheng J, Zhang Y. Mapping brain activity and neurotransmitters pre-cigarette smoking evolution: A study of male subjects. J Psychiatr Res 2024; 180:39-46. [PMID: 39369637 DOI: 10.1016/j.jpsychires.2024.09.051] [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: 05/08/2024] [Revised: 09/22/2024] [Accepted: 09/29/2024] [Indexed: 10/08/2024]
Abstract
BACKGROUND The impact of tobacco smoking on global health persists and it is essential to understand the progression of addiction and the involvement of neurotransmitters. METHODS This study assessed 47 participants with tobacco use disorder (TUD) categorized based on changes in Fagerström Test for Nicotine Dependence (FTND) scores over 6 years: progressive TUD (pTUD), regressive TUD (rTUD), and stable TUD (sTUD). Additionally, 35 healthy controls were included. Resting-state functional magnetic resonance imaging was used to evaluate brain regional homogeneity (ReHo) and correlations with neurotransmitter distributions using JuSpace. RESULTS Significant differences in ReHo were observed among pTUD, rTUD, sTUD, and controls. After strict Bonferroni correction, rTUD exhibited increased ReHo in the dorsolateral superior frontal gyrus compared to sTUD (p < 0.001) and controls (p < 0.001). Both pTUD (p < 0.001) and rTUD (p < 0.001) showed decreased ReHo in the superior temporal gyrus compared to sTUD. sTUD had increased ReHo in the supramarginal gyrus compared to all other groups (p < 0.001, p < 0.001, p = 0.002, separately). The strongest association, which survived rigorous Bonferroni correction, was between the ReHo changes in rTUD compared to sTUD and neurotransmitter distribution. This includes 5-hydroxytryptamine receptor 2A (p = 0.001), gamma-aminobutyric acid type A receptor (p < 0.001), norepinephrine transporter (p < 0.001), and N-Methyl-D-Aspartate (p = 0.002). CONCLUSIONS This study provides insights into how smoking behaviors correlate with alterations in brain activity and neurotransmitter function. By elucidating these neural links to tobacco use disorder progression, our findings contribute to a deeper understanding of smoking's neurological impact and potentially inform more targeted therapeutic strategies.
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Affiliation(s)
- Jieping Sun
- 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 Province, 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 Province, China
| | - Jinghan Dang
- 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 Province, 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 Province, China
| | - Qiuying Tao
- 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 Province, China
| | - Yimeng Kang
- 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 Province, China
| | - Longyao Ma
- 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 Province, China
| | - Bohui Mei
- 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 Province, 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 Province, 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 Province, 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 Province, 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 Province, 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 Province, China.
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Tabibnia G, Ghahremani DG, Pochon JBF, Diaz MP, London ED. Negative affect and craving during abstinence from smoking are both linked to default mode network connectivity. Drug Alcohol Depend 2023; 249:109919. [PMID: 37270935 PMCID: PMC10516582 DOI: 10.1016/j.drugalcdep.2023.109919] [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: 02/02/2023] [Revised: 04/11/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND Negative affect and craving during abstinence from cigarettes predict resumption of smoking. Therefore, understanding their neural substrates may guide development of new interventions. Negative affect and craving have traditionally been linked to functions of the brain's threat and reward networks, respectively. However, given the role of default mode network (DMN), particularly the posterior cingulate cortex (PCC), in self-related thought, we examined whether DMN activity underlies both craving and negative affective states in adults who smoke. METHODS 46 adults who smoke abstained from smoking overnight and underwent resting-state fMRI, after self-reporting their psychological symptoms (negative affect) and craving on the Shiffman-Jarvik Withdrawal Scale and state anxiety (negative affect) on the Spielberger State-Trait Anxiety Inventory. Within-DMN functional connectivity using 3 different anterior PCC seeds was tested for correlations with self-report measures. Additionally, independent component analysis with dual regression was performed to measure associations of self-report with whole-brain connectivity of the DMN component. RESULTS Craving correlated positively with connectivity of all three anterior PCC seeds with posterior PCC clusters (pcorr<0.04). The measures of negative affective states correlated positively with connectivity of the DMN component to various brain regions, including posterior PCC (pcorr=0.02) and striatum (pcorr<0.008). Craving and state anxiety were correlated with connectivity of an overlapping region of PCC (pcorr=0.003). Unlike the state measures, nicotine dependence and trait anxiety were not associated with PCC connectivity within DMN. CONCLUSIONS Although negative affect and craving are distinct subjective states, they appear to share a common neural pathway within the DMN, particularly involving the PCC.
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Affiliation(s)
- Golnaz Tabibnia
- Department of Psychological Science, University of California, Irvine, CA, USA.
| | - Dara G Ghahremani
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jean-Baptiste F Pochon
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Maylen Perez Diaz
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Edythe D London
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
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Yip SW, Lichenstein SD, Garrison K, Averill CL, Viswanath H, Salas R, Abdallah CG. Effects of Smoking Status and State on Intrinsic Connectivity. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:895-904. [PMID: 33618016 PMCID: PMC8373998 DOI: 10.1016/j.bpsc.2021.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/18/2021] [Accepted: 02/02/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Smoking behavior during the first 24 hours of a quit attempt is a significant predictor of longer-term abstinence, yet little is known about the neurobiology of early tobacco abstinence. Specifically, the effects of acute tobacco deprivation and reinstatement on brain function-particularly at the level of large-scale network dynamics and assessed across the entire brain-remain incompletely understood. To address this gap, this study used a mixed within- and between-subjects design to assess the effects of smoking status (yes/no smoker) and state (deprived vs. satiated) on whole-brain patterns of intrinsic connectivity. METHODS Participants included 42 tobacco smokers who underwent resting-state functional magnetic resonance imaging following overnight abstinence (deprived state) and following smoking reinstatement (satiated state, randomized order across participants). Sixty healthy control nonsmokers underwent a single resting-state scan using the same acquisition parameters. Functional connectivity data were analyzed using both a canonical network-of-interest approach and a whole-brain, data-driven approach, i.e., intrinsic connectivity distribution. RESULTS Network-of-interest-based analyses indicated decreased functional connectivity within frontoparietal and salience networks among smokers relative to nonsmokers as well as effects of smoking state on default mode connectivity. In addition, intrinsic connectivity distribution analyses identified novel between-group differences in subcortical-cerebellar and corticocerebellar networks that were largely smoking state dependent. CONCLUSIONS These data demonstrate the importance of considering smoking state and the utility of using both theory- and data-driven analysis approaches. These data provide much-needed insight into the functional neurobiology of early abstinence, which may be used in the development of novel treatments.
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Affiliation(s)
- Sarah W Yip
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| | - Sarah D Lichenstein
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Kathleen Garrison
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Christopher L Averill
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Clinical Neurosciences Division, Veterans Administration National Center for PTSD, West Haven, Connecticut; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas; Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
| | - Humsini Viswanath
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas; Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
| | - Chadi G Abdallah
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Clinical Neurosciences Division, Veterans Administration National Center for PTSD, West Haven, Connecticut; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas; Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas
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Bi Y, Hu L. Magnetic resonance imaging for smoking abstinence: symptoms, mechanisms, and interventions. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2021.9050016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Tobacco smoking is the leading preventable cause of morbidity and mortality worldwide. Although a number of smokers are aware of the adverse outcomes of smoking and express a strong desire to stop smoking, most smoking quit attempts end in relapse within the first few days of abstinence, primarily resulting from the aversive aspects of the nicotine withdrawal syndrome. Therefore, studying the neural mechanisms of smoking abstinence, identifying smokers with heightened relapse vulnerability prior to quit attempts, and developing effective smoking cessation treatments appear to be promising strategies for improving the success of quit attempts. In recent years, with the development of magnetic resonance imaging, the neural substrates of smoking abstinence have become extensively studied. In this review, we first introduce the psychophysiological changes induced by smoking abstinence, including affective, cognitive, and somatic signs. We then provide an overview of the magnetic resonance imaging-based evidence regarding abstinence-related functional changes accompanied by these psychophysiological changes. We conclude with a discussion of the neural markers that could predict relapse during quit attempts and a summary of the psychophysiological interventions that are currently often used to help with smoking cessation. This review extends our understanding of the role of the central nervous system in smoking abstinence.
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Affiliation(s)
- Yanzhi Bi
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Li Hu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
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Temporal Dynamics of Large-Scale Networks Predict Neural Cue Reactivity and Cue-Induced Craving. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:1011-1018. [PMID: 32900658 DOI: 10.1016/j.bpsc.2020.07.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/17/2020] [Accepted: 07/09/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Cue reactivity, a core characteristic of substance use disorders, commonly recruits brain regions that are key nodes in neurocognitive networks, including the default mode network (DMN) and salience network (SN). Whether resting-state temporal dynamic properties of these networks relate to subsequent cue reactivity and cue-induced craving is unknown. METHODS The resting-state data of 46 nicotine-dependent participants were assessed to define temporal dynamic properties of DMN and SN states. Temporal dynamics focused on the total time across the scan session that brain activity resides in these specific states. Using regression models, we examined how the total time in each state related to neural reactivity to smoking cues within key DMN (posterior cingulate cortex, medial prefrontal cortex) or SN (anterior insula, dorsal anterior cingulate cortex) nodes. Mediation analyses were subsequently conducted to study how neural cue reactivity mediates the relationship between total time in state at rest and subjective cue-induced craving. RESULTS Increased time spent in the DMN state and decreased time spent in the SN state predicted subsequent cue-induced increases in the anterior insula and dorsal anterior cingulate cortex, respectively. Cue-induced anterior insula and dorsal anterior cingulate cortex activity significantly mediated the relationship between time spent in DMN/SN and cue-induced subjective craving. CONCLUSIONS Our findings showed a significant relationship between resting-state dynamics of the DMN/SN and task-activated SN nodes that together predicted cue-induced craving changes in nicotine-dependent individuals. These findings propose a neurobiological pathway for cue-induced craving that begins with resting-state temporal dynamics, suggesting that brain responding to external stimuli is driven by resting temporal dynamics.
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Zhao H, Turel O, Brevers D, Bechara A, He Q. Smoking cues impair monitoring but not stopping during response inhibition in abstinent male smokers. Behav Brain Res 2020; 386:112605. [DOI: 10.1016/j.bbr.2020.112605] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/16/2020] [Accepted: 02/19/2020] [Indexed: 11/27/2022]
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Cui Y, Dong F, Li X, Xie D, Cheng Y, Tian S, Xue T, Li Y, Zhang M, Ren Y, Yuan K, Yu D. Electrophysiological Evidence of Event-Related Potential Changes Induced by 12 h Abstinence in Young Smokers Based on the Flanker Study. Front Psychiatry 2020; 11:424. [PMID: 32528322 PMCID: PMC7258559 DOI: 10.3389/fpsyt.2020.00424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 04/27/2020] [Indexed: 11/23/2022] Open
Abstract
The cognitive control processes may be disrupted by abstinence in smokers, which may be helpful in the development and maintenance of addictive behavior. The purpose of this study was to measure the performance of cognitive task after 12 h of smoking abstinence by using event-related potentials (ERPs), including the error-related negativity (ERN) and the error positivity (Pe). In Eriksen flanker task, electroencephalography (EEG) signals of 24 smokers were recorded in two conditions: satiety and 12 h abstinence. In the behavioral data, both conditions exhibited more errors and more time on the incongruent trials than congruence. Meantime, the Minnesota Nicotine Withdrawal Scale (MNWS) score was increased during abstinence. Smokers showed reduced ERN and Pe after 12 h of abstinence, compared with satiety condition. The results indicate that the diminished error processing in young smokers after 12 h of abstinence. It may be related to increased withdrawal symptoms. In conclusion, the disrupted neurophysiological indexes in the general behavior monitoring system may be caused by abstinence. The results of this study may provide us with new ideas about the effects of short-term abstinence on brain cognitive neuroscience and be helpful for the solution of relapse.
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Affiliation(s)
- Yongting Cui
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xiaojian Li
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Dongdong Xie
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yongxin Cheng
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Shiyu Tian
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yangding Li
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Ming Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yan Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.,Life Sciences Research Center, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
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Liu C, Dong F, Li Y, Ren Y, Xie D, Wang X, Xue T, Zhang M, Ren G, von Deneen KM, Yuan K, Yu D. 12 h Abstinence-Induced ERP Changes in Young Smokers: Electrophysiological Evidence From a Go/NoGo Study. Front Psychol 2019; 10:1814. [PMID: 31474901 PMCID: PMC6703154 DOI: 10.3389/fpsyg.2019.01814] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 07/22/2019] [Indexed: 11/13/2022] Open
Abstract
Decreased inhibition control ability and increased craving may be the most important causes of relapsing in smoking. Although inhibition control defects in young smokers were investigated, the effects of short-term abstinence on inhibition control in young smokers were still unclear. Thirty young smokers participated in the present study. The EEG signals during the Go/NoGo task were recorded in both satiety and 12 h abstinence conditions. The task performances were observed and compared between the two conditions. Event-related potential (ERP) analysis was used to investigate changes in N200 and P300 amplitude and latency induced by 12 h of abstinence. After 12 h of abstinence, the latency of N200 was prolonged in young smokers. No significant changes were found in the number of NoGo errors and the response time of Go in young smokers after 12 h of abstinence. Correlation analysis showed that the N200 latency of abstinence condition was significantly correlated with the number of NoGo errors and the response time of Go in the abstinence condition. The present findings may improve the understanding of the effect of short-term abstinence in young smokers. We suggested that the latency of N200 may be associated with inefficient inhibitory control of the abstinence condition in young smokers. Our results may contribute new insights into the neural mechanism of nicotine abstinence in young smokers.
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Affiliation(s)
- Chang Liu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yangding Li
- Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, China
| | - Yan Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Dongdong Xie
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xianfu Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ming Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Guoyin Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | | | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
- Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, China
- School of Life Sciences and Technology, Xidian University, Xi’an, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
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Shen Z, Huang P, Wang C, Qian W, Yang Y, Zhang M. Cerebellar Gray Matter Reductions Associate With Decreased Functional Connectivity in Nicotine-Dependent Individuals. Nicotine Tob Res 2019; 20:440-447. [PMID: 29065207 DOI: 10.1093/ntr/ntx168] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 07/25/2017] [Indexed: 01/19/2023]
Abstract
Introduction Nicotine dependence (ND) is a chronic, relapsing mental disorder characterized by compulsive cigarette seeking and smoking. Although the cerebellum plays an increasingly implicated role in ND, the exact cerebellar alterations in ND remain unclear. Identifying the localization of these cerebellar abnormalities in ND may help to further understand the role of the cerebellum in ND. Thus, we investigated the structural and functional alterations in the cerebellum in a large sample of smokers using the spatially unbiased infratentorial template (SUIT). Methods High-resolution structural magnetic resonance imaging (MRI) data were acquired from 85 smokers and 41 nonsmokers. We applied voxel-based morphometry (VBM) and the SUIT cerebellar atlas to compare the cerebellar gray matter (GM) volume between smokers and nonsmokers. Using resting-state functional MRI data, we also performed seed-based functional connectivity (FC) analysis to examine the functional correlates of the GM volume changes. Results Both VBM and lobular analyses revealed smaller GM volume in the bilateral Crus I in smokers. The GM volume of the left Crus I was inversely correlated with the severity of nicotine dependence as assessed by Fagerström Test for Nicotine Dependence (r = -.268, p = .013). We also found reduced FC between the bilateral Crus I and brain regions involved in the default mode network and motor system, as well as the frontal and temporal cortex in smokers. Conclusions Our results indicate that decreased cerebellar GM volume and corticocerebellar FC are associated with ND, and these may underlie the core ND phenotypes, including automatized smoking behavior, cognitive, and emotional deficits. Implications As smoking remains a worldwide public health problem, identifying the related neural alterations may help to understand the pathophysiology of ND. Based on previous findings in the cerebellum, we investigated the localization of the GM differences and related FC changes in ND subjects. Our findings highlight altered corticocerebellar circuits in ND, suggesting an association between the cerebellum and the phenotypes of ND.
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Affiliation(s)
- Zhujing Shen
- 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
| | - Chao Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Qian
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Yuan K, Yu D, Bi Y, Wang R, Li M, Zhang Y, Dong M, Zhai J, Li Y, Lu X, Tian J. The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue-induced craving of smokers. Hum Brain Mapp 2017; 38:4644-4656. [PMID: 28653791 PMCID: PMC6866730 DOI: 10.1002/hbm.23690] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 06/05/2017] [Accepted: 06/09/2017] [Indexed: 01/17/2023] Open
Abstract
Although the activation of the prefrontal cortex (PFC) and the striatum had been found in smoking cue induced craving task, whether and how the functional interactions and white matter integrity between these brain regions contribute to craving processing during smoking cue exposure remains unknown. Twenty-five young male smokers and 26 age- and gender-matched nonsmokers participated in the smoking cue-reactivity task. Craving related brain activation was extracted and psychophysiological interactions (PPI) analysis was used to specify the PFC-efferent pathways contributed to smoking cue-induced craving. Diffusion tensor imaging (DTI) and probabilistic tractography was used to explore whether the fiber connectivity strength facilitated functional coupling of the circuit with the smoking cue-induced craving. The PPI analysis revealed the negative functional coupling of the left dorsolateral prefrontal cortex (DLPFC) and the caudate during smoking cue induced craving task, which positively correlated with the craving score. Neither significant activation nor functional connectivity in smoking cue exposure task was detected in nonsmokers. DTI analyses revealed that fiber tract integrity negatively correlated with functional coupling in the DLPFC-caudate pathway and activation of the caudate induced by smoking cue in smokers. Moreover, the relationship between the fiber connectivity integrity of the left DLPFC-caudate and smoking cue induced caudate activation can be fully mediated by functional coupling strength of this circuit in smokers. The present study highlighted the left DLPFC-caudate pathway in smoking cue-induced craving in smokers, which may reflect top-down prefrontal modulation of striatal reward processing in smoking cue induced craving processing. Hum Brain Mapp 38:4644-4656, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Kai Yuan
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
- School of Information EngineeringInner Mongolia University of Science and TechnologyBaotouInner Mongolia014010People's Republic of China
| | - Dahua Yu
- School of Information EngineeringInner Mongolia University of Science and TechnologyBaotouInner Mongolia014010People's Republic of China
| | - Yanzhi Bi
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
| | - Ruonan Wang
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
| | - Min Li
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
| | - Yajuan Zhang
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
| | - Minghao Dong
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
| | - Jinquan Zhai
- Department of Medical ImagingThe First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and TechnologyBaotouInner Mongolia014010People's Republic of China
| | - Yangding Li
- Guangxi Key Laboratory of Multi‐Source Information Mining and SecurityGuangxi Normal UniversityGuilinPeople's Republic of China
| | - Xiaoqi Lu
- School of Information EngineeringInner Mongolia University of Science and TechnologyBaotouInner Mongolia014010People's Republic of China
| | - Jie Tian
- School of Life Science and TechnologyXidian UniversityXi'anShaanxi710071People's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education
- Institute of Automation, Chinese Academy of SciencesBeijing100190People's Republic of China
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Bu L, Yu D, Su S, Ma Y, von Deneen KM, Luo L, Zhai J, Liu B, Cheng J, Guan Y, Li Y, Bi Y, Xue T, Lu X, Yuan K. Functional Connectivity Abnormalities of Brain Regions with Structural Deficits in Young Adult Male Smokers. Front Hum Neurosci 2016; 10:494. [PMID: 27757078 PMCID: PMC5047919 DOI: 10.3389/fnhum.2016.00494] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 09/20/2016] [Indexed: 12/22/2022] Open
Abstract
Smoking is one of the most prevalent dependence disorders. Previous studies have detected structural and functional deficits in smokers. However, few studies focused on the changes of resting state functional connectivity (RSFC) of the brain regions with structural deficits in young adult smokers. Twenty-six young adult smokers and 26 well-matched healthy non-smokers participated in our study. Voxel-based morphometry (VBM) and RSFC were employed to investigate the structural and functional changes in young adult smokers. Compared with healthy non-smokers, young smokers showed increased gray matter (GM) volume in the left putamen and decreased GM volume in the left anterior cingulate cortex (ACC). Moreover, GM volume in the left ACC has a negative correlation trend with pack-years and GM volume in the left putamen was positively correlated with pack-years. The left ACC and putamen with abnormal volumes were chosen as the regions of interest (ROIs) for the RSFC analysis. We found that smokers showed increased RSFC between the left ACC and right amygdala and between the left putamen and right anterior insula. We revealed structural and functional deficits within the frontostriatal circuits in young smokers, which may shed new insights into the neural mechanisms of smoking.
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Affiliation(s)
- Limei Bu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Shaoping Su
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Yao Ma
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Karen M von Deneen
- School of Life Science and Technology, Xidian UniversityXian, People's Republic of China; Engineering Research Center of Molecular and NeuroImaging, Ministry of EducationPeople's Republic of China
| | - Lin Luo
- Department of Medical Imaging, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Jinquan Zhai
- Department of Medical Imaging, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Bo Liu
- Department of Medical Imaging, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Jiadong Cheng
- School of Life Science and Technology, Xidian UniversityXian, People's Republic of China; Engineering Research Center of Molecular and NeuroImaging, Ministry of EducationPeople's Republic of China
| | - Yanyan Guan
- School of Life Science and Technology, Xidian UniversityXian, People's Republic of China; Engineering Research Center of Molecular and NeuroImaging, Ministry of EducationPeople's Republic of China
| | - Yangding Li
- School of Life Science and Technology, Xidian UniversityXian, People's Republic of China; Engineering Research Center of Molecular and NeuroImaging, Ministry of EducationPeople's Republic of China
| | - Yanzhi Bi
- School of Life Science and Technology, Xidian UniversityXian, People's Republic of China; Engineering Research Center of Molecular and NeuroImaging, Ministry of EducationPeople's Republic of China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Xiaoqi Lu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology Baotou, People's Republic of China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and TechnologyBaotou, People's Republic of China; School of Life Science and Technology, Xidian UniversityXian, People's Republic of China; Engineering Research Center of Molecular and NeuroImaging, Ministry of EducationPeople's Republic of China
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12
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Li Y, Yuan K, Guan Y, Cheng J, Bi Y, Shi S, Xue T, Lu X, Qin W, Yu D, Tian J. The implication of salience network abnormalities in young male adult smokers. Brain Imaging Behav 2016; 11:943-953. [DOI: 10.1007/s11682-016-9568-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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