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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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3
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Duan R, Li Y, Jing L, Zhang T, Yao Y, Gong Z, Shao Y, Song Y, Wang W, Zhang Y, Cheng J, Zhu X, Peng Y, Jia Y. The altered functional connectivity density related to cognitive impairment in alcoholics. Front Psychol 2022; 13:973654. [PMID: 36092050 PMCID: PMC9453650 DOI: 10.3389/fpsyg.2022.973654] [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: 06/20/2022] [Accepted: 07/28/2022] [Indexed: 11/21/2022] Open
Abstract
Alcohol use disorder (AUD) is one of the most common substance use disorders contributing to both behavioral and cognitive impairments in patients with AUD. Recent neuroimaging studies point out that AUD is a typical disorder featured by altered functional connectivity. However, the details about how voxel-wise functional coordination remain unknown. Here, we adopted a newly proposed method named functional connectivity density (FCD) to depict altered voxel-wise functional coordination in AUD. The novel functional imaging technique, FCD, provides a comprehensive analytical method for brain's “scale-free” networks. We applied resting-state functional MRI (rs-fMRI) toward subjects to obtain their FCD, including global FCD (gFCD), local FCD (lFCD), and long-range FCD (lrFCD). Sixty-one patients with AUD and 29 healthy controls (HC) were recruited, and patients with AUD were further divided into alcohol-related cognitive impairment group (ARCI, n = 11) and non-cognitive impairment group (AUD-NCI, n = 50). All subjects were asked to stay stationary during the scan in order to calculate the resting-state gFCD, lFCD, and lrFCD values, and further investigate the abnormal connectivity alterations among AUD-NCI, ARCI, and HC. Compared to HC, both AUD groups exhibited significantly altered gFCD in the left inferior occipital lobe, left calcarine, altered lFCD in right lingual, and altered lrFCD in ventromedial frontal gyrus (VMPFC). It is notable that gFCD of the ARCI group was found to be significantly deviated from AUD-NCI and HC in left medial frontal gyrus, which changes probably contributed by the impairment in cognition. In addition, no significant differences in gFCD were found between ARCI and HC in left parahippocampal, while ARCI and HC were profoundly deviated from AUD-NCI, possibly reflecting a compensation of cognition impairment. Further analysis showed that within patients with AUD, gFCD values in left medial frontal gyrus are negatively correlated with MMSE scores, while lFCD values in left inferior occipital lobe are positively related to ADS scores. In conclusion, patients with AUD exhibited significantly altered functional connectivity patterns mainly in several left hemisphere brain regions, while patients with AUD with or without cognitive impairment also demonstrated intergroup FCD differences which correlated with symptom severity, and patients with AUD cognitive impairment would suffer less severe alcohol dependence. This difference in symptom severity probably served as a compensation for cognitive impairment, suggesting a difference in pathological pathways. These findings assisted future AUD studies by providing insight into possible pathological mechanisms.
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Affiliation(s)
- Ranran Duan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanfei Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lijun Jing
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Tian Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaobing Yao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhe Gong
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yingzhe Shao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yajun Song
- Department of Neurology, 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
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, 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
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, 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
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaofeng Zhu
- Mudanjiang Medical University, Mudanjiang, China
| | - Ying Peng
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yanjie Jia
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Yanjie Jia
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Yang Z, Wen M, Wei Y, Huang H, Zheng R, Wang W, Gao X, Zhang M, Cheng J, Han S, Zhang Y. Alternations in Dynamic and Static Functional Connectivity Density in Chronic Smokers. Front Psychiatry 2022; 13:843254. [PMID: 35530028 PMCID: PMC9068985 DOI: 10.3389/fpsyt.2022.843254] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Previous studies have implicated abnormal functional coordination in brain regions of smokers. Neuroimaging studies demonstrated alternations in brain connectivity by using the resting-state functional connectivity (rsFC) method which arbitrarily chooses specific networks or seed regions as priori selections and cannot provide a full picture of the FC changes in chronic smokers. The aim of this study was to investigate the whole-brain functional coordination measured by functional connectivity density (FCD). As the variance of brain activity, dynamic FCD (dFCD) was performed to investigate dynamic changes of whole-brain integration in chronic smokers. In total, 120 chronic smokers and 56 nonsmokers were recruited, and static FCD and dFCD were performed to investigate aberrance of whole-brain functional coordination. Shared aberrance in visual areas has been found in both static and dFCD study in chronic smokers. Furthermore, the results exhibited that both heavy and light smokers demonstrated decreased dFCD in the visual cortex and left precuneus, and also increased dFCD in the right orbitofrontal cortex, left caudate, right putamen, and left thalamus compared with nonsmokers. In addition, alternations of dFCD have been found between heavy and light smokers. Furthermore, the dFCD variations showed significant positive correlation with smoking-related behaviors. The results demonstrated that chronic smokers not only have some initial areas, but also have some regions associated with severity of cigarette smoking. Lastly, dFCD could provide more subtle variations in chronic smokers, and the combination of static and dFCD may deepen our understanding of the brain alternations in chronic smokers.
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Affiliation(s)
- 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
| | - 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.,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
| | - 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.,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
| | - 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.,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
| | - 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
| | - 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
| | - 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
| | - 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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5
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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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Zhang M, Liu S, Wang S, Xu Y, Chen L, Shao Z, Wen X, Yang W, Liu J, Yuan K. Reduced thalamic resting-state functional connectivity and impaired cognition in acute abstinent heroin users. Hum Brain Mapp 2021; 42:2077-2088. [PMID: 33459459 PMCID: PMC8046054 DOI: 10.1002/hbm.25346] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/05/2021] [Accepted: 01/07/2021] [Indexed: 12/13/2022] Open
Abstract
As a critical component of cortico-striato-thalamo-cortical loop in addiction, our understanding of the thalamus in impaired cognition of heroin users (HU) has been limited. Due to the complex thalamic connection with cortical and subcortical regions, thalamus was divided into prefrontal (PFC), occipital (OC), premotor, primary motor, sensory, temporal, and posterior parietal association subregions according to white matter tractography. We adopted seven subregions of bilateral thalamus as regions of interest to systematically study the implications of distinct thalamic nuclei in acute abstinent HU. The volume and resting-state functional connectivity (RSFC) differences of the thalamus were investigated between age-, gender-, and alcohol-matched 37 HU and 33 healthy controls (HCs). Trail making test-A (TMT-A) was adopted to assess cognitive function deficits, which were then correlated with neuroimaging findings. Although no significant different volumes were found, HU group showed decreased RSFC between left PFC_thalamus and middle temporal gyrus as well as between left OC_thalamus and inferior frontal gyrus and supplementary motor area relative to HCs. Meanwhile, the higher TMT-A scores in HU were negatively correlated with PFC_thalamic RSFC with inferior temporal gyrus, fusiform, and precuneus. Craving scores were negatively correlated with OC_thalamic RSFC with accumbens, hippocampus, and insula. Opiate Withdrawal Scale scores were negatively correlated with left PFC/OC_thalamic RSFC with orbitofrontal cortex and medial PFC. We indicated two thalamus subregions separately involvement in cognitive control and craving to reveal the implications of thalamic subnucleus in pathology of acute abstinent HU.
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Affiliation(s)
- Min Zhang
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Shuang Liu
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Shicong Wang
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Yan Xu
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Longmao Chen
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Ziqiang Shao
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Xinwen Wen
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
| | - Wenhan Yang
- Department of RadiologySecond Xiangya Hospital, Central South UniversityChangshaChina
| | - Jun Liu
- Department of RadiologySecond Xiangya Hospital, Central South UniversityChangshaChina
| | - Kai Yuan
- School of Life Science and TechnologyXidian UniversityXi'anShaanxiPeople's Republic of China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi'anPeople's Republic of China
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7
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Xue T, Dong F, Huang R, Tao Z, Tang J, Cheng Y, Zhou M, Hu Y, Li X, Yu D, Ju H, Yuan K. Dynamic Neuroimaging Biomarkers of Smoking in Young Smokers. Front Psychiatry 2020; 11:663. [PMID: 32754067 PMCID: PMC7367415 DOI: 10.3389/fpsyt.2020.00663] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 06/26/2020] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE To examine potential changes in the dynamic characteristics of regional neural activity in young smokers and to detect whether the changes were associated with smoking behavior. METHODS The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) in 40 young smokers and 42 nonsmokers were compared. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and smoking behavior [e.g., the Fagerström Test for Nicotine dependence (FTND) scores and pack-years]. RESULTS Significantly differences in dReHo variability were observed in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), medial frontal gyrus (MFG), insula, cuneus, postcentral gyrus, inferior semi-lunar lobule, orbitofrontal gyrus, and inferior temporal gyrus (ITG). Young smokers also showed significantly increased dALFF variability in the anterior cingulate cortex (ACC) and ITG. Furthermore, a significant positive correlation was found between dALFF variability in the ACC and the pack-years; whereas a significant negative correlation between dReHo variability in the IFG and the FTND scores was found in young smokers. CONCLUSION The pattern of resting state regional neural activity variability was different between young smokers and nonsmokers. Dynamic regional indexes might be a novel neuroimaging biomarker of smoking behavior in young smokers.
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Affiliation(s)
- Ting Xue
- School of Science, Inner Mongolia University of Science and Technology, Baotou, China
- 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
| | - Ruoyan Huang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Zhanlong Tao
- School of Science, Inner Mongolia University of Science and Technology, Baotou, China
| | - Jun Tang
- School of Science, 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
| | - Mi Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yiting Hu
- 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
| | - 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
| | - Haitao Ju
- Department of Neurosurgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 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
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8
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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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9
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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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10
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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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