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Koster M, Mannsdörfer L, van der Pluijm M, de Haan L, Ziermans T, van Wingen G, Vermeulen J. The Association Between Chronic Tobacco Smoking and Brain Alterations in Schizophrenia: A Systematic Review of Magnetic Resonance Imaging Studies. Schizophr Bull 2024:sbae088. [PMID: 38824451 DOI: 10.1093/schbul/sbae088] [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] [Indexed: 06/03/2024]
Abstract
BACKGROUND AND HYPOTHESIS The high co-occurrence of tobacco smoking in patients with schizophrenia spectrum disorders (SSD) poses a serious health concern, linked to increased mortality and worse clinical outcomes. The mechanisms underlying this co-occurrence are not fully understood. STUDY DESIGN Addressing the need for a comprehensive overview of the impact of tobacco use on SSD neurobiology, we conducted a systematic review of neuroimaging studies (including structural, functional, and neurochemical magnetic resonance imaging studies) that investigate the association between chronic tobacco smoking and brain alterations in patients with SSD. STUDY RESULTS Eight structural and fourteen functional studies were included. Structural studies show widespread independent and additive reductions in gray matter in relation to smoking and SSD. The majority of functional studies suggest that smoking might be associated with improvements in connectivity deficits linked to SSD. However, the limited number of and high amount of cross-sectional studies, and high between-studies sample overlap prevent a conclusive determination of the nature and extent of the impact of smoking on brain functioning in patients with SSD. Overall, functional results imply a distinct neurobiological mechanism for tobacco addiction in patients with SSD, possibly attributed to differences at the nicotinic acetylcholine receptor level. CONCLUSIONS Our findings highlight the need for more longitudinal and exposure-dependent studies to differentiate between inherent neurobiological differences and the (long-term) effects of smoking in SSD, and to unravel the complex interaction between smoking and schizophrenia at various disease stages. This could inform more effective strategies addressing smoking susceptibility in SSD, potentially improving clinical outcomes.
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Affiliation(s)
- Merel Koster
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lilli Mannsdörfer
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Marieke van der Pluijm
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Tim Ziermans
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Guido van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jentien Vermeulen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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2
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Murray L, Frederick BB, Janes AC. Data-driven connectivity profiles relate to smoking cessation outcomes. Neuropsychopharmacology 2024; 49:1007-1013. [PMID: 38280945 DOI: 10.1038/s41386-024-01802-9] [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: 08/28/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/29/2024]
Abstract
At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.
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Affiliation(s)
- Laura Murray
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
| | - Blaise B Frederick
- McLean Imaging Center, McLean Hospital, 115 Mill Street, Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02215, USA
| | - Amy C Janes
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Biomedical Research Center, 251 Bayview Blvd, Baltimore, MD, 21224, USA
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3
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Sanader Vukadinovic B, Karch S, Paolini M, Reidler P, Rauchmann B, Koller G, Pogarell O, Keeser D. Neurofeedback for alcohol addiction: Changes in resting state network activity ✰. Psychiatry Res Neuroimaging 2024; 339:111786. [PMID: 38281353 DOI: 10.1016/j.pscychresns.2024.111786] [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: 08/08/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Alcohol dependence continues to be a major global burden despite significant research progress and treatment development. The aim of this study was to investigate whether neurofeedback training can alter resting state fMRI activity in brain regions that play a crucial role in addiction disorders in patients with alcohol dependence. For this purpose, a total of 52 patients were recruited for the present study, randomized, and divided into an active and a sham group. Patients in the active group received three sessions of neurofeedback training. We compared the resting state data in the active group as part of the NF training on six measurement days. When comparing the results of the active group from neurofeedback day 3 with baseline 1, a significant reduction in activated voxels in the ventral attention network area was seen. This suggests that reduced activity over the course of therapy in subjects may lead to greater independence from external stimuli. Overall, a global decrease in activated voxels within all three analysed networks compared to baseline was observed in the study. The use of resting-state data as potential biomarkers, as activity changes within these networks, may be to help restore cognitive processes and alcohol abuse-related craving and emotions.
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Affiliation(s)
- B Sanader Vukadinovic
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany; University College London Hospitals NHS Foundation Trust (UCLH), London, United Kingdom.
| | - S Karch
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany
| | - M Paolini
- Department of Radiology, University Hospital LMU, Munich, Germany
| | - P Reidler
- Department of Radiology, University Hospital LMU, Munich, Germany
| | - B Rauchmann
- Department of Radiology, University Hospital LMU, Munich, Germany
| | - G Koller
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany
| | - O Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany
| | - D Keeser
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany; NeuroImaging Core Unit Munich (NICUM), University Hospital LMU, Munich, Germany
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4
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Frie JA, McCunn P, Eed A, Hassan A, Luciani KR, Chen C, Tyndale RF, Khokhar JY. Factors influencing JUUL e-cigarette nicotine vapour-induced reward, withdrawal, pharmacokinetics and brain connectivity in rats: sex matters. Neuropsychopharmacology 2024; 49:782-795. [PMID: 38057369 PMCID: PMC10948865 DOI: 10.1038/s41386-023-01773-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 11/05/2023] [Accepted: 11/14/2023] [Indexed: 12/08/2023]
Abstract
Though vaping likely represents a safer alternative to smoking, it is not without risks, many of which are not well understood, especially for vulnerable populations. Here we evaluate the sex- and age-dependent effects of JUUL nicotine vapour in rats. Following passive nicotine vapour exposures (from 59 mg/ml JUUL nicotine pods), rats were evaluated for reward-like behaviour, locomotion, and precipitated withdrawal. Pharmacokinetics of nicotine and its metabolites in brain and plasma and the long-term impact of nicotine vapour exposure on functional magnetic resonance imaging-based brain connectivity were assessed. Adult female rats acquired conditioned place preference (CPP) at a high dose (600 s of exposure) of nicotine vapour while female adolescents, as well as male adults and adolescents did not. Adult and adolescent male rats displayed nicotine vapour-induced precipitated withdrawal and hyperlocomotion, while both adult and adolescent female rats did not. Adult females showed higher venous and arterial plasma and brain nicotine and nicotine metabolite concentrations compared to adult males and adolescent females. Adolescent females showed higher brain nicotine concentration compared to adolescent males. Both network-based statistics and between-component group connectivity analyses uncovered reduced connectivity in nicotine-exposed rats, with a significant group by sex interaction observed in both analyses. The short- and long-term effects of nicotine vapour are affected by sex and age, with distinct behavioural, pharmacokinetic, and altered network connectivity outcomes dependent on these variables.
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Affiliation(s)
- Jude A Frie
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Patrick McCunn
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Amr Eed
- Department of Medical Biophysics and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Ahmad Hassan
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Karling R Luciani
- Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Chuyun Chen
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Rachel F Tyndale
- Departments of Psychiatry, and Pharmacology & Toxicology, University of Toronto, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jibran Y Khokhar
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
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5
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Panagopoulos VN, Bailey A, Kostopoulos GK, Ioannides AA. Changes in distinct brain systems identified with fMRI during smoking cessation treatment with varenicline: a review. Psychopharmacology (Berl) 2024; 241:653-685. [PMID: 38430396 DOI: 10.1007/s00213-024-06556-2] [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: 03/23/2023] [Accepted: 02/15/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND Varenicline is considered one of the most effective treatment options for smoking cessation. Nonetheless, it is only modestly effective. A deeper comprehension of the effects of varenicline by means of the in-depth review of relevant fMRI studies may assist in paving the development of more targeted and effective treatments. METHODOLOGY A search of PubMed and Google Scholar databases was conducted with the keywords "functional magnetic resonance imaging" or "fMRI", and "varenicline". All peer-reviewed articles regarding the assessment of smokers with fMRI while undergoing treatment with varenicline and meeting the predefined criteria were included. RESULTS Several studies utilizing different methodologies and targeting different aspects of brain function were identified. During nicotine withdrawal, decreased mesocorticolimbic activity and increased amygdala activity, as well as elevated amygdala-insula and insula-default-mode-network functional connectivity are alleviated by varenicline under specific testing conditions. However, other nicotine withdrawal-induced changes, including the decreased reward responsivity of the ventral striatum, the bilateral dorsal striatum and the anterior cingulate cortex are not influenced by varenicline suggesting a task-dependent divergence in neurocircuitry activation. Under satiety, varenicline treatment is associated with diminished cue-induced activation of the ventral striatum and medial orbitofrontal cortex concomitant with reduced cravings; during the resting state, varenicline induces activation of the lateral orbitofrontal cortex and suppression of the right amygdala. CONCLUSIONS The current review provides important clues with regard to the neurobiological mechanism of action of varenicline and highlights promising research opportunities regarding the development of more selective and effective treatments and predictive biomarkers for treatment efficacy.
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Affiliation(s)
- Vassilis N Panagopoulos
- Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, Cyprus.
- Department of Physiology, Medical School, University of Patras, Patras, Greece.
| | - Alexis Bailey
- Pharmacology Section, St. George's University of London, London, UK
| | | | - Andreas A Ioannides
- Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
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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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7
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Tosti B, Corrado S, Mancone S, Di Libero T, Rodio A, Andrade A, Diotaiuti P. Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: a narrative review. Front Neurosci 2024; 18:1358481. [PMID: 38567285 PMCID: PMC10985214 DOI: 10.3389/fnins.2024.1358481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024] Open
Abstract
In recent years, the scientific community has begun tо explore the efficacy оf an integrated neurofeedback + biofeedback approach іn various conditions, both pathological and non-pathological. Although several studies have contributed valuable insights into its potential benefits, this review aims tо further investigate its effectiveness by synthesizing current findings and identifying areas for future research. Our goal іs tо provide a comprehensive overview that may highlight gaps іn the existing literature and propose directions for subsequent studies. The search for articles was conducted on the digital databases PubMed, Scopus, and Web of Science. Studies to have used the integrated neurofeedback + biofeedback approach published between 2014 and 2023 and reviews to have analyzed the efficacy of neurofeedback and biofeedback, separately, related to the same time interval and topics were selected. The search identified five studies compatible with the objectives of the review, related to several conditions: nicotine addiction, sports performance, Autism Spectrum Disorder (ASD), and Attention Deficit Hyperactivity Disorder (ADHD). The integrated neurofeedback + biofeedback approach has been shown to be effective in improving several aspects of these conditions, such as a reduction in the presence of psychiatric symptoms, anxiety, depression, and withdrawal symptoms and an increase in self-esteem in smokers; improvements in communication, imitation, social/cognitive awareness, and social behavior in ASD subjects; improvements in attention, alertness, and reaction time in sports champions; and improvements in attention and inhibitory control in ADHD subjects. Further research, characterized by greater methodological rigor, is therefore needed to determine the effectiveness of this method and the superiority, if any, of this type of training over the single administration of either. This review іs intended tо serve as a catalyst for future research, signaling promising directions for the advancement оf biofeedback and neurofeedback methodologies.
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Affiliation(s)
- Beatrice Tosti
- Department of Human Sciences, Society and Health, University of Cassino, Cassino, Lazio, Italy
| | - Stefano Corrado
- Department of Human Sciences, Society and Health, University of Cassino, Cassino, Lazio, Italy
| | - Stefania Mancone
- Department of Human Sciences, Society and Health, University of Cassino, Cassino, Lazio, Italy
| | - Tommaso Di Libero
- Department of Human Sciences, Society and Health, University of Cassino, Cassino, Lazio, Italy
| | - Angelo Rodio
- Department of Human Sciences, Society and Health, University of Cassino, Cassino, Lazio, Italy
| | - Alexandro Andrade
- Department of Physical Education, CEFID, Santa Catarina State University, Florianopolis, Santa Catarina, Brazil
| | - Pierluigi Diotaiuti
- Department of Human Sciences, Society and Health, University of Cassino, Cassino, Lazio, Italy
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8
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Zhu T, Wang W, Chen Y, Kranzler HR, Li CSR, Bi J. Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:326-336. [PMID: 37696489 DOI: 10.1016/j.bpsc.2023.08.010] [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: 04/18/2023] [Revised: 07/23/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD. METHODS Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses. RESULTS Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets. CONCLUSIONS Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.
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Affiliation(s)
- Tan Zhu
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, Connecticut
| | - Wuyi Wang
- Data Analytics Department, Yale New Haven Health System, New Haven, Connecticut
| | - Yu Chen
- Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Chiang-Shan R Li
- Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut; Department of Neuroscience, School of Medicine, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut
| | - Jinbo Bi
- Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, Connecticut.
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Tian W, Zhao D, Ding J, Zhan S, Zhang Y, Etkin A, Wu W, Yuan TF. An electroencephalographic signature predicts craving for methamphetamine. Cell Rep Med 2024; 5:101347. [PMID: 38151021 PMCID: PMC10829728 DOI: 10.1016/j.xcrm.2023.101347] [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: 05/25/2023] [Revised: 09/17/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023]
Abstract
Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.
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Affiliation(s)
- Weiwen Tian
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Di Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Jinjun Ding
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Shulu Zhan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Yi Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Amit Etkin
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA 94305, USA; Alto Neuroscience, Inc., Los Altos, CA 94022, USA
| | - Wei Wu
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA 94305, USA; Alto Neuroscience, Inc., Los Altos, CA 94022, USA.
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China; Institute of Mental Health and Drug Discovery, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325000, China; Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu 226019, China.
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10
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Zhou Y, Xue T, Cheng Y, Wang J, Dong F, Jia S, Zhang F, Wang X, Lv X, Wang H, Yuan K, Yu D. The changes of intrinsic connectivity contrast in young smokers. Addict Biol 2023; 28:e13347. [PMID: 38017637 DOI: 10.1111/adb.13347] [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: 04/02/2023] [Revised: 07/07/2023] [Accepted: 09/26/2023] [Indexed: 11/30/2023]
Abstract
Previous studies demonstrated that reward circuit plays an important role in smoking. The differences of functional and structural connectivity were found among several brain regions such as thalamus and frontal lobe. However, few studies focused on functional connectivity (FC) in whole-brain voxel level of young smokers. In this study, intrinsic connectivity contrast (ICC) was used to perform voxel-based whole-brain analyses in 55 young smokers and 55 matched non-smokers to identify brain regions with significant group differences. ICC results showed that the connectivity of young smokers in medial frontal cortex (MedFC), supramarginal gyrus anterior division left (L_aSMG), central opercular cortex left (L_CO) and middle frontal gyrus left (L_MidFG) showed a significantly lower trend compared with the non-smokers. The seed-based FC analysis about MedFC indicated that young smokers showed reduced connectivity between the MedFC and left hippocampus, left amygdala compared to non-smokers. Correlation analysis showed that the ICC of MedFC in young smokers was significantly negatively correlated with Fagerstrom test for nicotine dependence (FTND) and Questionnaire on Smoking Urges (QSU). The FC between the MedFC and left hippocampus, left amygdala was significantly negatively correlated with Pack_years. The mediation analysis indicated that ICC of MedFC completely mediated FTND and QSU of young smokers. The results suggest that nicotine accumulation may affect the communication of the frontal lobe with the whole brain to some extent, leading to changes in smoking cravings. The above research also provides in-depth insights into the mechanism of adolescent smoking addiction and related intervention treatment.
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Affiliation(s)
- Yang Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 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, Inner Mongolia, 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, Inner Mongolia, China
| | - Juan Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 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, Inner Mongolia, China
| | - Shaodi Jia
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Fan Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Xiaoqing Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
| | - Xiaoqi Lv
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, China
| | - Hongde Wang
- Xilinguole Meng Mongolian General Hospital, Xilinhaote, Inner Mongolia, 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, Inner Mongolia, China
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 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, Inner Mongolia, China
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11
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O'Neill J, Diaz MP, Alger JR, Pochon JB, Ghahremani D, Dean AC, Tyndale RF, Petersen N, Marohnic S, Karaiskaki A, London ED. Smoking, tobacco dependence, and neurometabolites in the dorsal anterior cingulate cortex. Mol Psychiatry 2023; 28:4756-4765. [PMID: 37749232 PMCID: PMC10914613 DOI: 10.1038/s41380-023-02247-0] [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: 03/31/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/27/2023]
Abstract
Cigarette smoking has a major impact on global health and morbidity, and positron emission tomographic research has provided evidence for reduced inflammation in the human brain associated with cigarette smoking. Given the consequences of inflammatory dysfunction for health, the question of whether cigarette smoking affects neuroinflammation warrants further investigation. The goal of this project therefore was to validate and extend evidence of hypoinflammation related to smoking, and to examine the potential contribution of inflammation to clinical features of smoking. Using magnetic resonance spectroscopy, we measured levels of neurometabolites that are putative neuroinflammatory markers. N-acetyl compounds (N-acetylaspartate + N-acetylaspartylglutamate), glutamate, creatine, choline-compounds (phosphocholine + glycerophosphocholine), and myo-inositol, have all been linked to neuroinflammation, but they have not been examined as such with respect to smoking. We tested whether people who smoke cigarettes have brain levels of these metabolites consistent with decreased neuroinflammation, and whether clinical features of smoking are associated with levels of these metabolites. The dorsal anterior cingulate cortex was chosen as the region-of-interest because of previous evidence linking it to smoking and related states. Fifty-four adults who smoked daily maintained overnight smoking abstinence before testing and were compared with 37 nonsmoking participants. Among the smoking participants, we tested for associations of metabolite levels with tobacco dependence, smoking history, craving, and withdrawal. Levels of N-acetyl compounds and glutamate were higher, whereas levels of creatine and choline compounds were lower in the smoking group as compared with the nonsmoking group. In the smoking group, glutamate and creatine levels correlated negatively with tobacco dependence, and creatine correlated negatively with lifetime smoking, but none of the metabolite levels correlated with craving or withdrawal. The findings indicate a link between smoking and a hypoinflammatory state in the brain, specifically in the dorsal anterior cingulate cortex. Smoking may thereby increase vulnerability to infection and brain injury.
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Affiliation(s)
- Joseph O'Neill
- Division of Child & Adolescent Psychiatry, Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
- Brain Research Institute, University of California at Los Angeles, Los Angeles, CA, USA
| | - Maylen Perez Diaz
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
- Biogen, Inc., Nashville, TN, USA
| | - Jeffry R Alger
- Department of Neurology, University of California at Los Angeles, Los Angeles, CA, USA
| | - Jean-Baptiste Pochon
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
| | - Dara Ghahremani
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
| | - Andrew C Dean
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
| | - Rachel F Tyndale
- Department of Pharmacology & Toxicology, and Department of Psychiatry, University of Toronto, and Campbell Family Mental Health Research Institute, Centre for Addiction & Mental Health, Toronto, ON, Canada
| | - Nicole Petersen
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
| | - Shane Marohnic
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
| | - Andrea Karaiskaki
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA
| | - Edythe D London
- Brain Research Institute, University of California at Los Angeles, Los Angeles, CA, USA.
- Jane and Terry Semel Institute for Neuroscience and Human Behavior and the Department of Psychiatry, University of California at Los Angeles, Los Angeles, CA, USA.
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12
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Jordan T, Apostol MR, Nomi J, Petersen N. Unraveling Neural Complexity: Exploring Brain Entropy to Yield Mechanistic Insight in Neuromodulation Therapies for Tobacco Use Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557465. [PMID: 37745351 PMCID: PMC10515846 DOI: 10.1101/2023.09.12.557465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Neuromodulation therapies, such as repetitive transcranial magnetic stimulation (rTMS), have shown promise as treatments for tobacco use disorder (TUD). However, the underlying mechanisms of these therapies remain unclear, which may hamper optimization and personalization efforts. In this study, we investigated alteration of brain entropy as a potential mechanism underlying the neural effects of noninvasive brain stimulation by rTMS in people with TUD. We employed sample entropy (SampEn) to quantify the complexity and predictability of brain activity measured using resting-state fMRI data. Our study design included a randomized single-blind study with 42 participants who underwent 2 data collection sessions. During each session, participants received high-frequency (10Hz) stimulation to the dorsolateral prefrontal cortex (dlPFC) or a control region (visual cortex), and resting-state fMRI scans were acquired before and after rTMS. Our findings revealed that individuals who smoke exhibited higher baseline SampEn throughout the brain as compared to previously-published SampEn measurements in control participants. Furthermore, high-frequency rTMS to the dlPFC but not the control region reduced SampEn in the insula and dlPFC, regions implicated in TUD, and also reduced self-reported cigarette craving. These results suggest that brain entropy may serve as a potential biomarker for effects of rTMS, and provide insight into the neural mechanisms underlying rTMS effects on smoking cessation. Our study contributes to the growing understanding of brain-based interventions for TUD by highlighting the relevance of brain entropy in characterizing neural activity patterns associated with smoking. The observed reductions in entropy following dlPFC-targeted rTMS suggest a potential mechanism for the therapeutic effects of this intervention. These findings support the use of neuroimaging techniques to investigate the use of neuromodulation therapies for TUD.
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Affiliation(s)
- Timothy Jordan
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles CA
| | - Michael R. Apostol
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles CA
| | - Jason Nomi
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles CA
| | - Nicole Petersen
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles CA
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13
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Ngbokoli ML, Douton JE, Carelli RM. Prelimbic cortex and nucleus accumbens core resting state signaling dynamics as a biomarker for cocaine seeking behaviors. ADDICTION NEUROSCIENCE 2023; 7:100097. [PMID: 37396409 PMCID: PMC10310298 DOI: 10.1016/j.addicn.2023.100097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Substance use disorders (SUDs) are characterized by maladaptive signaling in the prefrontal cortex and associated regions, however precisely how these drug-induced abnormalities may be linked to drug seeking/taking behaviors is not well understood. Here, in vivo local field potential (LFP) electrophysiology was used in rats to examine the relationship between overall spontaneous (resting state) activity within the prelimbic cortex (PrL) and nucleus accumbens (NAc) core, and their functional connectivity, to cocaine taking and seeking behaviors. Adult, male Sprague-Dawley rats were trained to self-administer either intravenous cocaine (0.33 mg/inf) or water reinforcement during 6-hour daily sessions over 2 weeks; extinction sessions were completed immediately after self-administration training and following 30 days experimenter-imposed abstinence. Rest LFP recordings were completed during 3 recording periods (15 min each in a chamber different from the self-administration context) conducted (1) prior to self-administration training (rest LFP 1) (2) immediately after 2 weeks of self-administration training (rest LFP 2) and (3) following 1 month abstinence (rest LFP 3). Our findings show that resting state LFP power in the PrL recorded prior to training (Rest LFP 1) was positively correlated with total cocaine intake and escalation of cocaine seeking at the beta frequency range. Immediately after self-administration training (Rest LFP 2) power in the NAc core at gamma frequency was negatively correlated with incubation of cocaine craving. For rats trained to self-administer water, no significant correlations were observed. Together, these findings show that resting state LFP at specific timepoints in the addiction cycle can serve as unique predictors (biomarkers) of cocaine use disorders.
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Affiliation(s)
- Metika L. Ngbokoli
- Department of Psychology and Neuroscience, The University of North Carolina, CB #3270 Davie Hall, Chapel Hill, NC 27599, USA
| | - Joaquin E. Douton
- Department of Psychology and Neuroscience, The University of North Carolina, CB #3270 Davie Hall, Chapel Hill, NC 27599, USA
| | - Regina M. Carelli
- Department of Psychology and Neuroscience, The University of North Carolina, CB #3270 Davie Hall, Chapel Hill, NC 27599, USA
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14
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Meng Q, Zhu Y, Yuan Y, Yang L, Liu J, Zhang X, Bu J. Resting-state electroencephalography theta predicts neurofeedback treatment 4-month follow-up response in nicotine addiction. Gen Psychiatr 2023; 36:e101091. [PMID: 37663053 PMCID: PMC10471848 DOI: 10.1136/gpsych-2023-101091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Background The high rate of long-term relapse is a major cause of smoking cessation failure. Recently, neurofeedback training has been widely used in the treatment of nicotine addiction; however, approximately 30% of subjects fail to benefit from this intervention. Our previous randomised clinical trial (RCT) examined cognition-guided neurofeedback and demonstrated a significant decrease in daily cigarette consumption at the 4-month follow-up. However, significant individual differences were observed in the 4-month follow-up effects of decreased cigarette consumption. Therefore, it is critical to identify who will benefit from pre-neurofeedback. Aims We examined whether the resting-state electroencephalography (EEG) characteristics from pre-neurofeedback predicted the 4-month follow-up effects and explored the possible mechanisms. Methods This was a double-blind RCT. A total of 60 participants with nicotine dependence were randomly assigned to either the real-feedback or yoked-feedback group. They underwent 6 min closed-eye resting EEG recordings both before and after two neurofeedback sessions. A follow-up assessment was conducted after 4 months. Results The frontal resting-state theta power spectral density (PSD) was significantly altered in the real-feedback group after two neurofeedback visits. Higher theta PSD in the real-feedback group before neurofeedback was the only predictor of decreased cigarette consumption at the 4-month follow-up. Further reliability analysis revealed a significant positive correlation between theta PSD pre-neurofeedback and post-neurofeedback. A leave-one-out cross-validated linear regression of the theta PSD pre-neurofeedback demonstrated a significant correlation between the predicted and observed reductions in cigarette consumption at the 4-month follow-up. Finally, source analysis revealed that the brain mechanisms of the theta PSD predictor were located in the orbital frontal cortex. Conclusions Our study demonstrated changes in the resting-state theta PSD following neurofeedback training. Moreover, the resting-state theta PSD may serve as a prognostic marker of neurofeedback effects. A higher resting-state theta PSD predicts a better long-term response to neurofeedback treatment, which may facilitate the selection of individualised interventions. Trial registration number ChiCTR-IPR-17011710.
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Affiliation(s)
- Qiujian Meng
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
- Department of Psychology, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Ying Zhu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
| | - Ye Yuan
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
| | - Li Yang
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
| | - Jiafang Liu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
| | - Xiaochu Zhang
- Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, Anhui, China
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, Anhui, China
- Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, Anhui, China
- Institute of Health and Medicine, Hefei Comprehensive Science Center, Hefei, Anhui, China
| | - Junjie Bu
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
- Department of Psychology, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui, China
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15
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Wang W, Kang Y, Niu X, Zhang Z, Li S, Gao X, Zhang M, Cheng J, Zhang Y. Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers. Front Neurosci 2023; 17:1227422. [PMID: 37547147 PMCID: PMC10400777 DOI: 10.3389/fnins.2023.1227422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers. Methods A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing. Results As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks. Discussion These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.
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16
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Upton S, Brown AA, Golzy M, Garland EL, Froeliger B. Right inferior frontal gyrus theta-burst stimulation reduces smoking behaviors and strengthens fronto-striatal-limbic resting-state functional connectivity: a randomized crossover trial. Front Psychiatry 2023; 14:1166912. [PMID: 37457779 PMCID: PMC10338839 DOI: 10.3389/fpsyt.2023.1166912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/22/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Functional and anatomical irregularities in the right inferior frontal gyrus (rIFG), a ventrolateral prefrontal region that mediates top-down inhibitory control over prepotent behavioral responding, are implicated in the ongoing maintenance of nicotine dependence (ND). However, there is little research on the effects of neuromodulation of the rIFG on smoking behavior, inhibitory control, and resting-state functional connectivity (rsFC) among individuals with ND. Methods In this double-blind, crossover, theta-burst stimulation (TBS) study, adults with ND (N = 31; female: n = 15) completed a baseline session and were then randomized to two counterbalanced sessions of functionally neuronavigated TBS to the rIFG: continuous TBS (cTBS) on 1 day and intermittent TBS (iTBS) on another. Differences in cigarette cravings, smoking, and fronto-striatal-limbic rsFC were assessed. Results Relative to baseline, cTBS significantly reduced appetitive and withdrawal cravings immediately after treatment. The effects of cTBS on withdrawal craving persisted for 24 h, as well as produced a reduction in smoking. Furthermore, cTBS significantly strengthened rsFC between the rIFG pars opercularis and subcallosal cingulate (fronto-striatal circuit), and between the rIFG pars opercularis and the right posterior parahippocampal gyrus (fronto-limbic circuit). At post-24 h, cTBS-induced increase in fronto-striatal rsFC was significantly associated with less appetitive craving, while the increase in fronto-limbic rsFC was significantly associated with less withdrawal craving and smoking. Discussion These findings warrant further investigation into the potential value of rIFG cTBS to attenuate smoking behavior among individuals with ND.
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Affiliation(s)
- Spencer Upton
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
| | - Alexander A. Brown
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
- Department of Psychiatry, School of Medicine, University of Missouri, Columbia, MO, United States
- Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO, United States
| | - Mojgan Golzy
- Biostatistics Unit, Department of Family and Community Medicine, School of Medicine, University of Missouri, Columbia, MO, United States
| | - Eric L. Garland
- Center on Mindfulness and Integrative Health Intervention Development, College of Social Work, University of Utah, Salt Lake City, UT, United States
| | - Brett Froeliger
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
- Department of Psychiatry, School of Medicine, University of Missouri, Columbia, MO, United States
- Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO, United States
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17
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Carrette LLG, Kimbrough A, Davoudian PA, Kwan AC, Collazo A, George O. Hyperconnectivity of Two Separate Long-Range Cholinergic Systems Contributes to the Reorganization of the Brain Functional Connectivity during Nicotine Withdrawal in Male Mice. eNeuro 2023; 10:ENEURO.0019-23.2023. [PMID: 37295945 PMCID: PMC10306126 DOI: 10.1523/eneuro.0019-23.2023] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/13/2023] [Accepted: 04/25/2023] [Indexed: 06/12/2023] Open
Abstract
Chronic nicotine results in dependence with withdrawal symptoms on discontinuation of use, through desensitization of nicotinic acetylcholine receptors and altered cholinergic neurotransmission. Nicotine withdrawal is associated with increased whole-brain functional connectivity and decreased network modularity; however, the role of cholinergic neurons in those changes is unknown. To identify the contribution of nicotinic receptors and cholinergic regions to changes in the functional network, we analyzed the contribution of the main cholinergic regions to brain-wide activation of the immediate early-gene Fos during withdrawal in male mice and correlated these changes with the expression of nicotinic receptor mRNA throughout the brain. We show that the main functional connectivity modules included the main long-range cholinergic regions, which were highly synchronized with the rest of the brain. However, despite this hyperconnectivity, they were organized into two anticorrelated networks that were separated into basal forebrain-projecting and brainstem-thalamic-projecting cholinergic regions, validating a long-standing hypothesis of the organization of the brain cholinergic systems. Moreover, baseline (without nicotine) expression of Chrna2, Chrna3, Chrna10, and Chrnd mRNA of each brain region correlated with withdrawal-induced changes in Fos expression. Finally, by mining the Allen Brain mRNA expression database, we were able to identify 1755 gene candidates and three pathways (Sox2-Oct4-Nanog, JAK-STAT, and MeCP2-GABA) that may contribute to nicotine withdrawal-induced Fos expression. These results identify the dual contribution of the basal forebrain and brainstem-thalamic cholinergic systems to whole-brain functional connectivity during withdrawal; and identify nicotinic receptors and novel cellular pathways that may be critical for the transition to nicotine dependence.
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Affiliation(s)
| | - Adam Kimbrough
- Department of Psychiatry, UC San Diego, California 92093
| | - Pasha A Davoudian
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut 06511
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, Connecticut 06511
| | - Alex C Kwan
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853
| | - Andres Collazo
- Beckman Institute, California Institute of Technology, Pasadena, California 91125
| | - Olivier George
- Department of Psychiatry, UC San Diego, California 92093
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18
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Ghahremani DG, Pochon JBF, Diaz MP, Tyndale RF, Dean AC, London ED. Nicotine dependence and insula subregions: functional connectivity and cue-induced activation. Neuropsychopharmacology 2023; 48:936-945. [PMID: 36869233 PMCID: PMC10156746 DOI: 10.1038/s41386-023-01528-0] [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: 05/17/2022] [Revised: 12/29/2022] [Accepted: 01/02/2023] [Indexed: 03/05/2023]
Abstract
Nicotine dependence is a major predictor of relapse in people with Tobacco Use Disorder (TUD). Accordingly, therapies that reduce nicotine dependence may promote sustained abstinence from smoking. The insular cortex has been identified as a promising target in brain-based therapies for TUD, and has three major sub-regions (ventral anterior, dorsal anterior, and posterior) that serve distinct functional networks. How these subregions and associated networks contribute to nicotine dependence is not well understood, and therefore was the focus of this study. Sixty individuals (28 women; 18-45 years old), who smoked cigarettes daily, rated their level of nicotine dependence (on the Fagerström Test for Nicotine Dependence) and, after abstaining from smoking overnight (~12 h), underwent functional magnetic resonance imaging (fMRI) in a resting state. A subset of these participants (N = 48) also completing a cue-induced craving task during fMRI. Correlations between nicotine dependence and resting-state functional connectivity (RSFC) and cue-induced activation of the major insular sub-regions were evaluated. Nicotine dependence was negatively correlated with connectivity of the left and right dorsal, and left ventral anterior insula with regions within the superior parietal lobule (SPL), including the left precuneus. No relationship between posterior insula connectivity and nicotine dependence was found. Cue-induced activation in the left dorsal anterior insula was positively associated with nicotine dependence and negatively associated with RSFC of the same region with SPL, suggesting that craving-related responsivity in this subregion was greater among participants who were more dependent. These results may inform therapeutic approaches, such as brain stimulation, which may elicit differential clinical outcomes (e.g., dependence, craving) depending on the insular subnetwork that is targeted.
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Affiliation(s)
- 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
| | - Rachel F Tyndale
- Department of Pharmacology & Toxicology and Department of Psychiatry, University of Toronto, 1 King's College Circle, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Andy C Dean
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Brain Research Institute, 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.
- Brain Research Institute, University of California, Los Angeles, CA, USA.
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA, USA.
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19
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Rolls ET, Feng R, Feng J. Lifestyle risks associated with brain functional connectivity and structure. Hum Brain Mapp 2023; 44:2479-2492. [PMID: 36799566 PMCID: PMC10028639 DOI: 10.1002/hbm.26225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 02/18/2023] Open
Abstract
Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Ruiqing Feng
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
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20
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Gazula H, Rootes-Murdy K, Holla B, Basodi S, Zhang Z, Verner E, Kelly R, Murthy P, Chakrabarti A, Basu D, Bhagyalakshmi Nanjayya S, Lenin Singh R, Lourembam Singh R, Kalyanram K, Kartik K, Kalyanaraman K, Ghattu K, Kuriyan R, Kurpad SS, Barker GJ, Bharath RD, Desrivieres S, Purushottam M, Orfanos DP, Sharma E, Hickman M, Toledano M, Vaidya N, Banaschewski T, Bokde ALW, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillére Martinot ML, Artiges E, Nees F, Paus T, Poustka L, Fröhner JH, Robinson L, Smolka MN, Walter H, Winterer J, Whelan R, Turner JA, Sarwate AD, Plis SM, Benegal V, Schumann G, Calhoun VD. Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains. Neuroinformatics 2023; 21:287-301. [PMID: 36434478 DOI: 10.1007/s12021-022-09604-4] [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: 08/29/2022] [Indexed: 11/27/2022]
Abstract
With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.
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Affiliation(s)
- Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, USA.
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Bharath Holla
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
- Integrative Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
| | - Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Zuo Zhang
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
| | - Eric Verner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | | | - Debasish Basu
- Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | | | - Rajkumar Lenin Singh
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | - Roshan Lourembam Singh
- Department of Psychology, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | | | | | | | - Krishnaveni Ghattu
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St. John's Medical College & Hospital, Bangalore, India
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, NIMHANS, Bengaluru, India
| | - Sylvane Desrivieres
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
| | | | | | - Eesha Sharma
- Department of Child & Adolescent Psychiatry, NIMHANS, Bengaluru, India
| | | | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Nilakshi Vaidya
- Centre for Addiction Medicine, NIMHANS, Bengaluru, India
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68159, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, 68131, Germany
| | | | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
| | - Marie-Laure Paillére Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68159, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Tomás Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, Göttingen, 37075, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Lauren Robinson
- Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jeanne Winterer
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Jessica A Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, P.R. China
| | - 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, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
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Carrette LL, Kimbrough A, Davoudian PA, Kwan AC, Collazo A, George O. Hyperconnectivity of two separate long-range cholinergic systems contributes to the reorganization of the brain functional connectivity during nicotine withdrawal in male mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.29.534836. [PMID: 37034602 PMCID: PMC10081261 DOI: 10.1101/2023.03.29.534836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Chronic nicotine results in dependence with withdrawal symptoms upon discontinuation of use, through desensitization of nicotinic acetylcholine receptors and altered cholinergic neurotransmission. Nicotine withdrawal is associated with increased whole-brain functional connectivity and decreased network modularity, however, the role of cholinergic neurons in those changes is unknown. To identify the contribution of nicotinic receptors and cholinergic regions to changes in the functional network, we analyzed the contribution of the main cholinergic regions to brain-wide activation of the immediate early-gene FOS during withdrawal in male mice and correlated these changes with the expression of nicotinic receptor mRNA throughout the brain. We show that the main functional connectivity modules included the main long-range cholinergic regions, which were highly synchronized with the rest of the brain. However, despite this hyperconnectivity they were organized into two anticorrelated networks that were separated into basal forebrain projecting and brainstem-thalamic projecting cholinergic regions, validating a long-standing hypothesis of the organization of the brain cholinergic systems. Moreover, baseline (without nicotine) expression of Chrna2 , Chrna3 , Chrna10 , and Chrnd mRNA of each brain region correlated with withdrawal-induced changes in FOS expression. Finally, by mining the Allen Brain mRNA expression database, we were able to identify 1755 gene candidates and three pathways (Sox2-Oct4-Nanog, JAK-STAT, and MeCP2-GABA) that may contribute to nicotine withdrawal-induced FOS expression. These results identify the dual contribution of the basal forebrain and brainstem-thalamic cholinergic systems to whole-brain functional connectivity during withdrawal; and identify nicotinic receptors and novel cellular pathways that may be critical for the transition to nicotine dependence. Significance Statement Discontinuation of nicotine use in dependent users is associated with increased whole-brain activation and functional connectivity and leads to withdrawal symptoms. Here we investigated the contribution of the nicotinic cholinergic receptors and main cholinergic projecting brain areas in the whole-brain changes associated with withdrawal. This not only allowed us to visualize and confirm the previously described duality of the cholinergic brain system using this novel methodology, but also identify nicotinic receptors together with 1751 other genes that contribute, and could thus be targets for treatments against, nicotine withdrawal and dependence.
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Affiliation(s)
| | - Adam Kimbrough
- Department of Psychiatry, UC San Diego, La Jolla, CA, 92032, United States
| | - Pasha A. Davoudian
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT, 06511, United States
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, United States
| | - Alex C. Kwan
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, 14853, United States
| | - Andres Collazo
- Beckman Institute, CalTech, Pasadena, CA, 91125, United States
| | - Olivier George
- Department of Psychiatry, UC San Diego, La Jolla, CA, 92032, United States
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22
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Altered white matter functional network in nicotine addiction. Psychiatry Res 2023; 321:115073. [PMID: 36716553 DOI: 10.1016/j.psychres.2023.115073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.
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23
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Versace F, Robinson JD, Cinciripini PM. Toward neuromarkers for tailored smoking cessation treatments. ADDICTION NEUROSCIENCE 2023; 6. [PMID: 37034180 PMCID: PMC10081511 DOI: 10.1016/j.addicn.2023.100075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Vulnerability to compulsive drug use stems from dysregulated activity within the neural networks that underlie reward and executive functions. Empirical evidence suggests that a) attributing high motivational salience to drug-related stimuli leads to compulsive drug seeking and b) cognitive control deficits lead to compulsive drug taking. Noninvasive neuroimaging techniques enable brain activity monitoring during affective and cognitive processing and are paving the way to precision medicine for substance use disorders. Identifying robust neuromarkers of affective and cognitive dysregulation would allow clinicians to personalize treatments by targeting individual psychophysiological vulnerabilities. However, methodological choices have biased the field toward experimental paradigms that cannot optimally assess individual differences in the motivational salience of drug-related cues and in the ability to control drug-related decisions, choices which have hindered the identification of clinically relevant neuromarkers. Here, we show that once these shortcomings are amended, replicable neuromarkers of the tendency to attribute motivational salience to drug-related cues and the ability to control drug-related decisions emerge. While we use tobacco use disorder as a model, we also show that the methodological issues highlighted here are relevant to other disorders characterized by maladaptive appetitive behaviors.
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Pandria N, Athanasiou A, Styliadis C, Terzopoulos N, Mitsopoulos K, Paraskevopoulos E, Karagianni M, Pataka A, Kourtidou-Papadeli C, Makedou K, Iliadis S, Lymperaki E, Nimatoudis I, Argyropoulou-Pataka P, Bamidis PD. Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity? Front Behav Neurosci 2023; 17:1096122. [PMID: 36778131 PMCID: PMC9911884 DOI: 10.3389/fnbeh.2023.1096122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/02/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design. Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs). Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions. Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02991781.
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Affiliation(s)
- Niki Pandria
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Alkinoos Athanasiou
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Charis Styliadis
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Nikos Terzopoulos
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Konstantinos Mitsopoulos
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Evangelos Paraskevopoulos
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece,Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Maria Karagianni
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Athanasia Pataka
- Pulmonary Department-Oncology Unit, “G. Papanikolaou” General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Kali Makedou
- Laboratory of Biochemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stavros Iliadis
- Laboratory of Biochemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Evgenia Lymperaki
- Department of Biomedical Sciences, International Hellenic University, Thessaloniki, Greece
| | - Ioannis Nimatoudis
- Third Department of Psychiatry, AHEPA University General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Panagiotis D. Bamidis
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece,*Correspondence: Panagiotis D. Bamidis
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Karl D, Wieland A, Shevchenko Y, Grundinger N, Machunze N, Gerhardt S, Flor H, Vollstädt-Klein S. Using computer-based habit versus chess-based cognitive remediation training as add-on therapy to modify the imbalance between habitual behavior and cognitive control in tobacco use disorder: protocol of a randomized controlled, fMRI study. BMC Psychol 2023; 11:24. [PMID: 36698210 PMCID: PMC9875438 DOI: 10.1186/s40359-023-01055-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Although the vast majority of smokers are aware of the enormous preventable health hazards caused by smoking, only a small percentage of smokers manage to remain abstinent in the long term. One possible explanation for this discrepancy lies in the inflexibility of addictive behavior and associated disadvantageous decision-making. According to a dual-process theory of decision-making, two distinct decision systems can be identified. One slow deliberate system based on desirable expectations of outcome value described as goal-directed behavior and a fast reflexive system based on habitual instrumental behavior and driven by reinforcement experienced in the past. In the course of addiction development, an imbalance occurs between habitual behavior and goal-directed. The present study aims to investigate the modifiability of the balance between habitual and goal-directed behavior at the neurobiological and behavioral level in smokers using two different novel add-on therapies. We hypothesize that both interventions change the balance between goal-directed and habitual behavior, but by different mechanisms. Whereas a cognitive remediation treatment should directly improve cognitive control, in contrast an implicit priming task should affect the early processing and the emotional valence of smoking and smoking cues. METHODS We will conduct a randomized controlled study in treatment-seeking individuals with tobacco use disorder applying either chess-based cognitive remediation training (N = 30) or implicit computer-based habit-modifying training (N = 30) as add on therapy compared to the standard smoking cessation group therapy (N = 30) only. We will address neurobiological and neuropsychological correlates associated with craving, reward devaluation, cue reactivity and attentional bias. In addition, various effects of treatment and prediction of treatment outcome will be examined using behavioral and neural measures. DISCUSSION The present study will apply different examination methods such as functional magnetic resonance imaging, neuropsychological tests, and self-report before and after the interventions. This allows the identification of intervention-specific mechanisms and therefore potential neurobiology-based specific treatment targets for individuals with Tobacco Use Disorder. TRIAL REGISTRATION Registered at clinicaltrials.gov/ct2/show/NCT03764969 (05 December 2018).
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Affiliation(s)
- Damian Karl
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 21 20, PO Box 12, 68072 Mannheim, Germany
| | - Alfred Wieland
- grid.7700.00000 0001 2190 4373Institute of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Yury Shevchenko
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 21 20, PO Box 12, 68072 Mannheim, Germany ,grid.9811.10000 0001 0658 7699Research Methods, Assessment, and iScience, Department of Psychology, University of Konstanz, Constance, Germany
| | - Nadja Grundinger
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 21 20, PO Box 12, 68072 Mannheim, Germany
| | - Noah Machunze
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 21 20, PO Box 12, 68072 Mannheim, Germany
| | - Sarah Gerhardt
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 21 20, PO Box 12, 68072 Mannheim, Germany
| | - Herta Flor
- grid.7700.00000 0001 2190 4373Institute of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Sabine Vollstädt-Klein
- grid.7700.00000 0001 2190 4373Department of Addictive Behaviour and Addiction Medicine, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 21 20, PO Box 12, 68072 Mannheim, Germany ,grid.7700.00000 0001 2190 4373Mannheim Center for Translational Neurosciences (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Active versus sham transcranial direct current stimulation (tDCS) as an adjunct to varenicline treatment for smoking cessation: Study protocol for a double-blind single dummy randomized controlled trial. PLoS One 2022; 17:e0277408. [PMID: 36480510 PMCID: PMC9731486 DOI: 10.1371/journal.pone.0277408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 09/13/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Smoking is a chronic and relapsing disease, with up to 60% of quitters relapsing within the first year. Transcranial Direct Current Stimulation (tDCS), targets cortical circuits and acutely reduces craving and withdrawal symptoms among cigarette smokers. However, the efficacy of tDCS as an adjunct to standard smoking cessation treatments has not been studied. This study aims to investigate the effectiveness of tDCS in combination with varenicline for smoking cessation. We hypothesize that active tDCS combined with varenicline will improve cessation outcomes compared to sham tDCS combined with varenicline. METHODS This is a double-blind, sham-controlled randomized clinical trial where fifty healthy smokers will be recruited in Toronto, Canada. Participants will be randomized 1:1 to either active tDCS (20 minutes at 2 mA) or sham tDCS (30 seconds at 2 mA, 19 minutes at 0 mA) for 10 daily sessions (2 weeks) plus 5 follow up sessions, occurring every two weeks for 10 weeks. All participants will be given standard varenicline treatment concurrently for the 12-week treatment period. The primary outcome is 30 day continuous abstinence at end of treatment, confirmed with urinary cotinine. Measurements made at each study visit include expired carbon monoxide, self-reported craving and withdrawal. Three magnetic resonance imaging (MRI) scans will be conducted: two at baseline and one at end of treatment, to assess any functional or structural changes following treatment. DISCUSSION For every two smokers who quit, one life is saved from a tobacco-related mortality. Therefore, it is important to develop new and more effective treatment approaches that can improve and maintain long-term abstinence, in order to decrease the prevalence of tobacco-related deaths and disease. Furthermore, the addition of longitudinal neuroimaging can shed light on neural circuitry changes that might occur as a result of brain stimulation, furthering our understanding of tDCS in addiction treatment. TRIAL REGISTRATION This trial has been registered with Clinicaltrials.gov: NCT03841292 since February 15th 2019 (https://clinicaltrials.gov/ct2/show/NCT03841292)-retrospectively registered.
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Misconfigured striatal connectivity profiles in smokers. Neuropsychopharmacology 2022; 47:2081-2089. [PMID: 35752682 PMCID: PMC9556661 DOI: 10.1038/s41386-022-01366-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/19/2022] [Accepted: 06/14/2022] [Indexed: 11/09/2022]
Abstract
Dysregulation of frontal cortical inputs to the striatum is foundational in the neural basis of substance use disorder (SUD). Neuroanatomical and electrophysiological data increasingly show that striatal nodes receive appreciable input from numerous cortical areas, and that the combinational properties of these multivariate "connectivity profiles" play a predominant role in shaping striatal activity and function. Yet, how abnormal configuration of striatal connectivity profiles might contribute to SUD is unknown. Here, we implemented a novel "connectivity profile analysis" (CPA) approach using resting-state functional connectivity data to facilitate detection of different types of connectivity profile "misconfiguration" that may reflect distinct forms of aberrant circuit plasticity in SUD. We examined 46 nicotine-dependent smokers and 33 non-smokers and showed that both dorsal striatum (DS) and ventral striatum (VS) connectivity profiles with frontal cortex were misconfigured in smokers-but in doubly distinct fashions. DS misconfigurations were stable across sated and acute abstinent states (indicative of a "trait" circuit adaptation) whereas VS misconfigurations emerged only during acute abstinence (indicative of a "state" circuit adaptation). Moreover, DS misconfigurations involved abnormal connection strength rank order arrangement, whereas VS misconfigurations involved abnormal aggregate strength. We found that caudal ventral putamen in smokers uniquely displayed multiple types of connectivity profile misconfiguration, whose interactive magnitude was linked to dependence severity, and that VS misconfiguration magnitude correlated positively with withdrawal severity during acute abstinence. Findings underscore the potential for approaches that more aptly model the neurobiological composition of corticostriatal circuits to yield deeper insights into the neural basis of SUD.
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Hill-Bowen LD, Riedel MC, Salo T, Flannery JS, Poudel R, Laird AR, Sutherland MT. Convergent gray matter alterations across drugs of abuse and network-level implications: A meta-analysis of structural MRI studies. Drug Alcohol Depend 2022; 240:109625. [PMID: 36115222 DOI: 10.1016/j.drugalcdep.2022.109625] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 01/06/2023]
Abstract
BACKGROUND Neuroimaging studies often consider brain alterations linked with substance abuse within the context of individual drugs (e.g., nicotine), while neurobiological theories of addiction emphasize common brain network-level alterations across drug classes. Using emergent meta-analytic techniques, we identified common structural brain alterations across drugs and characterized the functionally-connected networks with which such structurally altered regions interact. METHODS We identified 82 articles characterizing gray matter (GM) volume differences for substance users vs. controls. Using the anatomical likelihood estimation algorithm, we identified convergent GM reductions across drug classes. Next, we performed resting-state and meta-analytic functional connectivity analyses using each structurally altered region as a seed and computed whole-brain functional connectivity profiles as the union of both maps. We characterized an "extended network" by identifying brain areas demonstrating the highest degree of functional coupling with structurally impacted regions. Finally, hierarchical clustering was performed leveraging extended network nodes' functional connectivity profiles to delineate subnetworks. RESULTS Across drug classes, we identified medial frontal/ventromedial prefrontal, and multiple regions in anterior cingulate (ACC) and insula as regions displaying convergent GM reductions among users. Overlap of these regions' functional connectivity profiles identified ACC, inferior frontal, PCC, insula, superior temporal, and putamen as regions of an impacted extended network. Hierarchical clustering revealed 3 subnetworks closely corresponding to default mode (PCC, angular), salience (dACC, caudate), and executive control networks (dlPFC and parietal). CONCLUSIONS These outcomes suggest that substance-related structural brain alterations likely have implications for the functioning of canonical large-scale networks and the perpetuation of substance use and neurocognitive alterations.
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Affiliation(s)
- Lauren D Hill-Bowen
- Department of Psychology, Florida International University, 11200 SW 8th Street, Miami, FL 33199, United States
| | - Michael C Riedel
- Department of Physics, Florida International University, 11200 SW 8th Street, Miami, FL 33199, United States
| | - Taylor Salo
- Department of Psychology, Florida International University, 11200 SW 8th Street, Miami, FL 33199, United States
| | - Jessica S Flannery
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, United States
| | - Ranjita Poudel
- Department of Psychology, Florida International University, 11200 SW 8th Street, Miami, FL 33199, United States
| | - Angela R Laird
- Department of Physics, Florida International University, 11200 SW 8th Street, Miami, FL 33199, United States
| | - Matthew T Sutherland
- Department of Psychology, Florida International University, 11200 SW 8th Street, Miami, FL 33199, United States.
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Lin X, Zhu X, Zhou W, Zhang Z, Li P, Dong G, Meng S, Deng J, Lu L. Connectome-based predictive modelling of smoking severity in smokers. Addict Biol 2022; 27:e13242. [PMID: 36301219 DOI: 10.1111/adb.13242] [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: 06/06/2022] [Revised: 08/11/2022] [Accepted: 09/27/2022] [Indexed: 01/24/2023]
Abstract
The functional connectivity within and between networks could provide a framework to characterize the neurobiological mechanism of nicotine addiction. This study examined the brain regions that were functionally connected in response to smoking cues and established the brain-behaviour relationships in smokers. Sixty-seven male smokers were enrolled and scanned while performing the cue-reactivity and Stroop task. A whole-brain analysis approach, connectome-based predictive modelling (CPM), was conducted on the data from the cue-reactivity task to identify the networks that could predict the smoking severity with the Shen atlas as templates. Then, the brain-behaviour relationships were verified in a different brain state (Stroop task). CPM identified the smoking severity-related network, as indicated by a significant correlation between predicted and actual smoking severity scores (r = 0.31, p = 0.02). Identified networks mainly involved the canonical networks implicated in the reward process (motor/sensory network and salience network) and executive control (frontoparietal network). Network strength in the Stroop task marginally significantly predicted smoking severity scores (r = 0.23, p = 0.06), partially replicating the brain-behaviour relationship. The CPM results identified the whole-brain neural network related to smoking severity, which was cross-validated by the AAL and Shen atlas. These findings contribute to more profound insights into neural substrates underlying the smoking severity.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Ximei Zhu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Weiran Zhou
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Zhibo Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Guangheng Dong
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Shiqiu Meng
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
| | - Jiahui Deng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China.,National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
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30
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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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31
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Paolini M, Keeser D, Rauchmann BS, Gschwendtner S, Jeanty H, Reckenfelderbäumer A, Yaseen O, Reidler P, Rabenstein A, Engelbregt HJ, Maywald M, Blautzik J, Ertl-Wagner B, Pogarell O, Rüther T, Karch S. Correlations Between the DMN and the Smoking Cessation Outcome of a Real-Time fMRI Neurofeedback Supported Exploratory Therapy Approach: Descriptive Statistics on Tobacco-Dependent Patients. Clin EEG Neurosci 2022; 53:287-296. [PMID: 34878329 PMCID: PMC9174614 DOI: 10.1177/15500594211062703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/28/2021] [Accepted: 10/28/2021] [Indexed: 11/30/2022]
Abstract
The aim of this study was to explore the potential of default mode network (DMN) functional connectivity for predicting the success of smoking cessation in patients with tobacco dependence in the context of a real-time function al MRI (RT-fMRI) neurofeedback (NF) supported therapy.Fifty-four tobacco-dependent patients underwent three RT-fMRI-NF sessions including resting-state functional connectivity (RSFC) runs over a period of 4 weeks during professionally assisted smoking cessation. Patients were randomized into two groups that performed either active NF of an addiction-related brain region or sham NF. After preprocessing, the RSFC baseline data were statistically evaluated using seed-based ROI (SBA) approaches taking into account the smoking status of patients after 3 months (abstinence/relapse).The results of the real study group showed a widespread functional connectivity in the relapse subgroup (n = 10) exceeding the DMN template and mainly low correlations and anticorrelations in the within-seed analysis. In contrast, the connectivity pattern of the abstinence subgroup (n = 8) primarily contained the core DMN in the seed-to-whole-brain analysis and a left lateralized correlation pattern in the within-seed analysis. Calculated Multi-Subject Dictionary Learning (MSDL) matrices showed anticorrelations between DMN regions and salience regions in the abstinence group. Concerning the sham group, results of the relapse subgroup (n = 4) and the abstinence subgroup (n = 6) showed similar trends only in the within-seed analysis.In the setting of a RT-fMRI-NF-assisted therapy, a widespread intrinsic DMN connectivity and a low negative coupling between the DMN and the salience network (SN) in patients with tobacco dependency during early withdrawal may be useful as an early indicator of later therapy nonresponse.
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Affiliation(s)
- Marco Paolini
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
| | - Daniel Keeser
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Sarah Gschwendtner
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Hannah Jeanty
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Arne Reckenfelderbäumer
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Omar Yaseen
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Paul Reidler
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
| | - Andrea Rabenstein
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Hessel Jan Engelbregt
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Hersencentrum Mental Health Institute, Amsterdam, the
Netherlands
| | - Maximilian Maywald
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Janusch Blautzik
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Institute for Radiology and Nuclear
Medicine St. Anna, Luzern, Switzerland
| | - Birgit Ertl-Wagner
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Division of Neuro-Radiology, The Hospital for Sick Children,
University of Toronto, Toronto, Canada
| | - Oliver Pogarell
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Tobias Rüther
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Susanne Karch
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
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32
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Taebi A, Becker B, Klugah-Brown B, Roecher E, Biswal B, Zweerings J, Mathiak K. Shared network-level functional alterations across substance use disorders: A multi-level kernel density meta-analysis of resting-state functional connectivity studies. Addict Biol 2022; 27:e13200. [PMID: 35754101 DOI: 10.1111/adb.13200] [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: 11/24/2021] [Revised: 04/21/2022] [Accepted: 06/02/2022] [Indexed: 11/30/2022]
Abstract
An increasing number of neuroimaging studies indicate functional alterations in cortico-striatal loops in individuals with substance use disorders (SUD). Dysregulations in these circuits may contribute to drug-seeking and drug-consuming behaviour by impeding inhibitory control, habit formation, and reward processing. Despite evidence of network-level changes in SUD, a shared pattern of functional alterations within and between spatially distributed brain networks has not been systematically investigated. The present meta-analytic investigation aims at identifying common alterations in resting-state functional connectivity patterns across different SUD, including stimulant, heroin, alcohol, cannabis, and nicotine use. To this aim, seed-based whole-brain connectivity maps for different functional networks were extracted and subjected to multi-level kernel density analysis to identify dysfunctional networks in individuals with SUD compared with healthy controls. In addition, an exploratory analysis examined substance-specific effects as well as the influence of drug use status on the main findings. Our findings indicate a hypoconnectivity pattern for the limbic, salience, and frontoparietal networks in individuals with SUD as compared with healthy controls. The default mode network additionally exhibited a complex pattern of hypo- and hyperconnectivity across the studies. The observed disrupted connectivity between networks in SUD may associate with deficient inhibitory control mechanisms that are thought to contribute to excessive craving and automatic drug-related behaviour as well as failure in substance use cessation. The identified network-based alterations in SUD represent potential treatment targets for neuromodulation, for example, network-based real-time functional magnetic resonance imaging (fMRI) neurofeedback. Such interventions can evaluate the behavioural relevance of the identified neural circuits.
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Affiliation(s)
- Arezoo Taebi
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany
| | - Benjamin Becker
- The Clinical Hospital of the Chengdu Brain Science Institute, School of Life Science and Technology, Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Klugah-Brown
- The Clinical Hospital of the Chengdu Brain Science Institute, School of Life Science and Technology, Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Erik Roecher
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany.,Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
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Thomas PJ, Leow A, Klumpp H, Phan KL, Ajilore O. Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies. Cereb Cortex 2022; 32:1823-1839. [PMID: 34521109 PMCID: PMC9070362 DOI: 10.1093/cercor/bhab314] [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: 05/18/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/14/2022] Open
Abstract
Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.
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Affiliation(s)
- Paul J Thomas
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH 43210, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN. Sci Data 2022; 9:133. [PMID: 35361781 PMCID: PMC8971535 DOI: 10.1038/s41597-022-01251-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 03/10/2022] [Indexed: 01/16/2023] Open
Abstract
Cocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of 74 CUD patients (9 female) and matched 64 healthy controls (6 female) that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1-weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. The repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform. Researchers can pursue brain variability between these groups or use a single group for a larger population sample. Measurement(s) | functional brain measurement • Diffusion Weighted Imaging • Abnormality of brain morphology • Alteration Of Cognitive Function • Clinical Study | Technology Type(s) | Functional Magnetic Resonance Imaging • Diffusion Weighted Imaging • Turbo Field Echo MRI • neuropsychological test • Clinical Evaluation | Factor Type(s) | Cocaine Dependence | Sample Characteristic - Organism | Homo | Sample Characteristic - Location | Mexico City |
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35
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Kim DY, Jang Y, Heo DW, Jo S, Kim HC, Lee JH. Electronic Cigarette Vaping Did Not Enhance the Neural Process of Working Memory for Regular Cigarette Smokers. Front Hum Neurosci 2022; 16:817538. [PMID: 35250518 PMCID: PMC8894252 DOI: 10.3389/fnhum.2022.817538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/20/2022] [Indexed: 12/01/2022] Open
Abstract
Background Electronic cigarettes (e-cigs) as substitute devices for regular tobacco cigarettes (r-cigs) have been increasing in recent times. We investigated neuronal substrates of vaping e-cigs and smoking r-cigs from r-cig smokers. Methods Twenty-two r-cig smokers made two visits following overnight smoking cessation. Functional magnetic resonance imaging (fMRI) data were acquired while participants watched smoking images. Participants were then allowed to smoke either an e-cig or r-cig until satiated and fMRI data were acquired. Their craving levels and performance on the Montreal Imaging Stress Task and a 3-back alphabet/digit recognition task were obtained and analyzed using two-way repeated-measures analysis of variance. Regions-of-interest (ROIs) were identified by comparing the abstained and satiated conditions. Neuronal activation within ROIs was regressed on the craving and behavioral data separately. Results Craving was more substantially reduced by smoking r-cigs than by vaping e-cigs. The response time (RT) for the 3-back task was significantly shorter following smoking r-cigs than following vaping e-cigs (interaction: F (1, 17) = 5.3, p = 0.035). Neuronal activations of the right vermis (r = 0.43, p = 0.037, CI = [-0.05, 0.74]), right caudate (r = 0.51, p = 0.015, CI = [0.05, 0.79]), and right superior frontal gyrus (r = −0.70, p = 0.001, CI = [−0.88, −0.34]) were significantly correlated with the RT for the 3-back task only for smoking r-cigs. Conclusion Our findings suggest that insufficient satiety from vaping e-cigs for r-cigs smokers may be insignificant effect on working memory function.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
| | - Yujin Jang
- Department of Psychology, Korea University, Seoul, South Korea
| | - Da-Woon Heo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Sungman Jo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Department of Artificial Intelligence, Kyungpook National University, Daegu, South Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- *Correspondence: Jong-Hwan Lee,
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36
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Tolomeo S, Yu R. Brain network dysfunctions in addiction: a meta-analysis of resting-state functional connectivity. Transl Psychiatry 2022; 12:41. [PMID: 35091540 PMCID: PMC8799706 DOI: 10.1038/s41398-022-01792-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 11/16/2022] Open
Abstract
Resting-state functional connectivity (rsFC) provides novel insights into variabilities in neural networks associated with the use of addictive drugs or with addictive behavioral repertoire. However, given the broad mix of inconsistent findings across studies, identifying specific consistent patterns of network abnormalities is warranted. Here we aimed at integrating rsFC abnormalities and systematically searching for large-scale functional brain networks in substance use disorder (SUD) and behavioral addictions (BA), through a coordinate-based meta-analysis of seed-based rsFC studies. A total of fifty-two studies are eligible in the meta-analysis, including 1911 SUD and BA patients and 1580 healthy controls. In addition, we performed multilevel kernel density analysis (MKDA) for the brain regions reliably involved in hyperconnectivity and hypoconnectivity in SUD and BA. Data from fifty-two studies showed that SUD was associated with putamen, caudate and middle frontal gyrus hyperconnectivity relative to healthy controls. Eight BA studies showed hyperconnectivity clusters within the putamen and medio-temporal lobe relative to healthy controls. Altered connectivity in salience or emotion-processing areas may be related to dysregulated affective and cognitive control-related networks, such as deficits in regulating elevated sensitivity to drug-related stimuli. These findings confirm that SUD and BA might be characterized by dysfunctions in specific brain networks, particularly those implicated in the core cognitive and affective functions. These findings might provide insight into the development of neural mechanistic biomarkers for SUD and BA.
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Affiliation(s)
- Serenella Tolomeo
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
| | - Rongjun Yu
- Department of Management, Hong Kong Baptist University, Hong Kong, China.
- Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, China.
- Department of Physics, Hong Kong Baptist University, Hong Kong, China.
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Soleimani G, Kupliki R, Bodurka J, Paulus M, Ekhtiari H. How structural and functional MRI can inform dual-site tACS parameters: A case study in a clinical population and its pragmatic implications. Brain Stimul 2022; 15:337-351. [PMID: 35042056 DOI: 10.1016/j.brs.2022.01.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 01/10/2022] [Accepted: 01/10/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Abnormalities in frontoparietal network (FPN) were observed in many neuropsychiatric diseases including substance use disorders. A growing number of studies are using dual-site-tACS with frontoparietal synchronization to engage this network. However, a computational pathway to inform and optimize parameter space for frontoparietal synchronization is still lacking. In this case study, in a group of participants with methamphetamine use disorders, we proposed a computational pathway to extract optimal electrode montage while accounting for stimulation intensity using structural and functional MRI. METHODS Sixty methamphetamine users completed an fMRI drug cue-reactivity task. Four main steps were taken to define electrode montage and adjust stimulation intensity using 4x1 high-definition (HD) electrodes for a dual-site-tACS; (1) Frontal seed was defined based on the maximum electric fields (EF) predicted by simulation of HD montage over DLPFC (F3/F4 in EEG 10-20), (2) frontal seed-to-whole brain context-dependent correlation was calculated to determine connected regions to frontal seeds, (3) center of connected cluster in parietal cortex was selected as a location for placing the second set of HD electrodes to shape the informed montage, (4) individualized head models were used to determine optimal stimulation intensity considering underlying brain structure. The informed montage was compared to montages with large electrodes and classic frontoparietal HD montages (F3-P3/F4-P4) in terms of tACS-induced EF and ROI-to-ROI task-based/resting-state connectivity. RESULTS Compared to the large electrodes, HD frontoparietal montages allow for a finer control of the spatial peak fields in the main nodes of the FPN at the cost of lower maximum EF (large-pad/HD: max EF[V/m] = 0.37/0.11, number of cortical sub-regions that EF exceeds 50% of the max = 77/13). For defining stimulation targets based on EF patterns, using group-level head models compared to a single standard head model results in comparable but significantly different seed locations (6.43mm Euclidean distance between the locations of the frontal maximum EF in standard-space). As expected, significant task-based/resting-state connections were only found between frontal-parietal locations in the informed montage. Cue-induced craving score was correlated with frontoparietal connectivity only in the informed montage (r = -0.24). Stimulation intensity in the informed montage, and not in the classic HD montage, needs 40% reduction in the parietal site to reduce the disparity in EF between sites. CONCLUSION This study provides some empirical insights to montage and dose selection in dual-site-tACS using individual brain structures and functions and proposes a computational pathway to use head models and functional MRI to define (1) optimum electrode montage for targeting FPN in a context of interest (drug-cue-reactivity) and (2) proper transcranial stimulation intensity.
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Affiliation(s)
- Ghazaleh Soleimani
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran; Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Rayus Kupliki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research, Tulsa, OK, United States.
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Li X, Dong F, Zhang Y, Wang J, Wang Z, Sun Y, Zhang M, Xue T, Ren Y, Lv X, Yuan K, Yu D. Altered resting-state electroencephalography microstate characteristics in young male smokers. Front Psychiatry 2022; 13:1008007. [PMID: 36267852 PMCID: PMC9577082 DOI: 10.3389/fpsyt.2022.1008007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.
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Affiliation(s)
- 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
| | - 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
| | - Yunmiao 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
| | - Juan 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
| | - Zhengxi 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
| | - Yaning Sun
- 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
| | - 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
| | - 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
| | - Xiaoqi Lv
- College of Information Engineering, Inner Mongolia University of Technology, 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.,School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 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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Abstract
Prior findings indicate that trait anhedonia enhances the likelihood of becoming a tobacco smoker, and preliminary evidence suggests that smoking abstinence leads to anhedonic states in some individuals and situations, and nicotine administration reduces anhedonic states. Nevertheless, many vital questions exist concerning relationships between anhedonia and nicotine dependence, including situational and individual difference factors that may moderate the strength of these associations. This chapter provides a critical review of the literature assessing relationships of anhedonia to nicotine dependence and the effects of acute nicotine through the lenses of the Research Domain Criteria's (RDoC) Positive Valence Systems (NIMH, RDoC changes to the matrix (CMAT) workgroup update: proposed positive valence domain revisions. A report by the national advisory mental health council workgroup on changes to the research domain criteria matrix, 2018) and the Situation x Trait Affective Response (STAR) model of nicotine's effects and nicotine dependence (Gilbert, Smoking individual differences, psychopathology, and emotion. Taylor and Francis, Washington, DC, 1995; Gilbert, Hum Psychopharmacol Clin Exp 12:S89-S102, 1997). The effects of nicotine and nicotine withdrawal on subjective, behavioral, and brain indices vary across the three RDoC Positive Valences Systems (Reward Responsiveness, Reward Learning, and Reward Valuation) in a manner that supports the research and potential clinical utility of using RDoC criteria and the STAR model to guide research and clinical innovation. We provide a revision of the STAR model that incorporates the three RDoC Positive Valence Systems with evidence that nicotine's effects on hedonic and affective processes vary as a function of the dominance/salience of (1) situational hedonic and affective cues and task/active coping cues, and (2) state executive functioning level/capacity and state reward sensitivity such that these effects of nicotine are maximal during states of suboptimal cognitive functioning and reward sensitivity, combined with low situational stimulus salience and low task-related cues/demands.
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Affiliation(s)
- David G Gilbert
- School of Psychological and Social Sciences, Southern Illinois University-Carbondale, Carbondale, IL, USA.
| | - Bryant M Stone
- School of Psychological and Social Sciences, Southern Illinois University-Carbondale, Carbondale, IL, USA
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40
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Ramaekers JG, Theunissen EL, van Ruitenbeek P, Mason NL. Cannabis Use and Neuroadaptation: A Call for Δ 9 -Tetrahydrocannabinol Challenge Studies. Front Psychiatry 2022; 13:870750. [PMID: 35492732 PMCID: PMC9046729 DOI: 10.3389/fpsyt.2022.870750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/18/2022] [Indexed: 11/26/2022] Open
Abstract
Currently, the assessment of the neurobehavioral consequences of repeated cannabis use is restricted to studies in which brain function of chronic cannabis users is compared to that of non-cannabis using controls. The assumption of such studies is that changes in brain function of chronic users are caused by repeated and prolonged exposure to acute cannabis intoxication. However, differences in brain function between chronic cannabis users and non-users might also arise from confounding factors such as polydrug use, alcohol use, withdrawal, economic status, or lifestyle conditions. We propose a methodology that highlights the relevance of acute Δ9-tetrahydrocannabinol (THC) dosing studies for a direct assessment of neuroadaptations in chronic cannabis users. The approach includes quantification of neurochemical, receptor, and functional brain network changes in response to an acute cannabis challenge, as well as stratification of cannabis using groups ranging from occasional to cannabis-dependent individuals. The methodology allows for an evaluation of THC induced neuroadaptive and neurocognitive changes across cannabis use history, that can inform neurobiological models on reward driven, compulsive cannabis use.
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Affiliation(s)
- Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Eef L Theunissen
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Peter van Ruitenbeek
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Natasha L Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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41
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Blum K, Bowirrat A, Baron D, Badgaiyan RD, Thanos PK, Elman I, Braverman ER, Gold MS. Understanding that Addiction Is a Brain Disorder Offers Help and Hope. Health (London) 2022. [DOI: 10.4236/health.2022.146050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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42
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Ghahremani DG, Pochon JB, Perez Diaz M, Tyndale RF, Dean AC, London ED. Functional connectivity of the anterior insula during withdrawal from cigarette smoking. Neuropsychopharmacology 2021; 46:2083-2089. [PMID: 34035468 PMCID: PMC8505622 DOI: 10.1038/s41386-021-01036-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/27/2021] [Accepted: 05/04/2021] [Indexed: 12/18/2022]
Abstract
Currently available therapies for smoking cessation have limited efficacy, and potential treatments that target specific brain regions are under evaluation, with a focus on the insula. The ventral and dorsal anterior subregions of the insula serve distinct functional networks, yet our understanding of how these subregions contribute to smoking behavior is unclear. Resting-state functional connectivity (RSFC) provides a window into network-level function associated with smoking-related internal states. The goal of this study was to determine potentially distinct relationships of ventral and dorsal anterior insula RSFC with cigarette withdrawal after brief abstinence from smoking. Forty-seven participants (24 women; 18-45 years old), who smoked cigarettes daily and were abstinent from smoking overnight (~12 h), provided self-reports of withdrawal and underwent resting-state fMRI before and after smoking the first cigarette of the day. Correlations between withdrawal and RSFC were computed separately for ventral and dorsal anterior insula seed regions in whole-brain voxel-wise analyses. Withdrawal was positively correlated with RSFC of the right ventral anterior insula and dorsal anterior cingulate cortex (dACC) before but not after smoking. The correlation was mainly due to a composite effect of craving and physical symptoms of withdrawal. These results suggest a role of right ventral anterior insula-dACC connectivity in the internal states that maintain smoking behavior (e.g., withdrawal) and present a specific neural target for brain-based therapies seeking to attenuate withdrawal symptoms in the critical early stages of smoking cessation.
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Affiliation(s)
- Dara G. Ghahremani
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA USA
| | - Jean-Baptiste Pochon
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA USA
| | - Maylen Perez Diaz
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA USA
| | - Rachel F. Tyndale
- grid.17063.330000 0001 2157 2938Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,grid.155956.b0000 0000 8793 5925Campbell Family Mental Health Research Institute, Centre for Addiction & Mental Health, Toronto, ON Canada
| | - Andy C. Dean
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Brain Research Institute, University of California, Los Angeles, CA USA
| | - Edythe D. London
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Brain Research Institute, University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA USA
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43
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Ely AV, Jagannathan K, Spilka N, Keyser H, Rao H, Franklin TR, Wetherill RR. Exploration of the influence of body mass index on intra-network resting-state connectivity in chronic cigarette smokers. Drug Alcohol Depend 2021; 227:108911. [PMID: 34364193 PMCID: PMC8464487 DOI: 10.1016/j.drugalcdep.2021.108911] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Obesity and cigarette smoking are two leading preventable causes of death. Previous research suggests that comorbid smoking and obesity likely share neurobehavioral underpinnings; however, the influence of body mass index (BMI) on resting-state functional connectivity (rsFC) in smokers remains unknown. In this study, we explore how BMI affects rsFC and associations between rsFC and smoking-related behavior. METHODS Treatment-seeking cigarette smokers (N = 87; 54 % men) completed a BOLD resting-state fMRI scan session. We grouped smokers into BMI groups (N = 23 with obesity, N = 33 with overweight, N = 31 lean) and used independent components analysis (ICA) to identify the resting state networks commonly associated with cigarette smoking: salience network (SN), right and left executive control networks (ECN) and default mode network (DMN). Average rsFC values were extracted (p < 0.001, k = 100) to determine group differences in rsFC and relationship to self-reported smoking and dependence. RESULTS Analyses revealed a significant relationship between BMI and connectivity in the SN and a significant quadratic effect of BMI on DMN connectivity. Heavier smoking was related to greater rsFC in the SN among lean and obese groups but reduced rsFC in the overweight group. CONCLUSIONS Findings build on research suggesting an influence of BMI on the neurobiology of smokers. In particular, dysfunction of SN-DMN-ECN circuitry in smokers with overweight may lead to a failure to modulate attention and behavior and subsequent difficulty quitting smoking. Future research is needed to elucidate the mechanism underlying the interaction of BMI and smoking and its impact on treatment.
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Affiliation(s)
- Alice V. Ely
- Corresponding authors: University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia PA 19104, ,
| | | | | | | | | | | | - Reagan R. Wetherill
- Corresponding authors: University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia PA 19104, ,
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44
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Jo S, Kim HC, Lustig N, Chen G, Lee JH. Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies. Hum Brain Mapp 2021; 42:5374-5396. [PMID: 34415651 PMCID: PMC8519860 DOI: 10.1002/hbm.25627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2 -test of the MEMA but not from the t-test, and the corresponding activations were significantly associated with the similarity scores (r = -.52, p = .041, confidence interval [CI] = [-0.78, -0.17]) and the urge-to-smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2 -tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = -.58, p = .01, CI = [-0.84, -0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.
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Affiliation(s)
- Sungman Jo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Niv Lustig
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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45
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Linli Z, Huang X, Liu Z, Guo S, Sariah A. A multivariate pattern analysis of resting-state functional MRI data in Naïve and chronic betel quid chewers. Brain Imaging Behav 2021; 15:1222-1234. [PMID: 32712800 DOI: 10.1007/s11682-020-00322-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Betel quid (BQ) is the fourth most commonly consumed psychoactive substance in the world. However, comprehensive functional magnetic resonance imaging (fMRI) studies exploring the neurophysiological mechanism of BQ addiction are lacking. Betel-quid-dependent (BQD) individuals (n = 24) and age-matched healthy controls (HC) (n = 26) underwent fMRI before and after chewing BQ. Multivariate pattern analysis (MVPA) was used to explore the acute effects of BQ-chewing in both groups. A cross-sectional comparison was conducted to explore the chronic effects of BQ-chewing. Regression analysis was used to investigate the relationship between altered circuits of BQD individuals and the severity of BQ addiction. MVPA achieved classification accuracies of up to 90% in both groups for acute BQ-chewing. Suppression of the default-mode network was the most prominent feature. BQD showed more extensive and intensive within- and between-network dysconnectivity of the default, frontal-parietal, and occipital regions associated with high-order brain functions such as self-awareness, inhibitory control, and decision-making. In contrast, the chronic effects of BQ on the brain function were mild, but impaired circuits were predominately located in the default and frontal-parietal networks which might be associated with compulsive drug use. Simultaneously quantifying the effects of both chronic and acute BQ exposure provides a possible neuroimaging-based BQ addiction foci. Results from this study may help us understand the neural mechanisms involved in BQ-chewing and BQ dependence.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, People's Republic of China
| | - Xiaojun Huang
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Zhening Liu
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China.
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, People's Republic of China.
| | - Adellah Sariah
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
- Department of Mental Health and Psychiatric Nursing, Hubert Kairuki Memorial University, Dar es Salaam, Tanzania.
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46
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Kim DY, Tegethoff M, Meinlschmidt G, Yoo SS, Lee JH. Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: empirical evidence from functional MRI data. Neuroreport 2021; 32:762-770. [PMID: 33901056 DOI: 10.1097/wnr.0000000000001653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. OBJECTIVES The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training. METHODS Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs). RESULTS Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs. CONCLUSION Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
| | - Marion Tegethoff
- Institute of Psychology, RWTH Aachen, Jägerstrasse, Aachen, Germany
- Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Gunther Meinlschmidt
- Division of Clinical Psychology and Cognitive Behavioral Therapy, International Psychoanalytic University, Stromstrasse, Berlin, Germany
- Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse, Basel, Switzerland
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Seung-Schik Yoo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
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Association between functional brain alterations and neuropsychological scales in male chronic smokers using resting-state fMRI. Psychopharmacology (Berl) 2021; 238:1387-1399. [PMID: 33772331 DOI: 10.1007/s00213-021-05819-6] [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: 07/28/2020] [Accepted: 03/08/2021] [Indexed: 10/21/2022]
Abstract
RATIONALE Recent studies have demonstrated that cigarette smoking is related to changes in brain structure and function. However, few studies focus on functional brain differences between male chronic smokers and nonsmokers in both local spontaneous activity and whole-brain functional networks. OBJECTIVES Our study recruited 67 chronic smokers and 43 nonsmokers who underwent functional magnetic resonance imaging (fMRI) scans to investigate functional activity and connectivity alterations in chronic smokers. METHODS We used the mean fractional amplitude of the low-frequency fluctuation (mfALFF) and mean regional homogeneity (mReHo) methods to investigate resting-state spontaneous activity in chronic smokers and nonsmokers. The graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also used to investigate functional connectivity alterations. RESULTS Compared with nonsmokers, chronic smokers exhibited higher activation in the reward system and portions of the prefrontal cortex but lower activation in the default mode networks (DMN) and visual-related regions. In addition, correlation analysis was conducted to assess the associations between neuroimaging findings and the severity of nicotine dependence or expectations of smoking effects. Our results showed that certain brain regions correlated with the Fagerström Test for Nicotine Dependence (FTND), the positive aspect of the Drug Use Disorders Identification Test Extended (DUDIT-E), and the negative aspect of the DUDIT-E, especially in the attentional control networks and hippocampus. The graph theoretical analysis (GTA) results indicated chronic smokers exhibited a trend toward increased assortativity. Our network-based statistical (NBS) analysis revealed reduced functional connections between the subnetwork in the prefrontal cortex, olfactory cortex, angular gyrus, and cingulate gyrus of chronic smokers. CONCLUSIONS We concluded that chronic smokers have neural adaptations in local spontaneous activity but remain healthy brain functional networks.
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48
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Wang X, Xue T, Dong F, Li Y, Xie D, Liu C, Zhang M, Bi Y, Yuan K, Yu D. The changes of brain functional networks in young adult smokers based on independent component analysis. Brain Imaging Behav 2021; 15:788-797. [PMID: 32314196 DOI: 10.1007/s11682-020-00289-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Intrinsic functional connectivity (FC) networks, including the default mode network (DMN), central executive network (CEN), and salience network (SN), have been implicated in nicotine addiction. However, litter evidence exists about the abnormalities in the three networks in young adult smokers. Forty-eight young adult smokers and 49 age- and gender-matched non-smokers were recruited in the present study. Resting-state functional magnetic resonance imaging (fMRI) data were analyzed by a combination of independent component analysis (ICA) and dual regression to identify potential differences of FC patterns in the DMN, CEN, and SN. Compared to non-smokers, young adult smokers showed enhanced FC of the left posterior cingulate cortex (LPCC), right medial prefrontal cortex (RMPFC) and right precuneus within the DMN network, of the right dorsolateral prefrontal cortex (DLPFC) within the right CEN, and of the left anterior insula (LAI) within the SN. We also found increased FC between the DMN, CEN and key node of the SN (anterior insula, AI). Correlation analysis showed that the increased FC within the networks was significantly correlated with smoking behaviors (pack-years, smoking duration, FTND, first smoking age, and number of cigarettes per day). Our findings may provide additional evidence for conceptualizing the framework of nicotine addiction as a disease of intercommunicating brain networks.
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Affiliation(s)
- 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, 014010, Inner Mongolia, China.,Information Center, Dezhou People's Hospital, Dezhou, 253000, Shandong, 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, 014010, Inner Mongolia, China.,School of Science, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, 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, 014010, Inner Mongolia, China
| | - Yangding Li
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, 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, 014010, Inner Mongolia, China
| | - 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, 014010, Inner Mongolia, 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, 014010, Inner Mongolia, China
| | - Yanzhi Bi
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, 100101, 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, 014010, Inner Mongolia, China. .,Life Sciences Research Center, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, 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, 014010, Inner Mongolia, China.
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Fedota JR, Ross TJ, Castillo J, McKenna MR, Matous AL, Salmeron BJ, Menon V, Stein EA. Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:459-469. [PMID: 33436331 PMCID: PMC8035238 DOI: 10.1016/j.bpsc.2020.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/15/2020] [Accepted: 10/03/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS. METHODS To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain. RESULTS During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms. CONCLUSIONS Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence.
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Affiliation(s)
- John R Fedota
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland.
| | - Thomas J Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland
| | - Juan Castillo
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland
| | - Michael R McKenna
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland; Department of Psychology, Ohio State University, Columbus, Ohio
| | - Allison L Matous
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland; Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California; Stanford Neuroscience Institute, Stanford, California
| | - Elliot A Stein
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland.
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50
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Smoking-induced craving relief relates to increased DLPFC-striatal coupling in nicotine-dependent women. Drug Alcohol Depend 2021; 221:108593. [PMID: 33611027 PMCID: PMC8026729 DOI: 10.1016/j.drugalcdep.2021.108593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND Craving is a major contributor to drug-seeking and relapse. Although the ventral striatum (VS) is a primary neural correlate of craving, strategies aimed at manipulating VS function have not resulted in efficacious treatments. This incongruity may be because the VS does not influence craving in isolation. Instead, craving is likely mediated by communication between the VS and other neural substrates. Thus, we examined how striatal functional connectivity (FC) with key nodes of networks involved in addiction affects relief of craving, which is an important step in identifying viable treatment targets. METHODS Twenty-four nicotine-dependent non-abstinent women completed two resting-state (rs) fMRI scans, one before and one following smoking a cigarette in the scanner, and provided craving ratings before and after smoking the cigarette. A seed-based approach was used to examine rsFC between the VS, putamen and germane craving-related brain regions; the dorsolateral prefrontal cortex (dlPFC), the posterior cingulate cortex, and the anterior ventral insula. RESULTS Smoking a cigarette was associated with a decrease in craving. Relief of craving correlated with increases in right dlPFC- bilateral VS (r = 0.57, p = 0.003, corrected) as did increased right dlPFC-left putamen coupling (r = 0.62, p = 0.001, corrected). CONCLUSIONS Smoking-induced relief of craving is associated with enhanced rsFC between the dlPFC, a region that plays a pivotal role in decision making, and the striatum, the neural structure underlying motivated behavior. These findings are highly consistent with a burgeoning literature implicating dlPFC-striatal interactions as a neurobiological substrate of craving.
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