1
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Ruan X, Song Z, Yu T, Chen J. A voxel-level resting-state fMRI study on patients with alcohol use disorders based on a power spectrum slope analysis method. Front Neurosci 2024; 18:1323741. [PMID: 38426022 PMCID: PMC10902125 DOI: 10.3389/fnins.2024.1323741] [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: 10/18/2023] [Accepted: 02/06/2024] [Indexed: 03/02/2024] Open
Abstract
Background Earlier neuroimaging investigations showed that abnormal brain activity in patients with alcohol use disorder (AUD) was frequency dependent. However, there is lacking of a comprehensive method to capture the amplitude of multi-frequency bands directly. Here, we used a new method, the power spectrum slope (PSS) to explore abnormal spontaneous activity of brain in patients with AUD. Methods Thirty-three AUD patients and 29 healthy controls (HCs) enrolled in this study. The coefficient b and the power-law slope b' were calculated and compared between two groups. We also used the receiver operating characteristic (ROC) curve to examine the ability of the PSS analysis to distinguish between AUD and HCs. We next examined the correlation between PSS difference in the brain areas and the severity of alcohol dependence. Results Thirty AUD patients and 26 HCs were retained after head motion correction. The two metrics of PSS values increased in the left precentral gyrus in AUD patients. The area under the curve values of PSS differences in the specific brain area were respectively 0.836 and 0.844, with sensitivities of 86.7% and 83.3% and specificities of 73.1% and 76.9%. The Michigan Alcoholism Screening Test (MAST) and Alcohol drinking scale (ADS) scores were not significantly correlated with the PSS values in the specific brain area. Conclusion As a novel method, the PSS can well detect abnormal local brain activity in the AUD patients and may offer new insights for future fMRI studies.
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Affiliation(s)
- Xia Ruan
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhiyan Song
- Department of Radiology, Wuhan No. 1 Hospital, Wuhan, Hubei, China
| | - Tingting Yu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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2
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Yi C, Fan Y, Wu Y. Cross-module switching diversity of brain network nodes in resting and cognitive states. Cogn Neurodyn 2023; 17:1485-1499. [PMID: 37974588 PMCID: PMC10640499 DOI: 10.1007/s11571-022-09894-z] [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: 12/13/2021] [Revised: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022] Open
Abstract
Large-scale brain network dynamics reflect state change in brain activities and have potential effects on cognition. Such dynamics can be described by node temporal switching between modules; however, there are only a few studies on the influence of brain network node switching on brain cognition. Based on the functional magnetic resonance imaging (fMRI) data of resting and task states, we constructed dynamic functional networks using overlap sliding-time windows and applied multilayer network analysis to study the behaviours of nodes across brain modules. We found that (i) nodes with a high level switching rate in the resting-state mainly come from the default network, while nodes with a low level of switching rate mainly come from the visual network, (ii) nodes with a high switching rate have lower clustering coefficients and shorter characteristic path lengths, which are mainly affected by the somatomotor network and dorsal attention network; and (iii) in task states, there is still a negative correlation between switching rate, clustering coefficient and characteristic path length. However, the main subsystems that affect brain functions are regulated by the tasks. Our findings not only reveal the relevant characteristics of network node switching behaviours but also provide new insights for further understanding the complex functions of the brain. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09894-z.
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Affiliation(s)
- Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an, 710049 China
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3
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Li Y, Wang Y, Chen A. Flexible integration and segregation of large-scale networks during adaptive control. Behav Brain Res 2023; 451:114521. [PMID: 37268251 DOI: 10.1016/j.bbr.2023.114521] [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: 01/26/2023] [Revised: 05/08/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
Adaptive control characterizes the dynamic adjustment of cognitive control to changing environmental demand, and has obtained growing interests in its neural mechanism for the past two decades. Recent years, interpreting network reconfiguration in terms of integration and segregation has been proved to shed light on neural structure underlying various cognitive tasks. However, the relationship between network architecture and adaptive control remains unclear. Here, we quantified the network integration (global efficiency, participation coefficient, inter-subnetwork efficiency) and segregation (local efficiency, modularity) in the whole-brain and analyzed how these graph theory metrics were modulated by adaptive control. The results showed that the integration of the cognitive control network (the fronto-parietal network, FPN), the visual network (VIN) and the sensori-motor network (SMN) was significantly improved when conflict was rare, so as to cope with the incongruent trials of high cognitive control demands. Additionally, as the conflict proportion increased, the segregation of the cingulo-opercular network (CON) and the default mode network (DMN) significantly enhanced, which may contribute to specialized functioning or automatic processing, and help to solve conflict in a less resource-intensive mode. Finally, using graph metrics as features, the multivariate classifier reliably predicted the context condition. These results demonstrate how large-scale brain networks support adaptive control through flexible integration and segregation.
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Affiliation(s)
- Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing 100101, China
| | - Antao Chen
- School of Psychology, Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai 200438, China.
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4
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Syan SK, McIntyre-Wood C, Vandehei E, Vidal ML, Hargreaves T, Levitt EE, Scarfe M, Marsden E, MacKillop E, Sarles-Whittlesey H, Amlung M, Sweet L, MacKillop J. Resting state functional connectivity as a predictor of brief intervention response in adults with alcohol use disorder: A preliminary study. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2023; 47:1590-1602. [PMID: 37572293 DOI: 10.1111/acer.15123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 05/17/2023] [Accepted: 05/24/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Brief interventions for alcohol use disorder (AUD) are generally efficacious, albeit with variability in response. Resting state functional connectivity (rsFC) may characterize neurobiological indicators that predict the response to brief interventions and is the focus of the current investigation. MATERIALS AND METHODS Forty-six individuals with AUD (65.2% female) completed a resting state functional magnetic resonance imaging (fMRI) scan immediately followed by a brief intervention aimed at reducing alcohol consumption. Positive clinical response was defined as a reduction in alcohol consumption by at least one World Health Organization (WHO) risk drinking level at 3-month follow-up. rsFC was analyzed using seed-to-voxel analysis with seed regions from four networks: salience network, reward network, frontoparietal network, and default mode network. RESULTS At baseline, responders had greater rsFC between the following seed regions in relation to voxel-based clusters than non-responders: (i) anterior cingulate cortex (ACC) in relation to left postcentral gyrus and right supramarginal gyrus (salience network); (ii) right posterior parietal cortex in relation to right ventral ACC (salience network); (iii) right interior frontal gyrus (IFG) pars opercularis in relation to right cerebellum and right occipital fusiform gyrus (frontoparietal); and (iv) right primary motor cortex in relation to left thalamus (default mode). Lower rsFC in responders vs. nonresponders was seen between the (i) right rostral prefrontal cortex in relation to left IFG pars triangularis (frontoparietal); (ii) right IFG pars triangularis in relation to right cerebellum (frontoparietal); (iii) right IFG pars triangularis in relation to right frontal eye fields and right angular gyrus (frontoparietal); and (iv) right nucleus accumbens in relation to right orbital frontal cortex and right insula (reward). CONCLUSIONS Resting state functional connectivity in the frontoparietal, salience, and reward networks predicts the response to a brief intervention in individuals with AUD and could reflect greater receptivity or motivation for behavior change.
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Affiliation(s)
- Sabrina K Syan
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Carly McIntyre-Wood
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Emily Vandehei
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Mae Linda Vidal
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Tegan Hargreaves
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Emily E Levitt
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Molly Scarfe
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Emma Marsden
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Emily MacKillop
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | | | - Michael Amlung
- Cofrin Logan Center for Addiction Research and Treatment, University of Kansas, Lawrence, Kansas, USA
- Department of Applied Behavioral Science, University of Kansas, Lawrence, Kansas, USA
| | - Lawrence Sweet
- Department of Psychology, University of Georgia, Athens, Georgia, USA
| | - James MacKillop
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
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5
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Xie JY, Li RH, Yuan W, Du J, Zhou DS, Cheng YQ, Xu XM, Liu H, Yuan TF. Advances in neuroimaging studies of alcohol use disorder (AUD). PSYCHORADIOLOGY 2022; 2:146-155. [PMID: 38665276 PMCID: PMC11003430 DOI: 10.1093/psyrad/kkac018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 04/28/2024]
Abstract
Alcohol use disorder (AUD) is a worldwide problem and the most common substance use disorder. Chronic alcohol consumption may have negative effects on the body, the mind, the family, and even society. With the progress of current neuroimaging methods, an increasing number of imaging techniques are being used to objectively detect brain impairment induced by alcoholism and serve a vital role in the diagnosis, prognosis, and treatment assessment of AUD. This article organizes and analyzes the research on alcohol dependence concerning the main noninvasive neuroimaging methods, structural magnetic resonance imaging, functional magnetic resonance imaging, and electroencephalography, as well as the most common noninvasive brain stimulation - transcranial magnetic stimulation, and intersperses the article with joint intra- and intergroup studies, providing an outlook on future research directions.
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Affiliation(s)
- Ji-Yu Xie
- School of Mental Health, Wenzhou Medical University, Wenzho 325000, Zhejiangu, China
| | - Rui-Hua Li
- Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong, China
| | - Wei Yuan
- Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong, China
| | - Jiang Du
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Dong-Sheng Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo 315000, Zhejiang, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming 650000, Yunnan, China
| | - Xue-Ming Xu
- Department of Psychiatry, Taizhou Second People's Hospital, Taizhou 318000, Zhejiang, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi 563000, Guizhou, China
| | - Ti-Fei Yuan
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
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6
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Sepe-Forrest L, Kim DJ, Quinn PD, Bolbecker AR, Wisner KM, Hetrick WP, O'Donnell BF. Evidence of familial confounding of the association between cannabis use and cerebellar-cortical functional connectivity using a twin study. Neuroimage Clin 2022; 36:103237. [PMID: 36451348 PMCID: PMC9668648 DOI: 10.1016/j.nicl.2022.103237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 09/26/2022] [Accepted: 10/16/2022] [Indexed: 11/11/2022]
Abstract
Cerebellar-cortical resting-state functional connectivity (rsFC) has been reported to be altered in cannabis users. However, this association may be due to genetic and environmental confounding rather than a causal relationship between cannabis use and changes in rsFC. In this co-twin control study, linear mixed models were used to assess relationships between the number of lifetime cannabis uses (NLCU) and age of cannabis onset (ACO) with cerebellar-cortical rsFC. The rsFC with seven functional networks was evaluated in 147 monozygotic and 82 dizygotic twin pairs. Importantly, the use of genetically informed models in this twin sample facilitated examining whether shared genetic or environmental effects underlie crude associations between cannabis measures and connectivity. Individual-level phenotypic analyses (i.e., accounting for twin-pair non-independence) showed that individuals in the full sample with earlier ACO and higher NLCU had lower cerebellar rsFC within the VA, DA, and FP networks. Yet, there were no significant differences in cerebellar-cortical rsFC between monozygotic twins who were discordant for cannabis measures. These findings suggest shared genetic or environmental confounds contribute to associations between cannabis use and altered cerebellar-cortical rsFC, rather than unique causal impacts of cannabis use on cerebellar-cortical rsFC.
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Affiliation(s)
- Linnea Sepe-Forrest
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States,Program in Neuroscience, Indiana University, Bloomington, IN, United States,Corresponding author-at: Indiana University Bloomington, Department of Psychology, Room A208A, United States.
| | - Dae-Jin Kim
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Patrick D. Quinn
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States,Program in Neuroscience, Indiana University, Bloomington, IN, United States,Department of Applied Health Science, School of Public Health, Indiana University, Bloomington, IN, United States
| | - Amanda R. Bolbecker
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Krista M. Wisner
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States,Program in Neuroscience, Indiana University, Bloomington, IN, United States
| | - William P. Hetrick
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States,Program in Neuroscience, Indiana University, Bloomington, IN, United States,Department of Psychiatry, Indiana University, Indianapolis, IN, United States
| | - Brian F. O'Donnell
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States,Program in Neuroscience, Indiana University, Bloomington, IN, United States,Department of Psychiatry, Indiana University, Indianapolis, IN, United States
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7
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Disruption of cerebellar-cortical functional connectivity predicts balance instability in alcohol use disorder. Drug Alcohol Depend 2022; 235:109435. [PMID: 35395501 PMCID: PMC9106918 DOI: 10.1016/j.drugalcdep.2022.109435] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/23/2022] [Accepted: 03/29/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND A neural substrate of alcohol-related instability of gait and balance is the cerebellum. Whether disruption of neural communication between cerebellar and cortical brain regions exerts an influence on ataxia in alcohol use disorder (AUD) was the focus of this study. METHODS Study groups comprised 32 abstinent AUD participants and 22 age- and sex-matched healthy controls (CTL). All participants underwent clinical screening, motor testing, and resting-state functional MR imaging analyzed for functional connectivity (FC) among 90 regions across the whole cerebrum and cerebellum. Ataxia testing quantified gait and balance with the Fregly-Graybiel Ataxia Battery conducted with and without vision. RESULTS The AUD group achieved lower scores than the CTL group on balance performance, which was disproportionately worse for eyes open than eyes closed in the AUD relative to the CTL group. Differences in ataxia were accompanied by differences in FC marked by cerebellar-frontal and cerebellar-parietal hyperconnectivity and cortico-cortical hypoconnectivity in the AUD relative to the control group. Lifetime alcohol consumption correlated significantly with AUD-related FC aberrations, which explained upwards of 69% of the AUD ataxia score variance. CONCLUSION Heavy, chronic alcohol consumption is associated with disorganized neural communication among cerebellar-cortical regions and contributes to ataxia in AUD. Ataxia, which is known to accelerate with age and be exacerbated with AUD, can threaten functional independence. Longitudinal studies are warranted to address whether extended sobriety quells ataxia and normalizes aberrant FC contributing to instability.
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8
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Zhang R, Wiers CE, Manza P, Tomasi D, Shokri-Kojori E, Kerich M, Almira E, Schwandt M, Diazgranados N, Momenan R, Volkow ND. Severity of alcohol use disorder influences sex differences in sleep, mood and brain functional connectivity impairments. Brain Commun 2022; 4:fcac127. [DOI: 10.1093/braincomms/fcac127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 03/14/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Growing evidence suggests greater vulnerability of women than men to the adverse effects of alcohol on mood and sleep. However, the underlying neurobiological mechanisms are still poorly understood.
Here we examined sex difference in resting state functional connectivity in alcohol use disorder using a whole-brain data driven approach and tested for relationships with mood and self-reported sleep. To examine whether sex effects vary by severity of alcohol use disorder, we studied two cohorts: non-treatment seeking n = 141 participants with alcohol use disorder (low severity; 58 females) from the Human Connectome project, and recently detoxified n = 102 treatment seeking participants with alcohol use disorder (high severity; 34 females) at the National Institute on Alcohol Abuse and Alcoholism.
For both cohorts, participants with alcohol use disorder had greater sleep and mood problems than HC, whereas sex by alcohol use effect varied by severity. Non-treatment seeking females with alcohol use disorder showed significant greater impairments in sleep but not mood compared to non-treatment seeking males with alcohol use disorder, whereas treatment-seeking females with alcohol use disorder reported greater negative mood but not sleep than treatment-seeking males with alcohol use disorder. Greater sleep problems in non-treatment seeking females with alcohol use disorder were associated with lower cerebello-parahippocampal functional connectivity, while greater mood problems in treatment-seeking females with alcohol use disorder were associated with lower fronto-occipital functional connectivity during rest.
The current study suggests that changes in resting state functional connectivity may account for sleep and mood impairments in females with alcohol use disorder. The effect of severity on sex differences might reflect neuroadaptive processes with progression of alcohol use disorder and needs to be tested with longitudinal data in the future.
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Affiliation(s)
- Rui Zhang
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Corinde E. Wiers
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Peter Manza
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Dardo Tomasi
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Ehsan Shokri-Kojori
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1013, USA
| | - Mike Kerich
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1108, USA
| | - Erika Almira
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1108, USA
| | - Melanie Schwandt
- Office of Clinical Director, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1108, USA
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1108, USA
| | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1108, USA
| | - Nora D. Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892-1013, USA
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD 20892-1013, USA
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9
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Wang Y, Chai L, Chu C, Li D, Gao C, Wu X, Yang Z, Zhang Y, Xu J, Nyengaard JR, Eickhoff SB, Liu B, Madsen KH, Jiang T, Fan L. Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum. Mol Psychiatry 2022; 27:2619-2634. [PMID: 35264730 DOI: 10.1038/s41380-022-01489-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 11/09/2022]
Abstract
The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.
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Affiliation(s)
- Yaping Wang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lin Chai
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Congying Chu
- University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Deying Li
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Chaohong Gao
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Xia Wu
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zhengyi Yang
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
| | - Junhai Xu
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300350, China
| | - Jens Randel Nyengaard
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Department of Pathology, Aarhus University Hospital, 8200, Aarhus, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875, Beijing, China
| | - Kristoffer Hougaard Madsen
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Department of Informatics and Mathematical Modelling, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, 2650, Hvidovre, Denmark
| | - Tianzi Jiang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lingzhong Fan
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China. .,University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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10
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Huang X, Wu Z, Liu Z, Liu D, Huang D, Long Y. Acute Effect of Betel Quid Chewing on Brain Network Dynamics: A Resting-State Functional Magnetic Resonance Imaging Study. Front Psychiatry 2021; 12:701420. [PMID: 34504445 PMCID: PMC8421637 DOI: 10.3389/fpsyt.2021.701420] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Betel quid (BQ) is one of the most popular addictive substances in the world. However, the neurophysiological mechanism underlying BQ addiction remains unclear. This study aimed to investigate whether and how BQ chewing would affect brain function in the framework of a dynamic brain network model. Resting-state functional magnetic resonance imaging scans were collected from 24 male BQ-dependent individuals and 26 male non-addictive healthy individuals before and promptly after chewing BQ. Switching rate, a measure of temporal stability of functional brain networks, was calculated at both global and local levels for each scan. The results showed that BQ-dependent and healthy groups did not significantly differ on switching rate before BQ chewing (F = 0.784, p = 0.381, analysis of covariance controlling for age, education, and head motion). After chewing BQ, both BQ-dependent (t = 2.674, p = 0.014, paired t-test) and healthy (t = 2.313, p = 0.029, paired t-test) individuals showed a significantly increased global switching rate compared to those before chewing BQ. Significant corresponding local-level effects were observed within the occipital areas for both groups, and within the cingulo-opercular, fronto-parietal, and cerebellum regions for BQ-dependent individuals. Moreover, in BQ-dependent individuals, switching rate was significantly correlated with the severity of BQ addiction assessed by the Betel Quid Dependence Scale scores (Spearman's rho = 0.471, p = 0.020) before BQ chewing. Our study provides preliminary evidence for the acute effects of BQ chewing on brain functional dynamism. These findings may provide insights into the neural mechanisms of substance addictions.
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Affiliation(s)
- Xiaojun Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.,Department of Clinical Psychology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Zhipeng Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Dayi Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Danqing Huang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yicheng Long
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
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