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Wang J, Xue T, Song D, Dong F, Cheng Y, Wang J, Ma Y, Zou M, Ding S, Tao Z, Xin W, Yu D, Yuan K. Investigation of white matter functional networks in young smokers. Neuroimage 2024; 303:120917. [PMID: 39510395 DOI: 10.1016/j.neuroimage.2024.120917] [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: 08/23/2024] [Revised: 10/11/2024] [Accepted: 11/04/2024] [Indexed: 11/15/2024] Open
Abstract
AIMS This study investigated the changes in the organizational and intrinsical activities of the white matter functional networks (WMFNs) in young smokers using resting-state functional magnetic resonance imaging. METHODS A data-driven approach was used to characterize the WMFNs of 30 young smokers and 30 non-smokers. We applied K-means clustering to the neuroimaging data to delineate the WMFNs. Functional neural activities of the WMFNs were compared between the two groups. Correlation analyses were also conducted for the WMFNs neural activities of and clinical indicators of smoking. RESULTS Eight WMFNs were identified in both groups. Compared to non-smokers, young smokers demonstrated a different dorsal attention network and lack of a frontostriatal network. The neural activities in the frontal network, deep frontoparietal network, and visual network were reduced in young smokers. Further correlation analyses showed that the decreased neural activity in the deep frontal network and deep frontoparietal network were significantly negatively correlated with the Fagerström Test for Nicotine Dependence. CONCLUSION Young smokers exhibited differences in the organizational structure and neural activity intensities of the WMFNs. The present findings may indicate the importance of WMFNs in young smokers, which can help in obtaining a comprehensive understanding of the neural mechanisms underlying smoking addiction.
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Affiliation(s)
- Junxuan Wang
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Ting Xue
- School of Science College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China.
| | - Daining Song
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Fang Dong
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Yongxin Cheng
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Juan Wang
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Yuxin Ma
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Mingze Zou
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Shuailin Ding
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Zhanlong Tao
- School of Science College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Wuyuan Xin
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Dahua Yu
- School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China.
| | - Kai Yuan
- School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China; School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China; Life Sciences Research Center, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China; Hainan Free Trade Port Health Medical Research Institute, Baoting, Hainan 572300, China.
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Li X, Ramos-Rolón AP, Kass G, Pereira-Rufino LS, Shifman N, Shi Z, Volkow ND, Wiers CE. Imaging neuroinflammation in individuals with substance use disorders. J Clin Invest 2024; 134:e172884. [PMID: 38828729 PMCID: PMC11142750 DOI: 10.1172/jci172884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
Increasing evidence suggests a role of neuroinflammation in substance use disorders (SUDs). This Review presents findings from neuroimaging studies assessing brain markers of inflammation in vivo in individuals with SUDs. Most studies investigated the translocator protein 18 kDa (TSPO) using PET; neuroimmune markers myo-inositol, choline-containing compounds, and N-acetyl aspartate using magnetic resonance spectroscopy; and fractional anisotropy using MRI. Study findings have contributed to a greater understanding of neuroimmune function in the pathophysiology of SUDs, including its temporal dynamics (i.e., acute versus chronic substance use) and new targets for SUD treatment.
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Affiliation(s)
- Xinyi Li
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Astrid P. Ramos-Rolón
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Gabriel Kass
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Lais S. Pereira-Rufino
- Departamento de Morfologia e Genética, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Naomi Shifman
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Zhenhao Shi
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
| | - Nora D. Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, NIH, Bethesda, Maryland, USA
| | - Corinde E. Wiers
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, Pennsylvania, USA
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Zhang P, Feng Y, Xu T, Li Y, Xia J, Zhang H, Sun Z, Tian W, Zhang J. Brain white matter microstructural alterations in patients with systemic lupus erythematosus: an automated fiber quantification study. Brain Imaging Behav 2024; 18:622-629. [PMID: 38332385 DOI: 10.1007/s11682-024-00861-2] [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] [Accepted: 01/30/2024] [Indexed: 02/10/2024]
Abstract
This study aimed to identify damaged segments of brain white matter fiber tracts in patients with systemic lupus erythematosus (SLE) using diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ), and analyze their relationship with cognitive impairment. Clinical and imaging data for 39 female patients with SLE and for 44 female healthy controls (HCs) were collected. AFQ was used to track whole-brain white matter tracts in each participant, and each tract was segmented into 100 equally spaced nodes. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated at each node. Correlations were also explored between DTI metrics in the damaged segments of white matter fiber tracts and neuropsychological test scores of patients with SLE. Compared with HCs, SLE patients exhibited significantly lower FA values, and significantly higher MD, AD, RD values in many white matter tracts (all P < 0.05, false discovery rate-corrected). FA values in nodes 97-100 of the left inferior fronto-occipital fasciculus (IFOF) positively correlated with the mini-mental state examination score. AFQ enables precise and accurate identification of damage to white matter fiber tracts in brains of patients with SLE. FA values in the left IFOF correlate with cognitive impairment in SLE.
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Affiliation(s)
- Peng Zhang
- Graduate School of Dalian Medical University, Liaoning, 116044, China
- The First Affiliated Hospital of Baotou Medical College, Baotou, 014010, China
| | - Yanhong Feng
- Graduate School of Dalian Medical University, Liaoning, 116044, China
| | - Tianye Xu
- Graduate School of Dalian Medical University, Liaoning, 116044, China
| | - Yifan Li
- School of Medicine, Nantong University, Jiangsu, 226019, China
| | - Jianguo Xia
- Department of Imaging, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Jiangsu, 225300, China.
| | - Hongxia Zhang
- Department of Imaging, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Jiangsu, 225300, China.
| | - Zhongru Sun
- Department of Imaging, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Jiangsu, 225300, China
| | - Weizhong Tian
- Department of Imaging, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Jiangsu, 225300, China
| | - Ji Zhang
- Department of Imaging, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Jiangsu, 225300, China
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Roediger DJ, Griffin C, Marin FV, Verdoorn H, Fiecas M, Mueller BA, Lim KO, Camchong J. Relating white matter microstructure in theoretically defined addiction networks to relapse in alcohol use disorder. Cereb Cortex 2023; 33:9756-9763. [PMID: 37415080 PMCID: PMC10472493 DOI: 10.1093/cercor/bhad241] [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: 01/23/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 07/08/2023] Open
Abstract
Theoretical models group maladaptive behaviors in addiction into neurocognitive domains such as incentive salience (IS), negative emotionality (NE), and executive functioning (EF). Alterations in these domains lead to relapse in alcohol use disorder (AUD). We examine whether microstructural measures in the white matter pathways supporting these domains are associated with relapse in AUD. Diffusion kurtosis imaging data were collected from 53 individuals with AUD during early abstinence. We used probabilistic tractography to delineate the fornix (IS), uncinate fasciculus (NE), and anterior thalamic radiation (EF) in each participant and extracted mean fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) within each tract. Binary (abstained vs. relapsed) and continuous (number of days abstinent) relapse measures were collected over a 4-month period. Across tracts, anisotropy measures were typically (i) lower in those that relapsed during the follow-up period and (ii) positively associated with the duration of sustained abstinence during the follow-up period. However, only KFA in the right fornix reached significance in our sample. The association between microstructural measures in these fiber tracts and treatment outcome in a small sample highlights the potential utility of the three-factor model of addiction and the role of white matter alterations in AUD.
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Affiliation(s)
- Donovan J Roediger
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Claire Griffin
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States
| | - Frances V Marin
- Center for Mindfulness and Compassion, Cambridge Health Alliance, Cambridge, MA 02141, United States
| | - Hannah Verdoorn
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Mark Fiecas
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, United States
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jazmin Camchong
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
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