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Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ. Genetic architecture of the structural connectome. Nat Commun 2024; 15:1962. [PMID: 38438384 PMCID: PMC10912129 DOI: 10.1038/s41467-024-46023-2] [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] [Received: 09/13/2022] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.
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Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Natalie J Forde
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Salim Mansour
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Isabel Kerrebijn
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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2
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Pan N, Qin K, Patino LR, Tallman MJ, Lei D, Lu L, Li W, Blom TJ, Bruns KM, Welge JA, Strawn JR, Gong Q, Sweeney JA, Singh MK, DelBello MP. Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study. J Child Psychol Psychiatry 2024. [PMID: 38220469 DOI: 10.1111/jcpp.13946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD. METHODS Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics. RESULTS High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal , p = .022) and clustering coefficient (Cp , p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal : p < .001; Cp : p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%. CONCLUSIONS Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Luis R Patino
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | | | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Thomas J Blom
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Kaitlyn M Bruns
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Jeffrey A Welge
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Jeffrey R Strawn
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, OH, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Manpreet K Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, California, USA
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Chen Y, Zhao P, Pan C, Chang M, Zhang X, Duan J, Wei Y, Tang Y, Wang F. State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study. J Psychiatry Neurosci 2024; 49:E11-E22. [PMID: 38238036 PMCID: PMC10803102 DOI: 10.1503/jpn.230069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/10/2023] [Accepted: 09/14/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. METHODS We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. RESULTS We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. LIMITATIONS Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. CONCLUSION Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.
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Affiliation(s)
- Yifan Chen
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Pengfei Zhao
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Chunyu Pan
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Miao Chang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Xizhe Zhang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Jia Duan
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Yange Wei
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Yanqing Tang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
| | - Fei Wang
- From the Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chen, Wang); the Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China (Chen, Zhao, Pan, Duan, Wang); the Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China (Chen, Zhao, Duan, Wei, Wang); the Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China (Chang); the School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China (Zhang); the School of Computer Science and Engineering, Northeastern University, Shenyang, China (Pan); and the Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China (Tang)
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Zhu W, Yuan N, Wan C, Huang M, Fang S, Chen M, Chen J, Ma Q, Chen J. Mapping the scientific research on bipolar disorder: A scientometric study of hotspots, bursts, and trends. J Affect Disord 2023; 340:626-638. [PMID: 37595897 DOI: 10.1016/j.jad.2023.08.069] [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: 06/18/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 08/20/2023]
Abstract
Bipolar disorder (BD) is a severe psychiatric illness with an increasing prevalence worldwide. Although the pathological mechanism of and pharmacological interventions for BD have been extensively investigated in preclinical and clinical studies, a scientometric analysis of the developmental trends, interdisciplinary frontiers, and research hotspots in this field has not yet been conducted. Therefore, we performed a comprehensive scientometric review of 55,358 published studies on BD over the past two decades (2002-2021) to identify the most frequently used keywords and explore research hotspots and trajectories. The present findings revealed the main distribution, knowledge structure, topic evolution, and emerging topics of BD research. Analysing the risk factors, pathogenesis, key brain regions, comorbid conditions, and treatment strategies for BD contributed to understanding of the aetiology, progression, and treatment of this disorder. These findings provided substantial support for continued research in this area.
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Affiliation(s)
- Wenjun Zhu
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China
| | - Naijun Yuan
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China; Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong 518020, PR China; Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou 510632, PR China
| | - Chunmiao Wan
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China
| | - Minyi Huang
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China
| | - Shaoyi Fang
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China
| | - Man Chen
- College of Basic Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei 430065, PR China
| | - Jianbei Chen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, PR China
| | - Qingyu Ma
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China.
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, PR China; School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, PR China.
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5
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Nabulsi L, Chandio BQ, McPhilemy G, Martyn FM, Roberts G, Hallahan B, Dannlowski U, Kircher T, Haarman B, Mitchell P, McDonald C, Cannon DM, Andreassen OA, Ching CRK, Thompson PM. Multi-Site Statistical Mapping of Along-Tract Microstructural Abnormalities in Bipolar Disorder with Diffusion MRI Tractometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.17.553762. [PMID: 37662230 PMCID: PMC10473593 DOI: 10.1101/2023.08.17.553762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Investigating alterations in brain circuitry associated with bipolar disorder (BD) may offer a valuable approach to discover brain biomarkers for genetic and interventional studies of the disorder and related mental illnesses. Some diffusion MRI studies report evidence of microstructural abnormalities in white matter regions of interest, but we lack a fine-scale spatial mapping of brain microstructural differences along tracts in BD. We also lack large-scale studies that integrate tractometry data from multiple sites, as larger datasets can greatly enhance power to detect subtle effects and assess whether effects replicate across larger international datasets. In this multisite diffusion MRI study, we used BUndle ANalytics (BUAN, Chandio 2020), a recently developed analytic approach for tractography, to extract, map, and visualize profiles of microstructural abnormalities on 3D models of fiber tracts in 148 participants with BD and 259 healthy controls from 6 independent scan sites. Modeling site differences as random effects, we investigated along-tract white matter (WM) microstructural differences between diagnostic groups. QQ plots showed that group differences were gradually enhanced as more sites were added. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic, interhemispheric, and posterior pathways; higher FA was also noted in posterior bundles, relative to controls. By integrating tractography and anatomical information, BUAN effectively captures unique effects along white matter (WM) tracts, providing valuable insights into anatomical variations that may assist in the classification of diseases.
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Affiliation(s)
- Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Bramsh Q Chandio
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Genevieve McPhilemy
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Fiona M Martyn
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Brian Hallahan
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Benno Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Philip Mitchell
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Dara M Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
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6
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Qin K, Sweeney JA, DelBello MP. The inferior frontal gyrus and familial risk for bipolar disorder. PSYCHORADIOLOGY 2022; 2:171-179. [PMID: 38665274 PMCID: PMC10917220 DOI: 10.1093/psyrad/kkac022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 04/28/2024]
Abstract
Bipolar disorder (BD) is a familial disorder with high heritability. Genetic factors have been linked to the pathogenesis of BD. Relatives of probands with BD who are at familial risk can exhibit brain abnormalities prior to illness onset. Given its involvement in prefrontal cognitive control and in frontolimbic circuitry that regulates emotional reactivity, the inferior frontal gyrus (IFG) has been a focus of research in studies of BD-related pathology and BD-risk mechanism. In this review, we discuss multimodal neuroimaging findings of the IFG based on studies comparing at-risk relatives and low-risk controls. Review of these studies in at-risk cases suggests the presence of both risk and resilience markers related to the IFG. At-risk individuals exhibited larger gray matter volume and increased functional activities in IFG compared with low-risk controls, which might result from an adaptive brain compensation to support emotion regulation as an aspect of psychological resilience. Functional connectivity between IFG and downstream limbic or striatal areas was typically decreased in at-risk individuals relative to controls, which could contribute to risk-related problems of cognitive and emotional control. Large-scale and longitudinal investigations on at-risk individuals will further elucidate the role of IFG and other brain regions in relation to familial risk for BD, and together guide identification of at-risk individuals for primary prevention.
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Affiliation(s)
- Kun Qin
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
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Roberts G, Wen W, Ridgway K, Ho C, Gooch P, Leung V, Williams T, Breakspear M, Mitchell PB. Hippocampal cingulum white matter increases over time in young people at high genetic risk for bipolar disorder. J Affect Disord 2022; 314:325-332. [PMID: 35878837 DOI: 10.1016/j.jad.2022.07.025] [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: 02/20/2022] [Revised: 06/23/2022] [Accepted: 07/17/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a strongly familial psychiatric disorder associated with white matter (WM) brain abnormalities. It is unclear whether such abnormalities are present in relatives without BD, and little is known about WM trajectories in those at increased genetic risk. METHODS Diffusion magnetic resonance imaging (dMRI) data were acquired at baseline and after two years in 91 unaffected individuals with a first-degree relative with bipolar disorder (HR), and 85 individuals with no family history of mental illness (CON). All participants were aged between 12 and 30 years at baseline. We examined longitudinal change in Fractional Anisotropy (FA) using tract-based spatial statistics (TBSS). RESULTS Compared to the CON group, HR participants showed a significant increase in FA in the right cingulum (hippocampus) (CGH) over a two-year period (p < .05, FDR corrected). This effect was more pronounced in HR individuals without a lifetime diagnosis of a mood disorder than those with a mood disorder. LIMITATIONS While our study is well powered to achieve the primary objectives, our sub-group analyses were under powered. CONCLUSIONS In one of the very few longitudinal neuroimaging studies of young people at high risk for BD, this study reports novel evidence of atypical white matter development in HR individuals in a key cortico-limbic tract involved in emotion regulation. Our findings also suggest that this different white matter developmental trajectory may be stronger in HR individuals without affective psychopathology. As such, increases in FA in the right CGH of HR participants may be a biomarker of resilience to mood disorders.
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Affiliation(s)
- G Roberts
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia.
| | - W Wen
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
| | - K Ridgway
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
| | - C Ho
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
| | - P Gooch
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
| | - V Leung
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
| | - T Williams
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
| | - M Breakspear
- School of Psychology, Faculty of Science, Discipline of Psychiatry, Faculty of Health and Medicine, University of Newcastle, NSW, Australia
| | - P B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
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8
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Scholly J, Gras A, Guye M, Bilger M, Valenti Hirsch MP, Hirsch E, Timofeev A, Vidailhet P, Bénar CG, Bartolomei F. Connectivity Alterations in Emotional and Cognitive Networks During a Manic State Induced by Direct Electrical Stimulation. Brain Topogr 2022; 35:627-635. [PMID: 36071370 DOI: 10.1007/s10548-022-00913-0] [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: 11/19/2021] [Accepted: 08/27/2022] [Indexed: 11/28/2022]
Abstract
Mania is characterized by affective and cognitive alterations, with heightened external and self-awareness that are opposite to the alteration of awareness during epileptic seizures. Electrical stimulations carried out routinely during stereotactic intracerebral EEG (SEEG) recordings for presurgical evaluation of epilepsy may represent a unique opportunity to study the pathophysiology of such complex emotional-behavioral phenomenon, particularly difficult to reproduce in experimental setting. We investigated SEEG signals-based functional connectivity between different brain regions involved in emotions and in consciousness processing during a manic state induced by electrical stimulation in a patient with drug-resistant focal epilepsy. The stimulation inducing manic state and an asymptomatic stimulation of the same site, as well as a seizure with alteration of awareness (AOA) were analyzed. Functional connectivity analysis was performed by measuring interdependencies (nonlinear regression analysis based on the h2 coefficient) between broadband SEEG signals and within typical sub-bands, before and after stimulation, or before and during the seizure with AOA, respectively. Stimulation of the right lateral prefrontal cortex induced a manic state lasting several hours. Its onset was associated with significant increase of broadband-signal functional coupling between the right hemispheric limbic nodes, the temporal pole and the claustrum, whereas significant decorrelation between the right lateral prefrontal and the anterior cingulate cortex was observed in theta-band. In contrast, ictal alteration of awareness was associated with increased broadband and sub-bands synchronization within and between the internal and external awareness networks, including the anterior and middle cingulate, the mesial and lateral prefrontal, the inferior parietal and the temporopolar cortex. Our data suggest the existence of network- and frequency-specific functional connectivity patterns during manic state. A transient desynchronization of theta activity between the external and internal awareness network hubs is likely to increase awareness, with potential therapeutic effect.
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Affiliation(s)
- Julia Scholly
- Service d'Epileptologie et de Rythmololgie Cérébrale, Hôpital Timone, AP-HM, Marseille, France. .,Aix Marseille Univ, CNRS, CRMBM, Marseille, France. .,Service d'Epileptologie et Rythmologie Cérébrale, Hôpital Timone, AP-HM, 264 Rue St Pierre, 13005, Marseille, France.
| | - Adrien Gras
- Consultation-Liaison Psychiatry Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Maxime Guye
- Service d'Epileptologie et de Rythmololgie Cérébrale, Hôpital Timone, AP-HM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Mathias Bilger
- Medical and Surgical Epilepsy Unit, University Hospital of Strasbourg, Strasbourg, France
| | | | - Edouard Hirsch
- Medical and Surgical Epilepsy Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Alexander Timofeev
- Medical and Surgical Epilepsy Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Pierre Vidailhet
- Consultation-Liaison Psychiatry Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- Service d'Epileptologie et de Rythmololgie Cérébrale, Hôpital Timone, AP-HM, Marseille, France.,Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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9
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Phenotypes, mechanisms and therapeutics: insights from bipolar disorder GWAS findings. Mol Psychiatry 2022; 27:2927-2939. [PMID: 35351989 DOI: 10.1038/s41380-022-01523-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/02/2022] [Accepted: 03/10/2022] [Indexed: 12/25/2022]
Abstract
Genome-wide association studies (GWAS) have reported substantial genomic loci significantly associated with clinical risk of bipolar disorder (BD), and studies combining techniques of genetics, neuroscience, neuroimaging, and pharmacology are believed to help tackle clinical problems (e.g., identifying novel therapeutic targets). However, translating findings of psychiatric genetics into biological mechanisms underlying BD pathogenesis remains less successful. Biological impacts of majority of BD GWAS risk loci are obscure, and the involvement of many GWAS risk genes in this illness is yet to be investigated. It is thus necessary to review the progress of applying BD GWAS risk genes in the research and intervention of the disorder. A comprehensive literature search found that a number of such risk genes had been investigated in cellular or animal models, even before they were highlighted in BD GWAS. Intriguingly, manipulation of many BD risk genes (e.g., ANK3, CACNA1C, CACNA1B, HOMER1, KCNB1, MCHR1, NCAN, SHANK2 etc.) resulted in altered murine behaviors largely restoring BD clinical manifestations, including mania-like symptoms such as hyperactivity, anxiolytic-like behavior, as well as antidepressant-like behavior, and these abnormalities could be attenuated by mood stabilizers. In addition to recapitulating phenotypic characteristics of BD, some GWAS risk genes further provided clues for the neurobiology of this illness, such as aberrant activation and functional connectivity of brain areas in the limbic system, and modulated dendritic spine morphogenesis as well as synaptic plasticity and transmission. Therefore, BD GWAS risk genes are undoubtedly pivotal resources for modeling this illness, and might be translational therapeutic targets in the future clinical management of BD. We discuss both promising prospects and cautions in utilizing the bulk of useful resources generated by GWAS studies. Systematic integrations of findings from genetic and neuroscience studies are called for to promote our understanding and intervention of BD.
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10
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Wang B, Zhang S, Yu X, Niu Y, Niu J, Li D, Zhang S, Xiang J, Yan T, Yang J, Wu J, Liu M. Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder. Hum Brain Mapp 2022; 43:3909-3922. [PMID: 35567336 PMCID: PMC9374889 DOI: 10.1002/hbm.25892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/17/2022] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
Emerging evidence suggests white matter network abnormalities in patients with schizophrenia (SZ) and bipolar disorder (BD), but the alterations in dynamics of the white matter network in patients with SZ and BD are largely unknown. The white matter network of patients with SZ (n = 45) and BD (n = 47) and that of healthy controls (HC, n = 105) were constructed. We used dynamics network control theory to quantify the dynamics metrics of the network, including controllability and synchronizability, to measure the ability to transfer between different states. Experiments show that the patients with SZ and BD showed decreasing modal controllability and synchronizability and increasing average controllability. The correlations between the average controllability and synchronizability of patients were broken, especially for those with SZ. The patients also showed alterations in brain regions with supercontroller roles and their distribution in the cognitive system. Finally, we were able to accurately discriminate and predict patients with SZ and BD. Our findings provide novel dynamic metrics evidence that patients with SZ and BD are characterized by a selective disruption of brain network controllability, potentially leading to reduced brain state transfer capacity, and offer new guidance for the clinical diagnosis of mental illness.
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Affiliation(s)
- Bin Wang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shanshan Zhang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xuexue Yu
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jinliang Niu
- Department of Medical Imaging, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Dandan Li
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shan Zhang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Jiajia Yang
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama, Japan
| | - Jinglong Wu
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama, Japan
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, Shenzhen, China
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11
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Roberts G, Perry A, Ridgway K, Leung V, Campbell M, Lenroot R, Mitchell PB, Breakspear M. Longitudinal Changes in Structural Connectivity in Young People at High Genetic Risk for Bipolar Disorder. Am J Psychiatry 2022; 179:350-361. [PMID: 35343756 DOI: 10.1176/appi.ajp.21010047] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Recent studies of patients with bipolar disorder or at high genetic risk reveal structural dysconnections among key brain networks supporting cognitive and affective processes. Understanding the longitudinal trajectories of these networks across the peak age range of bipolar disorder onset could inform mechanisms of illness onset or resilience. METHODS Longitudinal diffusion-weighted MRI and phenotypic data were acquired at baseline and after 2 years in 183 individuals ages 12-30 years in two cohorts: 97 unaffected individuals with a first-degree relative with bipolar disorder (the high-risk group) and 86 individuals with no family history of mental illness (the control group). Whole-brain structural networks were derived using tractography, and longitudinal changes in these networks were studied using network-based statistics and mixed linear models. RESULTS Both groups showed widespread longitudinal changes, comprising both increases and decreases in structural connectivity, consistent with a shared neurodevelopmental process. On top of these shared changes, high-risk participants showed weakening of connectivity in a network encompassing the left inferior and middle frontal areas, left striatal and thalamic structures, the left fusiform, and right parietal and occipital regions. Connections among these regions strengthened in the control group, whereas they weakened in the high-risk group, shifting toward a cohort with established bipolar disorder. There was marginal evidence for even greater network weakening in those who had their first manic or hypomanic episode before follow-up. CONCLUSIONS Neurodevelopment from adolescence into early adulthood is associated with a substantial reorganization of structural brain networks. Differences in these maturational processes occur in a multisystem network in individuals at high genetic risk of bipolar disorder. This may represent a novel candidate to understand resilience and predict conversion to bipolar disorder.
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Affiliation(s)
- Gloria Roberts
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Alistair Perry
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Kate Ridgway
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Vivian Leung
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Megan Campbell
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Rhoshel Lenroot
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
| | - Michael Breakspear
- School of Psychiatry, University of New South Wales, Randwick, Australia (Roberts, Ridgway, Leung, Mitchell); Department of Clinical Neurosciences, University of Cambridge, and Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, U.K. (Perry); Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, U.K. (Perry); QIMR Berghofer Medical Research Institute, Brisbane, Australia (Perry, Breakspear); School of Psychology, College of Science, and Discipline of Psychiatry, College of Health and Medicine, University of Newcastle, Newcastle, Australia (Campbell, Breakspear); Neuroscience Research Australia, Randwick, Australia (Lenroot); University of New Mexico, Albuquerque (Lenroot)
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12
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Morán-Kneer J, Ríos U, Costa-Cordella S, Barría C, Carvajal V, Valenzuela K, Wasserman D. Childhood Trauma and Social Cognition in participants with Bipolar Disorder: The Moderating Role of Attachment. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2022. [DOI: 10.1016/j.jadr.2022.100359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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13
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Roberts G, Lenroot R, Overs B, Fullerton J, Leung V, Ridgway K, Stuart A, Frankland A, Levy F, Hadzi-Pavlovic D, Breakspear M, Mitchell PB. Accelerated cortical thinning and volume reduction over time in young people at high genetic risk for bipolar disorder. Psychol Med 2022; 52:1344-1355. [PMID: 32892764 DOI: 10.1017/s0033291720003153] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a familial psychiatric disorder associated with frontotemporal and subcortical brain abnormalities. It is unclear whether such abnormalities are present in relatives without BD, and little is known about structural brain trajectories in those at risk. METHOD Neuroimaging was conducted at baseline and at 2-year follow-up interval in 90 high-risk individuals with a first-degree BD relative (HR), and 56 participants with no family history of mental illness who could have non-BD diagnoses. All 146 subjects were aged 12-30 years at baseline. We examined longitudinal change in gray and white matter volume, cortical thickness, and surface area in the frontotemporal cortex and subcortical regions. RESULTS Compared to controls, HR participants showed accelerated cortical thinning and volume reduction in right lateralised frontal regions, including the inferior frontal gyrus, lateral orbitofrontal cortex, frontal pole and rostral middle frontal gyrus. Independent of time, the HR group had greater cortical thickness in the left caudal anterior cingulate cortex, larger volume in the right medial orbitofrontal cortex and greater area of right accumbens, compared to controls. This pattern was evident even in those without the new onset of psychopathology during the inter-scan interval. CONCLUSIONS This study suggests that differences previously observed in BD are developing prior to the onset of the disorder. The pattern of pathological acceleration of cortical thinning is likely consistent with a disturbance of molecular mechanisms responsible for normal cortical thinning. We also demonstrate that neuroanatomical differences in HR individuals may be progressive in some regions and stable in others.
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Affiliation(s)
- G Roberts
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - R Lenroot
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Medicine, University of New Mexico, Albuquerque, New Mexico
| | - B Overs
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - J Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - V Leung
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - K Ridgway
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - A Stuart
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - A Frankland
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - F Levy
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Prince of Wales Hospital, Randwick, NSW, Australia
| | - D Hadzi-Pavlovic
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - M Breakspear
- School of psychology, University of Newcastle, Callaghan, NSW, Australia
| | - P B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
- Prince of Wales Hospital, Randwick, NSW, Australia
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14
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Cha J, Spielberg JM, Hu B, Altinay M, Anand A. Differences in network properties of the structural connectome in bipolar and unipolar depression. Psychiatry Res Neuroimaging 2022; 321:111442. [PMID: 35152051 PMCID: PMC10577577 DOI: 10.1016/j.pscychresns.2022.111442] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Differentiation between Bipolar Disorder Depression (BDD) and Unipolar Major Depressive Disorder (MDD) is critical to clinical practice. This study investigated machine learning classification of BDD and MDD using graph properties of Diffusion-weighted Imaging (DWI)-based structural connectome. METHODS This study included a large number of medication-free (N =229) subjects: 60 BDD, 95 MDD, and 74 Healthy Control (HC) subjects. DWI probabilistic tractography was performed to create Fractional Anisotropy (FA) and Total Streamline (TS)-based structural connectivity matrices. Global and nodal graph properties were computed from these matrices and tested for group differences. Next, using identified graph properties, machine learning classification (MLC) between BDD, MDD, MDD with risk factors for developing BD (MDD+), and MDD without risk factors for developing BD (MDD-) was conducted. RESULTS Communicability Efficiency of the left superior frontal gyrus (SFG) was significantly higher in BDD vs. MDD. In particular, Communicability Efficiency using TS-based connectivity in the left SFG as well as FA-based connectivity in the right middle anterior cingulate area was higher in the BDD vs. MDD- group. There were no significant differences in graph properties between BDD and MDD+. Direct comparison between MDD+ and MDD- showed differences in Eigenvector Centrality (TS-based connectivity) of the left middle frontal sulcus. Acceptable Area Under Curve (AUC) for classification were seen between the BDD and MDD- groups, and between the MDD+ and MDD- groups, using the differing graph properties. CONCLUSION Graph properties of DWI-based connectivity can discriminate between BDD and MDD subjects without risk factors for BD.
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Affiliation(s)
- Jungwon Cha
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, USA; Center for Behavioral Health, Cleveland Clinic, USA
| | | | - Bo Hu
- Center for Quantitative Health Sciences, Cleveland Clinic, USA
| | | | - Amit Anand
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, USA; Center for Behavioral Health, Cleveland Clinic, USA
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15
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Maliske L, Kanske P. The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience. Front Psychiatry 2022; 13:845492. [PMID: 35449570 PMCID: PMC9016142 DOI: 10.3389/fpsyt.2022.845492] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.
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Affiliation(s)
- Lara Maliske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
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16
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Calcium imaging reveals depressive- and manic-phase-specific brain neural activity patterns in a murine model of bipolar disorder: a pilot study. Transl Psychiatry 2021; 11:619. [PMID: 34876553 PMCID: PMC8651770 DOI: 10.1038/s41398-021-01750-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/18/2021] [Accepted: 11/29/2021] [Indexed: 12/25/2022] Open
Abstract
Brain pathological features during manic/hypomanic and depressive episodes in the same patients with bipolar disorder (BPD) have not been described precisely. The study aimed to investigate depressive and manic-phase-specific brain neural activity patterns of BPD in the same murine model to provide information guiding investigation of the mechanism of phase switching and tailored prevention and treatment for patients with BPD. In vivo two-photon imaging was used to observe brain activity alterations in the depressive and manic phases in the same murine model of BPD. Two-photon imaging showed significantly reduced Ca2+ activity in temporal cortex pyramidal neurons in the depression phase in mice exposed to chronic unpredictable mild stress (CUMS), but not in the manic phase in mice exposed to CUMS and ketamine. Total integrated calcium values correlated significantly with immobility times. Brain Ca2+ hypoactivity was observed in the depression and manic phases in the same mice exposed to CUMS and ketamine relative to naïve controls. The novel object recognition preference ratio correlated negatively with the immobility time in the depression phase and the total distance traveled in the manic phase. With recognition of its limitations, this study revealed brain neural activity impairment indicating that intrinsic emotional network disturbance is a mechanism of BPD and that brain neural activity is associated with cognitive impairment in the depressive and manic phases of this disorder. These findings are consistent with those from macro-imaging studies of patients with BPD. The observed correlation of brain neural activity with the severity of depressive, but not manic, symptoms need to be investigated further.
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17
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Nabulsi L, McPhilemy G, O'Donoghue S, Cannon DM, Kilmartin L, O'Hora D, Sarrazin S, Poupon C, D'Albis MA, Versace A, Delavest M, Linke J, Wessa M, Phillips ML, Houenou J, McDonald C. Aberrant Subnetwork and Hub Dysconnectivity in Adult Bipolar Disorder: A Multicenter Graph Theory Analysis. Cereb Cortex 2021; 32:2254-2264. [PMID: 34607352 DOI: 10.1093/cercor/bhab356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/14/2022] Open
Abstract
Neuroimaging evidence implicates structural network-level abnormalities in bipolar disorder (BD); however, there remain conflicting results in the current literature hampered by sample size limitations and clinical heterogeneity. Here, we set out to perform a multisite graph theory analysis to assess the extent of neuroanatomical dysconnectivity in a large representative study of individuals with BD. This cross-sectional multicenter international study assessed structural and diffusion-weighted magnetic resonance imaging data obtained from 109 subjects with BD type 1 and 103 psychiatrically healthy volunteers. Whole-brain metrics, permutation-based statistics, and connectivity of highly connected nodes were used to compare network-level connectivity patterns in individuals with BD compared with controls. The BD group displayed longer characteristic path length, a weakly connected left frontotemporal network, and increased rich-club dysconnectivity compared with healthy controls. Our multisite findings implicate emotion and reward networks dysconnectivity in bipolar illness and may guide larger scale global efforts in understanding how human brain architecture impacts mood regulation in BD.
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Affiliation(s)
- Leila Nabulsi
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway H91 TK33, Ireland.,Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Genevieve McPhilemy
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway H91 TK33, Ireland
| | - Stefani O'Donoghue
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway H91 TK33, Ireland
| | - Dara M Cannon
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway H91 TK33, Ireland
| | - Liam Kilmartin
- College of Engineering and Informatics, National University of Ireland Galway, Galway H91 TK33, Ireland
| | - Denis O'Hora
- School of Psychology, National University of Ireland Galway, Galway H91 TK33, Ireland
| | - Samuel Sarrazin
- APHP, Hôpitaux Universitaires Mondor, Pôle de psychiatrie, DHU PePsy, INSERM U955, Equipe 15, Faculté de medicine de Créteil, Université Paris Est, Créteil, France.,NeuroSpin, CEA Saclay, Gif-Sur-Yvette, France
| | | | - Marc-Antoine D'Albis
- APHP, Hôpitaux Universitaires Mondor, Pôle de psychiatrie, DHU PePsy, INSERM U955, Equipe 15, Faculté de medicine de Créteil, Université Paris Est, Créteil, France.,NeuroSpin, CEA Saclay, Gif-Sur-Yvette, France
| | - Amelia Versace
- Department of Psychiatry, Pittsburgh University Medicine School, Pittsburgh, PA, USA.,Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, PA, USA
| | - Marine Delavest
- APHP, GH Fernand Widal-Lariboisière, Service de psychiatrie, Paris, France
| | - Julia Linke
- Department of Clinical Psychology and Neuropsychology, Institute for Psychology, Johannes Gutenberg-University Mainz, Wallstraße 3, Mainz 55122, Germany
| | - Michèle Wessa
- Department of Clinical Psychology and Neuropsychology, Institute for Psychology, Johannes Gutenberg-University Mainz, Wallstraße 3, Mainz 55122, Germany
| | - Mary L Phillips
- Department of Psychiatry, Pittsburgh University Medicine School, Pittsburgh, PA, USA.,Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, PA, USA
| | - Josselin Houenou
- APHP, Hôpitaux Universitaires Mondor, Pôle de psychiatrie, DHU PePsy, INSERM U955, Equipe 15, Faculté de medicine de Créteil, Université Paris Est, Créteil, France.,NeuroSpin, CEA Saclay, Gif-Sur-Yvette, France
| | - Colm McDonald
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Lab, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway H91 TK33, Ireland
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18
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Association between symbol digit modalities test and regional cortex thickness in young adults with relapsing-remitting multiple sclerosis. Clin Neurol Neurosurg 2021; 207:106805. [PMID: 34280674 DOI: 10.1016/j.clineuro.2021.106805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) is a demyelinating disease of the central nervous system, predominating within young adults. Cognitive disorders are common in MS and have are associated with several Magnetic Resonance Imaging (MRI) markers, especially brain atrophy. Many have found the symbol digit modalities test (SDMT) to be the most sensitive individual cognitive measure relevant to MS. However, the relationship between SDMT and regional brain cortex thickness in young adults with relapsing-remitting multiple sclerosis (YA-RRMS) has been little explored. The purpose of this study was to investigate the association between the SDMT and regional cortex thickness in YA-RRMS by FreeSurfer, which is an automatic brain structure segmentation method. METHOD Twenty-eight YA-RRMS patients (18-35 years old) were enrolled in the present study. Informed consent and information including gender, age, disease duration, number of relapses, annual relapse rate was collected from all patients. Clinical cognitive evaluations (SDMT and auditory verbal learning test (AVLT)) and daily performance: activities of daily living (ADL) were assessed in the present study. MRI scans were performed at the Institute of Neurosurgery of Tiantan Hospital. Twenty-eight matched healthy controls (HC) MRI data were obtained from Tiantan Hospital database. Data on thirty-four points of bilateral cortical structure thickness using statistically defined brain regions-of-interest from FreeSurfer were obtained from all participants. RESULTS Patients with RRMS exhibited extensively thinner cerebellar cortex compared with HC. SDMT scores were significantly correlated with AVLT subentries (IM, immediate memory; DRM, delayed recall memory; LTRM, long-term recognition memory) in YA-RRMS patients (P < 0.05). SDMT was strongly correlated with regional cortex thickness differences of the right temporal pole (r = 0.68) and bilateral parahippocampal areas (right r = 0.62; left r = 0.60), and moderately correlated with regional cortex thickness differences including the left superior temporal and right insula (r = 0.57 and 0.56, respectively) in YA-RRMS patients. CONCLUSION The present study has shown the SDMT is strongly correlated with selected cortex regions including the bilateral parahippocampal area and the right temporal pole which are involved in geometric structures processing.
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19
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Song W, Qian W, Wang W, Yu S, Lin GN. Mendelian randomization studies of brain MRI yield insights into the pathogenesis of neuropsychiatric disorders. BMC Genomics 2021; 22:342. [PMID: 34078268 PMCID: PMC8171058 DOI: 10.1186/s12864-021-07661-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 04/29/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Observational studies have identified various associations between neuroimaging alterations and neuropsychiatric disorders. However, whether such associations could truly reflect causal relations remains still unknown. RESULTS Here, we leveraged genome-wide association studies (GWAS) summary statistics for (1) 11 psychiatric disorders (sample sizes varied from n = 9,725 to 1,331,010); (2) 110 diffusion tensor imaging (DTI) measurement (sample size n = 17,706); (3) 101 region-of-interest (ROI) volumes, and investigate the causal relationship between brain structures and neuropsychiatric disorders by two-sample Mendelian randomization. Among all DTI-Disorder combinations, we observed a significant causal association between the superior longitudinal fasciculus (SLF) and the risk of Anorexia nervosa (AN) (Odds Ratio [OR] = 0.62, 95 % confidence interval: 0.50 ~ 0.76, P = 6.4 × 10- 6). Similar significant associations were also observed between the body of the corpus callosum (fractional anisotropy) and Alzheimer's disease (OR = 1.07, 95 % CI: 1.03 ~ 1.11, P = 4.1 × 10- 5). By combining all observations, we found that the overall p-value for DTI - Disorder associations was significantly elevated compared to the null distribution (Kolmogorov-Smirnov P = 0.009, inflation factor λ = 1.37), especially for DTI - Bipolar disorder (BP) (λ = 2.64) and DTI - AN (λ = 1.82). In contrast, for ROI-Disorder combinations, we only found a significant association between the brain region of pars triangularis and Schizophrenia (OR = 0.48, 95 % CI: 0.34 ~ 0.69, P = 5.9 × 10- 5) and no overall p-value elevation for ROI-Disorder analysis compared to the null expectation. CONCLUSIONS As a whole, we show that SLF degeneration may be a risk factor for AN, while DTI variations could be causally related to some neuropsychiatric disorders, such as BP and AN. Also, the white matter structure might have a larger impact on neuropsychiatric disorders than subregion volumes.
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Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Wei Qian
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Weidi Wang
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 200030, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, 200030, Shanghai, China
| | - Shunying Yu
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 200030, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, 200030, Shanghai, China
| | - Guan Ning Lin
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 200030, Shanghai, China.
- Shanghai Key Laboratory of Psychotic Disorders, 200030, Shanghai, China.
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20
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Abstract
Objective: Previous studies have shown differences in the regional brain structure and function between patients with bipolar disorder (BD) and healthy subjects, but little is known about the structural connectivity between BD patients and healthy subjects. In this study, we evaluated the disease-related changes in regional structural connectivity derived from gray matter magnetic resonance imaging (MRI) scans. Methods: The subjects were 73 patients with BD and 80 healthy volunteers who underwent 3-Tesla MRI. Network metrics, such as the small world properties, were computed. We also performed rendering of the network metric images such as the degree, betweenness centrality, and clustering coefficient, on individual brain image. Then, we estimated the differences between them, and evaluate the relationships between the clinical symptoms and the network metrics in the patients with BD. Results: BD patients showed a lower clustering coefficient in the right parietal region and left occipital region, compared with healthy subjects. A weak negative correlation between Young mania rating scale and clustering coefficient was found in left anterior cingulate cortex. Conclusions: We found differences in gray matter structural connectivity between BD patients and healthy subjects by a similarity-based approach. These points may provide objective biological information as an adjunct to the clinical diagnosis of BD.
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21
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Qin K, Lei D, Yang J, Li W, Tallman MJ, Duran LRP, Blom TJ, Bruns KM, Cotton S, Sweeney JA, Gong Q, DelBello MP. Network-level functional topological changes after mindfulness-based cognitive therapy in mood dysregulated adolescents at familial risk for bipolar disorder: a pilot study. BMC Psychiatry 2021; 21:213. [PMID: 33910549 PMCID: PMC8080341 DOI: 10.1186/s12888-021-03211-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/09/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Given that psychopharmacological approaches routinely used to treat mood-related problems may result in adverse outcomes in mood dysregulated adolescents at familial risk for bipolar disorder (BD), Mindfulness-Based Cognitive Therapy for Children (MBCT-C) provides an alternative effective and safe option. However, little is known about the brain mechanisms of beneficial outcomes from this intervention. Herein, we aimed to investigate the network-level neurofunctional effects of MBCT-C in mood dysregulated adolescents. METHODS Ten mood dysregulated adolescents at familial risk for BD underwent a 12-week MBCT-C intervention. Resting-state functional magnetic resonance imaging (fMRI) was performed prior to and following MBCT-C. Topological metrics of three intrinsic functional networks (default mode network (DMN), fronto-parietal network (FPN) and cingulo-opercular network (CON)) were investigated respectively using graph theory analysis. RESULTS Following MBCT-C, mood dysregulated adolescents showed increased global efficiency and decreased characteristic path length within both CON and FPN. Enhanced functional connectivity strength of frontal and limbic areas were identified within the DMN and CON. Moreover, change in characteristic path length within the CON was suggested to be significantly related to change in the Emotion Regulation Checklist score. CONCLUSIONS 12-week MBCT-C treatment in mood dysregulated adolescents at familial risk for BD yield network-level neurofunctional effects within the FPN and CON, suggesting enhanced functional integration of the dual-network. Decreased characteristic path length of the CON may be associated with the improvement of emotion regulation following mindfulness training. However, current findings derived from small sample size should be interpreted with caution. Future randomized controlled trials including larger samples are critical to validate our findings.
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Affiliation(s)
- Kun Qin
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Du Lei
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Jing Yang
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wenbin Li
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Maxwell J. Tallman
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Luis Rodrigo Patino Duran
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Thomas J. Blom
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Kaitlyn M. Bruns
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Sian Cotton
- grid.24827.3b0000 0001 2179 9593Department of Family and Community Medicine, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - John A. Sweeney
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China. .,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China. .,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China.
| | - Melissa P. DelBello
- grid.24827.3b0000 0001 2179 9593Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH USA
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22
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Lou C, Cross AM, Peters L, Ansari D, Joanisse MF. Rich-club structure contributes to individual variance of reading skills via feeder connections in children with reading disabilities. Dev Cogn Neurosci 2021; 49:100957. [PMID: 33894677 PMCID: PMC8093404 DOI: 10.1016/j.dcn.2021.100957] [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] [Received: 10/14/2020] [Revised: 03/29/2021] [Accepted: 04/15/2021] [Indexed: 01/18/2023] Open
Abstract
The present work considers how connectome-wide differences in brain organization might distinguish good and poor readers. The connectome comprises a ‘rich-club’ organization in which a small number of hub regions play a focal role in assisting global communication across the whole brain. Prior work indicates that this rich-club structure is associated with typical and impaired cognitive function although no work so far has examined how this relates to skilled reading or its disorders. Here we investigated the rich-club structure of brain’s white matter connectome and its relationship to reading subskills in 64 children with and without reading disabilities. Among three types of white matter connections, the strength of feeder connections that connect hub and non-hub nodes was significantly correlated with word reading efficiency and phonemic decoding. Phonemic decoding was also positively correlated with connectivity between connectome-wide hubs and nodes within the left-hemisphere reading network, as well as the local efficiency of the reading network. Exploratory analyses also identified sex differences indicating these effects were stronger in girls. This work highlights the independent roles of connectome-wide structure and the more narrowly-defined reading network in understanding the neural bases of skilled and impaired reading in children.
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Affiliation(s)
- Chenglin Lou
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada.
| | - Alexandra M Cross
- Brain and Mind Institute, The University of Western Ontario, London, Canada; Health and Rehabilitation Sciences, The University of Western Ontario, London, Canada
| | - Lien Peters
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada
| | - Daniel Ansari
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada; Faculty of Education, The University of Western Ontario, London, Canada
| | - Marc F Joanisse
- Department of Psychology, The University of Western Ontario, London, Canada; Brain and Mind Institute, The University of Western Ontario, London, Canada; Haskins Laboratories, New Haven, CT, USA
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23
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Collantoni E, Meneguzzo P, Tenconi E, Meregalli V, Manara R, Favaro A. Shift Toward Randomness in Brain Networks of Patients With Anorexia Nervosa: The Role of Malnutrition. Front Neurosci 2021; 15:645139. [PMID: 33841085 PMCID: PMC8024518 DOI: 10.3389/fnins.2021.645139] [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] [Received: 12/22/2020] [Accepted: 02/15/2021] [Indexed: 01/12/2023] Open
Abstract
No study to date investigated structural white matter (WM) connectome characteristics in patients with anorexia nervosa (AN). Previous research in AN found evidence of imbalances in global and regional connectomic brain architecture and highlighted a role of malnutrition in determining structural brain changes. The aim of our study was to explore the characteristics of the WM network architecture in a sample of patients with AN. Thirty-six patients with AN and 36 healthy women underwent magnetic resonance imaging to obtain a high-resolution three-dimensional T1-weighted anatomical image and a diffusion tensor imaging scan. Probabilistic tractography data were extracted and analyzed in their network properties through graph theory tools. In comparison to healthy women, patients with AN showed lower global network segregation (normalized clustering: p = 0.029), an imbalance between global network integration and segregation (i.e., lower small-worldness: p = 0.031), and the loss of some of the most integrative and influential hubs. Both clustering and small-worldness correlated with the lowest lifetime body mass index. A significant relationship was found between the average regional loss of cortical volume and changes in network properties of brain nodes: the more the difference in the cortical volume of brain areas, the more the increase in the centrality of corresponding nodes in the whole brain, and the decrease in clustering and efficiency of the nodes of parietal cortex. Our findings showed an unbalanced connectome wiring in AN patients, which seems to be influenced by malnutrition and loss of cortical volume. The role of this rearrangement in the maintenance and prognosis of AN and its reversibility with clinical improvement needs to be established by future studies.
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Affiliation(s)
| | - Paolo Meneguzzo
- Department of Neurosciences, University of Padua, Padua, Italy
| | - Elena Tenconi
- Department of Neurosciences, University of Padua, Padua, Italy.,Padova Neuroscience Center, University of Padua, Padua, Italy
| | | | - Renzo Manara
- Department of Neurosciences, University of Padua, Padua, Italy
| | - Angela Favaro
- Department of Neurosciences, University of Padua, Padua, Italy.,Padova Neuroscience Center, University of Padua, Padua, Italy
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24
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Bora E, Can G, Zorlu N, Ulas G, Inal N, Ozerdem A. Structural dysconnectivity in offspring of individuals with bipolar disorder: The effect of co-existing clinical-high-risk for bipolar disorder. J Affect Disord 2021; 281:109-116. [PMID: 33310660 DOI: 10.1016/j.jad.2020.11.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/22/2020] [Accepted: 11/26/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND Bipolar disorder (BD) might be associated in disturbances in brain networks. However, little is known about the abnormalities in structural brain connectivity which might be related to vulnerability to BD and predictive of the emergence of manic symptoms. No previous study has investigated the effect of subthreshold syndromes on structural dysconnectivity in offspring of parents with BD (BDoff). METHODS We investigated diffusion weighted images of 70 BDoff and 48 healthy controls (HC). Nineteen of the 70 BDoff had presented with subthreshold syndromes indicating a clinical high-risk (BDoff-CHR) and other 51 BDoff had no such history (BDoff-non-CHR). Global and regional network properties, rich club organization and inter-regional connectivity in BDoff and healthy controls were investigated using graph analytical methods and network-based-statistics (NBS). RESULTS Global properties of WM networks appeared to be intact in BDoff-CHR and BDoff-non-CHR. However, decreased regional connectivity in right occipito-parietal areas and cerebellum was a common feature of both BDoff groups. Importantly, decreased interregional connectivity between nodes in right and left prefrontal regions, nodes in right prefrontal lobe and right temporal lobe and nodes in left occipital area and left cerebellum were evident in BDoff-CHR but not BDoff-non-CHR. LIMITATIONS The cross-sectional nature of the study was the main consideration. CONCLUSION Decreased regional connectivity in right posterior brain regions might be related to vulnerability to BD. On the other hand, interregional dysconnectivity in anterior frontal and limbic regions and left posterior brain regions might be evident in individuals genetically at risk for developing BD who had experienced subthreshold mood symptoms.
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Affiliation(s)
- Emre Bora
- Dokuz Eylul University, Faculty of Medicine, Department of Psychiatry, Izmir, Turkey; Dokuz Eylul University, Institute of Neuroscience, Izmir, Turkey; Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne.
| | - Gunes Can
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey
| | - Nabi Zorlu
- Department of Psychiatry, Faculty of Medicine, Ataturk Training and Research Hospital, Izmir Katip Celebi University, İzmir, Turkey
| | - Gozde Ulas
- Department of Child and Adolescent Psychiatry, Tepecik Research and Training Hospital, İzmir, Turkey
| | - Neslihan Inal
- Dokuz Eylul University Faculty of Medicine, Department of Child and Adolescent Psychiatry, Izmir, Turkey
| | - Aysegul Ozerdem
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
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25
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van Dellen E, Börner C, Schutte M, van Montfort S, Abramovic L, Boks MP, Cahn W, van Haren N, Mandl R, Stam CJ, Sommer I. Functional brain networks in the schizophrenia spectrum and bipolar disorder with psychosis. NPJ SCHIZOPHRENIA 2020; 6:22. [PMID: 32879316 PMCID: PMC7468123 DOI: 10.1038/s41537-020-00111-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/23/2020] [Indexed: 12/22/2022]
Abstract
Psychotic experiences have been proposed to lie on a spectrum, ranging from subclinical experiences to treatment-resistant schizophrenia. We aimed to characterize functional connectivity and brain network characteristics in relation to the schizophrenia spectrum and bipolar disorder with psychosis to disentangle neural correlates to psychosis. Additionally, we studied antipsychotic medication and lithium effects on network characteristics. We analyzed functional connectivity strength and network topology in 487 resting-state functional MRI scans of individuals with schizophrenia spectrum disorder (SCZ), bipolar disorder with a history of psychotic experiences (BD), treatment-naïve subclinical psychosis (SCP), and healthy controls (HC). Since differences in connectivity strength may confound group comparisons of brain network topology, we analyzed characteristics of the minimum spanning tree (MST), a relatively unbiased backbone of the network. SCZ and SCP subjects had a lower connectivity strength than BD and HC individuals but showed no differences in network topology. In contrast, BD patients showed a less integrated network topology but no disturbances in connectivity strength. No differences in outcome measures were found between SCP and SCZ, or between BD patients that used antipsychotic medication or lithium and those that did not. We conclude that functional networks in patients prone to psychosis have different signatures for chronic SCZ patients and SCP compared to euthymic BD patients, with a limited role for medication. Connectivity strength effects may have confounded previous studies, as no functional network alterations were found in SCZ after strict correction for connectivity strength.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
- Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Corinna Börner
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maya Schutte
- University of Groningen, Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - Simone van Montfort
- Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lucija Abramovic
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marco P Boks
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center, Sophia Children's Hospital, Rotterdam, The Netherlands
| | - René Mandl
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Iris Sommer
- University of Groningen, Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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26
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Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
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Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
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Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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27
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Heinze K, Shen X, Hawkins E, Harris MA, de Nooij L, McIntosh AM, Wood SJ, Whalley HC. Aberrant structural covariance networks in youth at high familial risk for mood disorder. Bipolar Disord 2020; 22:155-162. [PMID: 31724284 PMCID: PMC7155114 DOI: 10.1111/bdi.12868] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Current research suggests significant disruptions in functional brain networks in individuals with mood disorder, and in those at familial risk. Studies of structural brain networks provide important insights into synchronized maturational change but have received less attention. We aimed to investigate developmental relationships of large-scale brain networks in mood disorder using structural covariance (SC) analyses. METHODS We conducted SC analysis of baseline structural imaging data from 121 at the time of scanning unaffected high risk (HR) individuals (29 later developed mood disorder after a median time of 4.95 years), and 89 healthy controls (C-well) with no familial risk from the Scottish Bipolar Family Study (age 15-27, 64% female). Voxel-wise analyses of covariance were conducted to compare the associations between each seed region in visual, auditory, motor, speech, semantic, executive-control, salience and default-mode networks and the whole brain signal. SC maps were compared for (a) HR(all) versus C-well individuals, and (b) between those who remained well (HR-well), versus those who subsequently developed mood disorder (HR-MD), and C-well. RESULTS There were no significant differences between HR(all) and C-well individuals. On splitting the HR group based on subsequent clinical outcome, the HR-MD group however displayed greater baseline SC in the salience and executive-control network, and HR-well individuals showed less SC in the salience network, compared to C-well, respectively (P < .001). CONCLUSIONS These findings indicate differences in network-level inter-regional relationships, especially within the salience network, which precede onset of mood disorder in those at familial risk.
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Affiliation(s)
- Kareen Heinze
- School of PsychologyUniversity of BirminghamBirminghamUK,Institute for Mental HealthUniversity of BirminghamBirminghamUK,Centre for Human Brain HealthUniversity of BirminghamBirminghamUK
| | - Xueyi Shen
- Division of PsychiatryUniversity of EdinburghEdinburghUK
| | - Emma Hawkins
- Division of PsychiatryUniversity of EdinburghEdinburghUK
| | | | - Laura de Nooij
- Division of PsychiatryUniversity of EdinburghEdinburghUK
| | | | - Stephen J. Wood
- School of PsychologyUniversity of BirminghamBirminghamUK,Institute for Mental HealthUniversity of BirminghamBirminghamUK,Orygen, The National Centre of Excellence in Youth Mental HealthMelbourneVic.Australia,Centre for Youth Mental HealthUniversity of MelbourneMelbourneVic.Australia
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28
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Nabulsi L, McPhilemy G, Kilmartin L, O'Hora D, O'Donoghue S, Forcellini G, Najt P, Ambati S, Costello L, Byrne F, McLoughlin J, Hallahan B, McDonald C, Cannon DM. Bipolar Disorder and Gender Are Associated with Frontolimbic and Basal Ganglia Dysconnectivity: A Study of Topological Variance Using Network Analysis. Brain Connect 2019; 9:745-759. [PMID: 31591898 DOI: 10.1089/brain.2019.0667] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Well-established structural abnormalities, mostly involving the limbic system, have been associated with disorders of emotion regulation. Understanding the arrangement and connections of these regions with other functionally specialized cortico-subcortical subnetworks is key to understanding how the human brain's architecture underpins abnormalities of mood and emotion. We investigated topological patterns in bipolar disorder (BD) with the anatomically improved precision conferred by combining subject-specific parcellation/segmentation with nontensor-based tractograms derived using a high-angular resolution diffusion-weighted approach. Connectivity matrices were constructed using 34 cortical and 9 subcortical bilateral nodes (Desikan-Killiany), and edges that were weighted by fractional anisotropy and streamline count derived from deterministic tractography using constrained spherical deconvolution. Whole-brain and rich-club connectivity alongside a permutation-based statistical approach was used to investigate topological variance in predominantly euthymic BD relative to healthy volunteers. BP patients (n = 40) demonstrated impairments across whole-brain topological arrangements (density, degree, and efficiency), and a dysconnected subnetwork involving limbic and basal ganglia relative to controls (n = 45). Increased rich-club connectivity was most evident in females with BD, with frontolimbic and parieto-occipital nodes not members of BD rich-club. Increased centrality in females relative to males was driven by basal ganglia and fronto-temporo-limbic nodes. Our subject-specific cortico-subcortical nontensor-based connectome map presents a neuroanatomical model of BD dysconnectivity that differentially involves communication within and between emotion-regulatory and reward-related subsystems. Moreover, the female brain positions more dependence on nodes belonging to these two differently specialized subsystems for communication relative to males, which may confer increased susceptibility to processes dependent on integration of emotion and reward-related information.
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Affiliation(s)
- Leila Nabulsi
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Genevieve McPhilemy
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Liam Kilmartin
- College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland
| | - Denis O'Hora
- School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Stefani O'Donoghue
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Giulia Forcellini
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland.,Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Pablo Najt
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Srinath Ambati
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Laura Costello
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Fintan Byrne
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - James McLoughlin
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Brian Hallahan
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Dara M Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine, Nursing, and Health Sciences, National University of Ireland Galway, Galway, Ireland
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30
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Fernandes HM, Cabral J, van Hartevelt TJ, Lord LD, Gleesborg C, Møller A, Deco G, Whybrow PC, Petrovic P, James AC, Kringelbach ML. Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis. Sci Rep 2019; 9:13638. [PMID: 31541155 PMCID: PMC6754428 DOI: 10.1038/s41598-019-50093-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 09/06/2019] [Indexed: 12/22/2022] Open
Abstract
Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of euthymic PBD patients with psychosis, a variant of PBD, and matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients with psychosis in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed, revealing trends of association between patients’ IQ and affective psychotic symptoms with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD with psychosis is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which show trends of association with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.
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Affiliation(s)
- Henrique M Fernandes
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark. .,Department of Psychiatry, University of Oxford, Oxford, UK. .,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark.
| | - Joana Cabral
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.,Department of Psychiatry, University of Oxford, Oxford, UK.,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Tim J van Hartevelt
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.,Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Carsten Gleesborg
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark.,Sino-Danish Center for Education and Research (SDC), Aarhus, Denmark
| | - Arne Møller
- Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Peter C Whybrow
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USA
| | - Predrag Petrovic
- Cognitive Neurophysiology Research Group, Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Anthony C James
- Department of Psychiatry, University of Oxford, Oxford, UK.,Highfield Unit, Warneford Hospital, Oxford, UK
| | - Morten L Kringelbach
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.,Department of Psychiatry, University of Oxford, Oxford, UK.,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Aarhus, Denmark.,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
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32
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Zhang R, Shao R, Xu G, Lu W, Zheng W, Miao Q, Chen K, Gao Y, Bi Y, Guan L, McIntyre RS, Deng Y, Huang X, So KF, Lin K. Aberrant brain structural-functional connectivity coupling in euthymic bipolar disorder. Hum Brain Mapp 2019; 40:3452-3463. [PMID: 31282606 DOI: 10.1002/hbm.24608] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/28/2019] [Accepted: 04/16/2019] [Indexed: 12/14/2022] Open
Abstract
Aberrant structural (diffusion tensor imaging [DTI]) and resting-state functional magnetic resonance imagining connectivity are core features of bipolar disorder. However, few studies have explored the integrity agreement between structural and functional connectivity (SC-FC) in bipolar disorder. We examine SC connectivity coupling index whether could potentially provide additional clinical predictive value for bipolar disorder spectrum disorders besides the intramodality network measures. By examining the structural (DTI) and resting-state functional network properties, as well as their coupling index, among 57 euthymic bipolar disorder patients (age 13-28 years, 18 females) and 42 age- and gender-matched healthy controls (age 13-28 years, 16 females), we found that compared to controls, bipolar disorder patients showed increased structural rich-club connectivity as well as decreased functional modularity. Importantly, the coupling strength between structural and functional connectome was decreased in patients compared to controls, which emerged as the most powerful feature discriminating the two groups. Our findings suggest that structural-functional coupling strength could serve as a valuable biological trait-like feature for bipolar disorder over and above the intramodality network measures. Such measure can have important clinical implications for early identification of bipolar disorder individuals, and inform strategies for prevention of bipolar disorder onset and relapse.
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Affiliation(s)
- Ruibin Zhang
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Neuropsychology, Laboratory of Social Cognitive Affective Neuroscience, Department of Psychology, The University of Hong Kong, Hong Kong, China.,Department of Psychology, School of Public Health, Southern Medical University (Guangdong Provincial Key Laboratory of Tropical Disease Research), Guangzhou, China
| | - Robin Shao
- Laboratory of Neuropsychology, Laboratory of Social Cognitive Affective Neuroscience, Department of Psychology, The University of Hong Kong, Hong Kong, China
| | - Guiyun Xu
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Academician workstation of Mood and Brain Sciences, Guangzhou Medical University, Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Weicong Lu
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenjing Zheng
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qingzhe Miao
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Kun Chen
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yanling Gao
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yanan Bi
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lijie Guan
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Roger S McIntyre
- Academician workstation of Mood and Brain Sciences, Guangzhou Medical University, Guangzhou, China.,Department of Psychiatry, University of Toronto, Toronto, Canada.,Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada
| | - Yue Deng
- Department of Psychology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xuejun Huang
- Department of Psychology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kwok-Fai So
- Academician workstation of Mood and Brain Sciences, Guangzhou Medical University, Guangzhou, China.,GMH Institute of CNS Regeneration, Jinan University, Guangzhou, China.,The State Key Laboratory of Brain and Cognitive Sciences and Department of Ophthalmology, University of Hong Kong, Hong Kong, China
| | - Kangguang Lin
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Laboratory of Neuropsychology, Laboratory of Social Cognitive Affective Neuroscience, Department of Psychology, The University of Hong Kong, Hong Kong, China.,Academician workstation of Mood and Brain Sciences, Guangzhou Medical University, Guangzhou, China.,Laboratory of Emotion and Cognition, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.,GMH Institute of CNS Regeneration, Jinan University, Guangzhou, China
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Bo Q, Dong F, Li X, Li F, Li P, Yu H, He F, Zhang G, Wang Z, Ma X, Wang C. Comparison of cognitive performance in bipolar disorder, major depressive disorder, unaffected first-degree relatives, and healthy controls. Psychiatry Clin Neurosci 2019; 73:70-76. [PMID: 30393945 DOI: 10.1111/pcn.12797] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 09/18/2018] [Accepted: 10/29/2018] [Indexed: 12/20/2022]
Abstract
AIM The extent and specifics regarding cognitive dysfunction in patients with bipolar disorder (BD) or major depressive disorder (MDD) and their unaffected first-degree relatives (FDR) have not been addressed in any single study. The present study compared the cognitive function of patients with BD or MDD, their FDR, and healthy control (HC) individuals. METHODS The study population comprised adults (aged 18-55 years) with BD, adults with MDD, FDR (children or siblings of patients with BD or MDD), and HC (n = 105, 109, 85, and 95, respectively). The Repeatable Battery for the Assessment of Neuropsychological Status was used to assess neurocognitive functions, with five domains and 12 tests. A Wechsler Adult Intelligence Scale brief form was applied to evaluate IQ. Status of mood was assessed using the Young Mania Rating Scale and the Hamilton Depression Scale. RESULTS The mixed model indicated significant variation among the four groups in cognitive function. Cognitive impairments, compared to HC, progressively greater from least to most were found in: FDR, MDD, and BD (F = 32.74, P < 0.001). Years of education correlated with cognitive performance (F = 17.04, P < 0.001), as did IQ (F = 240.63, P < 0.001). The total score for the Hamilton Rating Scale for Depression negatively correlated with cognitive function (F = 5.78, P = 0.017). CONCLUSION Among the study groups, patients with BD had the most severe deficits, followed by MDD patients and FDR. Cognitive deficits could not be associated with a specific psychiatric disorder, but differences in degree were noted.
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Affiliation(s)
- Qijing Bo
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Fang Dong
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xianbin Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Feng Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Peng Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Haiting Yu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Fan He
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Guofu Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Zhimin Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xin Ma
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, China
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Lee I, Nielsen K, Nawaz U, Hall MH, Öngür D, Keshavan M, Brady R. Diverse pathophysiological processes converge on network disruption in mania. J Affect Disord 2019; 244:115-123. [PMID: 30340100 PMCID: PMC6785980 DOI: 10.1016/j.jad.2018.10.087] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 09/19/2018] [Accepted: 10/05/2018] [Indexed: 12/15/2022]
Abstract
BACKGROUND Neuroimaging of psychiatric disease is challenged by the difficulty of establishing the causal role of neuroimaging abnormalities. Lesions that cause mania present a unique opportunity to understand how brain network disruption may cause mania in both lesions and in bipolar disorder. METHODS A literature search revealed 23 case reports with imaged lesions that caused mania in patients without history of bipolar disorder. We traced these lesions and examined resting-state functional Magnetic Resonance Imaging (rsfMRI) connectivity to these lesions and control lesions to find networks that would be disrupted specifically by mania-causing lesions. The results were then used as regions-of-interest to examine rsfMRI connectivity in patients with bipolar disorder (n = 16) who underwent imaging longitudinally across states of both mania and euthymia alongside a cohort of healthy participants scanned longitudinally. We then sought to replicate these results in independent cohorts of manic (n = 26) and euthymic (n = 21) participants with bipolar disorder. RESULTS Mania-inducing lesions overlap significantly in network connectivity. Mania-causing lesions selectively disrupt networks that include orbitofrontal cortex, dorsolateral prefrontal cortex, and temporal lobes. In bipolar disorder, the manic state was reflected in strong, significant, and specific disruption in network communication between these regions and regions implicated in bipolar pathophysiology: the amygdala and ventro-lateral prefrontal cortex. LIMITATIONS There was heterogeneity in the clinical characterization of mania causing lesions. CONCLUSIONS Lesions causing mania demonstrate shared and specific network disruptions. These disruptions are also observed in bipolar mania and suggest a convergence of multiple disorders on shared circuit dysfunction to cause mania.
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Affiliation(s)
- Ivy Lee
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kathryn Nielsen
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA,Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA
| | - Uzma Nawaz
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Mei-Hua Hall
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA,Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA
| | - Dost Öngür
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA,Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Roscoe Brady
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.
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35
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Perry A, Roberts G, Mitchell PB, Breakspear M. Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks. Mol Psychiatry 2019; 24:1296-1318. [PMID: 30279458 PMCID: PMC6756092 DOI: 10.1038/s41380-018-0267-2] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 08/14/2018] [Accepted: 09/07/2018] [Indexed: 12/31/2022]
Abstract
The notion that specific cognitive and emotional processes arise from functionally distinct brain regions has lately shifted toward a connectivity-based approach that emphasizes the role of network-mediated integration across regions. The clinical neurosciences have likewise shifted from a predominantly lesion-based approach to a connectomic paradigm-framing disorders as diverse as stroke, schizophrenia (SCZ), and dementia as "dysconnection syndromes". Here we position bipolar disorder (BD) within this paradigm. We first summarise the disruptions in structural, functional and effective connectivity that have been documented in BD. Not surprisingly, these disturbances show a preferential impact on circuits that support emotional processes, cognitive control and executive functions. Those at high risk (HR) for BD also show patterns of connectivity that differ from both matched control populations and those with BD, and which may thus speak to neurobiological markers of both risk and resilience. We highlight research fields that aim to link brain network disturbances to the phenotype of BD, including the study of large-scale brain dynamics, the principles of network stability and control, and the study of interoception (the perception of physiological states). Together, these findings suggest that the affective dysregulation of BD arises from dynamic instabilities in interoceptive circuits which subsequently impact on fear circuitry and cognitive control systems. We describe the resulting disturbance as a "psychosis of interoception".
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Affiliation(s)
- Alistair Perry
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin/London, Germany. .,Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
| | - Gloria Roberts
- 0000 0004 4902 0432grid.1005.4School of Psychiatry, University of New South Wales, Randwick, NSW Australia ,grid.415193.bBlack Dog Institute, Prince of Wales Hospital, Randwick, NSW Australia
| | - Philip B. Mitchell
- 0000 0004 4902 0432grid.1005.4School of Psychiatry, University of New South Wales, Randwick, NSW Australia ,grid.415193.bBlack Dog Institute, Prince of Wales Hospital, Randwick, NSW Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. .,Metro North Mental Health Service, Brisbane, QLD, Australia.
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Development of Neuroimaging-Based Biomarkers in Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1192:159-195. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.
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Wang Y, Zhong S, Chen G, Liu T, Zhao L, Sun Y, Jia Y, Huang L. Altered cerebellar functional connectivity in remitted bipolar disorder: A resting-state functional magnetic resonance imaging study. Aust N Z J Psychiatry 2018; 52:962-971. [PMID: 29232968 DOI: 10.1177/0004867417745996] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
OBJECTIVES Several recent studies have reported a strong association between the cerebellar structural and functional abnormalities and psychiatric disorders. However, there are no studies to investigate possible changes in cerebellar functional connectivity in bipolar disorder. This study aimed to examine the whole-brain functional connectivity pattern of patients with remitted bipolar disorder II, in particular in the cerebellum. METHODS A total of 25 patients with remitted bipolar disorder II and 25 controls underwent resting-state functional magnetic resonance imaging and neuropsychological tests. Voxel-wise whole-brain connectivity was analyzed using a graph theory approach: functional connectivity strength. A seed-based resting-state functional connectivity analysis was further performed to investigate abnormal functional connectivity pattern of those regions with changed functional connectivity strength. RESULTS Remitted bipolar disorder II patients had significantly decreased functional connectivity strength in the bilateral posterior lobes of cerebellum (mainly lobules VIIb/VIIIa). The seed-based functional connectivity analyses revealed decreased functional connectivity between the right posterior cerebellum and the default mode network (i.e. right posterior cingulate cortex/precuneus and right superior temporal gyrus), bilateral hippocampus, right putamen, left paracentral lobule and bilateral posterior cerebellum and decreased functional connectivity between the left posterior cerebellum and the right inferior parietal lobule and bilateral posterior cerebellum in patients with remitted bipolar disorder II. CONCLUSION Our results suggest that cerebellar dysconnectivity, in particular distributed cerebellar-cerebral functional connectivity, might be associated with the pathogenesis of bipolar disorder.
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Affiliation(s)
- Ying Wang
- 1 Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,2 Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Shuming Zhong
- 3 Department of Psychiatry, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Guanmao Chen
- 1 Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Tao Liu
- 3 Department of Psychiatry, The First Affiliated Hospital of Jinan University, Guangzhou, China.,4 The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Lianping Zhao
- 1 Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yao Sun
- 2 Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
| | - Yanbin Jia
- 3 Department of Psychiatry, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Li Huang
- 2 Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, China
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Moreno-Fernández RD, Nieto-Quero A, Gómez-Salas FJ, Chun J, Estivill-Torrús G, Rodríguez de Fonseca F, Santín LJ, Pérez-Martín M, Pedraza C. Effects of genetic deletion versus pharmacological blockade of the LPA 1 receptor on depression-like behaviour and related brain functional activity. Dis Model Mech 2018; 11:dmm.035519. [PMID: 30061118 PMCID: PMC6177006 DOI: 10.1242/dmm.035519] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Accepted: 07/13/2018] [Indexed: 12/17/2022] Open
Abstract
Animal models of psychopathology are particularly useful for studying the neurobiology of depression and characterising the subtypes. Recently, our group was the first to identify a possible relationship between the LPA1 receptor and a mixed anxiety-depression phenotype. Specifically, maLPA1-null mice exhibited a phenotype characterised by depressive and anxious features. However, the constitutive lack of the gene encoding the LPA1 receptor (Lpar1) can induce compensatory mechanisms that might have resulted in the observed deficits. Therefore, in the present study, we have compared the impact of permanent loss and acute pharmacological inhibition of the LPA1 receptor on despair-like behaviours and on the functional brain map associated with these behaviours, as well as on the degree of functional connectivity among structures. Although the antagonist (intracerebroventricularly administered Ki16425) mimicked some, but not all, effects of genetic deletion of the LPA1 receptor on the results of behavioural tests and engaged different brain circuits, both treatments induced depression-like behaviours with an agitation component that was linked to functional changes in key brain regions involved in the stress response and emotional regulation. In addition, both Ki16425 treatment and LPA1 receptor deletion modified the functional brain maps in a way similar to the changes observed in depressed patients. In summary, the pharmacological and genetic approaches could ultimately assist in dissecting the function of the LPA1 receptor in emotional regulation and brain responses, and a combination of those approaches might provide researchers with an opportunity to develop useful drugs that target the LPA1 receptor as treatments for depression, mainly the anxious subtype. This article has an associated First Person interview with the first author of the paper. Summary: Animal models of psychopathology are useful for studying the neurobiology of depression. Here, we have assessed by pharmacological approach and knockout models the contribution of the LPA-LPA1 signalling pathway to anxious depression.
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Affiliation(s)
- Román Darío Moreno-Fernández
- Departamento de Psicobiologia y Metodologia en las CC, Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga 29071, Spain
| | - Andrea Nieto-Quero
- Departamento de Psicobiologia y Metodologia en las CC, Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga 29071, Spain
| | - Francisco Javier Gómez-Salas
- Departamento de Psicobiologia y Metodologia en las CC, Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga 29071, Spain
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Guillermo Estivill-Torrús
- Unidad de Gestión Clínica de Neurociencias, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Regional Universitario de Málaga, Málaga 29010, Spain
| | - Fernando Rodríguez de Fonseca
- Unidad de Gestión Clínica de Salud Mental, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Regional Universitario de Málaga, Málaga 29010, Spain
| | - Luis Javier Santín
- Departamento de Psicobiologia y Metodologia en las CC, Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga 29071, Spain
| | - Margarita Pérez-Martín
- Departamento de Biología Celular, Genética y Fisiología. Facultad de Ciencias. Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga 29071, Spain
| | - Carmen Pedraza
- Departamento de Psicobiologia y Metodologia en las CC, Instituto de Investigación Biomédica de Málaga (IBIMA), Universidad de Málaga, Málaga 29071, Spain
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Wang B, Li T, Zhou M, Zhao S, Niu Y, Wang X, Yan T, Cao R, Xiang J, Li D. The Abnormality of Topological Asymmetry in Hemispheric Brain Anatomical Networks in Bipolar Disorder. Front Neurosci 2018; 12:618. [PMID: 30233301 PMCID: PMC6129594 DOI: 10.3389/fnins.2018.00618] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 08/14/2018] [Indexed: 12/24/2022] Open
Abstract
Convergent evidences have demonstrated a variety of regional abnormalities of asymmetry in bipolar disorder (BD). However, little is known about the alterations in hemispheric topological asymmetries. In this study, we used diffusion tensor imaging to construct the hemispheric brain anatomical network of 49 patients with BD and 61 matched normal controls. Graph theory was then applied to quantify topological properties of the hemispheric networks. Although small-world properties were preserved in the hemispheric networks of BD, the degrees of the asymmetry in global efficiency, characteristic path length, and small-world property were significantly decreased. More changes in topological properties of the right hemisphere than those of left hemisphere were found in patients compared with normal controls. Consistent with such changes, the nodal efficiency in patients with BD also showed less rightward asymmetry mainly in the frontal, occipital, parietal, and temporal lobes. In contrast to leftward asymmetry, significant rightward asymmetry was found in supplementary motor area of BD, and attributed to more deficits in nodal efficiency of the left hemisphere. Finally, these asymmetry score of nodal efficiency in the inferior parietal lobule and rolandic operculum were significantly associated with symptom severity of BD. Our results suggested that abnormal hemispheric asymmetries in brain anatomical networks were associated with aberrant neurodevelopment, and providing insights into the potential neural biomarkers of BD by measuring the topological asymmetry in hemispheric brain anatomical networks.
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Affiliation(s)
- Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Mengni Zhou
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shuo Zhao
- Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Rui Cao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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White matter alterations in the internal capsule and psychomotor impairment in melancholic depression. PLoS One 2018; 13:e0195672. [PMID: 29672517 PMCID: PMC5908181 DOI: 10.1371/journal.pone.0195672] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 03/27/2018] [Indexed: 12/13/2022] Open
Abstract
Emerging evidence suggests that structural brain abnormalities may play a role in the pathophysiology of melancholic depression. We set out to test whether diffusion-derived estimates of white matter structure were disrupted in melancholia in regions underpinning psychomotor function. We hypothesized that those with melancholia (and evidencing impaired psychomotor function) would show disrupted white matter organization in internal capsule subdivisions. Diffusion magnetic resonance imaging (dMRI) data were acquired from 22 melancholic depressed, 23 non-melancholic depressed, and 29 healthy control participants. Voxel-wise fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) values were derived for anterior, posterior, and retrolenticular limbs of the internal capsule and compared between groups. Neuropsychological (reaction time) and psychomotor functioning were assessed and correlated against FA. Fractional anisotropy was distinctly increased, whilst RD was decreased, in the right anterior internal capsule in those with melancholia, compared to controls. The right anterior limb of the internal capsule correlated with clinical ratings of psychomotor disturbance, and reduced psychomotor speed was associated with increased FA values in the right retrolenticular limb in those with melancholia. Our findings highlight a distinct disturbance in the local white matter arrangement in specific regions of the internal capsule in melancholia, which in turn is associated with psychomotor dysfunction. This study clarifies the contribution of structural brain integrity to the phenomenology of melancholia, and may assist future efforts seeking to integrate neurobiological markers into depression subtyping.
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Jeganathan J, Perry A, Bassett DS, Roberts G, Mitchell PB, Breakspear M. Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk. NEUROIMAGE-CLINICAL 2018; 19:71-81. [PMID: 30035004 PMCID: PMC6051310 DOI: 10.1016/j.nicl.2018.03.032] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/20/2018] [Accepted: 03/25/2018] [Indexed: 01/19/2023]
Abstract
Recent investigations have used diffusion-weighted imaging to reveal disturbances in the neurocircuitry that underlie cognitive-emotional control in bipolar disorder (BD) and in unaffected siblings or children at high genetic risk (HR). It has been difficult to quantify the mechanism by which structural changes disrupt the superimposed brain dynamics, leading to the emotional lability that is characteristic of BD. Average controllability is a concept from network control theory that extends structural connectivity data to estimate the manner in which local neuronal fluctuations spread from a node or subnetwork to alter the state of the rest of the brain. We used this theory to ask whether structural connectivity deficits previously observed in HR individuals (n = 84, mean age 22.4), patients with BD (n = 38, mean age 23.9), and age- and gender-matched controls (n = 96, mean age 22.6) translate to differences in the ability of brain systems to be manipulated between states. Localized impairments in network controllability were seen in the left parahippocampal, left middle occipital, left superior frontal, right inferior frontal, and right precentral gyri in BD and HR groups. Subjects with BD had distributed deficits in a subnetwork containing the left superior and inferior frontal gyri, postcentral gyrus, and insula (p = 0.004). HR participants had controllability deficits in a right-lateralized subnetwork involving connections between the dorsomedial and ventrolateral prefrontal cortex, the superior temporal pole, putamen, and caudate nucleus (p = 0.008). Between-group controllability differences were attenuated after removal of topological factors by network randomization. Some previously reported differences in network connectivity were not associated with controllability-differences, likely reflecting the contribution of more complex brain network properties. These analyses highlight the potential functional consequences of altered brain networks in BD, and may guide future clinical interventions. Control theory estimates how neuronal fluctuations spread from local networks. We compare brain controllability in bipolar disorder and their high-risk relatives. These groups have impaired controllability in networks supporting cognitive and emotional control. Weaker connectivity as well as topological alterations contribute to these changes.
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Affiliation(s)
- Jayson Jeganathan
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
| | - Alistair Perry
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; School of Psychiatry, University of New South Wales, Randwick, NSW, Australia; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Gloria Roberts
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Michael Breakspear
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Metro North Mental Health Service, Brisbane, QLD, Australia
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42
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Collin G, Scholtens LH, Kahn RS, Hillegers MHJ, van den Heuvel MP. Affected Anatomical Rich Club and Structural-Functional Coupling in Young Offspring of Schizophrenia and Bipolar Disorder Patients. Biol Psychiatry 2017; 82:746-755. [PMID: 28734460 DOI: 10.1016/j.biopsych.2017.06.013] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 06/09/2017] [Accepted: 06/12/2017] [Indexed: 01/01/2023]
Abstract
BACKGROUND Emerging evidence suggests disruptions in the wiring organization of the brain's network in schizophrenia (SZ) and bipolar disorder (BD). As the importance of genetic predisposition has been firmly established in these illnesses, children (offspring) of patients constitute an at-risk population. This study examines connectome organization in children at familial high risk for psychosis. METHODS Diffusion-weighted magnetic resonance imaging scans were collected from 127 nonpsychotic offspring 8 to 18 years of age (average age = 13.5 years) of a parent diagnosed with SZ (SZ offspring; n = 28) or BD (BD offspring; N = 60) and community control subjects (n = 39). Resting-state functional magnetic resonance imaging scans were available for 82 subjects. Anatomical and functional brain networks were reconstructed and examined using graph theoretical analysis. RESULTS SZ offspring were found to show connectivity deficits of the brain's central rich club (RC) system relative to both control subjects and BD offspring. The disruption in anatomical RC connectivity in SZ offspring was associated with increased modularity of the functional connectome. In addition, increased coupling between structural and functional connectivity of long-distance connections was observed in both SZ offspring and BD offspring. CONCLUSIONS This study shows lower levels of anatomical RC connectivity in nonpsychotic young offspring of SZ patients. This finding suggests that the brain's anatomical RC system is affected in at-risk youths, reflecting a connectome signature of familial risk for psychotic illness. Moreover, finding no RC deficits in offspring of BD patients suggest a differential effect of genetic predisposition for SZ versus BD on the developmental formation of the connectome.
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Affiliation(s)
- Guusje Collin
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Lianne H Scholtens
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - René S Kahn
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Manon H J Hillegers
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Erasmus University Medical Center-Sophia Kinderziekenhuis, Rotterdam, the Netherlands
| | - Martijn P van den Heuvel
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
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Sannino S, Stramaglia S, Lacasa L, Marinazzo D. Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks. Netw Neurosci 2017; 1:208-221. [PMID: 29911672 PMCID: PMC5988401 DOI: 10.1162/netn_a_00012] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 04/06/2017] [Indexed: 01/02/2023] Open
Abstract
Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.
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Affiliation(s)
- Speranza Sannino
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, University of Ghent, Belgium
- Department of Electric and Electronic Engineering, University of Cagliari, Italy
| | | | - Lucas Lacasa
- School of Mathematical Sciences, Queen Mary University of London, United Kingdom
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, University of Ghent, Belgium
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