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Li M, Hou X, Yan W, Wang D, Yu R, Li X, Li F, Chen J, Wei L, Liu J, Wang H, Zeng Q. Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01279-4. [PMID: 39327378 DOI: 10.1007/s10278-024-01279-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 09/03/2024] [Accepted: 09/18/2024] [Indexed: 09/28/2024]
Abstract
With the increasing prevalence of mental illness, accurate clinical diagnosis of mental illness is crucial. Compared with MRI, CT has the advantages of wide application, low price, short scanning time, and high patient cooperation. This study aims to construct a deep learning (DL) model based on CT images to make identification of bipolar disorder (BD) and schizophrenia (SZ). A total of 506 patients (BD = 227, SZ = 279) and 179 healthy controls (HC) was collected from January 2022 to May 2023 at two hospitals, and divided into an internal training set and an internal validation set according to a ratio of 4:1. An additional 65 patients (BD = 35, SZ = 30) and 40 HC were recruited from different hospitals, and served as an external test set. All subjects accepted the conventional brain CT examination. The DenseMD model for identify BD and SZ using multiple instance learning was developed and compared with other classical DL models. The results showed that DenseMD performed excellently with an accuracy of 0.745 in the internal validation set, whereas the accuracy of the ResNet-18, ResNeXt-50, and DenseNet-121model was 0.672, 0.664, and 0.679, respectively. For the external test set, DenseMD again outperformed other models with an accuracy of 0.724; however, the accuracy of the ResNet-18, ResNeXt-50, and DenseNet-121model was 0.657, 0.638, and 0.676, respectively. Therefore, the potential of DL models for identification of BD and SZ based on brain CT images was established, and identification ability of the DenseMD model was better than other classical DL models.
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Affiliation(s)
- Meilin Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250000, China
- Shandong First Medical University, Jinan, 250000, China
| | - Xingyu Hou
- Department of Psychiatry, Shandong Mental Health Center, Shandong University, Jinan, 250000, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd, Beijing, 100000, China
| | - Dawei Wang
- Infervision Medical Technology Co., Ltd, Beijing, 100000, China
| | - Ruize Yu
- Infervision Medical Technology Co., Ltd, Beijing, 100000, China
| | - Xixiang Li
- Department of Radiology, Zaozhuang Mental Health Center (Zaozhuang Municipal No. 2 Hospital), Zaozhuang, 277000, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital Afliated to Shandong First Medical University, Jinan, 250000, China
| | - Jinming Chen
- Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250000, China
| | - Lingzhen Wei
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250000, China
- School of Clinical Medicine, Jining Medical University, Jining, 272000, China
| | - Jiahao Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250000, China
- Shandong First Medical University, Jinan, 250000, China
| | - Huaizhen Wang
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250000, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250000, China.
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Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns MJ, Loughland C, Carr VJ, Catts SV, Jablensky A, Green MJ, Henskens F, Kiltschewskij D, Michie PT, Mowry B, Pantelis C, Rasser PE, Reay WR, Schall U, Scott RJ, Watkeys OJ, Roberts G, Mitchell PB, Fullerton JM, Overs BJ, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev PS, Brodaty H, Wen W, Jiang J, Fani N, Ely TD, Lorio A, Stevens JS, Ressler K, Jovanovic T, van Rooij SJ, Federmann LM, Jockwitz C, Teumer A, Forstner AJ, Caspers S, Cichon S, Plis SM, Sarwate AD, Calhoun VD. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. PATTERNS (NEW YORK, N.Y.) 2024; 5:100987. [PMID: 39081570 PMCID: PMC11284501 DOI: 10.1016/j.patter.2024.100987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/02/2024] [Accepted: 04/10/2024] [Indexed: 08/02/2024]
Abstract
Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.
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Affiliation(s)
- Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Sandeep Panta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Javier Romero
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yann Quidé
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Carmel Loughland
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Vaughan J. Carr
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
- Department of Psychiatry, Monash University, Clayton, VIC, Australia
| | - Stanley V. Catts
- School of Medicine, University of Queensland, Brisbane, QLD, Australia
| | | | - Melissa J. Green
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Frans Henskens
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Medicine & Public Health, University of Newcastle, Newcastle, NSW, Australia
- Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, NSW, Australia
| | - Dylan Kiltschewskij
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Patricia T. Michie
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Psychological Sciences, University of Newcastle, Callaghan, NSW, Australia
| | - Bryan Mowry
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, University of Queensland, Brisbane, QLD, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Carlton South, VIC, Australia
- Florey Institute of Neuroscience & Mental Health, Parkville, VIC, Australia
| | - Paul E. Rasser
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, NSW, Australia
| | - William R. Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Ulrich Schall
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Rodney J. Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
| | - Oliver J. Watkeys
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Philip B. Mitchell
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Janice M. Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | | | - Masataka Kikuchi
- Department of Computational Biology and Medical Sciences, University of Tokyo, Chiba, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Masaki Fukunaga
- Section of Brain Function Information, National Institute for Physiological Sciences, Aichi, Japan
| | - Perminder S. Sachdev
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Timothy D. Ely
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | | | - Jennifer S. Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Atlanta VA Medical Center, Decatur, GA, USA
| | - Kerry Ressler
- McLean Hospital, Harvard Medical School, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Sanne J.H. van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lydia M. Federmann
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Alexander Teumer
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Andreas J. Forstner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Sergey M. Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Anand D. Sarwate
- Department of Electrical and Computer Engineering, Rutgers University-New Brunswick, Piscataway, NJ, USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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Thomas-Odenthal F, Stein F, Vogelbacher C, Alexander N, Bechdolf A, Bermpohl F, Bröckel K, Brosch K, Correll CU, Evermann U, Falkenberg I, Fallgatter A, Flinkenflügel K, Grotegerd D, Hahn T, Hautzinger M, Jansen A, Juckel G, Krug A, Lambert M, Leicht G, Leopold K, Meinert S, Mikolas P, Mulert C, Nenadić I, Pfarr JK, Reif A, Ringwald K, Ritter P, Stamm T, Straube B, Teutenberg L, Thiel K, Usemann P, Winter A, Wroblewski A, Dannlowski U, Bauer M, Pfennig A, Kircher T. Larger putamen in individuals at risk and with manifest bipolar disorder. Psychol Med 2024:1-11. [PMID: 38801091 DOI: 10.1017/s0033291724001193] [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] [Indexed: 05/29/2024]
Abstract
BACKGROUND Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations. METHODS In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites. RESULTS Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake. CONCLUSIONS Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.
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Affiliation(s)
- Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Christoph Vogelbacher
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
- Translational Clinical Psychology, Department of Psychology, Philipps-University Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Campus Mitte, Berlin, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Kyra Bröckel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Christoph U Correll
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Irina Falkenberg
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Andreas Fallgatter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Germany; German Center for Mental Health (DZPG), partner site Tübingen, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Martin Hautzinger
- Department of Psychology, Clinical Psychology and Psychotherapy, Eberhard Karls University, Tübingen, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
- Core-Facility BrainImaging, Faculty of Medicine, Philipps-Universität Marburg, Marburg, Germany
| | - Georg Juckel
- Department of Psychiatry, Ruhr University Bochum, Bochum, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital of Bonn, Bonn, Germany
| | - Martin Lambert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gregor Leicht
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karolina Leopold
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Pavol Mikolas
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Christoph Mulert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Psychiatry, Justus Liebig University, Giessen, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Thomas Stamm
- Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Clinical Psychiatry and Psychotherapy Brandenburg Medical School, Neuruppin, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
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4
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Wang Y, Yang Y, Xu W, Yao X, Xie X, Zhang L, Sun J, Wang L, Hua Q, He K, Tian Y, Wang K, Ji GJ. Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission. Schizophr Bull 2024; 50:545-556. [PMID: 38253437 PMCID: PMC11059819 DOI: 10.1093/schbul/sbae003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
BACKGROUND AND HYPOTHESIS There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network. STUDY DESIGN We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions. STUDY RESULTS We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission. CONCLUSIONS Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.
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Affiliation(s)
- Yingru Wang
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Yinian Yang
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Wenqiang Xu
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaoqing Yao
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Xiaohui Xie
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Long Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qiang Hua
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kongliang He
- Department of Psychiatry, Fourth People’s Hospital of Hefei, Anhui Mental Health Center, Hefei, China
| | - Yanghua Tian
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
| | - Gong-Jun Ji
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
- Department of Clinical Psychiatry, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders,Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Anhui Institute of Translational Medicine, Hefei, China
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5
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Long JY, Li B, Ding P, Mei H, Li Y. Correlations between multimodal neuroimaging and peripheral inflammation in different subtypes and mood states of bipolar disorder: a systematic review. Int J Bipolar Disord 2024; 12:5. [PMID: 38388844 PMCID: PMC10884387 DOI: 10.1186/s40345-024-00327-w] [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] [Received: 11/02/2023] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Systemic inflammation-immune dysregulation and brain abnormalities are believed to contribute to the pathogenesis of bipolar disorder (BD). However, the connections between peripheral inflammation and the brain, especially the interactions between different BD subtypes and episodes, remain to be elucidated. Therefore, we conducted the present study to provide a comprehensive understanding of the complex association between peripheral inflammation and neuroimaging findings in patients with bipolar spectrum disorders. METHODS This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42023447044) and conducted according to the Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) framework. Online literature databases (PubMed, Web of Science, Scopus, EMBASE, MEDLINE, PsycINFO, and the Cochrane Library) were searched for studies that simultaneously investigated both peripheral inflammation-related factors and magnetic resonance neurography of BD patients up to July 01, 2023. Then, we analysed the correlations between peripheral inflammation and neuroimaging, as well as the variation trends and the shared and specific patterns of these correlations according to different clinical dimensions. RESULTS In total, 34 publications ultimately met the inclusion criteria for this systematic review, with 2993 subjects included. Among all patterns of interaction between peripheral inflammation and neuroimaging, the most common pattern was a positive relationship between elevated inflammation levels and decreased neuroimaging measurements. The brain regions most susceptible to inflammatory activation were the anterior cingulate cortex, amygdala, prefrontal cortex, striatum, hippocampus, orbitofrontal cortex, parahippocampal gyrus, postcentral gyrus, and posterior cingulate cortex. LIMITATIONS The small sample size, insufficiently explicit categorization of BD subtypes and episodes, and heterogeneity of the research methods limited further implementation of quantitative data synthesis. CONCLUSIONS Disturbed interactions between peripheral inflammation and the brain play a critical role in BD, and these interactions exhibit certain commonalities and differences across various clinical dimensions of BD. Our study further confirmed that the fronto-limbic-striatal system may be the central neural substrate in BD patients.
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Affiliation(s)
- Jing-Yi Long
- Wuhan Mental Health Center, No. 89, Gongnongbing Rd., Jiang'an District, Wuhan, 430012, Hubei Province, China
- Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Bo Li
- School of Public Administration, China University of Geosciences, Wuhan, 430074, China
| | - Pei Ding
- Wuhan Mental Health Center, No. 89, Gongnongbing Rd., Jiang'an District, Wuhan, 430012, Hubei Province, China
- Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Hao Mei
- Zhongnan Hospital of Wuhan University, No. 169, East Lake Rd., Wuchang District, Wuhan, 430062, Hubei Province, China.
| | - Yi Li
- Wuhan Mental Health Center, No. 89, Gongnongbing Rd., Jiang'an District, Wuhan, 430012, Hubei Province, China.
- Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
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Cattarinussi G, Pouya P, Grimaldi DA, Dini MZ, Sambataro F, Brambilla P, Delvecchio G. Cortical alterations in relatives of patients with bipolar disorder: A review of magnetic resonance imaging studies. J Affect Disord 2024; 345:234-243. [PMID: 37865341 DOI: 10.1016/j.jad.2023.10.097] [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: 05/29/2023] [Revised: 09/11/2023] [Accepted: 10/15/2023] [Indexed: 10/23/2023]
Abstract
INTRODUCTION Bipolar disorder (BD) is a severe mental disorder characterized by high heritability rates. Widespread brain cortical alterations have been reported in BD patients, mostly involving the frontal, temporal and parietal regions. Importantly, also unaffected relatives of BD patients (BD-RELs) present abnormalities in cortical measures, which are not influenced by disease-related factors, such as medication use and illness duration. Here, we collected all available evidence on cortical measures in BD-RELs to further our knowledge on the potential cortical alterations associated with the vulnerability and the resilience to BD. METHODS A search on PubMed, Web of Science and Scopus was performed to identify neuroimaging studies exploring cortical alterations in BD-RELs, including cortical thickness (CT), surface area (SA), gyrification (GI) and cortical complexity. Eleven studies were included. Of these, five assessed CT, five examined CT and SA and one explored CT, SA and GI. RESULTS Overall, a heterogeneous pattern of cortical alterations emerged. The areas more consistently linked with genetic liability for BD were the prefrontal and sensorimotor regions. Mixed evidence was reported in the temporal and cingulate areas. LIMITATIONS The small sample size and the heterogeneity in terms of methodologies and the characteristics of the participants limit the generalizability of our results. CONCLUSIONS Our findings suggest that the genetic liability for BD is related to reduced CT in the prefrontal cortex, which might be a marker of risk for BD, and increased CT within the sensorimotor cortex, which could represent a marker of resilience.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), Padua Neuroscience Center, University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Parnia Pouya
- Research Center for Evidence-Based Medicine, Health Management and Safety Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran; Iranian EBM Center: A Joanna Briggs Institute Affiliated Group, Iran; Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Mahta Zare Dini
- Research Center for Evidence-Based Medicine, Health Management and Safety Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran; Iranian EBM Center: A Joanna Briggs Institute Affiliated Group, Iran; Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fabio Sambataro
- Department of Neuroscience (DNS), Padua Neuroscience Center, University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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7
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He J, Antonyan L, Zhu H, Ardila K, Li Q, Enoma D, Zhang W, Liu A, Chekouo T, Cao B, MacDonald ME, Arnold PD, Long Q. A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders. Am J Hum Genet 2024; 111:48-69. [PMID: 38118447 PMCID: PMC10806749 DOI: 10.1016/j.ajhg.2023.11.006] [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: 07/03/2023] [Revised: 11/04/2023] [Accepted: 11/16/2023] [Indexed: 12/22/2023] Open
Abstract
Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.
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Affiliation(s)
- Jingni He
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lilit Antonyan
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Harold Zhu
- Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary, AB, Canada
| | - Karen Ardila
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Qing Li
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - David Enoma
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Andy Liu
- Sir Winston Churchill High School, Calgary, AB, Canada; College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Thierry Chekouo
- Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada
| | - M Ethan MacDonald
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Paul D Arnold
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| | - Quan Long
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada.
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8
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Guo L, Ma J, Cai M, Zhang M, Xu Q, Wang H, Zhang Y, Yao J, Sun Z, Chen Y, Xue H, Zhang Y, Wang S, Xue K, Zhu D, Liu F. Transcriptional signatures of the whole-brain voxel-wise resting-state functional network centrality alterations in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:87. [PMID: 38104130 PMCID: PMC10725456 DOI: 10.1038/s41537-023-00422-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.
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Affiliation(s)
- Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghui Zhang
- Department of Ultrasound, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jia Yao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
| | - Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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9
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Petruso F, Giff A, Milano B, De Rossi M, Saccaro L. Inflammation and emotion regulation: a narrative review of evidence and mechanisms in emotion dysregulation disorders. Neuronal Signal 2023; 7:NS20220077. [PMID: 38026703 PMCID: PMC10653990 DOI: 10.1042/ns20220077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Emotion dysregulation (ED) describes a difficulty with the modulation of which emotions are felt, as well as when and how these emotions are experienced or expressed. It is a focal overarching symptom in many severe and prevalent neuropsychiatric diseases, including bipolar disorders (BD), attention deficit/hyperactivity disorder (ADHD), and borderline personality disorder (BPD). In all these disorders, ED can manifest through symptoms of depression, anxiety, or affective lability. Considering the many symptomatic similarities between BD, ADHD, and BPD, a transdiagnostic approach is a promising lens of investigation. Mounting evidence supports the role of peripheral inflammatory markers and stress in the multifactorial aetiology and physiopathology of BD, ADHD, and BPD. Of note, neural circuits that regulate emotions appear particularly vulnerable to inflammatory insults and peripheral inflammation, which can impact the neuroimmune milieu of the central nervous system. Thus far, few studies have examined the link between ED and inflammation in BD, ADHD, and BPD. To our knowledge, no specific work has provided a critical comparison of the results from these disorders. To fill this gap in the literature, we review the known associations and mechanisms linking ED and inflammation in general, and clinically, in BD, ADHD, and BD. Our narrative review begins with an examination of the routes linking ED and inflammation, followed by a discussion of disorder-specific results accounting for methodological limitations and relevant confounding factors. Finally, we critically discuss both correspondences and discrepancies in the results and comment on potential vulnerability markers and promising therapeutic interventions.
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Affiliation(s)
| | - Alexis E. Giff
- Department of Neuroscience, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland
| | - Beatrice A. Milano
- Sant’Anna School of Advanced Studies, Pisa, Italy
- University of Pisa, Pisa, Italy
| | | | - Luigi Francesco Saccaro
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Switzerland
- Department of Psychiatry, Geneva University Hospital, Switzerland
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10
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Zhu Z, Lei D, Qin K, Tallman MJ, Patino LR, Fleck DE, Gong Q, Sweeney JA, DelBello MP, McNamara RK. Cortical and subcortical structural differences in psychostimulant-free ADHD youth with and without a family history of bipolar I disorder: a cross-sectional morphometric comparison. Transl Psychiatry 2023; 13:368. [PMID: 38036505 PMCID: PMC10689449 DOI: 10.1038/s41398-023-02667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/31/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Although attention-deficit/hyperactivity disorder (ADHD) and a family history of bipolar I disorder (BD) are associated with increased risk for developing BD, their neuroanatomical substrates remain poorly understood. This study compared cortical and subcortical gray matter morphology in psychostimulant-free ADHD youth with and without a first-degree relative with BD and typically developing healthy controls. ADHD youth (ages 10-18 years) with ('high-risk', HR) or without ('low-risk', LR) a first-degree relative with BD and healthy comparison youth (HC) were enrolled. High-resolution 3D T1-weighted images were acquired using a Philips 3.0 T MR scanner. The FreeSurfer image analysis suite was used to measure cortical thickness, surface area, and subcortical volumes. A general linear model evaluated group differences in MRI features with age and sex as covariates, and exploratory correlational analyses evaluated associations with symptom ratings. A total of n = 142 youth (mean age: 14.16 ± 2.54 years, 35.9% female) were included in the analysis (HC, n = 48; LR, n = 49; HR, n = 45). The HR group exhibited a more severe symptom profile, including higher mania and dysregulation scores, compared to the LR group. For subcortical volumes, the HR group exhibited smaller bilateral thalamic, hippocampal, and left caudate nucleus volumes compared to both LR and HC, and smaller right caudate nucleus compared with LR. No differences were found between LR and HC groups. For cortical surface area, the HR group exhibited lower parietal and temporal surface area compared with HC and LR, and lower orbitofrontal and superior frontal surface area compared to LR. The HR group exhibited lower left anterior cingulate surface area compared with HC. LR participants exhibited greater right pars opercularis surface area compared with the HC. Some cortical alterations correlated with symptom severity ratings. These findings suggest that ADHD in youth with a BD family history is associated with a more a severe symptom profile and a neuroanatomical phenotype that distinguishes it from ADHD without a BD family history.
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Affiliation(s)
- Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, PR China.
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, PR China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442012, PR China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, 361021, Fujian, PR China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
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11
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Paunova R, Ramponi C, Kandilarova S, Todeva-Radneva A, Latypova A, Stoyanov D, Kherif F. Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes. Front Psychiatry 2023; 14:1272933. [PMID: 37908595 PMCID: PMC10614636 DOI: 10.3389/fpsyt.2023.1272933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54). Methods We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups. Results As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups. Discussion Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.
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Affiliation(s)
- Rositsa Paunova
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Cristina Ramponi
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University Plovdiv, Plovdiv, Bulgaria
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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12
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Limbic and cortical regions as functional biomarkers associated with emotion regulation in bipolar disorder: A meta-analysis of neuroimaging studies. J Affect Disord 2023; 323:506-513. [PMID: 36462610 DOI: 10.1016/j.jad.2022.11.071] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/28/2022] [Accepted: 11/20/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a psychiatric disorder characterized by episodes of depression and mania, associated with impaired emotion processing. Several functional MRI (fMRI) studies have been used to investigate the structural and functional alteration in BD. Here, we aim to investigate the current fMRI findings of brain activation during emotion-regulation tasks between BD patients and healthy controls (HC). METHODS A systematic search through PubMed database for fMRI studies on bipolar patients and HC yielded 685 studies. We performed an activation likelihood estimation (ALE) on 21 studies for emotion regulation in BD patients and HC. Furthermore, we performed subgroup analyses for task performances in response time and accuracy between bipolar patients and HC. RESULTS The total sample included 21 fMRI studies, comprising 543 BD patients, compared to 565 HC. ALE maps for emotion-related tasks showed hyperactivation in BD patients in the caudate, amygdala, precentral gyrus, middle frontal gyri, and sub-gyrus. Whereas hypoactivation was seen in the inferior frontal gyrus and anterior cingulate gyrus. LIMITATIONS We could not apply a correction for p-value thresholds, as it needs large number of foci. Second, functional abnormalities were investigated for adult BD patients only, as BD patients have functional differences correlated with age. CONCLUSIONS Our results showed that limbic and cortical regions can represent a potential biomarker for the diagnosis and management of BD, by showing clustered brain regions of abnormal patterns of increased activation between BD patients and HC.
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13
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Shang MY, Zhang CY, Wu Y, Wang L, Wang C, Li M. Genetic associations between bipolar disorder and brain structural phenotypes. Cereb Cortex 2023:7024717. [PMID: 36734292 DOI: 10.1093/cercor/bhad014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 02/04/2023] Open
Abstract
Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4-53% on a 0-100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR < 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.
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Affiliation(s)
- Meng-Yuan Shang
- Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo, 315211, Zhejiang, China.,School of Basic Medical Science, School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo, 315211, Zhejiang, China
| | - Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 17 Long-Xin Lu, Kunming, 650201, Yunnan, China
| | - Yong Wu
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, No. 920 Jianshe Road, Wuhan, 430012, Hubei, China
| | - Lu Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 17 Long-Xin Lu, Kunming, 650201, Yunnan, China
| | - Chuang Wang
- Zhejiang Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo, 315211, Zhejiang, China.,School of Basic Medical Science, School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo, 315211, Zhejiang, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, No. 17 Long-Xin Lu, Kunming, 650201, Yunnan, China
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14
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Singh A, Arya A, Agarwal V, Shree R, Kumar U. Computing brain cortical complexity in euthymic children with bipolar disorder: A surface-based approach. Asian J Psychiatr 2023; 80:103352. [PMID: 36481621 DOI: 10.1016/j.ajp.2022.103352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/17/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Anshita Singh
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India; Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, India
| | - Amit Arya
- Department of Psychiatry, King George Medical University, Lucknow, India
| | - Vivek Agarwal
- Department of Psychiatry, King George Medical University, Lucknow, India
| | - Raj Shree
- Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, India
| | - Uttam Kumar
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India.
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15
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Chen H, Wang L, Li H, Song H, Zhang X, Wang D. Altered intrinsic brain activity and cognitive impairment in euthymic, unmedicated individuals with bipolar disorder. Asian J Psychiatr 2023; 80:103386. [PMID: 36495730 DOI: 10.1016/j.ajp.2022.103386] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/07/2022] [Accepted: 10/08/2022] [Indexed: 12/12/2022]
Abstract
Cognitive impairment in euthymic bipolar disorder (BD) contributes to poor functional outcomes. Resting-state magnetic resonance imaging (MRI)may help us understand the neurobiology of cognitive impairment in BD. Here, forty unmedicated euthymic BD patients and thirty-nine healthy controls were recruited, undergoing MRI scans and neuropsychological measures. The amplitude of low-frequency fluctuation (ALFF) and ALFF-based functional connectivity (FC) analysis was employed to explore the potential alterations of neural activity. Voxel-wised correlation was calculated between clinical and cognitive variables and abnormal brain activity. Compared with healthy controls, euthymic BD patients showed worse cognitive performance in Trail Making Test, Digit Span Test, and Stroop Color-Word Test (SCWT). The euthymic BD group had significantly lower ALFF in the left medial frontal gyrus, right middle frontal gyrus, right postcentral gyrus, and left superior frontal gyrus. Furthermore, we found decreased ALFF values in the right middle frontal gyrus that was negatively correlated with cognitive inhibition, (r = -0.43, P = 0.015). ALFF-based FC analysis showed that BD group showed significantly decreased FC between the right middle frontal gyrus (seed) and left middle temporal gyrus and left medial frontal gyrus, (Two-tailed, PFWE < 0.05, TFCE corrected). The findings demonstrated that individuals with BD during the euthymic phase exhibited decreased ALFF and hypoconnectivity of key brain areas within the frontoparietal network. These altered spontaneous brain activity in euthymic BD patients may be involved in the pathophysiology mechanism of cognitive deficits.
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Affiliation(s)
- Hao Chen
- Department of Radiology, Suzhou Municipal Hospital, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Longxi Wang
- Department of laboratory, Rongfu Military Hospital of Jining city, Jining, China
| | - Hong Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huihui Song
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, the Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiaobin Zhang
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, the Affiliated Guangji Hospital of Soochow University, Suzhou, China.
| | - Dong Wang
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, the Affiliated Guangji Hospital of Soochow University, Suzhou, China.
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16
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Cai M, Liu J, Wang X, Ma J, Ma L, Liu M, Zhao Y, Wang H, Fu D, Wang W, Xu Q, Guo L, Liu F. Spontaneous brain activity abnormalities in migraine: A meta-analysis of functional neuroimaging. Hum Brain Mapp 2023; 44:571-584. [PMID: 36129066 PMCID: PMC9842892 DOI: 10.1002/hbm.26085] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 08/21/2022] [Accepted: 09/02/2022] [Indexed: 01/25/2023] Open
Abstract
Neuroimaging studies have demonstrated that migraine is accompanied by spontaneous brain activity alterations in specific regions. However, these findings are inconsistent, thus hindering our understanding of the potential neuropathology. Hence, we performed a quantitative whole-brain meta-analysis of relevant resting-state functional imaging studies to identify brain regions consistently involved in migraine. A systematic search of studies that investigated the differences in spontaneous brain activity patterns between migraineurs and healthy controls up to April 2022 was conducted. We then performed a whole-brain voxel-wise meta-analysis using the anisotropic effect size version of seed-based d mapping software. Complementary analyses including jackknife sensitivity analysis, heterogeneity test, publication bias test, subgroup analysis, and meta-regression analysis were conducted as well. In total, 24 studies that reported 31 datasets were finally eligible for our meta-analysis, including 748 patients and 690 controls. In contrast to healthy controls, migraineurs demonstrated consistent and robust decreased spontaneous brain activity in the angular gyrus, visual cortex, and cerebellum, while increased activity in the caudate, thalamus, pons, and prefrontal cortex. Results were robust and highly replicable in the following jackknife sensitivity analysis and subgroup analysis. Meta-regression analyses revealed that a higher visual analog scale score in the patient sample was associated with increased spontaneous brain activity in the left thalamus. These findings provided not only a comprehensive overview of spontaneous brain activity patterns impairments, but also useful insights into the pathophysiology of dysfunction in migraine.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Jiawei Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Xuexiang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
- Department of RadiologyTianjin Hongqiao HospitalTianjinChina
| | - Juanwei Ma
- Department of RadiologyTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yao Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Dianxun Fu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Wenqin Wang
- School of Mathematical SciencesTiangong UniversityTianjinChina
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
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17
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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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18
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Cai M, Wang R, Liu M, Du X, Xue K, Ji Y, Wang Z, Zhang Y, Guo L, Qin W, Zhu W, Fu J, Liu F. Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:93. [PMID: 36347874 PMCID: PMC9643538 DOI: 10.1038/s41537-022-00311-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/31/2022] [Indexed: 06/06/2023]
Abstract
Neuroimaging studies have shown that schizophrenia is associated with disruption of resting-state local functional connectivity. However, these findings vary considerably, which hampers our understanding of the underlying pathophysiological mechanisms of schizophrenia. Here, we performed an updated and extended meta-analysis to identify the most consistent changes of local functional connectivity measured by regional homogeneity (ReHo) in schizophrenia. Specifically, a systematic search of ReHo studies in patients with schizophrenia in PubMed, Embase, and Web of Science identified 18 studies (20 datasets), including 652 patients and 596 healthy controls. In addition, we included three whole-brain statistical maps of ReHo differences calculated based on independent datasets (163 patients and 194 controls). A voxel-wise meta-analysis was then conducted to investigate ReHo alterations and their relationship with clinical characteristics using the newly developed seed-based d mapping with permutation of subject images (SDM-PSI) meta-analytic approach. Compared with healthy controls, patients with schizophrenia showed significantly higher ReHo in the bilateral medial superior frontal gyrus, while lower ReHo in the bilateral postcentral gyrus, right precentral gyrus, and right middle occipital gyrus. The following sensitivity analyses including jackknife analysis, subgroup analysis, heterogeneity test, and publication bias test demonstrated that our results were robust and highly reliable. Meta-regression analysis revealed that illness duration was negatively correlated with ReHo abnormalities in the right precentral/postcentral gyrus. This comprehensive meta-analysis not only identified consistent and reliably aberrant local functional connectivity in schizophrenia but also helped to further deepen our understanding of its pathophysiology.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Rui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yuan Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zirui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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