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Poortman SR, Barendse ME, Setiaman N, van den Heuvel MP, de Lange SC, Hillegers MH, van Haren NE. Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100336. [PMID: 39040431 PMCID: PMC11260845 DOI: 10.1016/j.bpsgos.2024.100336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/08/2024] [Accepted: 05/09/2024] [Indexed: 07/24/2024] Open
Abstract
Background Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated. Methods A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity. Results Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder. Conclusions The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.
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Affiliation(s)
- Simon R. Poortman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
| | - Marjolein E.A. Barendse
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
| | - Nikita Setiaman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Martijn P. van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Siemon C. de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands
| | - Manon H.J. Hillegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Neeltje E.M. van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children’s Hospital, Rotterdam, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
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Georgiadis F, Larivière S, Glahn D, Hong LE, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens FA, Green MJ, Cairns MJ, Michie PT, Rasser PE, Catts S, Tooney P, Scott RJ, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite TD, Karuk A, Pomarol-Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay IS, Sponheim SR, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein DJ, Howells F, Temmingh HS, Diaz Zuluaga AM, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk SL, Thompson P, van Erp TGM, Turner JA, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Mol Psychiatry 2024; 29:1869-1881. [PMID: 38336840 PMCID: PMC11371638 DOI: 10.1038/s41380-024-02442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/08/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.
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Affiliation(s)
- Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
| | - Sara Larivière
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - David Glahn
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Carmel Loughland
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Frans A Henskens
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Patricia T Michie
- School of Psychological Sciences, University of Newcastle, Newcastle, NSW, Australia
| | - Paul E Rasser
- School of Medicine and Public Health, College of Health, Medicine, and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia
| | - Stanley Catts
- Faculty of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Paul Tooney
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Rodney J Scott
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Vaughan Carr
- School of Clinical Medicine, Discipline of Psychiatry, UNSW Sydney, Sydney, NSW, Australia
| | - Yann Quidé
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Axel Krug
- University Hospital Bonn, Department of Psychiatry and Psychotherapy, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frederike Stein
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Igor Nenadić
- Department. of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Raquel Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruben Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | | | - Aristotle Voineskos
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | | | - Diana Tordesillas Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental Neurosciences, Technischen Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - Nicolas Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Caroline Demro
- University of Minnesota Department of Psychology, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Ian S Ramsay
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Andre Schmidt
- University of Basel, Department of Psychiatry, Basel, Switzerland
| | | | | | - Irina Lebedeva
- Mental Health Research Center, Moscow, Russian Federation
| | - Cyril Höschl
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Dana Nguyen
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
| | - Anne Uhlmann
- Department of child and adolescent psychiatry, TU Dresden, Dresden, Germany
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Fleur Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Henk S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Ana M Diaz Zuluaga
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Carlos López Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Felice Iasevoli
- University of Naples, Department of Neuroscience, Naples, Italy
| | - Ellen Ji
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Bratislav Misic
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Sofie L Valk
- Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA
| | - Boris Bernhardt
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
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3
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Daneshvar R, Naghib M, Fayyazi Bordbar MR, Faridhosseini F, Fotouhi M, Motamed Shariati M. Optic nerve head neurovascular assessments in patients with schizophrenia: A cross-sectional study. Health Sci Rep 2024; 7:e2100. [PMID: 38725558 PMCID: PMC11079145 DOI: 10.1002/hsr2.2100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/06/2024] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
Objective The retina is a protrusion of the brain, so researchers have recently proposed retinal changes as a new marker for studying central nervous system diseases. To investigate optic nerve head neurovascular structure assessed by optical coherence tomography angiography (OCTA) in schizophrenia compared to healthy subjects. Methods The study was conducted from 2019 to 2021 at the Ibn Sina Psychiatric Hospital in Mashhad, Iran. We enrolled 22 hospitalized known cases of schizophrenia, treated with risperidone as an antipsychotic drug, and 22 healthy subjects. The two groups were matched in age and gender. In the schizophrenic group, the positive and negative syndrome scale test was used to assess the illness severity. All subjects underwent complete ophthalmic evaluations and OCTA imaging. Results We found that the cup/disc area ratio, vertical cup/disc ratio, and horizontal cup/disc ratio are significantly higher in patients with schizophrenia than in healthy subjects (with p-values of 0.019, 0.015, and 0.022, respectively). No statistically significant difference in the peripapillary retinal nerve fiber layer and vascular parameters of the optic nerve head was observed between schizophrenia and healthy groups. Conclusion We found evidence regarding the difference in the optic nerve head tomographic properties in schizophrenia compared to healthy subjects. However, ONH vascular parameters showed no significant difference. More studies are needed for a definite conclusion.
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Affiliation(s)
- Ramin Daneshvar
- Eye Research CenterMashhad University of Medical SciencesMashhadIran
| | - Maryam Naghib
- Psychiatry and Behavioral Sciences Research CenterMashhad University of Medical SciencesMashhadIran
| | | | - Farhad Faridhosseini
- Psychiatry and Behavioral Sciences Research CenterMashhad University of Medical SciencesMashhadIran
| | - Marziyeh Fotouhi
- Eye Research CenterMashhad University of Medical SciencesMashhadIran
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Grosu C, Klauser P, Dwir D, Khadimallah I, Alemán-Gómez Y, Laaboub N, Piras M, Fournier M, Preisig M, Conus P, Draganski B, Eap CB. Associations between antipsychotics-induced weight gain and brain networks of impulsivity. Transl Psychiatry 2024; 14:162. [PMID: 38531873 DOI: 10.1038/s41398-024-02881-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 03/28/2024] Open
Abstract
Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics, there is a pressing need to get insights into the underlying processes at the brain system level that will allow stratification of "at risk" patients. The pathophysiological hypothesis at hand is focused on brain networks governing impulsivity that are modulated by neuro-inflammatory processes. To this aim, we investigated brain anatomy and functional connectivity in patients with early psychosis (median age: 23 years, IQR = 21-27) using anthropometric data and magnetic resonance imaging acquired one month to one year after initiation of AP medication. Our analyses included 19 patients with high and rapid weight gain (i.e., ≥5% from baseline weight after one month) and 23 patients with low weight gain (i.e., <5% from baseline weight after one month). We replicated our analyses in young (26 years, IQR = 22-33, N = 102) and middle-aged (56 years, IQR = 51-62, N = 875) healthy individuals from the general population. In early psychosis patients, higher weight gain was associated with poor impulse control score (β = 1.35; P = 0.03). Here, the observed brain differences comprised nodes of impulsivity networks - reduced frontal lobe grey matter volume (Pcorrected = 0.007) and higher striatal volume (Pcorrected = 0.048) paralleled by disruption of fronto-striatal functional connectivity (R = -0.32; P = 0.04). Weight gain was associated with the inflammatory biomarker plasminogen activator inhibitor-1 (β = 4.9, P = 0.002). There was no significant association between increased BMI or weight gain and brain anatomy characteristics in both cohorts of young and middle-aged healthy individuals. Our findings support the notion of weight gain in treated psychotic patients associated with poor impulse control, impulsivity-related brain networks and chronic inflammation.
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Affiliation(s)
- Claire Grosu
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland.
| | - Paul Klauser
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Ines Khadimallah
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
- Connectomics Lab, Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nermine Laaboub
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Marianna Piras
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Margot Fournier
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Martin Preisig
- Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience - Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Chin B Eap
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland.
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva and University of Lausanne, Lausanne, Switzerland.
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Huang Y, Li Y, Yuan Y, Zhang X, Yan W, Li T, Niu Y, Xu M, Yan T, Li X, Li D, Xiang J, Wang B, Yan T. Beta-informativeness-diffusion multilayer graph embedding for brain network analysis. Front Neurosci 2024; 18:1303741. [PMID: 38525375 PMCID: PMC10957763 DOI: 10.3389/fnins.2024.1303741] [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: 09/28/2023] [Accepted: 02/07/2024] [Indexed: 03/26/2024] Open
Abstract
Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.
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Affiliation(s)
- Yin Huang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Yuting Yuan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Xingyu Zhang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Wenjie Yan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yan Niu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Mengzhou Xu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Xiaowen Li
- Computer Information Engineering Institute, Shanxi Technology and Business College, Taiyuan, China
| | - Dandan Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Tianyi Yan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
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Ishibashi T, Nobukawa S, Tobe M, Kikuchi M, Takahashi T. Alterations in the hub structure of whole-brain functional networks in patients with drug-naïve schizophrenia: Insights from electroencephalography-based research. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2024; 3:e164. [PMID: 38868477 PMCID: PMC11114440 DOI: 10.1002/pcn5.164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 06/14/2024]
Abstract
Aim This study aimed to identify atypical hubs in the whole-brain networks of patients with schizophrenia (SZ) and examine the effects of antipsychotic medications, using electroencephalography (EEG) data. Methods We estimated the functional connectivity across all electrodes by applying the phase lag index to the EEG signals of 21 drug-naïve patients with SZ and 31 age-matched healthy controls. Betweenness centrality (BC), a measure of hub status, was calculated for each electrode and frequency band. Data from 14 patients were re-evaluated after initiating treatment with antipsychotic medications. Results BC values decreased significantly at the Fz site in the beta band, decreased significantly at Pz in the gamma band, and increased significantly at O1 in the gamma band among patients with SZ. These changes persisted after antipsychotic treatment and were unrelated to clinical symptoms. Conclusion The abnormal hub topology we observed, especially in the high-frequency band, may reflect the pathophysiology of SZ, and this study highlights the utility of BC analysis of EEG data for detecting alterations in the whole-brain networks of patients with SZ.
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Affiliation(s)
| | - Sou Nobukawa
- Department of Computer ScienceChiba Institute of TechnologyChibaJapan
- Graduate School of Information and Computer ScienceChiba Institute of TechnologyChibaJapan
- Research Center for Mathematical EngineeringChiba Institute of TechnologyChibaJapan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental HealthNational Center of Neurology and PsychiatryTokyoJapan
| | - Mayuna Tobe
- Graduate School of Information and Computer ScienceChiba Institute of TechnologyChibaJapan
| | - Mitsuru Kikuchi
- Department of Psychiatry & Behavioral ScienceKanazawa UniversityIshikawaJapan
- Research Center for Child Mental DevelopmentKanazawa UniversityIshikawaJapan
| | - Tetsuya Takahashi
- Department of NeuropsychiatryUniversity of FukuiFukuiJapan
- Research Center for Child Mental DevelopmentKanazawa UniversityIshikawaJapan
- Uozu Shinkei SanatoriumUozuJapan
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Lewis L, Corcoran M, Cho KIK, Kwak Y, Hayes RA, Larsen B, Jalbrzikowski M. Age-associated alterations in thalamocortical structural connectivity in youths with a psychosis-spectrum disorder. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:86. [PMID: 38081873 PMCID: PMC10713597 DOI: 10.1038/s41537-023-00411-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/30/2023] [Indexed: 12/23/2023]
Abstract
Psychotic symptoms typically emerge in adolescence. Age-associated thalamocortical connectivity differences in psychosis remain unclear. We analyzed diffusion-weighted imaging data from 1254 participants 8-23 years old (typically developing (TD):N = 626, psychosis-spectrum (PS): N = 329, other psychopathology (OP): N = 299) from the Philadelphia Neurodevelopmental Cohort. We modeled thalamocortical tracts using deterministic fiber tractography, extracted Q-Space Diffeomorphic Reconstruction (QSDR) and diffusion tensor imaging (DTI) measures, and then used generalized additive models to determine group and age-associated thalamocortical connectivity differences. Compared to other groups, PS exhibited thalamocortical reductions in QSDR global fractional anisotropy (GFA, p-values range = 3.0 × 10-6-0.05) and DTI fractional anisotropy (FA, p-values range = 4.2 × 10-4-0.03). Compared to TD, PS exhibited shallower thalamus-prefrontal age-associated increases in GFA and FA during mid-childhood, but steeper age-associated increases during adolescence. TD and OP exhibited decreases in thalamus-frontal mean and radial diffusivities during adolescence; PS did not. Altered developmental trajectories of thalamocortical connectivity may contribute to the disruptions observed in adults with psychosis.
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Affiliation(s)
- Lydia Lewis
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Mary Corcoran
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Kang Ik K Cho
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - YooBin Kwak
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Rebecca A Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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8
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Rubinstein DY, Eisenberg DP, Carver FW, Holroyd T, Apud JA, Coppola R, Berman KF. Spatiotemporal Alterations in Working Memory-Related Beta Band Neuromagnetic Activity of Patients With Schizophrenia On and Off Antipsychotic Medication: Investigation With MEG. Schizophr Bull 2023; 49:669-678. [PMID: 36772948 PMCID: PMC10154700 DOI: 10.1093/schbul/sbac178] [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] [Indexed: 02/12/2023]
Abstract
BACKGROUND AND HYPOTHESIS We used the uniquely high combined spatial and temporal resolution of magnetoencephalography to characterize working memory (WM)-related modulation of beta band activity in neuroleptic-free patients with schizophrenia in comparison to a large sample of performance-matched healthy controls. We also tested for effects of antipsychotic medication on identified differences in these same patients. STUDY DESIGN Inpatients with schizophrenia (n = 21) or psychotic disorder not otherwise specified (n = 4) completed N-back and control tasks during magnetoencephalography while on placebo and during antipsychotic medication treatment, in a blinded, randomized, counterbalanced manner. Healthy, performance-matched controls (N = 100) completed the same tasks. WM-related neural activation was estimated as beta band (14-30 Hz) desynchronization throughout the brain in successive 400 ms time windows. Voxel-wise statistical comparisons were performed between controls and patients while off-medication at each time window. Significant clusters resulting from this between-groups analysis were then used as regions-of-interest, the activations of which were compared between on- and off-medication conditions in patients. STUDY RESULTS Controls showed beta-band desynchronization (activation) of a fronto-parietal network immediately preceding correct button press responses-the time associated with WM updating and task execution. Altered activation in medication-free patients occurred largely during this time, in prefrontal, parietal, and visual cortices. Medication altered patients' neural responses such that the activation time courses in these regions-of-interest more closely resembled those of controls. CONCLUSIONS These findings demonstrate that WM-related beta band alterations in schizophrenia are time-specific and associated with neural systems targeted by antipsychotic medications. Future studies may investigate this association by examining its potential neurochemical basis.
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Affiliation(s)
- Daniel Y Rubinstein
- Section on Integrative Neuroimaging, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Daniel P Eisenberg
- Section on Integrative Neuroimaging, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | | | - Tom Holroyd
- MEG Core Facility, NIH, DHHS, Bethesda, MD, USA
| | - Jose A Apud
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
| | - Richard Coppola
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- MEG Core Facility, NIH, DHHS, Bethesda, MD, USA
| | - Karen F Berman
- Section on Integrative Neuroimaging, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA
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9
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Schizophrenia and psychedelic state: Dysconnection versus hyper-connection. A perspective on two different models of psychosis stemming from dysfunctional integration processes. Mol Psychiatry 2023; 28:59-67. [PMID: 35931756 DOI: 10.1038/s41380-022-01721-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/15/2022] [Accepted: 07/22/2022] [Indexed: 01/07/2023]
Abstract
Psychotic symptoms are a cross-sectional dimension affecting multiple diagnostic categories, despite schizophrenia represents the prototype of psychoses. Initially, dopamine was considered the most involved molecule in the neurobiology of schizophrenia. Over the next years, several biological factors were added to the discussion helping to constitute the concept of schizophrenia as a disease marked by a deficit of functional integration, contributing to the formulation of the Dysconnection Hypothesis in 1995. Nowadays the notion of dysconnection persists in the conceptualization of schizophrenia enriched by neuroimaging findings which corroborate the hypothesis. At the same time, in recent years, psychedelics received a lot of attention by the scientific community and astonishing findings emerged about the rearrangement of brain networks under the effect of these compounds. Specifically, a global decrease in functional connectivity was found, highlighting the disintegration of preserved and functional circuits and an increase of overall connectivity in the brain. The aim of this paper is to compare the biological bases of dysconnection in schizophrenia with the alterations of neuronal cyto-architecture induced by psychedelics and the consequent state of cerebral hyper-connection. These two models of psychosis, despite diametrically opposed, imply a substantial deficit of integration of neural signaling reached through two opposite paths.
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10
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Roychaudhuri R, Gadalla MM, West T, Snyder SH. A Novel Stereospecific Bioluminescent Assay for Detection of Endogenous d-Cysteine. ACS Chem Neurosci 2022; 13:3257-3262. [PMID: 36403160 DOI: 10.1021/acschemneuro.2c00528] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The presence of endogenous d-stereoisomers of amino acids in mammals dispels a long-standing dogma about their existence. d-Serine and d-aspartate function as novel neurotransmitters in mammals. However, the stereoisomer with the fastest, spontaneous in vitro racemization rate, d-cysteine, has not been reported. We utilized a novel, stereospecific, bioluminescent assay to identify endogenous d-cysteine in substantial amounts in the eye, brain, and pancreas of mice. d-Cysteine is enriched in mice embryonic brains at day E9.5 (4.5 mM) and decreases progressively with development (μM levels). d-Cysteine is also present in significantly higher amounts in the human brain white matter compared with gray matter. In the luciferase assay, d-cysteine conjugates with cyano hydroxy benzothiazole in the presence of a base and reducing agent to form d-luciferin. d-Luciferin, subsequently, in the presence of firefly luciferase and ATP, emits bioluminescence proportional to the concentration of d-cysteine. The assay is stereospecific and allows the quantitative estimation of endogenous d-cysteine in tissues in addition to its specificity for d-cysteine. Future efforts aimed at bioluminescent in vivo imaging of d-cysteine may allow a more noninvasive means of its detection, thereby elucidating its function.
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Affiliation(s)
- Robin Roychaudhuri
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Moataz M Gadalla
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, United States
| | - Timothy West
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Solomon H Snyder
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, United States.,Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, United States
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11
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Feng S, Zheng S, Zou H, Dong L, Zhu H, Liu S, Wang D, Ning Y, Jia H. Altered functional connectivity of cerebellar networks in first-episode schizophrenia. Front Cell Neurosci 2022; 16:1024192. [PMID: 36439199 PMCID: PMC9692071 DOI: 10.3389/fncel.2022.1024192] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Abnormalities of the cerebellum have been displayed to be a manifestation of schizophrenia (SCH) which is a detrimental psychiatric disorder. It has been recognized that the cerebellum contributes to motor function, sensorimotor function, cognition, and other brain functions in association with cerebral functions. Multiple studies have observed that abnormal alterations in cerebro-cerebellar functional connectivity (FC) were shown in patients with SCH. However, the FC of cerebellar networks in SCH remains unclear. Methods In this study, we explored the FC of cerebellar networks of 45 patients with first-episode SCH and 45 healthy control (HC) subjects by using a defined Yeo 17 network parcellation system. Furthermore, we performed a correlation analysis between cerebellar networks' FC and positive and negative symptoms in patients with first-episode SCH. Finally, we established the classification model to provide relatively suitable features for patients with first-episode SCH concerning the cerebellar networks. Results We found lower between-network FCs between 14 distinct cerebellar network pairs in patients with first-episode SCH, compared to the HCs. Significantly, the between-network FC in N2-N15 was positively associated with positive symptom severity; meanwhile, N4-N15 was negatively associated with negative symptom severity. Besides, our results revealed a satisfactory classification accuracy (79%) of these decreased between-network FCs of cerebellar networks for correctly identifying patients with first-episode SCH. Conclusion Conclusively, between-network abnormalities in the cerebellum are closely related to positive and negative symptoms of patients with first-episode SCH. In addition, the classification results suggest that the cerebellar networks can be a potential target for further elucidating the underlying mechanisms in first-episode SCH.
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Affiliation(s)
- Sitong Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sisi Zheng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Haoming Zou
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Linrui Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hong Zhu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shanshan Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Dan Wang
- Inner Mongolia Autonomous Region Mental Health Center, Hohhot, China
| | - Yanzhe Ning
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hongxiao Jia
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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12
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Schnellbächer GJ, Rajkumar R, Veselinović T, Ramkiran S, Hagen J, Shah NJ, Neuner I. Structural alterations of the insula in depression patients - A 7-Tesla-MRI study. Neuroimage Clin 2022; 36:103249. [PMID: 36451355 PMCID: PMC9668670 DOI: 10.1016/j.nicl.2022.103249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/26/2022] [Accepted: 10/23/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION The insular cortex is part of a network of highly connected cerebral "rich club" - regions and has been implicated in the pathophysiology of various psychiatric and neurological disorders, of which major depressive disease is one of the most prevalent. "Rich club" vulnerability can be a contributing factor in disease development. High-resolution structural subfield analysis of insular volume in combination with cortical thickness measurements and psychological testing might elucidate the way in which the insula is changed in depression. MATERIAL AND METHODS High-resolution structural images of the brain were acquired using a 7T-MRI scanner. The mean grey matter volume and cortical thickness within the insular subfields were analysed using voxel-based morphometry (VBM) and surface analysis techniques respectively. Insular subfields were defined according to the Brainnetome Atlas for VBM - and the Destrieux-Atlas for cortical thickness - analysis. Thirty-three patients with confirmed major depressive disease, as well as thirty-one healthy controls matched for age and gender, were measured. The severity of depression in MDD patients was measured via a BDI-II score and objective clinical assessment (AMDP). Intergroup statistical analysis was performed using ANCOVA. An intragroup multivariate regression analysis of patient psychological test results was calculated. Corrections for multiple comparisons was performed using FDR. RESULTS Significant differences between groups were observed in the left granular dorsal insula according to VBM-analysis. AMDP-scores positively correlated with cortical thickness in the right superior segment of the circular insular sulcus. CONCLUSIONS The combination of differences in grey matter volume between healthy controls and patients with a positive correlation of cortical thickness with disease severity underscores the insula's role in the pathogeneses of MDD. The connectivity hub insular cortex seems vulnerable to disruption in context of affective disease.
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Affiliation(s)
- Gereon J. Schnellbächer
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - Ravichandran Rajkumar
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Shukti Ramkiran
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Jana Hagen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany,Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 11, INM-11, Forschungszentrum Jülich, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany,Corresponding author.
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13
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Boudriot E, Schworm B, Slapakova L, Hanken K, Jäger I, Stephan M, Gabriel V, Ioannou G, Melcher J, Hasanaj G, Campana M, Moussiopoulou J, Löhrs L, Hasan A, Falkai P, Pogarell O, Priglinger S, Keeser D, Kern C, Wagner E, Raabe FJ. Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. Eur Arch Psychiatry Clin Neurosci 2022; 273:575-588. [PMID: 35930031 PMCID: PMC10085905 DOI: 10.1007/s00406-022-01455-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/20/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Schizophrenia spectrum disorders (SSDs) are presumed to be associated with retinal thinning. However, evidence is lacking as to whether these retinal alterations reflect a disease-specific process or are rather a consequence of comorbid diseases or concomitant microvascular impairment. METHODS The study included 126 eyes of 65 patients with SSDs and 143 eyes of 72 healthy controls. We examined macula and optic disc measures by optical coherence tomography (OCT) and OCT angiography (OCT-A). Additive mixed models were used to assess the impact of SSDs on retinal thickness and perfusion and to explore the association of retinal and clinical disease-related parameters by controlling for several ocular and systemic covariates (age, sex, spherical equivalent, intraocular pressure, body mass index, diabetes, hypertension, smoking status, and OCT signal strength). RESULTS OCT revealed significantly lower parafoveal macular, macular ganglion cell-inner plexiform layer (GCIPL), and macular retinal nerve fiber layer (RNFL) thickness and thinner mean and superior peripapillary RNFL in SSDs. In contrast, the applied OCT-A investigations, which included macular and peripapillary perfusion density, macular vessel density, and size of the foveal avascular zone, did not reveal any significant between-group differences. Finally, a longer duration of illness and higher chlorpromazine equivalent doses were associated with lower parafoveal macular and macular RNFL thickness. CONCLUSIONS This study strengthens the evidence for disease-related retinal thinning in SSDs.
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Affiliation(s)
- Emanuel Boudriot
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Benedikt Schworm
- Department of Ophthalmology, University Hospital, LMU Munich, 80336, Munich, Germany
| | - Lenka Slapakova
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany
| | - Katharina Hanken
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Iris Jäger
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Marius Stephan
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany
| | - Vanessa Gabriel
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Georgios Ioannou
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Julian Melcher
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Genc Hasanaj
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Mattia Campana
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Joanna Moussiopoulou
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Lisa Löhrs
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, University of Augsburg, 86156, Augsburg, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany.,Max Planck Institute of Psychiatry, 80804, Munich, Germany
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Siegfried Priglinger
- Department of Ophthalmology, University Hospital, LMU Munich, 80336, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany.,NeuroImaging Core Unit Munich (NICUM), University Hospital, LMU Munich, 80336, Munich, Germany.,Munich Center for Neurosciences (MCN), LMU Munich, 82152, Planegg-Martinsried, Germany
| | - Christoph Kern
- Department of Ophthalmology, University Hospital, LMU Munich, 80336, Munich, Germany
| | - Elias Wagner
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany
| | - Florian J Raabe
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Nußbaumstraße 7, 80336, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), 80804, Munich, Germany.
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14
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Bayrakçı A, Zorlu N, Karakılıç M, Gülyüksel F, Yalınçetin B, Oral E, Gelal F, Bora E. Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2022; 273:565-574. [PMID: 35661912 DOI: 10.1007/s00406-022-01433-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/15/2022] [Indexed: 11/30/2022]
Abstract
Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.
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Affiliation(s)
- Adem Bayrakçı
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Nabi Zorlu
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey.
| | - Merve Karakılıç
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Funda Gülyüksel
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Berna Yalınçetin
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Elif Oral
- Department of Psychiatry, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Fazıl Gelal
- Department of Radiodiagnostics, Katip Celebi University, Ataturk Education and Research Hospital, Izmir, Turkey
| | - Emre Bora
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.,Faculty of Medicine, Department of Psychiatry, Dokuz Eylul University, Izmir, Turkey.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
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15
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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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16
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Cuenod M, Steullet P, Cabungcal JH, Dwir D, Khadimallah I, Klauser P, Conus P, Do KQ. Caught in vicious circles: a perspective on dynamic feed-forward loops driving oxidative stress in schizophrenia. Mol Psychiatry 2022; 27:1886-1897. [PMID: 34759358 PMCID: PMC9126811 DOI: 10.1038/s41380-021-01374-w] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022]
Abstract
A growing body of evidence has emerged demonstrating a pathological link between oxidative stress and schizophrenia. This evidence identifies oxidative stress as a convergence point or "central hub" for schizophrenia genetic and environmental risk factors. Here we review the existing experimental and translational research pinpointing the complex dynamics of oxidative stress mechanisms and their modulation in relation to schizophrenia pathophysiology. We focus on evidence supporting the crucial role of either redox dysregulation, N-methyl-D-aspartate receptor hypofunction, neuroinflammation or mitochondria bioenergetics dysfunction, initiating "vicious circles" centered on oxidative stress during neurodevelopment. These processes would amplify one another in positive feed-forward loops, leading to persistent impairments of the maturation and function of local parvalbumin-GABAergic neurons microcircuits and myelinated fibers of long-range macrocircuitry. This is at the basis of neural circuit synchronization impairments and cognitive, emotional, social and sensory deficits characteristic of schizophrenia. Potential therapeutic approaches that aim at breaking these different vicious circles represent promising strategies for timely and safe interventions. In order to improve early detection and increase the signal-to-noise ratio for adjunctive trials of antioxidant, anti-inflammatory and NMDAR modulator drugs, a reverse translation of validated circuitry approach is needed. The above presented processes allow to identify mechanism based biomarkers guiding stratification of homogenous patients groups and target engagement required for successful clinical trials, paving the way towards precision medicine in psychiatry.
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Affiliation(s)
- Michel Cuenod
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland
| | - Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland
| | - Jan-Harry Cabungcal
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland
| | - Ines Khadimallah
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Prilly, Lausanne, Switzerland.
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17
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Plechawska-Wójcik M, Karczmarek P, Krukow P, Kaczorowska M, Tokovarov M, Jonak K. Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals. Front Neuroinform 2022; 15:744355. [PMID: 34970131 PMCID: PMC8712566 DOI: 10.3389/fninf.2021.744355] [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: 07/20/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.
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Affiliation(s)
| | - Paweł Karczmarek
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Monika Kaczorowska
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Mikhail Tokovarov
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Kamil Jonak
- Department of Computer Science, Lublin University of Technology, Lublin, Poland.,Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
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18
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Elad D, Cetin‐Karayumak S, Zhang F, Cho KIK, Lyall AE, Seitz‐Holland J, Ben‐Ari R, Pearlson GD, Tamminga CA, Sweeney JA, Clementz BA, Schretlen DJ, Viher PV, Stegmayer K, Walther S, Lee J, Crow TJ, James A, Voineskos AN, Buchanan RW, Szeszko PR, Malhotra AK, Keshavan MS, Shenton ME, Rathi Y, Bouix S, Sochen N, Kubicki MR, Pasternak O. Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification. Hum Brain Mapp 2021; 42:4658-4670. [PMID: 34322947 PMCID: PMC8410550 DOI: 10.1002/hbm.25574] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 05/04/2021] [Accepted: 05/27/2021] [Indexed: 12/11/2022] Open
Abstract
Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.
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Affiliation(s)
- Doron Elad
- Department of MathematicsTel‐Aviv UniversityTel‐AvivIsrael
| | - Suheyla Cetin‐Karayumak
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Kang Ik K. Cho
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Amanda E. Lyall
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry and NeuroscienceMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Johanna Seitz‐Holland
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryUniversity Hospital, Ludwig Maximilian University of MunichMunichGermany
| | | | | | - Carol A. Tamminga
- Department of PsychiatryUT Southwestern Medical CenterDallasTexasUSA
| | - John A. Sweeney
- Department of Psychiatry and Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Brett A. Clementz
- Departments of Psychology and NeuroscienceBio‐Imaging Research Center, University of GeorgiaAthensGeorgiaUSA
| | - David J. Schretlen
- Department of Psychiatry and Behavioral Sciences, Morgan Department of Radiology and Radiological ScienceJohns Hopkins Medical InstitutionsBaltimoreMarylandUSA
| | - Petra Verena Viher
- Translational Research CenterUniversity Hospital of Psychiatry, University of BernBernSwitzerland
| | - Katharina Stegmayer
- Translational Research CenterUniversity Hospital of Psychiatry, University of BernBernSwitzerland
| | - Sebastian Walther
- Translational Research CenterUniversity Hospital of Psychiatry, University of BernBernSwitzerland
| | - Jungsun Lee
- Department of PsychiatryUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulSouth Korea
| | - Tim J. Crow
- Department of Psychiatry, SANE POWICWarneford Hospital, University of OxfordOxfordUK
| | - Anthony James
- Department of Psychiatry, SANE POWICWarneford Hospital, University of OxfordOxfordUK
| | - Aristotle N. Voineskos
- Centre for Addiction and Mental Health, Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Robert W. Buchanan
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Philip R. Szeszko
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mental Illness Research, Education and Clinical CenterJames J. Peters VA Medical CenterNew YorkNew YorkUSA
| | - Anil K. Malhotra
- The Feinstein Institute for Medical Research and Zucker Hillside HospitalManhassetNew YorkUSA
| | - Matcheri S. Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical CentreHarvard Medical SchoolBostonMassachusettsUSA
| | - Martha E. Shenton
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry and NeuroscienceMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Sylvain Bouix
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nir Sochen
- Department of MathematicsTel‐Aviv UniversityTel‐AvivIsrael
| | - Marek R. Kubicki
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry and NeuroscienceMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Ofer Pasternak
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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19
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Jiang Y, Duan M, Li X, Huang H, Zhao G, Li X, Li S, Song X, He H, Yao D, Luo C. Function-structure coupling: White matter functional magnetic resonance imaging hyper-activation associates with structural integrity reductions in schizophrenia. Hum Brain Mapp 2021; 42:4022-4034. [PMID: 34110075 PMCID: PMC8288085 DOI: 10.1002/hbm.25536] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 05/04/2021] [Accepted: 05/08/2021] [Indexed: 01/12/2023] Open
Abstract
White matter (WM) microstructure deficit may be an underlying factor in the brain dysconnectivity hypothesis of schizophrenia using diffusion tensor imaging (DTI). However, WM dysfunction is unclear in schizophrenia. This study aimed to investigate the association between structural deficits and functional disturbances in major WM tracts in schizophrenia. Using functional magnetic resonance imaging (fMRI) and DTI, we developed the skeleton-based WM functional analysis, which could achieve voxel-wise function-structure coupling by projecting the fMRI signals onto a skeleton in WM. We measured the fractional anisotropy (FA) and WM low-frequency oscillation (LFO) and their couplings in 93 schizophrenia patients and 122 healthy controls (HCs). An independent open database (62 schizophrenia patients and 71 HCs) was used to test the reproducibility. Finally, associations between WM LFO and five behaviour assessment categories (cognition, emotion, motor, personality and sensory) were examined. This study revealed a reversed pattern of structure and function in frontotemporal tracts, as follows. (a) WM hyper-LFO was associated with reduced FA in schizophrenia. (b) The function-structure association was positive in HCs but negative in schizophrenia patients. Furthermore, function-structure dissociation was exacerbated by long illness duration and severe negative symptoms. (c) WM activations were significantly related to cognition and emotion. This study indicated function-structure dys-coupling, with higher LFO and reduced structural integration in frontotemporal WM, which may reflect a potential mechanism in WM neuropathologic processing of schizophrenia.
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Affiliation(s)
- Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- Department of Psychiatry, Chengdu Mental Health CenterInstitute of Chengdu Brain Science in University of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Guocheng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- Department of Radiology, Chengdu Mental Health CenterInstitute of Chengdu Brain Science in University of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Xuan Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Shicai Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- Department of Psychiatry, Chengdu Mental Health CenterInstitute of Chengdu Brain Science in University of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Xufeng Song
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- Department of Psychiatry, Chengdu Mental Health CenterInstitute of Chengdu Brain Science in University of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical SciencesChengduPeople's Republic of China
- Department of NeurologyThe First Affiliated Hospital of Hainan Medical UniversityHaikouPeople's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduPeople's Republic of China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical SciencesChengduPeople's Republic of China
- Department of NeurologyThe First Affiliated Hospital of Hainan Medical UniversityHaikouPeople's Republic of China
- Radiation Oncology Key Laboratory of Sichuan ProvinceSichuan Cancer HospitalChengduPeople's Republic of China
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20
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Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol Psychiatry 2021; 26:3512-3523. [PMID: 32963336 PMCID: PMC8329928 DOI: 10.1038/s41380-020-00882-5] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 08/21/2020] [Accepted: 09/04/2020] [Indexed: 12/12/2022]
Abstract
The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n = 195). Individuals with schizophrenia (n = 322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region. Furthermore, 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59 ± 2%, p < 0.001). Thus, while infra-normal deviations were common among patients, their anatomical loci were highly inconsistent between individuals. Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r = -0.17, p = 0.006). We conclude that anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.
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21
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Zhou C, Ping L, Chen W, He M, Xu J, Shen Z, Lu Y, Shang B, Xu X, Cheng Y. Altered white matter structural networks in drug-naïve patients with obsessive-compulsive disorder. Brain Imaging Behav 2021; 15:700-710. [PMID: 32314200 DOI: 10.1007/s11682-020-00278-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
White matter (WM) alteration is considered to be a vital neurological mechanism of obsessive-compulsive disorder (OCD). However, little is known regarding the changes in topological organization of WM structural network in OCD. We acquired diffusion tensor imaging (DTI) datasets from 28 drug-naïve OCD patients and 28 well-matched healthy controls (HC). A deterministic fiber tracking approach was used to construct the whole-brain structural connectome. Group differences in global and nodal topological properties as well as rich-club organizations were compared by using graph theory analysis. The relationship between the altered network metrics and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) was calculated. Compared with controls, OCD patients exhibited a significantly decreased small-worldness (σ), normalized clustering coefficient (γ) and shortest path length (Lp), as well as an increased global efficiency (Eglob). The nodal efficiency (Enodal) was found to be reduced in the left middle frontal gyrus, and increased in the right parahippocampal gyrus and bilateral putamen in OCD patients. Besides, OCD patients showed increased rich-club, feeder and local connection strength, and the connection strength of the rich-club was positively correlated with the total Y-BOCS score. Our findings emphasized a central role for the complicatedly changed topological architecture of brain structural networks in the pathological mechanism underlying OCD.
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Affiliation(s)
- Cong Zhou
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, 361000, China
| | - Wei Chen
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Mengxin He
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Jian Xu
- Department of Internal Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Yi Lu
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Binli Shang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, 650032, China. .,The NHC Key Laboratory of Drug Addiction Medicine, Kunming, 650032, China.
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22
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Zhu Y, Zhu Y, Zhu Y, Ren Q, Zhou T. An analysis of the dynamic changes in the self-efficacy and quality of life of elderly patients with chronic obstructive pulmonary disease following community-based rehabilitation. Am J Transl Res 2021; 13:2745-2751. [PMID: 34017437 PMCID: PMC8129375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/24/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVE This study aimed to investigate the effects of community-based rehabilitation (CBR) on the self-efficacy and quality of life in elderly patients with chronic obstructive pulmonary disease (COPD). METHOD Eighty-one elderly patients with COPD admitted to our hospital were recruited as the study cohort and were randomly divided into a control group (n=41) and a study group (n=40). The control group underwent outpatient rehabilitative treatment, and the study group additionally underwent CBR. The treatment effects of the two groups at 1 month, 3 months, and 6 months of intervention were assessed using their pulmonary function and quality of life scores. RESULTS After completion of the CBR, the patients in the study group and the control group were scored using the CSES scale, which did not differ at 1 month of intervention, but the scores were higher in the study group than they were in the control group at 3 and 6 months of intervention (P < 0.05). The patients in the study group also scored higher on the WHOQOL-BREF scale than the control group (P < 0.05). CONCLUSION CBR improves the self-efficacy and quality of life in elderly patients with COPD.
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Affiliation(s)
- Yunyan Zhu
- Department of Nursing, Nantong Hospital of Traditional Chinese MedicineNantong 226000, Jiangsu, China
| | - Yiqing Zhu
- Department of Respiration, Nantong Hospital of Traditional Chinese MedicineNantong 226000, Jiangsu, China
| | - Yonglei Zhu
- Department of Respiration, Nantong Hospital of Traditional Chinese MedicineNantong 226000, Jiangsu, China
| | - Qiuhong Ren
- Department of Respiration, Nantong Hospital of Traditional Chinese MedicineNantong 226000, Jiangsu, China
| | - Tingting Zhou
- Department of Respiration, Nantong Hospital of Traditional Chinese MedicineNantong 226000, Jiangsu, China
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23
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Bagautdinova J, Padula MC, Zöller D, Sandini C, Schneider M, Schaer M, Eliez S. Identifying neurodevelopmental anomalies of white matter microstructure associated with high risk for psychosis in 22q11.2DS. Transl Psychiatry 2020; 10:408. [PMID: 33235187 PMCID: PMC7686319 DOI: 10.1038/s41398-020-01090-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/25/2020] [Accepted: 10/19/2020] [Indexed: 12/11/2022] Open
Abstract
Disruptions of white matter microstructure have been widely reported in schizophrenia. However, the emergence of these alterations during preclinical stages remains poorly understood. 22q11.2 Deletion Syndrome (22q11.2DS) represents a unique model to study the interplay of different risk factors that may impact neurodevelopment in premorbid psychosis. To identify the impact of genetic predisposition for psychosis on white matter development, we acquired longitudinal MRI data in 201 individuals (22q11.2DS = 101; controls = 100) aged 5-35 years with 1-3 time points and reconstructed 18 white matter tracts using TRACULA. Mixed model regression was used to characterize developmental trajectories of four diffusion measures-fractional anisotropy (FA), axial (AD), radial (RD), and mean diffusivity (MD) in each tract. To disentangle the impact of additional environmental and developmental risk factors on white matter maturation, we used a multivariate approach (partial least squares (PLS) correlation) in a subset of 39 individuals with 22q11.2DS. Results revealed no divergent white matter developmental trajectories in patients with 22q11.2DS compared to controls. However, 22q11.2DS showed consistently increased FA and reduced AD, RD, and MD in most white matter tracts. PLS correlation further revealed a significant white matter-clinical risk factors relationship. These results indicate that while age-related changes are preserved in 22q11.2DS, white matter microstructure is widely disrupted, suggesting that genetic high risk for psychosis involves early occurring neurodevelopmental insults. In addition, multivariate modeling showed that clinical risk factors further impact white matter development. Together, these findings suggest that genetic, developmental, and environmental risk factors may play a cumulative role in altering normative white matter development during premorbid stages of psychosis.
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Affiliation(s)
- Joëlle Bagautdinova
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland.
| | - Maria C Padula
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Daniela Zöller
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
- Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Institute of Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tübingen, Tübingen, Germany
| | - Corrado Sandini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Maude Schneider
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
- Clinical Psychology Unit for Intellectual and Developmental Disabilities, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Marie Schaer
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
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24
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Disruption of Conscious Access in Psychosis Is Associated with Altered Structural Brain Connectivity. J Neurosci 2020; 41:513-523. [PMID: 33229501 DOI: 10.1523/jneurosci.0945-20.2020] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/10/2020] [Accepted: 09/20/2020] [Indexed: 11/21/2022] Open
Abstract
According to global neuronal workspace (GNW) theory, conscious access relies on long-distance cerebral connectivity to allow a global neuronal ignition coding for conscious content. In patients with schizophrenia and bipolar disorder, both alterations in cerebral connectivity and an increased threshold for conscious perception have been reported. The implications of abnormal structural connectivity for disrupted conscious access and the relationship between these two deficits and psychopathology remain unclear. The aim of this study was to determine the extent to which structural connectivity is correlated with consciousness threshold, particularly in psychosis. We used a visual masking paradigm to measure consciousness threshold, and diffusion MRI tractography to assess structural connectivity in 97 humans of either sex with varying degrees of psychosis: healthy control subjects (n = 46), schizophrenia patients (n = 25), and bipolar disorder patients with (n = 17) and without (n = 9) a history of psychosis. Patients with psychosis (schizophrenia and bipolar disorder with psychotic features) had an elevated masking threshold compared with control subjects and bipolar disorder patients without psychotic features. Masking threshold correlated negatively with the mean general fractional anisotropy of white matter tracts exclusively within the GNW network (inferior frontal-occipital fasciculus, cingulum, and corpus callosum). Mediation analysis demonstrated that alterations in long-distance connectivity were associated with an increased masking threshold, which in turn was linked to psychotic symptoms. Our findings support the hypothesis that long-distance structural connectivity within the GNW plays a crucial role in conscious access, and that conscious access may mediate the association between impaired structural connectivity and psychosis.
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25
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Michielse S, Lange I, Bakker J, Goossens L, Verhagen S, Wichers M, Lieverse R, Schruers K, van Amelsvoort T, van Os J, Marcelis M. White matter microstructure and network-connectivity in emerging adults with subclinical psychotic experiences. Brain Imaging Behav 2020; 14:1876-1888. [PMID: 31183775 PMCID: PMC7572337 DOI: 10.1007/s11682-019-00129-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Group comparisons of individuals with psychotic disorder and controls have shown alterations in white matter microstructure. Whether white matter microstructure and network connectivity is altered in adolescents with subclinical psychotic experiences (PE) at the lowest end of the psychosis severity spectrum is less clear. DWI scan were acquired in 48 individuals with PE and 43 healthy controls (HC). Traditional tensor-derived indices: Fractional Anisotropy, Axial Diffusivity, Mean Diffusivity and Radial Diffusivity, as well as network connectivity measures (global/local efficiency and clustering coefficient) were compared between the groups. Subclinical psychopathology was assessed with the Community Assessment of Psychic Experiences (CAPE) and Montgomery-Åsberg Depression Rating Scale (MADRS) questionnaires and, in order to capture momentary subclinical expression of psychosis, the Experience Sampling Method (ESM) questionnaires. Within the PE-group, interactions between subclinical (momentary) symptoms and brain regions in the model of tensor-derived indices and network connectivity measures were investigated in a hypothesis-generating fashion. Whole brain analyses showed no group differences in tensor-derived indices and network connectivity measures. In the PE-group, a higher positive symptom distress score was associated with both higher local efficiency and clustering coefficient in the right middle temporal pole. The findings indicate absence of microstructural white matter differences between emerging adults with subclinical PE and controls. In the PE-group, attenuated symptoms were positively associated with network efficiency/cohesion, which requires replication and may indicate network alterations in emerging mild psychopathology.
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Affiliation(s)
- Stijn Michielse
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands.
| | - Iris Lange
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Jindra Bakker
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- Department of Neuroscience, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - Liesbet Goossens
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Simone Verhagen
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Marieke Wichers
- University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, Groningen, The Netherlands
| | - Ritsaert Lieverse
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Koen Schruers
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- Faculty of Psychology, Center for Experimental and Learning Psychology, University of Leuven, Leuven, Belgium
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, England
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, PO Box 616, 6200, MD, Maastricht, the Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, the Netherlands
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26
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Luo C, Lencer R, Hu N, Xiao Y, Zhang W, Li S, Lui S, Gong Q. Characteristics of White Matter Structural Networks in Chronic Schizophrenia Treated With Clozapine or Risperidone and Those Never Treated. Int J Neuropsychopharmacol 2020; 23:799-810. [PMID: 32808036 PMCID: PMC7770521 DOI: 10.1093/ijnp/pyaa061] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/24/2020] [Accepted: 08/12/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Despite its benefits, a major concern regarding antipsychotic treatment is its possible impact on the brain's structure and function. This study sought to explore the characteristics of white matter structural networks in chronic never-treated schizophrenia and those treated with clozapine or risperidone, and its potential association with cognitive function. METHODS Diffusion tensor imaging was performed on a unique sample of 34 schizophrenia patients treated with antipsychotic monotherapy for over 5 years (17 treated with clozapine and 17 treated with risperidone), 17 never-treated schizophrenia patients with illness duration over 5 years, and 27 healthy control participants. Graph theory and network-based statistic approaches were employed. RESULTS We observed a disrupted organization of white matter structural networks as well as decreased nodal and connectivity characteristics across the schizophrenia groups, mainly involving thalamus, prefrontal, and occipital regions. Alterations in nodal and connectivity characteristics were relatively milder in risperidone-treated patients than clozapine-treated patients and never-treated patients. Altered global network measures were significantly associated with cognitive performance levels. Structural connectivity as reflected by network-based statistic mediated the difference in cognitive performance levels between clozapine-treated and risperidone-treated patients. LIMITATIONS These results are constrained by the lack of random assignment to different types of antipsychotic treatment. CONCLUSION These findings provide insight into the white matter structural network deficits in patients with chronic schizophrenia, either being treated or untreated, and suggest white matter structural networks supporting cognitive function may benefit from antipsychotic treatment, especially in those treated with risperidone.
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Affiliation(s)
- Chunyan Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China,Correspondence: Dr Su Lui, MD, Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China ()
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
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27
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Peng X, Zhang R, Yan W, Zhou M, Lu S, Xie S. Reduced white matter integrity associated with cognitive deficits in patients with drug-naive first-episode schizophrenia revealed by diffusion tensor imaging. Am J Transl Res 2020; 12:4410-4421. [PMID: 32913515 PMCID: PMC7476109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 07/16/2020] [Indexed: 06/11/2023]
Abstract
Patients with schizophrenia have shown widespread white matter microstructural abnormalities and cognitive deficits, but the definitive relationship between white matter and cognitive performance remains unclear. In this study, we investigated the possible associations between white matter integrity and cognitive deficits in drug-naive first-episode schizophrenia (dn-FES) using diffusion tensor imaging (DTI). A total of 96 participants, including 46 dn-FES patients and 50 healthy individuals, underwent 3.0 T magnetic resonance diffusion-weighted imaging and cognitive assessments using the Chinese version of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB). Group differences were tested using tract-based spatial statistics (TBSS). Compared with the control group, the dn-FES group exhibited reduced white matter integrity, as indexed using fractional anisotropy (FA) metrics, in the right-hemispheric cluster comprising the posterior thalamic radiation, posterior corona radiata, superior longitudinal fasciculus, retrolenticular part of the internal capsule, tapetum, splenium of the corpus callosum, sagittal stratum, and inferior longitudinal fasciculus. We found that social cognitive deficit is significantly correlated with reduced FA in these white matter regions, except the sagittal stratum and inferior longitudinal fasciculus. Furthermore, we found that speed of processing is positively correlated with reduced FA in the right superior longitudinal fasciculus of dn-FES patients. In summary, white matter deficits were validated in dn-FES patients and could be associated with speed of processing and social cognition, providing clues about a neural basis of schizophrenia and a potential biomarker for clinical studies.
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Affiliation(s)
- Xiaohui Peng
- Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University Nanjing 210029, China
| | - Rongrong Zhang
- Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University Nanjing 210029, China
| | - Wei Yan
- Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University Nanjing 210029, China
| | - Min Zhou
- Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University Nanjing 210029, China
| | - Shuiping Lu
- Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University Nanjing 210029, China
| | - Shiping Xie
- Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University Nanjing 210029, China
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28
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Perkins DO, Jeffries CD, Do KQ. Potential Roles of Redox Dysregulation in the Development of Schizophrenia. Biol Psychiatry 2020; 88:326-336. [PMID: 32560962 PMCID: PMC7395886 DOI: 10.1016/j.biopsych.2020.03.016] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 03/03/2020] [Accepted: 03/22/2020] [Indexed: 12/20/2022]
Abstract
Converging evidence implicates redox dysregulation as a pathological mechanism driving the emergence of psychosis. Increased oxidative damage and decreased capacity of intracellular redox modulatory systems are consistent findings in persons with schizophrenia as well as in persons at clinical high risk who subsequently developed frank psychosis. Levels of glutathione, a key regulator of cellular redox status, are reduced in the medial prefrontal cortex, striatum, and thalamus in schizophrenia. In humans with schizophrenia and in rodent models recapitulating various features of schizophrenia, redox dysregulation is linked to reductions of parvalbumin containing gamma-aminobutyric acid (GABA) interneurons and volumes of their perineuronal nets, white matter abnormalities, and microglia activation. Importantly, the activity of transcription factors, kinases, and phosphatases regulating diverse aspects of neurodevelopment and synaptic plasticity varies according to cellular redox state. Molecules regulating interneuron function under redox control include NMDA receptor subunits GluN1 and GluN2A as well as KEAP1 (regulator of transcription factor NRF2). In a rodent schizophrenia model characterized by impaired glutathione synthesis, the Gclm knockout mouse, oxidative stress activated MMP9 (matrix metalloprotease 9) via its redox-responsive regulatory sites, causing a cascade of molecular events leading to microglia activation, perineural net degradation, and impaired NMDA receptor function. Molecular pathways under redox control are implicated in the etiopathology of schizophrenia and are attractive drug targets for individualized drug therapy trials in the contexts of prevention and treatment of psychosis.
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Affiliation(s)
- Diana O. Perkins
- corresponding author: CB 7160, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, Office: 919-962-1401, Cell: 919-360-1602,
| | - Clark D. Jeffries
- Renaissance Computing Institute, University of North Carolina, Chapel Hill NC
| | - Kim Q. Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
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29
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Alemán-Gómez Y, Najdenovska E, Roine T, Fartaria MJ, Canales-Rodríguez EJ, Rovó Z, Hagmann P, Conus P, Do KQ, Klauser P, Steullet P, Baumann PS, Bach Cuadra M. Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia. Hum Brain Mapp 2020; 41:4041-4061. [PMID: 33448519 PMCID: PMC7469814 DOI: 10.1002/hbm.25108] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/22/2020] [Accepted: 06/14/2020] [Indexed: 12/20/2022] Open
Abstract
The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.
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Affiliation(s)
- Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland
| | - Elena Najdenovska
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Timo Roine
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland
| | - Mário João Fartaria
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Erick J Canales-Rodríguez
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Zita Rovó
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philipp S Baumann
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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30
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Selvadurai LP, Corben LA, Delatycki MB, Storey E, Egan GF, Georgiou‐Karistianis N, Harding IH. Multiple mechanisms underpin cerebral and cerebellar white matter deficits in Friedreich ataxia: The IMAGE-FRDA study. Hum Brain Mapp 2020; 41:1920-1933. [PMID: 31904895 PMCID: PMC7267947 DOI: 10.1002/hbm.24921] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 12/20/2019] [Accepted: 12/29/2019] [Indexed: 01/16/2023] Open
Abstract
Friedreich ataxia is a progressive neurodegenerative disorder with reported abnormalities in cerebellar, brainstem, and cerebral white matter. White matter structure can be measured using in vivo neuroimaging indices sensitive to different white matter features. For the first time, we examined the relative sensitivity and relationship between multiple white matter indices in Friedreich ataxia to more richly characterize disease expression and infer possible mechanisms underlying the observed white matter abnormalities. Diffusion-tensor, magnetization transfer, and T1-weighted structural images were acquired from 31 individuals with Friedreich ataxia and 36 controls. Six white matter indices were extracted: fractional anisotropy, diffusivity (mean, axial, radial), magnetization transfer ratio (microstructure), and volume (macrostructure). For each index, whole-brain voxel-wise between-group comparisons and correlations with disease severity, onset age, and gene triplet-repeat length were undertaken. Correlations between pairs of indices were assessed in the Friedreich ataxia cohort. Spatial similarities in the voxel-level pattern of between-group differences across the indices were also assessed. Microstructural abnormalities were maximal in cerebellar and brainstem regions, but evident throughout the brain, while macroscopic abnormalities were restricted to the brainstem. Poorer microstructure and reduced macrostructural volume correlated with greater disease severity and earlier onset, particularly in peri-dentate nuclei and brainstem regions. Microstructural and macrostructural abnormalities were largely independent. Reduced fractional anisotropy was most strongly associated with axial diffusivity in cerebral tracts, and magnetization transfer in cerebellar tracts. Multiple mechanisms likely underpin white matter abnormalities in Friedreich ataxia, with differential impacts in cerebellar and cerebral pathways.
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Affiliation(s)
- Louisa P. Selvadurai
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
| | - Louise A. Corben
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Bruce Lefroy Centre for Genetic Health ResearchMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsThe University of MelbourneParkvilleVictoriaAustralia
| | - Martin B. Delatycki
- Bruce Lefroy Centre for Genetic Health ResearchMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsThe University of MelbourneParkvilleVictoriaAustralia
- Victorian Clinical Genetics ServicesParkvilleVictoriaAustralia
| | - Elsdon Storey
- Department of MedicineMonash UniversityPrahranVictoriaAustralia
| | - Gary F. Egan
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
| | - Nellie Georgiou‐Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
| | - Ian H. Harding
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
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31
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Kim D, Moussa‐Tooks AB, Bolbecker AR, Apthorp D, Newman SD, O'Donnell BF, Hetrick WP. Cerebellar-cortical dysconnectivity in resting-state associated with sensorimotor tasks in schizophrenia. Hum Brain Mapp 2020; 41:3119-3132. [PMID: 32250008 PMCID: PMC7336143 DOI: 10.1002/hbm.25002] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/15/2020] [Accepted: 03/25/2020] [Indexed: 12/11/2022] Open
Abstract
Abnormalities of cerebellar function have been implicated in the pathophysiology of schizophrenia. Since the cerebellum has afferent and efferent projections to diverse brain regions, abnormalities in cerebellar lobules could affect functional connectivity with multiple functional systems in the brain. Prior studies, however, have not examined the relationship of individual cerebellar lobules with motor and nonmotor resting‐state functional networks. We evaluated these relationships using resting‐state fMRI in 30 patients with a schizophrenia‐spectrum disorder and 37 healthy comparison participants. For connectivity analyses, the cerebellum was parcellated into 18 lobular and vermal regions, and functional connectivity of each lobule to 10 major functional networks in the cerebrum was evaluated. The relationship between functional connectivity measures and behavioral performance on sensorimotor tasks (i.e., finger‐tapping and postural sway) was also examined. We found cerebellar–cortical hyperconnectivity in schizophrenia, which was predominantly associated with Crus I, Crus II, lobule IX, and lobule X. Specifically, abnormal cerebellar connectivity was found to the cerebral ventral attention, motor, and auditory networks. This cerebellar–cortical connectivity in the resting‐state was differentially associated with sensorimotor task‐based behavioral measures in schizophrenia and healthy comparison participants—that is, dissociation with motor network and association with nonmotor network in schizophrenia. These findings suggest that functional association between individual cerebellar lobules and the ventral attentional, motor, and auditory networks is particularly affected in schizophrenia. They are also consistent with dysconnectivity models of schizophrenia suggesting cerebellar contributions to a broad range of sensorimotor and cognitive operations.
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Affiliation(s)
- Dae‐Jin Kim
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
| | - Alexandra B. Moussa‐Tooks
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Program in NeuroscienceIndiana UniversityBloomingtonIndianaUSA
| | - Amanda R. Bolbecker
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - Deborah Apthorp
- School of Psychology, Faculty of Medicine and HealthUniversity of New EnglandArmidaleNew South WalesAustralia
- Research School of Computer Science, College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia
| | - Sharlene D. Newman
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Program in NeuroscienceIndiana UniversityBloomingtonIndianaUSA
| | - Brian F. O'Donnell
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Program in NeuroscienceIndiana UniversityBloomingtonIndianaUSA
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - William P. Hetrick
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndianaUSA
- Program in NeuroscienceIndiana UniversityBloomingtonIndianaUSA
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
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32
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Jo YT, Lee J, Joo SW, Kim H, Shon SH, Yoon W, Hong Y. Additive Burden of Abnormal Diffusivity in the Brain with Schizophrenia: A Diffusion Tensor Imaging Study with Public Neuroimaging Data. Psychiatry Investig 2020; 17:341-349. [PMID: 32252513 PMCID: PMC7176571 DOI: 10.30773/pi.2019.0200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 01/20/2020] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Diffusion tensor imaging has been extensively applied to schizophrenia research. In this study, we counted the number of abnormal brain regions with altered diffusion measures in patients with schizophrenia to enumerate the burden of abnormal diffusivity in the brain. METHODS The public neuroimaging data of the COBRE project from SchizConnect were used for the study. The studied dataset consisted of data from 57 patients with schizophrenia and 71 healthy participants. FreeSurfer and FSL were applied for image processing and analysis. After verifying 161 regions of interest (ROIs), mean diffusion measures in every single ROI in all study participants were measured and normalized into Z-scores. Each ROI was then defined as normal or abnormal on the basis of a cutoff absolute Z-score of 1.96. The number of abnormal ROIs was obtained by each diffusion measure. RESULTS The numbers of ROIs with increased radial diffusivity and increased trace were significantly larger in the patient group than in healthy participants. CONCLUSION Thus, the patient group showed a significant increase in abnormal ROIs, strongly indicating that schizophrenia is not caused by the pathology of a single brain region, but is instead attributable to the additive burden of structural alterations within multiple brain regions.
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Affiliation(s)
- Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sung Woo Joo
- Republic of Korea Navy, Donghae, Republic of Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung-Hyun Shon
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woon Yoon
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Youjin Hong
- Department of Psychiatry, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea
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Jiang JB, Cao Y, An NY, Yang Q, Cui LB. Magnetic Resonance Imaging-Based Connectomics in First-Episode Schizophrenia: From Preclinical Study to Clinical Translation. Front Psychiatry 2020; 11:565056. [PMID: 33061921 PMCID: PMC7518111 DOI: 10.3389/fpsyt.2020.565056] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 08/24/2020] [Indexed: 01/11/2023] Open
Affiliation(s)
- Jin-Bo Jiang
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Yang Cao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Ning-Yu An
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qun Yang
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
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Synaptic Plasticity Shapes Brain Connectivity: Implications for Network Topology. Int J Mol Sci 2019; 20:ijms20246193. [PMID: 31817968 PMCID: PMC6940892 DOI: 10.3390/ijms20246193] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/02/2019] [Accepted: 12/06/2019] [Indexed: 12/13/2022] Open
Abstract
Studies of brain network connectivity improved understanding on brain changes and adaptation in response to different pathologies. Synaptic plasticity, the ability of neurons to modify their connections, is involved in brain network remodeling following different types of brain damage (e.g., vascular, neurodegenerative, inflammatory). Although synaptic plasticity mechanisms have been extensively elucidated, how neural plasticity can shape network organization is far from being completely understood. Similarities existing between synaptic plasticity and principles governing brain network organization could be helpful to define brain network properties and reorganization profiles after damage. In this review, we discuss how different forms of synaptic plasticity, including homeostatic and anti-homeostatic mechanisms, could be directly involved in generating specific brain network characteristics. We propose that long-term potentiation could represent the neurophysiological basis for the formation of highly connected nodes (hubs). Conversely, homeostatic plasticity may contribute to stabilize network activity preventing poor and excessive connectivity in the peripheral nodes. In addition, synaptic plasticity dysfunction may drive brain network disruption in neuropsychiatric conditions such as Alzheimer's disease and schizophrenia. Optimal network architecture, characterized by efficient information processing and resilience, and reorganization after damage strictly depend on the balance between these forms of plasticity.
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van den Heuvel MP, Scholtens LH, de Lange SC, Pijnenburg R, Cahn W, van Haren NEM, Sommer IE, Bozzali M, Koch K, Boks MP, Repple J, Pievani M, Li L, Preuss TM, Rilling JK. Evolutionary modifications in human brain connectivity associated with schizophrenia. Brain 2019; 142:3991-4002. [PMID: 31724729 PMCID: PMC6906591 DOI: 10.1093/brain/awz330] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 08/13/2019] [Accepted: 09/05/2019] [Indexed: 12/21/2022] Open
Abstract
The genetic basis and human-specific character of schizophrenia has led to the hypothesis that human brain evolution may have played a role in the development of the disorder. We examined schizophrenia-related changes in brain connectivity in the context of evolutionary changes in human brain wiring by comparing in vivo neuroimaging data from humans and chimpanzees, one of our closest living evolutionary relatives and a species with which we share a very recent common ancestor. We contrasted the connectome layout between the chimpanzee and human brain and compared differences with the pattern of schizophrenia-related changes in brain connectivity as observed in patients. We show evidence of evolutionary modifications of human brain connectivity to significantly overlap with the cortical pattern of schizophrenia-related dysconnectivity (P < 0.001, permutation testing). We validated these effects in three additional, independent schizophrenia datasets. We further assessed the specificity of effects by examining brain dysconnectivity patterns in seven other psychiatric and neurological brain disorders (including, among others, major depressive disorder and obsessive-compulsive disorder, arguably characterized by behavioural symptoms that are less specific to humans), which showed no such associations with modifications of human brain connectivity. Comparisons of brain connectivity across humans, chimpanzee and macaques further suggest that features of connectivity that evolved in the human lineage showed the strongest association to the disorder, that is, brain circuits potentially related to human evolutionary specializations. Taken together, our findings suggest that human-specific features of connectome organization may be enriched for changes in brain connectivity related to schizophrenia. Modifications in human brain connectivity in service of higher order brain functions may have potentially also rendered the brain vulnerable to brain dysfunction.
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Affiliation(s)
- Martijn P van den Heuvel
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Iris E Sommer
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Department of Neuroscience and Department of Psychiatry, University Medical Center Groningen, The Netherlands
| | - Marco Bozzali
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, East Sussex, UK
- Neuroimaging Laboratory, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Kathrin Koch
- Department of Neuroradiology and TUM-Neuroimaging Center (TUM-NIC), School of Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Graduate School of Systemic Neurosciences GSN, Ludwig-Maximilians-Universität, Biocenter, Munich, Germany
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Jonathan Repple
- Department of Psychiatry, University of Muenster, Muenster, Germany
| | - Michela Pievani
- Lab Alzheimer’s Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Longchuan Li
- Marcus Autism Center, Children’s Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, USA
- Center for Behavioral Neuroscience, Atlanta, GA, USA
| | - James K Rilling
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, USA
- Center for Behavioral Neuroscience, Atlanta, GA, USA
- Department of Anthropology, Emory University, 1557 Dickey Drive, Atlanta, GA 30322, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
- Division of Developmental and Cognitive Neuroscience, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
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36
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Vanes LD, Moutoussis M, Ziegler G, Goodyer IM, Fonagy P, Jones PB, Bullmore ET, Dolan RJ. White matter tract myelin maturation and its association with general psychopathology in adolescence and early adulthood. Hum Brain Mapp 2019; 41:827-839. [PMID: 31661180 PMCID: PMC7268015 DOI: 10.1002/hbm.24842] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/30/2019] [Accepted: 10/14/2019] [Indexed: 12/12/2022] Open
Abstract
Adolescence is a time period associated with marked brain maturation that coincides with an enhanced risk for onset of psychiatric disorder. White matter tract myelination, a process that continues to unfold throughout adolescence, is reported to be abnormal in several psychiatric disorders. Here, we ask whether psychiatric vulnerability is linked to aberrant developmental myelination trajectories. We assessed a marker of myelin maturation, using magnetisation transfer (MT) imaging, in 10 major white matter tracts. We then investigated its relationship to the expression of a general psychopathology "p-factor" in a longitudinal analysis of 293 healthy participants between the ages of 14 and 24. We observed significant longitudinal MT increase across the full age spectrum in anterior thalamic radiation, hippocampal cingulum, dorsal cingulum and superior longitudinal fasciculus. MT increase in the inferior fronto-occipital fasciculus, inferior longitudinal fasciculus and uncinate fasciculus was pronounced in younger participants but levelled off during the transition into young adulthood. Crucially, longitudinal MT increase in dorsal cingulum and uncinate fasciculus decelerated as a function of mean p-factor scores over the study period. This suggests that an increased expression of psychopathology is closely linked to lower rates of myelin maturation in selective brain tracts over time. Impaired myelin growth in limbic association fibres may serve as a neural marker for emerging mental illness during the course of adolescence and early adulthood.
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Affiliation(s)
- Lucy D Vanes
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Michael Moutoussis
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | - Peter Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | -
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
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37
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Cui LB, Wei Y, Xi YB, Griffa A, De Lange SC, Kahn RS, Yin H, Van den Heuvel MP. Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia. Schizophr Bull 2019; 45:1291-1299. [PMID: 30926985 PMCID: PMC6811827 DOI: 10.1093/schbul/sbz014] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder.
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Affiliation(s)
- Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- School of Medical Psychology, Fourth Military Medical University, Xi’an, China
| | - Yongbin Wei
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Alessandra Griffa
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Siemon C De Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - René S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Martijn P Van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
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38
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Arnatkevičiūtė A, Fulcher BD, Fornito A. Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks. Front Neural Circuits 2019; 13:47. [PMID: 31379515 PMCID: PMC6659348 DOI: 10.3389/fncir.2019.00047] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 07/04/2019] [Indexed: 12/04/2022] Open
Abstract
Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.
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Affiliation(s)
- Aurina Arnatkevičiūtė
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
| | - Ben D. Fulcher
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Alex Fornito
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
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39
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Mullier E, Roine T, Griffa A, Xin L, Baumann PS, Klauser P, Cleusix M, Jenni R, Alemàn-Gómez Y, Gruetter R, Conus P, Do KQ, Hagmann P. N-Acetyl-Cysteine Supplementation Improves Functional Connectivity Within the Cingulate Cortex in Early Psychosis: A Pilot Study. Int J Neuropsychopharmacol 2019; 22:478-487. [PMID: 31283822 PMCID: PMC6672595 DOI: 10.1093/ijnp/pyz022] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 04/10/2019] [Accepted: 06/26/2019] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND There is increasing evidence that redox dysregulation, which can lead to oxidative stress and eventually to impairment of oligodendrocytes and parvalbumin interneurons, may underlie brain connectivity alterations in schizophrenia. Accordingly, we previously reported that levels of brain antioxidant glutathione in the medial prefrontal cortex were positively correlated with increased functional connectivity along the cingulum bundle in healthy controls but not in early psychosis patients. In a recent randomized controlled trial, we observed that 6-month supplementation with a glutathione precursor, N-acetyl-cysteine, increased brain glutathione levels and improved symptomatic expression and processing speed. METHODS We investigated the effect of N-acetyl-cysteine supplementation on the functional connectivity between regions of the cingulate cortex, which have been linked to positive symptoms and processing speed decline. In this pilot study, we compared structural connectivity and resting-state functional connectivity between early psychosis patients treated with 6-month N-acetyl-cysteine (n = 9) or placebo (n = 11) supplementation with sex- and age-matched healthy control subjects (n = 74). RESULTS We observed that 6-month N-acetyl-cysteine supplementation increases functional connectivity along the cingulum and more precisely between the caudal anterior part and the isthmus of the cingulate cortex. These functional changes can be partially explained by an increase of centrality of these regions in the functional brain network. CONCLUSIONS N-acetyl-cysteine supplementation has a positive effect on functional connectivity within the cingulate cortex in early psychosis patients. To our knowledge, this is the first study suggesting that increased brain glutathione levels via N-acetyl-cysteine supplementation may improve brain functional connectivity.
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Affiliation(s)
- Emeline Mullier
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland,Correspondence: Emeline Mullier, Centre de recherche en Radiologie RC7, CHUV, Rue du Bugnon 46, 1011 Lausanne, Suisse ()
| | - Timo Roine
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland,Turku Brain and Mind Center, University of Turku, Turku, Finland,Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Alessandra Griffa
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland,Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Amsterdam, Amsterdam, The Netherlands
| | - Lijing Xin
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philipp S Baumann
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Martine Cleusix
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Raoul Jenni
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Yasser Alemàn-Gómez
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland,Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland,Medical Image Analysis Laboratory (MIAL), Centre d’Imagerie BioMédicale (CIBM), Lausanne, Switzerland
| | - Rolf Gruetter
- Laboratory of Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Philippe Conus
- Treatment and Early Intervention in Psychosis Program (TIPP), Service of General Psychiatry, Department of Psychiatry, Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland,Treatment and Early Intervention in Psychosis Program (TIPP), Service of General Psychiatry, Department of Psychiatry, Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
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Girdler SJ, Confino JE, Woesner ME. Exercise as a Treatment for Schizophrenia: A Review. PSYCHOPHARMACOLOGY BULLETIN 2019; 49:56-69. [PMID: 30858639 PMCID: PMC6386427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Schizophrenia is a mental disorder that is characterized by progressive cognitive impairment in areas of attention, working memory, and executive functioning. Although no clear etiology of schizophrenia has been discovered, many factors have been identified that contribute to the development of the disease, such as neurotransmitter alterations, decreased synaptic plasticity, and diminished hippocampal volume. Historically, antipsychotic medications have targeted biochemical alterations in the brains of patients with schizophrenia but have been ineffective in alleviating cognitive and hippocampal deficits. Other modalities, such as exercise therapy, have been proposed as adjuvant or primary therapy options. Exercise therapy has been shown to improve positive and negative symptoms, quality of life, cognition, and hippocampal plasticity, and to increase hippocampal volume in the brains of patients with schizophrenia. This article will briefly review the clinical signs, symptoms and proposed etiologies of schizophrenia, and describe the current understanding of exercise programs as an effective treatment in patients with the disease.
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Affiliation(s)
- Steven J Girdler
- Girdler, MD, Icahn School of Medicine at Mount Sinai, New York, NY. Confino, BS, Department of Psychiatry & Behavioral Sciences, Albert Einstein College of Medicine, Bronx NY. Woesner, MD, Department of Psychiatry & Behavioral Sciences, Albert Einstein College of Medicine, Bronx NY, and Bronx Psychiatric Center, Bronx, NY
| | - Jamie E Confino
- Girdler, MD, Icahn School of Medicine at Mount Sinai, New York, NY. Confino, BS, Department of Psychiatry & Behavioral Sciences, Albert Einstein College of Medicine, Bronx NY. Woesner, MD, Department of Psychiatry & Behavioral Sciences, Albert Einstein College of Medicine, Bronx NY, and Bronx Psychiatric Center, Bronx, NY
| | - Mary E Woesner
- Girdler, MD, Icahn School of Medicine at Mount Sinai, New York, NY. Confino, BS, Department of Psychiatry & Behavioral Sciences, Albert Einstein College of Medicine, Bronx NY. Woesner, MD, Department of Psychiatry & Behavioral Sciences, Albert Einstein College of Medicine, Bronx NY, and Bronx Psychiatric Center, Bronx, NY
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Griffa A, Baumann PS, Klauser P, Mullier E, Cleusix M, Jenni R, van den Heuvel MP, Do KQ, Conus P, Hagmann P. Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging. Transl Psychiatry 2019; 9:62. [PMID: 30718455 PMCID: PMC6362225 DOI: 10.1038/s41398-019-0392-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 01/10/2019] [Indexed: 12/11/2022] Open
Abstract
Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model.
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Affiliation(s)
- Alessandra Griffa
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland. .,Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands.
| | - Philipp S. Baumann
- 0000 0001 0423 4662grid.8515.9Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland ,0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Paul Klauser
- 0000 0001 0423 4662grid.8515.9Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland ,0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Emeline Mullier
- 0000 0001 0423 4662grid.8515.9Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Martine Cleusix
- 0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Raoul Jenni
- 0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Martijn P. van den Heuvel
- grid.484519.5Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, The Netherlands
| | - Kim Q. Do
- 0000 0001 0423 4662grid.8515.9Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Conus
- 0000 0001 0423 4662grid.8515.9Service of General Psychiatry and Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Patric Hagmann
- 0000 0001 0423 4662grid.8515.9Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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42
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Psychiatric and Cognitive Symptoms Associated with Niemann-Pick Type C Disease: Neurobiology and Management. CNS Drugs 2019; 33:125-142. [PMID: 30632019 DOI: 10.1007/s40263-018-0599-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Niemann-Pick disease type C (NPC) is a lysosomal storage disorder that presents with a spectrum of clinical manifestations from infancy and childhood or in early or mid-adulthood. Progressive neurological symptoms including ataxia, dystonia and vertical gaze palsy are a hallmark of the disease, and psychiatric symptoms such as psychosis and mood disorders are common. These latter symptoms often present early in the course of NPC and thus these patients are often diagnosed with a major psychotic or affective disorder before neurological and cognitive signs present and the diagnosis is revised. The commonalities and characteristics of psychotic symptoms in both NPC and schizophrenia may share neuronal pathways and mechanisms and provide potential targets for research in both disorders. The neurobiology of NPC and its relationship to the pattern of neuropsychiatric and cognitive symptoms is described in this review. A number of neurobiological models are proposed as mechanisms by which NPC causes psychiatric and cognitive symptoms, informed from models proposed in schizophrenia and other metabolic disorders. There are a number of symptomatic and illness-modifying treatments for NPC currently available. The current evidence is discussed; focussing on two medications which have shown promise, miglustat and hydroxypropyl-β-cyclodextrin.
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43
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Griffa A, Van den Heuvel MP. Rich-club neurocircuitry: function, evolution, and vulnerability. DIALOGUES IN CLINICAL NEUROSCIENCE 2018. [PMID: 30250389 PMCID: PMC6136122 DOI: 10.31887/dcns.2018.20.2/agriffa] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.
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Affiliation(s)
- Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
| | - Martijn P Van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
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44
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Crocker CE, Tibbo PG. Confused Connections? Targeting White Matter to Address Treatment Resistant Schizophrenia. Front Pharmacol 2018; 9:1172. [PMID: 30405407 PMCID: PMC6201564 DOI: 10.3389/fphar.2018.01172] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 09/28/2018] [Indexed: 12/14/2022] Open
Abstract
Despite development of comprehensive approaches to treat schizophrenia and other psychotic disorders and improve outcomes, there remains a proportion (approximately one-third) of patients who are treatment resistant and will not have remission of psychotic symptoms despite adequate trials of pharmacotherapy. This level of treatment response is stable across all stages of the spectrum of psychotic disorders, including early phase psychosis and chronic schizophrenia. Our current pharmacotherapies are beneficial in decreasing positive symptomology in most cases, however, with little to no impact on negative or cognitive symptoms. Not all individuals with treatment resistant psychosis unfortunately, even benefit from the potential pharmacological reductions in positive symptoms. The existing pharmacotherapy for psychosis is targeted at neurotransmitter receptors. The current first and second generation antipsychotic medications all act on dopamine type 2 receptors with the second generation drugs also interacting significantly with serotonin type 1 and 2 receptors, and with varying pharmacodynamic profiles overall. This focus on developing dopaminergic/serotonergic antipsychotics, while beneficial, has not reduced the proportion of patients experiencing treatment resistance to date. Another pharmacological approach is imperative to address treatment resistance both for response overall and for negative symptoms in particular. There is research suggesting that changes in white matter integrity occur in schizophrenia and these may be more associated with cognition and even negative symptomology. Here we review the evidence that white matter abnormalities in the brain may be contributing to the symptomology of psychotic disorders. Additionally, we propose that white matter may be a viable pharmacological target for pharmacoresistant schizophrenia and discuss current treatments in development for schizophrenia that target white matter.
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Affiliation(s)
- Candice E Crocker
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.,Department of Diagnostic Imaging, Nova Scotia Health Authority, Halifax, NS, Canada
| | - Philip G Tibbo
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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45
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Oestreich LKL, Randeniya R, Garrido MI. White matter connectivity reductions in the pre-clinical continuum of psychosis: A connectome study. Hum Brain Mapp 2018; 40:529-537. [PMID: 30251761 DOI: 10.1002/hbm.24392] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 08/20/2018] [Accepted: 08/30/2018] [Indexed: 12/23/2022] Open
Abstract
Widespread white matter connectivity disruptions have commonly been reported in schizophrenia. However, it is questionable whether structural connectivity decline is specifically associated with schizophrenia or whether it extends along a continuum of psychosis into the healthy population. Elucidating brain structure changes associated with psychotic-like experiences in healthy individuals is insofar important as it is a necessary first step towards our understanding of brain pathology preceding florid psychosis. High resolution, multishell diffusion-weighted magnetic resonance images (MRI) were acquired from 89 healthy individuals. Whole-brain white matter fibre tracking was performed to quantify the strength of white matter connections. Network-based statistics were applied to white matter connections in a regression model in order to test for a linear relationship between streamline count and psychotic-like experiences. A significant subnetwork was identified whereby streamline count declined with increasing psychotic-like experiences. This network of structural connectivity reductions affected all cortical lobes, subcortical structures and the cerebellum and spanned along prominent association and commissural white matter pathways. A widespread network of linearly declining connectivity strength with increasing number of psychotic-like experiences was identified in healthy individuals. This finding is in line with white matter connectivity reductions reported from early to chronic stages of schizophrenia and might therefore aid the development of tools to identify individuals at risk of transitioning to psychosis.
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Affiliation(s)
- Lena K L Oestreich
- UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia.,Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Roshini Randeniya
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia.,Australian Centre of Excellence for Integrative Brain Function, The University of Queensland, Brisbane, Australia
| | - Marta I Garrido
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia.,Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia.,Australian Centre of Excellence for Integrative Brain Function, The University of Queensland, Brisbane, Australia.,School of Mathematics and Physics, The University of Queensland, Brisbane, Australia
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46
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Tønnesen S, Kaufmann T, Doan NT, Alnæs D, Córdova-Palomera A, Meer DVD, Rokicki J, Moberget T, Gurholt TP, Haukvik UK, Ueland T, Lagerberg TV, Agartz I, Andreassen OA, Westlye LT. White matter aberrations and age-related trajectories in patients with schizophrenia and bipolar disorder revealed by diffusion tensor imaging. Sci Rep 2018; 8:14129. [PMID: 30237410 PMCID: PMC6147807 DOI: 10.1038/s41598-018-32355-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 09/06/2018] [Indexed: 12/18/2022] Open
Abstract
Supported by histological and genetic evidence implicating myelin, neuroinflammation and oligodendrocyte dysfunction in schizophrenia spectrum disorders (SZ), diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) abnormalities when compared to healthy controls (HC). The diagnostic specificity remains unclear, with bipolar disorders (BD) frequently conceptualized as a less severe clinical manifestation along a psychotic spectrum. Further, the age-related dynamics and possible sex differences of WM abnormalities in SZ and BD are currently understudied. Using tract-based spatial statistics (TBSS) we compared DTI-based microstructural indices between SZ (n = 128), BD (n = 61), and HC (n = 293). We tested for age-by-group and sex-by-group interactions, computed effect sizes within different age-bins and within genders. TBSS revealed global reductions in fractional anisotropy (FA) and increases in radial (RD) diffusivity in SZ compared to HC, with strongest effects in the body and splenium of the corpus callosum, and lower FA in SZ compared to BD in right inferior longitudinal fasciculus and right inferior fronto-occipital fasciculus, and no significant differences between BD and HC. The results were not strongly dependent on age or sex. Despite lack of significant group-by-age interactions, a sliding-window approach supported widespread WM involvement in SZ with most profound differences in FA from the late 20 s.
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Affiliation(s)
- Siren Tønnesen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aldo Córdova-Palomera
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jaroslav Rokicki
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Torgeir Moberget
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tiril P Gurholt
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Unn K Haukvik
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Trine Vik Lagerberg
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
- Department of Psychology, University of Oslo, Oslo, Norway.
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47
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Griffa A. Rich-club neurocircuitry: function, evolution, and vulnerability. DIALOGUES IN CLINICAL NEUROSCIENCE 2018; 20:121-132. [PMID: 30250389 PMCID: PMC6136122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.
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Affiliation(s)
- Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
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48
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Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, Andreassen OA, Arango C, Banaj N, Bouix S, Bousman CA, Brouwer RM, Bruggemann J, Bustillo J, Cahn W, Calhoun V, Cannon D, Carr V, Catts S, Chen J, Chen JX, Chen X, Chiapponi C, Cho KK, Ciullo V, Corvin AS, Crespo-Facorro B, Cropley V, De Rossi P, Diaz-Caneja CM, Dickie EW, Ehrlich S, Fan FM, Faskowitz J, Fatouros-Bergman H, Flyckt L, Ford JM, Fouche JP, Fukunaga M, Gill M, Glahn DC, Gollub R, Goudzwaard ED, Guo H, Gur RE, Gur RC, Gurholt TP, Hashimoto R, Hatton SN, Henskens FA, Hibar DP, Hickie IB, Hong LE, Horacek J, Howells FM, Hulshoff Pol HE, Hyde CL, Isaev D, Jablensky A, Jansen PR, Janssen J, Jönsson EG, Jung LA, Kahn RS, Kikinis Z, Liu K, Klauser P, Knöchel C, Kubicki M, Lagopoulos J, Langen C, Lawrie S, Lenroot RK, Lim KO, Lopez-Jaramillo C, Lyall A, Magnotta V, Mandl RCW, Mathalon DH, McCarley RW, McCarthy-Jones S, McDonald C, McEwen S, McIntosh A, Melicher T, Mesholam-Gately RI, Michie PT, Mowry B, Mueller BA, Newell DT, O'Donnell P, Oertel-Knöchel V, Oestreich L, Paciga SA, Pantelis C, Pasternak O, Pearlson G, Pellicano GR, Pereira A, Pineda Zapata J, Piras F, Potkin SG, Preda A, Rasser PE, Roalf DR, Roiz R, Roos A, Rotenberg D, Satterthwaite TD, Savadjiev P, Schall U, Scott RJ, Seal ML, Seidman LJ, Shannon Weickert C, Whelan CD, Shenton ME, Kwon JS, Spalletta G, Spaniel F, Sprooten E, Stäblein M, Stein DJ, Sundram S, Tan Y, Tan S, Tang S, Temmingh HS, Westlye LT, Tønnesen S, Tordesillas-Gutierrez D, Doan NT, Vaidya J, van Haren NEM, Vargas CD, Vecchio D, Velakoulis D, Voineskos A, Voyvodic JQ, Wang Z, Wan P, Wei D, Weickert TW, Whalley H, White T, Whitford TJ, Wojcik JD, Xiang H, Xie Z, Yamamori H, Yang F, Yao N, Zhang G, Zhao J, van Erp TGM, Turner J, Thompson PM, Donohoe G. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry 2018; 23:1261-1269. [PMID: 29038599 PMCID: PMC5984078 DOI: 10.1038/mp.2017.170] [Citation(s) in RCA: 439] [Impact Index Per Article: 73.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 05/02/2017] [Accepted: 06/07/2017] [Indexed: 12/15/2022]
Abstract
The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
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Affiliation(s)
- S Kelly
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA,Harvard Medical School, Boston, MA, USA,Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA. E-mail:
| | - N Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - I Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - C Alloza
- University of Edinburgh, Edinburgh, UK
| | | | - C Arango
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - N Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - S Bouix
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - C A Bousman
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia,Department of General Practice, The University of Melbourne, Parkville, VIC, Australia,Swinburne University of Technology, Melbourne, VIC, Australia
| | - R M Brouwer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J Bruggemann
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - J Bustillo
- University of New Mexico, Albuquerque, NM, USA
| | - W Cahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - V Calhoun
- The Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA,The Mind Research Network, Albuquerque, NM, USA
| | - D Cannon
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - V Carr
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - S Catts
- Discipline of Psychiatry, School of Medicine, University of Queensland, Herston, QLD, Australia
| | - J Chen
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - J-x Chen
- Beijing Huilongguan Hospital, Beijing, China
| | - X Chen
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | | | - Kl K Cho
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - V Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - A S Corvin
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - B Crespo-Facorro
- University Hospital Marqués de Valdecilla, IDIVAL, Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain
| | - V Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - P De Rossi
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Department NESMOS, Faculty of Medicine and Psychology, University ‘Sapienza’ of Rome, Rome, Italy,Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - C M Diaz-Caneja
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - E W Dickie
- Center for Addiction and Mental Health, Toronto, ON, Canada
| | - S Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Technische Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - F-m Fan
- Beijing Huilongguan Hospital, Beijing, China
| | - J Faskowitz
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - H Fatouros-Bergman
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - L Flyckt
- University of New South Wales, School of Psychiatry, Sydney, NSW, Australia,The University of Queensland, Queensland Brain Institute and Centre for Advanced Imaging, Brisbane, QLD, Australia
| | - J M Ford
- University of California, VAMC, San Francisco, CA, USA
| | - J-P Fouche
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - M Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi, Japan
| | - M Gill
- Department of Psychiatry and Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland
| | - D C Glahn
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - R Gollub
- Harvard Medical School, Boston, MA, USA,Departments of Psychiatry and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - E D Goudzwaard
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - H Guo
- Zhumadian Psychiatry Hospital, Henan Province, China
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - T P Gurholt
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - R Hashimoto
- Molecular Research Center for Children's Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan,Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - S N Hatton
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - F A Henskens
- School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia,Health Behaviour Research Group, University of Newcastle, Callaghan, NSW, Australia,Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - D P Hibar
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - I B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - L E Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - J Horacek
- National Institute of Mental Health, Klecany, Czech Republic,Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - F M Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - H E Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C L Hyde
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - D Isaev
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - A Jablensky
- University of Western Australia, Perth, WA, Australia
| | - P R Jansen
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - J Janssen
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain,Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E G Jönsson
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - L A Jung
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - R S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Z Kikinis
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - K Liu
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - P Klauser
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia,Department of Psychiatry, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland
| | - C Knöchel
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - M Kubicki
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Lagopoulos
- Sunshine Coast Mind and Neuroscience Institute, University of the Sunshine Coast QLD, Australia, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - C Langen
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - S Lawrie
- University of Edinburgh, Edinburgh, UK
| | - R K Lenroot
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - K O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - C Lopez-Jaramillo
- Research Group in Psychiatry (GIPSI), Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Mood Disorder Program, Hospital Universitario San Vicente Fundación, Medellín, Colombia
| | - A Lyall
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - R C W Mandl
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - D H Mathalon
- University of California, VAMC, San Francisco, CA, USA
| | | | - S McCarthy-Jones
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - C McDonald
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - T Melicher
- Third Faculty of Medicine, Charles University, Prague, Czech Republic,The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - R I Mesholam-Gately
- Harvard Medical School and Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess, Medical Center, Boston, MA, USA
| | - P T Michie
- Hunter Medical Research Institute, Newcastle, NSW, Australia,The University of Newcastle, Newcastle, NSW, Australia,Schizophrenia Research Institute, Sydney, NSW, Australia
| | - B Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia and Queensland Centre for Mental Health Research, Brisbane and Queensland Centre for Mental Health Research, Brisbane, QLD, Australia
| | - B A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - D T Newell
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - P O'Donnell
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - V Oertel-Knöchel
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - L Oestreich
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia and Queensland Centre for Mental Health Research, Brisbane and Queensland Centre for Mental Health Research, Brisbane, QLD, Australia
| | - S A Paciga
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia,Schizophrenia Research Institute, Sydney, NSW, Australia,Centre for Neural Engineering (CfNE), Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
| | - O Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - G Pearlson
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - G R Pellicano
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - A Pereira
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | | | - F Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,School of Biomedical Sciences, Faculty of Health, the University of Newcastle, Callaghan, NSW, Australia
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - P E Rasser
- Hunter Medical Research Institute, Newcastle, NSW, Australia,Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - D R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - R Roiz
- University Hospital Marqués de Valdecilla, IDIVAL, Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain,CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain
| | - A Roos
- SU/UCT MRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - D Rotenberg
- Center for Addiction and Mental Health, Toronto, ON, Canada
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - P Savadjiev
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - U Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia,Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - R J Scott
- Hunter Medical Research Institute, Newcastle, NSW, Australia,School of Biomedical Sciences, Faculty of Health, the University of Newcastle, Callaghan, NSW, Australia
| | - M L Seal
- Murdoch Childrens Research Institute, The Royal Children’s Hospital, Parkville, VIC, Australia
| | - L J Seidman
- Harvard Medical School, Boston, MA, USA,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA,Harvard Medical School and Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess, Medical Center, Boston, MA, USA
| | - C Shannon Weickert
- Schizophrenia Research Institute, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia,School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - C D Whelan
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - M E Shenton
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA,VA Boston Healthcare System, Boston, MA, USA
| | - J S Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - G Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - F Spaniel
- National Institute of Mental Health, Klecany, Czech Republic,Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - E Sprooten
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - M Stäblein
- Laboratory for Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany
| | - D J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa,Department of Psychiatry and MRC Unit on Anxiety and Stress Disorders, University of Cape Town, Cape Town, South Africa
| | - S Sundram
- Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia,Department of Psychiatry, School of Clinical Sciences, Monash University and Monash Health, Clayton, VIC, Australia
| | - Y Tan
- Beijing Huilongguan Hospital, Beijing, China
| | - S Tan
- Beijing Huilongguan Hospital, Beijing, China
| | - S Tang
- Chongqing Three Gorges Central Hospital, Chongqing, China
| | - H S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - L T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - S Tønnesen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - D Tordesillas-Gutierrez
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Santander, Spain,Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - N T Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - J Vaidya
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - N E M van Haren
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C D Vargas
- Research Group in Psychiatry (GIPSI), Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia
| | - D Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - D Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - A Voineskos
- Kimel Family Translational Imaging-Genetics Research Laboratory, Campbell Family Mental Health Research Institute, CAMH Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - J Q Voyvodic
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Z Wang
- Beijing Huilongguan Hospital, Beijing, China
| | - P Wan
- Zhumadian Psychiatry Hospital, Henan Province, China
| | - D Wei
- Luoyang Fifth People's Hospital, Henan Province, China
| | - T W Weickert
- Schizophrenia Research Institute, Sydney, NSW, Australia,Neuroscience Research Australia, Sydney, NSW, Australia,School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - H Whalley
- University of Edinburgh, Edinburgh, UK
| | - T White
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - T J Whitford
- University of New South Wales, School of Psychiatry, Sydney, NSW, Australia
| | - J D Wojcik
- Harvard Medical School and Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess, Medical Center, Boston, MA, USA
| | - H Xiang
- Chongqing Three Gorges Central Hospital, Chongqing, China
| | - Z Xie
- Worldwide Research and Development, Pfizer, Cambridge, MA, USA
| | - H Yamamori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - F Yang
- Beijing Huilongguan Hospital, Beijing, China
| | - N Yao
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - G Zhang
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, USA
| | - J Zhao
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland,School of Psychology, Shaanxi Normal University and Key Laboratory for Behavior and Cognitive Neuroscience of Shaanxi Province, Xi’an, Shaanxi, China
| | - T G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J Turner
- Psychology Department & Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - P M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - G Donohoe
- Centre for Neuroimaging and Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
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49
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Yu Q, Du Y, Chen J, Sui J, Adali T, Pearlson G, Calhoun VD. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:886-906. [PMID: 30364630 PMCID: PMC6197492 DOI: 10.1109/jproc.2018.2825200] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.
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Affiliation(s)
- Qingbao Yu
- Mind Research Network, Albuquerque NM 87106 USA
| | - Yuhui Du
- Mind Research Network, Albuquerque NM 87106 USA. And also with School of Computer & Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Jiayu Chen
- Mind Research Network, Albuquerque NM 87106 USA
| | - Jing Sui
- University of Chinese Academy of Sciences, Beijing 100049 China. And also with CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Science (CAS), University of CAS, Beijing 100190 China
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA. And also with Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque NM 87106 USA. And also with Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
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50
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van Dellen E, Sommer IE, Bohlken MM, Tewarie P, Draaisma L, Zalesky A, Di Biase M, Brown JA, Douw L, Otte WM, Mandl RCW, Stam CJ. Minimum spanning tree analysis of the human connectome. Hum Brain Mapp 2018; 39:2455-2471. [PMID: 29468769 PMCID: PMC5969238 DOI: 10.1002/hbm.24014] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 01/15/2018] [Accepted: 02/10/2018] [Indexed: 12/18/2022] Open
Abstract
One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion‐weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null‐model. The MST of individual subjects matched this reference MST for a mean 58%–88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so‐called rich club nodes (a subset of high‐degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical–subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia
| | - Iris E Sommer
- Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - Marc M Bohlken
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Laurijn Draaisma
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia.,Melbourne School of Engineering, The University of Melbourne, Melbourne, Australia
| | - Maria Di Biase
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia
| | - Jesse A Brown
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, California
| | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - René C W Mandl
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
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