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      Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study

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      1 , 1 , 1 , 2 , 2 , 3 , 4 , 5 , 6 , 7 , 7 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 13 , 14 , 14 , 15 , 16 , 15 , 16 , 15 , 16 , 16 , 17 , 18 , 19 , 20 , 17 , 20 , 17 , 20 , 21 , 22 , 23 , 23 , 24 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 30 , 32 , 33 , 34 , 34 , 35 , 36 , 37 , 38 , 38 , 38 , 39 , 40 , 40 , 40 , 41 , 42 , 43 , 44 , 42 , 43 , 17 , 45 , 42 , 43 , 46 , 2 , 3 , 47 , 46 , 1 , *
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      American Association for the Advancement of Science

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          Abstract

          Brain atrophy in human epilepsy syndromes is explainable by network architecture and strongest in hub regions.

          Abstract

          Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.

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          Most cited references52

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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              Cortical surface-based analysis. I. Segmentation and surface reconstruction.

              Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                November 2020
                18 November 2020
                : 6
                : 47
                : eabc6457
                Affiliations
                [1 ]Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
                [2 ]Neuroscience Research Center, University Magna Græcia, Catanzaro, CZ, Italy.
                [3 ]Institute of Neurology, University Magna Græcia, Catanzaro, CZ, Italy.
                [4 ]Institute of Neurobiology, Universidad Nacional Autónoma de México, Querétaro, México.
                [5 ]Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
                [6 ]Walton Centre NHS Foundation Trust, Liverpool, UK.
                [7 ]Department of Neurology, University of Campinas–UNICAMP, Campinas, São Paulo, Brazil.
                [8 ]Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
                [9 ]Department of Clinical Neurophysiology, University of Medicine Göttingen, Göttingen, Germany.
                [10 ]Institute of Diagnostic Radiology and Neuroradiology, Functional Imaging Unit, University Medicine Greifswald, Greifswald, Germany.
                [11 ]Department of Neurology, University Medicine Greifswald, Greifswald, Germany.
                [12 ]Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.
                [13 ]Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Bern, Switzerland.
                [14 ]Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
                [15 ]Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Victoria, Australia.
                [16 ]Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
                [17 ]Department of Molecular and Cellular Therapeutics, The Royal College of Surgeons in Ireland, Dublin, Ireland.
                [18 ]Department of Neurology, Yale University School of Medicine, New Haven, CT, USA.
                [19 ]Department of Neurology, St. James’ Hospital, Dublin, Ireland.
                [20 ]FutureNeuro SFI Research Centre, Dublin, Ireland.
                [21 ]Epilepsy Center, Neuro Center, Kuopio University Hospital, European Reference Network for Rare and Complex Epilepsies EpiCARE, Kuopio, Finland.
                [22 ]Faculty of Health Sciences, School of Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
                [23 ]Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia.
                [24 ]Neuroradiology Research Program, Meyer Children Hospital of Florence, University of Florence, Florence, Italy.
                [25 ]Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
                [26 ]Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
                [27 ]Departments of Research Administration and Radiology, Henry Ford Health System, Detroit, MI, USA.
                [28 ]Department of Neurology, Henry Ford Health System, Detroit, MI, USA.
                [29 ]Cognitive Science Department, Xiamen University, Xiamen, China.
                [30 ]Child Neurology Unit and Laboratories, Neuroscience Department, Children’s Hospital A. Meyer—University of Florence, Italy.
                [31 ]Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children’s Hospital A. Meyer—University of Florence, Italy.
                [32 ]USL Centro Toscana, Neurology Unit, Nuovo Ospedale Santo Stefano, Prato, Italy.
                [33 ]Cardiff University Brain Research Imaging Centre (CUBRIC), College of Biomedical Sciences, Cardiff University, Cardiff, UK.
                [34 ]Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK.
                [35 ]Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany.
                [36 ]Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium.
                [37 ]Department of Radiology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium.
                [38 ]Division of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK.
                [39 ]Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
                [40 ]IRCCS Istituto Giannina Gaslini, Genova, Italy.
                [41 ]Department of Neurosciences, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA.
                [42 ]Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.
                [43 ]Chalfont Centre for Epilepsy, Bucks, UK.
                [44 ]Centre for Medical Image Computing, University College London, London, UK.
                [45 ]Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Los Angeles, CA, USA.
                [46 ]Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
                [47 ]Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA.
                Author notes
                [* ]Corresponding author. Email: boris.bernhardt@ 123456mcgill.ca
                Author information
                http://orcid.org/0000-0001-5701-1307
                http://orcid.org/0000-0001-9084-7173
                http://orcid.org/0000-0002-9802-0506
                http://orcid.org/0000-0002-8452-5319
                http://orcid.org/0000-0002-4720-8867
                http://orcid.org/0000-0001-9256-6041
                Article
                abc6457
                10.1126/sciadv.abc6457
                7673818
                33208365
                038e0367-9af9-4227-8124-49a0b8d7bd24
                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 06 May 2020
                : 05 October 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NINDS R01NS065838
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R21 NS107739
                Funded by: doi http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: FDN-154298
                Funded by: doi http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: MOP-123520
                Funded by: doi http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Funded by: doi http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award ID: Discovery-1304413
                Funded by: Fundacao de Amparo a Pesquisa do Estado de Sao Paulo;
                Award ID: 2013/07559-3
                Funded by: doi http://dx.doi.org/10.13039/501100000165, Sick Kids Foundation;
                Award ID: NI17-039
                Funded by: UK Medical Research Council;
                Award ID: MR/S00355X/1
                Funded by: National Institutes of Health Big Data to Knowledge;
                Award ID: U54 EB020403
                Funded by: ENIGMA World Aging Center;
                Award ID: R56 AG058854
                Funded by: ENIGMA Sex Differences Initiative;
                Award ID: R01 MH116147;
                Funded by: doi http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 1091593
                Categories
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                Diseases and Disorders
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                Karla Peñamante

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