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Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium
Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7009583/ https://www.ncbi.nlm.nih.gov/pubmed/32040561 http://dx.doi.org/10.1093/brain/awaa001 |
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author | Yun, Je-Yeon Boedhoe, Premika S W Vriend, Chris Jahanshad, Neda Abe, Yoshinari Ameis, Stephanie H Anticevic, Alan Arnold, Paul D Batistuzzo, Marcelo C Benedetti, Francesco Beucke, Jan C Bollettini, Irene Bose, Anushree Brem, Silvia Calvo, Anna Cheng, Yuqi Cho, Kang Ik K Ciullo, Valentina Dallaspezia, Sara Denys, Damiaan Feusner, Jamie D Fouche, Jean-Paul Giménez, Mònica Gruner, Patricia Hibar, Derrek P Hoexter, Marcelo Q Hu, Hao Huyser, Chaim Ikari, Keisuke Kathmann, Norbert Kaufmann, Christian Koch, Kathrin Lazaro, Luisa Lochner, Christine Marques, Paulo Marsh, Rachel Martínez-Zalacaín, Ignacio Mataix-Cols, David Menchón, José M Minuzzi, Luciano Morgado, Pedro Moreira, Pedro Nakamae, Takashi Nakao, Tomohiro Narayanaswamy, Janardhanan C Nurmi, Erika L O’Neill, Joseph Piacentini, John Piras, Fabrizio Piras, Federica Reddy, Y C Janardhan Sato, Joao R Simpson, H Blair Soreni, Noam Soriano-Mas, Carles Spalletta, Gianfranco Stevens, Michael C Szeszko, Philip R Tolin, David F Venkatasubramanian, Ganesan Walitza, Susanne Wang, Zhen van Wingen, Guido A Xu, Jian Xu, Xiufeng Zhao, Qing Thompson, Paul M Stein, Dan J van den Heuvel, Odile A Kwon, Jun Soo |
author_facet | Yun, Je-Yeon Boedhoe, Premika S W Vriend, Chris Jahanshad, Neda Abe, Yoshinari Ameis, Stephanie H Anticevic, Alan Arnold, Paul D Batistuzzo, Marcelo C Benedetti, Francesco Beucke, Jan C Bollettini, Irene Bose, Anushree Brem, Silvia Calvo, Anna Cheng, Yuqi Cho, Kang Ik K Ciullo, Valentina Dallaspezia, Sara Denys, Damiaan Feusner, Jamie D Fouche, Jean-Paul Giménez, Mònica Gruner, Patricia Hibar, Derrek P Hoexter, Marcelo Q Hu, Hao Huyser, Chaim Ikari, Keisuke Kathmann, Norbert Kaufmann, Christian Koch, Kathrin Lazaro, Luisa Lochner, Christine Marques, Paulo Marsh, Rachel Martínez-Zalacaín, Ignacio Mataix-Cols, David Menchón, José M Minuzzi, Luciano Morgado, Pedro Moreira, Pedro Nakamae, Takashi Nakao, Tomohiro Narayanaswamy, Janardhanan C Nurmi, Erika L O’Neill, Joseph Piacentini, John Piras, Fabrizio Piras, Federica Reddy, Y C Janardhan Sato, Joao R Simpson, H Blair Soreni, Noam Soriano-Mas, Carles Spalletta, Gianfranco Stevens, Michael C Szeszko, Philip R Tolin, David F Venkatasubramanian, Ganesan Walitza, Susanne Wang, Zhen van Wingen, Guido A Xu, Jian Xu, Xiufeng Zhao, Qing Thompson, Paul M Stein, Dan J van den Heuvel, Odile A Kwon, Jun Soo |
author_sort | Yun, Je-Yeon |
collection | PubMed |
description | Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T(1)-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10–0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions. |
format | Online Article Text |
id | pubmed-7009583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-70095832020-02-13 Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium Yun, Je-Yeon Boedhoe, Premika S W Vriend, Chris Jahanshad, Neda Abe, Yoshinari Ameis, Stephanie H Anticevic, Alan Arnold, Paul D Batistuzzo, Marcelo C Benedetti, Francesco Beucke, Jan C Bollettini, Irene Bose, Anushree Brem, Silvia Calvo, Anna Cheng, Yuqi Cho, Kang Ik K Ciullo, Valentina Dallaspezia, Sara Denys, Damiaan Feusner, Jamie D Fouche, Jean-Paul Giménez, Mònica Gruner, Patricia Hibar, Derrek P Hoexter, Marcelo Q Hu, Hao Huyser, Chaim Ikari, Keisuke Kathmann, Norbert Kaufmann, Christian Koch, Kathrin Lazaro, Luisa Lochner, Christine Marques, Paulo Marsh, Rachel Martínez-Zalacaín, Ignacio Mataix-Cols, David Menchón, José M Minuzzi, Luciano Morgado, Pedro Moreira, Pedro Nakamae, Takashi Nakao, Tomohiro Narayanaswamy, Janardhanan C Nurmi, Erika L O’Neill, Joseph Piacentini, John Piras, Fabrizio Piras, Federica Reddy, Y C Janardhan Sato, Joao R Simpson, H Blair Soreni, Noam Soriano-Mas, Carles Spalletta, Gianfranco Stevens, Michael C Szeszko, Philip R Tolin, David F Venkatasubramanian, Ganesan Walitza, Susanne Wang, Zhen van Wingen, Guido A Xu, Jian Xu, Xiufeng Zhao, Qing Thompson, Paul M Stein, Dan J van den Heuvel, Odile A Kwon, Jun Soo Brain Original Articles Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T(1)-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10–0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions. Oxford University Press 2020-02 2020-02-10 /pmc/articles/PMC7009583/ /pubmed/32040561 http://dx.doi.org/10.1093/brain/awaa001 Text en © The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Articles Yun, Je-Yeon Boedhoe, Premika S W Vriend, Chris Jahanshad, Neda Abe, Yoshinari Ameis, Stephanie H Anticevic, Alan Arnold, Paul D Batistuzzo, Marcelo C Benedetti, Francesco Beucke, Jan C Bollettini, Irene Bose, Anushree Brem, Silvia Calvo, Anna Cheng, Yuqi Cho, Kang Ik K Ciullo, Valentina Dallaspezia, Sara Denys, Damiaan Feusner, Jamie D Fouche, Jean-Paul Giménez, Mònica Gruner, Patricia Hibar, Derrek P Hoexter, Marcelo Q Hu, Hao Huyser, Chaim Ikari, Keisuke Kathmann, Norbert Kaufmann, Christian Koch, Kathrin Lazaro, Luisa Lochner, Christine Marques, Paulo Marsh, Rachel Martínez-Zalacaín, Ignacio Mataix-Cols, David Menchón, José M Minuzzi, Luciano Morgado, Pedro Moreira, Pedro Nakamae, Takashi Nakao, Tomohiro Narayanaswamy, Janardhanan C Nurmi, Erika L O’Neill, Joseph Piacentini, John Piras, Fabrizio Piras, Federica Reddy, Y C Janardhan Sato, Joao R Simpson, H Blair Soreni, Noam Soriano-Mas, Carles Spalletta, Gianfranco Stevens, Michael C Szeszko, Philip R Tolin, David F Venkatasubramanian, Ganesan Walitza, Susanne Wang, Zhen van Wingen, Guido A Xu, Jian Xu, Xiufeng Zhao, Qing Thompson, Paul M Stein, Dan J van den Heuvel, Odile A Kwon, Jun Soo Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium |
title | Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium |
title_full | Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium |
title_fullStr | Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium |
title_full_unstemmed | Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium |
title_short | Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium |
title_sort | brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the enigma consortium |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7009583/ https://www.ncbi.nlm.nih.gov/pubmed/32040561 http://dx.doi.org/10.1093/brain/awaa001 |
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