4 results on '"ENIGMA-OCD Working Group"'
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2. The thalamus and its subnuclei—a gateway to obsessive-compulsive disorder
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Cees J. Weeland, Selina Kasprzak, Niels T. de Joode, Yoshinari Abe, Pino Alonso, Stephanie H. Ameis, Alan Anticevic, Paul D. Arnold, Srinivas Balachander, Nerisa Banaj, Nuria Bargallo, Marcelo C. Batistuzzo, Francesco Benedetti, Jan C. Beucke, Irene Bollettini, Vilde Brecke, Silvia Brem, Carolina Cappi, Yuqi Cheng, Kang Ik K. Cho, Daniel L. C. Costa, Sara Dallaspezia, Damiaan Denys, Goi Khia Eng, Sónia Ferreira, Jamie D. Feusner, Martine Fontaine, Jean-Paul Fouche, Rachael G. Grazioplene, Patricia Gruner, Mengxin He, Yoshiyuki Hirano, Marcelo Q. Hoexter, Chaim Huyser, Hao Hu, Fern Jaspers-Fayer, Norbert Kathmann, Christian Kaufmann, Minah Kim, Kathrin Koch, Yoo Bin Kwak, Jun Soo Kwon, Luisa Lazaro, Chiang-shan R. Li, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, David Mataix-Cols, Jose M. Menchón, Luciano Minnuzi, Pedro Silva Moreira, Pedro Morgado, Akiko Nakagawa, Takashi Nakamae, Janardhanan C. Narayanaswamy, Erika L. Nurmi, Ana E. Ortiz, Jose C. Pariente, John Piacentini, Maria Picó-Pérez, Fabrizio Piras, Federica Piras, Christopher Pittenger, Y. C. Janardhan Reddy, Daniela Rodriguez-Manrique, Yuki Sakai, Eiji Shimizu, Venkataram Shivakumar, Helen Blair Simpson, Noam Soreni, Carles Soriano-Mas, Nuno Sousa, Gianfranco Spalletta, Emily R. Stern, Michael C. Stevens, S. Evelyn Stewart, Philip R. Szeszko, Jumpei Takahashi, Tais Tanamatis, Jinsong Tang, Anders Lillevik Thorsen, David Tolin, Ysbrand D. van der Werf, Hein van Marle, Guido A. van Wingen, Daniela Vecchio, G. Venkatasubramanian, Susanne Walitza, Jicai Wang, Zhen Wang, Anri Watanabe, Lidewij H. Wolters, Xiufeng Xu, Je-Yeon Yun, Qing Zhao, ENIGMA OCD Working Group, Tonya White, Paul M. Thompson, Dan J. Stein, Odile A. van den Heuvel, and Chris Vriend
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Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (
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- 2022
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3. White matter microstructure and its relation to clinical features of obsessive–compulsive disorder: findings from the ENIGMA OCD Working Group
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Fabrizio Piras, Federica Piras, Yoshinari Abe, Sri Mahavir Agarwal, Alan Anticevic, Stephanie Ameis, Paul Arnold, Nerisa Banaj, Núria Bargalló, Marcelo C. Batistuzzo, Francesco Benedetti, Jan-Carl Beucke, Premika S. W. Boedhoe, Irene Bollettini, Silvia Brem, Anna Calvo, Kang Ik Kevin Cho, Valentina Ciullo, Sara Dallaspezia, Erin Dickie, Benjamin Adam Ely, Siyan Fan, Jean-Paul Fouche, Patricia Gruner, Deniz A. Gürsel, Tobias Hauser, Yoshiyuki Hirano, Marcelo Q. Hoexter, Mariangela Iorio, Anthony James, Y. C. Janardhan Reddy, Christian Kaufmann, Kathrin Koch, Peter Kochunov, Jun Soo Kwon, Luisa Lazaro, Christine Lochner, Rachel Marsh, Akiko Nakagawa, Takashi Nakamae, Janardhanan C. Narayanaswamy, Yuki Sakai, Eiji Shimizu, Daniela Simon, Helen Blair Simpson, Noam Soreni, Philipp Stämpfli, Emily R. Stern, Philip Szeszko, Jumpei Takahashi, Ganesan Venkatasubramanian, Zhen Wang, Je-Yeon Yun, ENIGMA OCD Working Group, Dan J. Stein, Neda Jahanshad, Paul M. Thompson, Odile A. van den Heuvel, and Gianfranco Spalletta
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Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Microstructural alterations in cortico-subcortical connections are thought to be present in obsessive–compulsive disorder (OCD). However, prior studies have yielded inconsistent findings, perhaps because small sample sizes provided insufficient power to detect subtle abnormalities. Here we investigated microstructural white matter alterations and their relation to clinical features in the largest dataset of adult and pediatric OCD to date. We analyzed diffusion tensor imaging metrics from 700 adult patients and 645 adult controls, as well as 174 pediatric patients and 144 pediatric controls across 19 sites participating in the ENIGMA OCD Working Group, in a cross-sectional case-control magnetic resonance study. We extracted measures of fractional anisotropy (FA) as main outcome, and mean diffusivity, radial diffusivity, and axial diffusivity as secondary outcomes for 25 white matter regions. We meta-analyzed patient-control group differences (Cohen’s d) across sites, after adjusting for age and sex, and investigated associations with clinical characteristics. Adult OCD patients showed significant FA reduction in the sagittal stratum (d = −0.21, z = −3.21, p = 0.001) and posterior thalamic radiation (d = −0.26, z = −4.57, p
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- 2021
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4. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group
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Premika S. W. Boedhoe, Martijn W. Heymans, Lianne Schmaal, Yoshinari Abe, Pino Alonso, Stephanie H. Ameis, Alan Anticevic, Paul D. Arnold, Marcelo C. Batistuzzo, Francesco Benedetti, Jan C. Beucke, Irene Bollettini, Anushree Bose, Silvia Brem, Anna Calvo, Rosa Calvo, Yuqi Cheng, Kang Ik K. Cho, Valentina Ciullo, Sara Dallaspezia, Damiaan Denys, Jamie D. Feusner, Kate D. Fitzgerald, Jean-Paul Fouche, Egill A. Fridgeirsson, Patricia Gruner, Gregory L. Hanna, Derrek P. Hibar, Marcelo Q. Hoexter, Hao Hu, Chaim Huyser, Neda Jahanshad, Anthony James, Norbert Kathmann, Christian Kaufmann, Kathrin Koch, Jun Soo Kwon, Luisa Lazaro, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, David Mataix-Cols, José M. Menchón, Luciano Minuzzi, Astrid Morer, Takashi Nakamae, Tomohiro Nakao, Janardhanan C. Narayanaswamy, Seiji Nishida, Erika L. Nurmi, Joseph O'Neill, John Piacentini, Fabrizio Piras, Federica Piras, Y. C. Janardhan Reddy, Tim J. Reess, Yuki Sakai, Joao R. Sato, H. Blair Simpson, Noam Soreni, Carles Soriano-Mas, Gianfranco Spalletta, Michael C. Stevens, Philip R. Szeszko, David F. Tolin, Guido A. van Wingen, Ganesan Venkatasubramanian, Susanne Walitza, Zhen Wang, Je-Yeon Yun, ENIGMA-OCD Working-Group, Paul M. Thompson, Dan J. Stein, Odile A. van den Heuvel, and Jos W. R. Twisk
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neuroimaging ,MRI ,IPD meta-analysis ,mega-analysis ,linear mixed-effect models ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.
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- 2019
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