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1. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author

Boedhoe, Premika SW, Heymans, Martijn W, Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Hanna, Gregory L, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nishida, Seiji, Nurmi, Erika L, O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Reess, Tim J, Sakai, Yuki, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Working-Group, ENIGMA-OCD, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, and Twisk, Jos WR

Subjects
Biomedical and Clinical Sciences, Information and Computing Sciences, Neurosciences, Applied Computing, Machine Learning, neuroimaging, MRI, IPD meta-analysis, mega-analysis, linear mixed-effect models, ENIGMA-OCD Working-Group, Cognitive Sciences, Applied computing, Machine learning
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.

Published
2019

2. Cortical Abnormalities Associated With Pediatric and Adult Obsessive-Compulsive Disorder: Findings From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author

Boedhoe, Premika SW, Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Hanna, Gregory L, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nishida, Seiji, Nurmi, Erika, O’Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Reess, Tim J, Sakai, Yuki, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, Bargalló, Nuria, Brandeis, Daniel, Buimer, Elizabeth, Busatto, Geraldo F, de Vries, Froukje E, de Wit, Stella J, Drechsler, Renate, and Falini, Andrea

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Pediatric, Neurosciences, Mental Health, Serious Mental Illness, Brain Disorders, Neurological, Mental health, Adolescent, Adult, Age of Onset, Cerebral Cortex, Child, Frontal Lobe, Humans, Magnetic Resonance Imaging, Obsessive-Compulsive Disorder, Parietal Lobe, Reference Values, Temporal Lobe, Young Adult, ENIGMA-OCD Working Group, ENIGMA OCD Working Group, Cortical Thickness, FreeSurfer, MRI, Surface Area, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Clinical and health psychology
Abstract

ObjectiveBrain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.MethodT1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.ResultsIn adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.ConclusionsThe parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

Published
2018

3. Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders

Author

de Lange, Siemon C., Scholtens, Lianne H., Alzheimer’s Disease Neuroimaging Initiative, van den Berg, Leonard H., Boks, Marco P., Bozzali, Marco, Cahn, Wiepke, Dannlowski, Udo, Durston, Sarah, Geuze, Elbert, van Haren, Neeltje E. M., Hillegers, Manon H. J., Koch, Kathrin, Jurado, María Ángeles, Mancini, Matteo, Marqués-Iturria, Idoia, Meinert, Susanne, Ophoff, Roel A., Reess, Tim J., Repple, Jonathan, Kahn, René S., and van den Heuvel, Martijn P.

Published
2019
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6. Effects of Mindfulness Training on Emotion Regulation in Patients With Depression: Reduced Dorsolateral Prefrontal Cortex Activation Indexes Early Beneficial Changes

Author

Barnhofer, Thorsten, primary, Reess, Tim J., additional, Fissler, Maria, additional, Winnebeck, Emilia, additional, Grimm, Simone, additional, Gärtner, Matti, additional, Fan, Yan, additional, Huntenburg, Julia M., additional, Schroeter, Titus A., additional, Gummersbach, Marie, additional, Bajbouj, Malek, additional, and Hölzel, Britta K., additional

Published
2021
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7. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author

Boedhoe, Premika S. W., Heymans, Martijn W., Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Anticevic, Alan, Arnold, Paul D., Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K., Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D., Fitzgerald, Kate D., Fouche, Jean-Paul, Fridgeirsson, Egill A., Gruner, Patricia, Hanna, Gregory L., Hibar, Derrek P., Hoexter, Marcelo Q., Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M., Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C., Nishida, Seiji, Nurmi, Erika L., O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, Y. C. Janardhan, Reess, Tim J., Sakai, Yuki, Sato, Joao R., Simpson, H. Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C., Szeszko, Philip R., Tolin, David F., van Wingen, Guido A., Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., Twisk, Jos W. R., Boedhoe, Premika S. W., Heymans, Martijn W., Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Anticevic, Alan, Arnold, Paul D., Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K., Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D., Fitzgerald, Kate D., Fouche, Jean-Paul, Fridgeirsson, Egill A., Gruner, Patricia, Hanna, Gregory L., Hibar, Derrek P., Hoexter, Marcelo Q., Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M., Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C., Nishida, Seiji, Nurmi, Erika L., O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, Y. C. Janardhan, Reess, Tim J., Sakai, Yuki, Sato, Joao R., Simpson, H. Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C., Szeszko, Philip R., Tolin, David F., van Wingen, Guido A., Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., and Twisk, Jos W. R.

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., Peer Reviewed

Published
2019

8. Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders

Author

UMC Utrecht, Projectafdeling ALS, ZL Neuromusculaire Ziekten Medisch, Brain, Regenerative Medicine and Stem Cells, Onderzoeksgroep 2, Onderzoek, Ontwikkelingsstoornissen Ond., MGGZ, Onderzoeksgroep 1, Diagnostiek & Vroege Psychose Medisch, de Lange, Siemon C., Scholtens, Lianne H., van den Berg, Leonard H., Boks, Marco P., Bozzali, Marco, Cahn, Wiepke, Dannlowski, Udo, Durston, Sarah, Geuze, Elbert, van Haren, Neeltje E.M., Hillegers, Manon H.J., Koch, Kathrin, Jurado, María Ángeles, Mancini, Matteo, Marqués-Iturria, Idoia, Meinert, Susanne, Ophoff, Roel A., Reess, Tim J., Repple, Jonathan, Kahn, René S., van den Heuvel, Martijn P., Alzheimer's Disease Neuroimaging Initiative, UMC Utrecht, Projectafdeling ALS, ZL Neuromusculaire Ziekten Medisch, Brain, Regenerative Medicine and Stem Cells, Onderzoeksgroep 2, Onderzoek, Ontwikkelingsstoornissen Ond., MGGZ, Onderzoeksgroep 1, Diagnostiek & Vroege Psychose Medisch, de Lange, Siemon C., Scholtens, Lianne H., van den Berg, Leonard H., Boks, Marco P., Bozzali, Marco, Cahn, Wiepke, Dannlowski, Udo, Durston, Sarah, Geuze, Elbert, van Haren, Neeltje E.M., Hillegers, Manon H.J., Koch, Kathrin, Jurado, María Ángeles, Mancini, Matteo, Marqués-Iturria, Idoia, Meinert, Susanne, Ophoff, Roel A., Reess, Tim J., Repple, Jonathan, Kahn, René S., van den Heuvel, Martijn P., and Alzheimer's Disease Neuroimaging Initiative

Published
2019

12. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author

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, Jos W. R. Twisk, Anatomy and neurosciences, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Psychiatry, Epidemiology and Data Science, APH - Personalized Medicine, APH - Methodology, APH - Health Behaviors & Chronic Diseases, ACS - Atherosclerosis & ischemic syndromes, Netherlands Institute for Neuroscience (NIN), Boedhoe, Premika S W, Heymans, Martijn W, Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Hanna, Gregory L, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nishida, Seiji, Nurmi, Erika L, O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, Y C Janardhan, Reess, Tim J, Sakai, Yuki, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carle, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, Twisk, Jos W R, Adult Psychiatry, Graduate School, and Child Psychiatry

Subjects
mega-analysis, Mega-analysis, Biomedical Engineering, Neuroscience (miscellaneous), Neuroimaging, Linear mixed-effect models, 050105 experimental psychology, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Obsessive compulsive, Statistics, Linear regression, IPD meta-analysi, 0501 psychology and cognitive sciences, ddc:610, IPD meta-analysis, Association (psychology), lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, mega-analysi, Mathematics, Original Research, linear mixed-effect model, neuroimaging, 05 social sciences, Variance (accounting), Confidence interval, Computer Science Applications, Standard error, linear mixed-effect models, Mega analysis, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery, Neuroscience, MRI
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.

Published
2019
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13. Cortical Abnormalities Associated With Pediatric and Adult Obsessive-Compulsive Disorder: Findings From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author

Alan Anticevic, Valentina Ciullo, Janardhanan C. Narayanaswamy, Paul D. Arnold, Pino Alonso, Seiji Nishida, Luisa Lázaro, Christine Lochner, Zhen Wang, Joseph O'Neill, Silvia Brem, Guido A. Van Wingen, Jean Paul Fouche, Marcelo Queiroz Hoexter, Y. C.Janardhan Reddy, Michael C. Stevens, Stephanie H. Ameis, Fabrizio Piras, Jun Soo Kwon, Rosa Calvo, Marcelo C. Batistuzzo, Gregory L. Hanna, Yoshinari Abe, Yuki Sakai, David F. Tolin, Irene Bollettini, Kathrin Koch, H. Blair Simpson, Noam Soreni, Erika L. Nurmi, Kang Ik Kevin Cho, Luciano Minuzzi, Jan C. Beucke, Susanne Walitza, Jamie D. Feusner, Derrek P. Hibar, Chaim Huyser, Anna Calvo, Tomohiro Nakao, Ignacio Martínez-Zalacaín, Patricia Gruner, Francesco Benedetti, Je Yeon Yun, David Mataix-Cols, Paul M. Thompson, Odile A. van den Heuvel, Anthony A. James, Premika S.W. Boedhoe, Yuqi Cheng, Hao Hu, Tim Jonas Reess, John Piacentini, Carles Soriano-Mas, Neda Jahanshad, Kate D. Fitzgerald, Astrid Morer, Sara Dallaspezia, Gianfranco Spalletta, Dan J. Stein, Takashi Nakamae, João Ricardo Sato, Egill A. Fridgeirsson, Christian Kaufmann, Ganesan Venkatasubramanian, Philip R. Szeszko, Lianne Schmaal, Rachel Marsh, Norbert Kathmann, Anushree Bose, Federica Piras, José M. Menchón, D. Denys, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Boedhoe, Premika S W, Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Hanna, Gregory L, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nishida, Seiji, Nurmi, Erika, O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, Y C Janardhan, Reess, Tim J, Sakai, Yuki, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carle, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, ANS - Compulsivity, Impulsivity & Attention, Adult Psychiatry, Graduate School, Child Psychiatry, and Netherlands Institute for Neuroscience (NIN)

Subjects
Adult, Obsessive-Compulsive Disorder, medicine.medical_specialty, Adolescent, FreeSurfer, behavioral disciplines and activities, Article, Temporal lobe, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Reference Values, Cortical abnormalities, Parietal Lobe, mental disorders, Journal Article, medicine, Obsessive-compulsive disorder, Humans, Cortical Thickne, Age of Onset, Young adult, Child, Psychiatry, Cerebral Cortex, Neurosi obsessiva, Parietal lobe, Surface Area, Voxel-based morphometry, Magnetic Resonance Imaging, Temporal Lobe, humanities, Frontal Lobe, 030227 psychiatry, Psychiatry and Mental health, Diagnòstic per la imatge, Frontal lobe, Diagnostic imaging, Age of onset, Psychology, 030217 neurology & neurosurgery, MRI
Abstract

Objective: Brain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken. Method: T1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume. Results: In adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortexand a thinner inferiorparietalcortex.Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showedsignificant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from 20.10 to 20.33. Conclusions: The parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication.

Published
2018
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14. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group.

Author

Boedhoe PSW, Heymans MW, Schmaal L, Abe Y, Alonso P, Ameis SH, Anticevic A, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Calvo R, Cheng Y, Cho KIK, Ciullo V, Dallaspezia S, Denys D, Feusner JD, Fitzgerald KD, Fouche JP, Fridgeirsson EA, Gruner P, Hanna GL, Hibar DP, Hoexter MQ, Hu H, Huyser C, Jahanshad N, James A, Kathmann N, Kaufmann C, Koch K, Kwon JS, Lazaro L, Lochner C, Marsh R, Martínez-Zalacaín I, Mataix-Cols D, Menchón JM, Minuzzi L, Morer A, Nakamae T, Nakao T, Narayanaswamy JC, Nishida S, Nurmi EL, O'Neill J, Piacentini J, Piras F, Piras F, Reddy YCJ, Reess TJ, Sakai Y, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, van Wingen GA, Venkatasubramanian G, Walitza S, Wang Z, Yun JY, Thompson PM, Stein DJ, van den Heuvel OA, and Twisk JWR

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.

Published
2019
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