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6. Correction: The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Author

Bruin, Willem B, Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea L, Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo C, Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan AJP, Echevarria, Marco AN, Eng, Goi Khia, Ferreira, Sónia, Feusner, Jamie D, Grazioplene, Rachael G, Gruner, Patricia, Guo, Joyce Y, Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun Soo, Lazaro, Luisa, Li, Chiang-Shan R, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose M, Moreira, Pedro S, Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, Zorrilla, Jose C Pariente, Piacentini, John, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan YC, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair H, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily R, Evelyn Stewart, S, Szeszko, Philip R, Tang, Jinsong, Thomopoulos, Sophia I, Thorsen, Anders L, Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya M, Venkatasubramanian, Ganesan, Vetter, Nora C, Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke A, van Marle, Hein JF, van de Mortel, Laurens A, van der Straten, Anouk, van der Werf, Ysbrand D, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, and van Wingen, Guido A

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Machine Learning and Artificial Intelligence, Mental Illness, Serious Mental Illness, Brain Disorders, Anxiety Disorders, Mental Health, Good Health and Well Being, ENIGMA-OCD Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Correction to: Molecular Psychiatry, published online 2 May 2023 In this article Honami Arai, Irene Bollettini, Rosa Calvo Escalona, Ana Coelho, Federica Colombo, Leila Darwich, Martine Fontaine, Toshikazu Ikuta, Jonathan C. Ipser, Asier Juaneda-Seguí, Hitomi Kitagawa, Gerd Kvale, Mafalda Machado-Sousa, Astrid Morer, Takashi Nakamae, Jin Narumoto, Joseph O’Neill, Sho Okawa, Eva Real, Veit Roessner, Joao R. Sato, Cinto Segalàs, Roseli G. Shavitt, Dick J. Veltman, Kei Yamada were missing from the author list indexed under the ENIGMA-OCD Working Group. Additionally, there was an error regarding Tokiko Yoshida’s name, where the first name and last name were written in the wrong order. The original article has been corrected.

Published
2023

7. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Author

Bruin, Willem, Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea, Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo, Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan, Echevarria, Marco, Eng, Goi, Ferreira, Sónia, Feusner, Jamie, Grazioplene, Rachael, Gruner, Patricia, Guo, Joyce, Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo, Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun, Lazaro, Luisa, Li, Chiang-Shan, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose, Moreira, Pedro, Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan, Nurmi, Erika, Zorrilla, Jose, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily, Evelyn Stewart, S, Szeszko, Philip, Tang, Jinsong, Thomopoulos, Sophia, Thorsen, Anders, Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya, Venkatasubramanian, Ganesan, Vetter, Nora, Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke, van Marle, Hein, van de Mortel, Laurens, van der Straten, Anouk, van der Werf, Ysbrand, Thompson, Paul, Stein, Dan, van den Heuvel, Odile, van Wingen, Guido, and Piacentini, John

Subjects
Humans, Connectome, Brain Mapping, Magnetic Resonance Imaging, Brain, Obsessive-Compulsive Disorder, Biomarkers, Neural Pathways
Abstract

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Published
2023

8. White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author

Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook

Published
2024
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9. Benchmarking Graph Neural Networks for FMRI analysis

Author

ElGazzar, Ahmed, Thomas, Rajat, and van Wingen, Guido

Subjects
Computer Science - Machine Learning
Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data. A paramount example of such data is the brain, which operates as a network, from the micro-scale of neurons, to the macro-scale of regions. This organization deemed GNNs a natural tool of choice to model brain activity, and have consequently attracted a lot of attention in the neuroimaging community. Yet, the advantage of adopting these models over conventional methods has not yet been assessed in a systematic way to gauge if GNNs are capable of leveraging the underlying structure of the data to improve learning. In this work, we study and evaluate the performance of five popular GNN architectures in diagnosing major depression disorder and autism spectrum disorder in two multi-site clinical datasets, and sex classification on the UKBioBank, from functional brain scans under a general uniform framework. Our results show that GNNs fail to outperform kernel-based and structure-agnostic deep learning models, in which 1D CNNs outperform the other methods in all scenarios. We highlight that creating optimal graph structures for functional brain data is a major bottleneck hindering the performance of GNNs, where existing works use arbitrary measures to define the edges resulting in noisy graphs. We therefore propose to integrate graph diffusion into existing architectures and show that it can alleviate this problem and improve their performance. Our results call for increased moderation and rigorous validation when evaluating graph methods and advocate for more data-centeric approaches in developing GNNs for functional neuroimaging applications., Comment: 14 pages, 3 figures

Published
2022

11. fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models

Author

El-Gazzar, Ahmed, Thomas, Rajat Mani, and Van Wingen, Guido

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Single-subject mapping of resting-state brain functional activity to non-imaging phenotypes is a major goal of neuroimaging. The large majority of learning approaches applied today rely either on static representations or on short-term temporal correlations. This is at odds with the nature of brain activity which is dynamic and exhibit both short- and long-range dependencies. Further, new sophisticated deep learning approaches have been developed and validated on single tasks/datasets. The application of these models for the study of a different targets typically require exhaustive hyperparameter search, model engineering and trial and error to obtain competitive results with simpler linear models. This in turn limit their adoption and hinder fair benchmarking in a rapidly developing area of research. To this end, we propose fMRI-S4; a versatile deep learning model for the classification of phenotypes and psychiatric disorders from the timecourses of resting-state functional magnetic resonance imaging scans. fMRI-S4 capture short- and long- range temporal dependencies in the signal using 1D convolutions and the recently introduced state-space models S4. The proposed architecture is lightweight, sample-efficient and robust across tasks/datasets. We validate fMRI-S4 on the tasks of diagnosing major depressive disorder (MDD), autism spectrum disorder (ASD) and sex classifcation on three multi-site rs-fMRI datasets. We show that fMRI-S4 can outperform existing methods on all three tasks and can be trained as a plug&play model without special hyperpararameter tuning for each setting, Comment: 11 pages, 3 Figures, Accepted at MLCN 2022

Published
2022

12. Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression

Author

Argyelan, Miklos, Deng, Zhi-De, Ousdal, Olga Therese, Oltedal, Leif, Angulo, Brian, Baradits, Mate, Spitzberg, Andrew J., Kessler, Ute, Sartorius, Alexander, Dols, Annemiek, Narr, Katherine L., Espinoza, Randall, van Waarde, Jeroen A., Tendolkar, Indira, van Eijndhoven, Philip, van Wingen, Guido A., Takamiya, Akihiro, Kishimoto, Taishiro, Jorgensen, Martin B., Jorgensen, Anders, Paulson, Olaf B., Yrondi, Antoine, Péran, Patrice, Soriano-Mas, Carles, Cardoner, Narcis, Cano, Marta, van Diermen, Linda, Schrijvers, Didier, Belge, Jean-Baptiste, Emsell, Louise, Bouckaert, Filip, Vandenbulcke, Mathieu, Kiebs, Maximilian, Hurlemann, René, Mulders, Peter CR., Redlich, Ronny, Dannlowski, Udo, Kavakbasi, Erhan, Kritzer, Michael D., Ellard, Kristen K., Camprodon, Joan A., Petrides, Georgios, Malhotra, Anil K., and Abbott, Christopher C.

Published
2024
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13. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Author

Bruin, Willem B., Zhutovsky, Paul, van Wingen, Guido A., Bas-Hoogendam, Janna Marie, Groenewold, Nynke A., Hilbert, Kevin, Winkler, Anderson M., Zugman, Andre, Agosta, Federica, Åhs, Fredrik, Andreescu, Carmen, Antonacci, Chase, Asami, Takeshi, Assaf, Michal, Barber, Jacques P., Bauer, Jochen, Bavdekar, Shreya Y., Beesdo-Baum, Katja, Benedetti, Francesco, Bernstein, Rachel, Björkstrand, Johannes, Blair, Robert J., Blair, Karina S., Blanco-Hinojo, Laura, Böhnlein, Joscha, Brambilla, Paolo, Bressan, Rodrigo A., Breuer, Fabian, Cano, Marta, Canu, Elisa, Cardinale, Elise M., Cardoner, Narcís, Cividini, Camilla, Cremers, Henk, Dannlowski, Udo, Diefenbach, Gretchen J., Domschke, Katharina, Doruyter, Alexander G. G., Dresler, Thomas, Erhardt, Angelika, Filippi, Massimo, Fonzo, Gregory A., Freitag, Gabrielle F., Furmark, Tomas, Ge, Tian, Gerber, Andrew J., Gosnell, Savannah N., Grabe, Hans J., Grotegerd, Dominik, Gur, Ruben C., Gur, Raquel E., Hamm, Alfons O., Han, Laura K. M., Harper, Jennifer C., Harrewijn, Anita, Heeren, Alexandre, Hofmann, David, Jackowski, Andrea P., Jahanshad, Neda, Jett, Laura, Kaczkurkin, Antonia N., Khosravi, Parmis, Kingsley, Ellen N., Kircher, Tilo, Kostic, Milutin, Larsen, Bart, Lee, Sang-Hyuk, Leehr, Elisabeth J., Leibenluft, Ellen, Lochner, Christine, Lui, Su, Maggioni, Eleonora, Manfro, Gisele G., Månsson, Kristoffer N. T., Marino, Claire E., Meeten, Frances, Milrod, Barbara, Jovanovic, Ana Munjiza, Mwangi, Benson, Myers, Michael J., Neufang, Susanne, Nielsen, Jared A., Ohrmann, Patricia A., Ottaviani, Cristina, Paulus, Martin P., Perino, Michael T., Phan, K. Luan, Poletti, Sara, Porta-Casteràs, Daniel, Pujol, Jesus, Reinecke, Andrea, Ringlein, Grace V., Rjabtsenkov, Pavel, Roelofs, Karin, Salas, Ramiro, Salum, Giovanni A., Satterthwaite, Theodore D., Schrammen, Elisabeth, Sindermann, Lisa, Smoller, Jordan W., Soares, Jair C., Stark, Rudolf, Stein, Frederike, Straube, Thomas, Straube, Benjamin, Strawn, Jeffrey R., Suarez-Jimenez, Benjamin, Sylvester, Chad M., Talati, Ardesheer, Thomopoulos, Sophia I., Tükel, Raşit, van Nieuwenhuizen, Helena, Werwath, Kathryn, Wittfeld, Katharina, Wright, Barry, Wu, Mon-Ju, Yang, Yunbo, Zilverstand, Anna, Zwanzger, Peter, Blackford, Jennifer U., Avery, Suzanne N., Clauss, Jacqueline A., Lueken, Ulrike, Thompson, Paul M., Pine, Daniel S., Stein, Dan J., van der Wee, Nic J. A., Veltman, Dick J., and Aghajani, Moji

Published
2024
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14. Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models

Author

Gazzar, Ahmed El, Thomas, Rajat Mani, and Van Wingen, Guido

Subjects
Computer Science - Machine Learning
Abstract

Single subject prediction of brain disorders from neuroimaging data has gained increasing attention in recent years. Yet, for some heterogeneous disorders such as major depression disorder (MDD) and autism spectrum disorder (ASD), the performance of prediction models on large-scale multi-site datasets remains poor. We present a two-stage framework to improve the diagnosis of heterogeneous psychiatric disorders from resting-state functional magnetic resonance imaging (rs-fMRI). First, we propose a self-supervised mask prediction task on data from healthy individuals that can exploit differences between healthy controls and patients in clinical datasets. Next, we train a supervised classifier on the learned discriminative representations. To model rs-fMRI data, we develop Graph-S4; an extension to the recently proposed state-space model S4 to graph settings where the underlying graph structure is not known in advance. We show that combining the framework and Graph-S4 can significantly improve the diagnostic performance of neuroimaging-based single subject prediction models of MDD and ASD on three open-source multi-center rs-fMRI clinical datasets., Comment: 20 pages

Published
2022

16. Electroconvulsive therapy and cognitive performance from the Global ECT MRI Research Collaboration

Author

Kiebs, Maximilian, Farrar, Danielle C., Yrondi, Antoine, Cardoner, Narcis, Tuovinen, Noora, Redlich, Ronny, Dannlowski, Udo, Soriano-Mas, Carles, Dols, Annemiek, Takamiya, Akihiro, Tendolkar, Indira, Narr, Katherine L., Espinoza, Randall, Laroy, Maarten, van Eijndhoven, Philip, Verwijk, Esmée, van Waarde, Jeroen, Verdijk, Joey, Maier, Hannah B., Nordanskog, Pia, van Wingen, Guido, van Diermen, Linda, Emsell, Louise, Bouckaert, Filip, Repple, Jonathan, Camprodon, Joan A., Wade, Benjamin S.C., Donaldson, K. Tristan, Oltedal, Leif, Kessler, Ute, Hammar, Åsa, Sienaert, Pascal, Hebbrecht, Kaat, Urretavizcaya, Mikel, Belge, Jean-Baptiste, Argyelan, Miklos, Baradits, Mate, Obbels, Jasmien, Draganski, Bogdan, Philipsen, Alexandra, Sartorius, Alexander, Rhebergen, Didericke, Ousdal, Olga Therese, Hurlemann, René, McClintock, Shawn, Erhardt, Erik B., and Abbott, Christopher C.

Published
2024
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18. Characterization of gray matter volume changes from one week to 6 months after termination of electroconvulsive therapy in depressed patients

Author

Laroy, Maarten, Bouckaert, Filip, Ousdal, Olga Therese, Dols, Annemieke, Rhebergen, Didi, van Exel, Eric, van Wingen, Guido, van Waarde, Jeroen, Verdijk, Joey, Kessler, Ute, Bartsch, Hauke, Jorgensen, Martin Balslev, Paulson, Olaf B., Nordanskog, Pia, Prudic, Joan, Sienaert, Pascal, Vandenbulcke, Mathieu, Oltedal, Leif, and Emsell, Louise

Published
2024
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19. Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling

Author

El-Gazzar, Ahmed, Thomas, Rajat Mani, and van Wingen, Guido

Subjects
Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this representation has advanced our understanding of brain function, it represents a simplified model of brain connectivity that has a complex dynamic spatio-temporal nature. Oversimplification of the data may hinder the merits of applying advanced non-linear feature extraction algorithms. To this end, we propose a dynamic adaptive spatio-temporal graph convolution (DAST-GCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module while mapping brain connectivity to a phenotype in a supervised learning framework. This leverages the computational power of the model, data and targets to represent brain connectivity, and could enable the identification of potential biomarkers for the supervised target in question. We evaluate our pipeline on the UKBiobank dataset for age and gender classification tasks from resting-state functional scans and show that it outperforms currently adapted linear and non-linear methods in neuroimaging. Further, we assess the generalizability of the inferred graph structure by transferring the pre-trained graph to an independent dataset for the same task. Our results demonstrate the task-robustness of the graph against different scanning parameters and demographics., Comment: Accepted at International Workshop on Machine Learning in Clinical Neuroimaging (MLCN2021)

Published
2021
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20. An overview of the first 5 years of the ENIGMA obsessive–compulsive disorder working group: The power of worldwide collaboration

Author

van den Heuvel, Odile A, Boedhoe, Premika SW, Bertolin, Sara, Bruin, Willem B, Francks, Clyde, Ivanov, Iliyan, Jahanshad, Neda, Kong, Xiang‐Zhen, Kwon, Jun Soo, O'Neill, Joseph, Paus, Tomas, Patel, Yash, Piras, Fabrizio, Schmaal, Lianne, Soriano‐Mas, Carles, Spalletta, Gianfranco, van Wingen, Guido A, Yun, Je‐Yeon, Vriend, Chris, Simpson, H Blair, van Rooij, Daan, Hoexter, Marcelo Q, Hoogman, Martine, Buitelaar, Jan K, Arnold, Paul, Beucke, Jan C, Benedetti, Francesco, Bollettini, Irene, Bose, Anushree, Brennan, Brian P, De Nadai, Alessandro S, Fitzgerald, Kate, Gruner, Patricia, Grünblatt, Edna, Hirano, Yoshiyuki, Huyser, Chaim, James, Anthony, Koch, Kathrin, Kvale, Gerd, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Mataix‐Cols, David, Morgado, Pedro, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika, Pittenger, Christopher, Reddy, YC Janardhan, Sato, João R, Soreni, Noam, Stewart, S Evelyn, Taylor, Stephan F, Tolin, David, Thomopoulos, Sophia I, Veltman, Dick J, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Thompson, Paul M, Stein, Dan J, Abe, Yoshinari, Alonso, Pino, Assogna, Francesca, Banaj, Nerisa, Batistuzzo, Marcelo C, Brem, Silvia, Ciullo, Valentina, Feusner, Jamie, Martínez‐Zalacaín, Ignacio, Menchón, José M, Miguel, Euripedes C, Piacentini, John, Piras, Federica, Sakai, Yuki, Wolters, Lidewij, and Yamada, Kei

Subjects
Biological Psychology, Psychology, Brain Disorders, Clinical Research, Serious Mental Illness, Pediatric, Neurosciences, Mental Health, Mental health, Neurological, Cerebral Cortex, Humans, Machine Learning, Multicenter Studies as Topic, Neuroimaging, Obsessive-Compulsive Disorder, cortical thickness, ENIGMA, mega-analysis, meta-analysis, MRI, obsessive-compulsive disorder, surface area, volume, ENIGMA-OCD working group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Published
2022

21. Brain abnormalities in survivors of COVID-19 after 2-year recovery: a functional MRI study

Author

Zhao, Yimiao, Liang, Qiongdan, Jiang, Zhendong, Mei, Huan, Zeng, Na, Su, Sizhen, Wu, Shanshan, Ge, Yinghong, Li, Peng, Lin, Xiao, Yuan, Kai, Shi, Le, Yan, Wei, Liu, Xiaoxing, Sun, Jie, Liu, Weijian, van Wingen, Guido, Gao, Yujun, Tan, Yiqing, Hong, Yi, Lu, Yu, Wu, Ping, Zhang, Xiujun, Wang, Yongxiang, Shi, Jie, Wang, Yumei, Lu, Lin, Li, Xiangyou, and Bao, Yanping

Published
2024
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23. Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

Author

Gallo, Selene, El-Gazzar, Ahmed, Zhutovsky, Paul, Thomas, Rajat M., Javaheripour, Nooshin, Li, Meng, Bartova, Lucie, Bathula, Deepti, Dannlowski, Udo, Davey, Christopher, Frodl, Thomas, Gotlib, Ian, Grimm, Simone, Grotegerd, Dominik, Hahn, Tim, Hamilton, Paul J., Harrison, Ben J., Jansen, Andreas, Kircher, Tilo, Meyer, Bernhard, Nenadić, Igor, Olbrich, Sebastian, Paul, Elisabeth, Pezawas, Lukas, Sacchet, Matthew D., Sämann, Philipp, Wagner, Gerd, Walter, Henrik, Walter, Martin, and van Wingen, Guido

Published
2023
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24. Negative Valence in Obsessive-Compulsive Disorder: A Worldwide Mega-Analysis of Task-Based Functional Neuroimaging Data of the ENIGMA-OCD Consortium

Author

Bertolin, Sara, Beyer, Nicole, Castelhano, Joao, Choi, Sunah, Couto, Beatriz, Real, Eva, Segalas, Cinto, Sousa, Nuno, Uhlmann, Anne, Zhao, Yijie, Dzinalija, Nadza, Vriend, Chris, Waller, Lea, Simpson, H. Blair, Ivanov, Iliyan, Agarwal, Sri Mahavir, Alonso, Pino, Backhausen, Lea L., Balachander, Srinivas, Broekhuizen, Aniek, Castelo-Branco, Miguel, Costa, Ana Daniela, Cui, Hailun, Denys, Damiaan, Duarte, Isabel Catarina, Eng, Goi Khia, Erk, Susanne, Fitzsimmons, Sophie M.D.D., Ipser, Jonathan, Jaspers-Fayer, Fern, de Joode, Niels T., Kim, Minah, Koch, Kathrin, Kwon, Jun Soo, van Leeuwen, Wieke, Lochner, Christine, van Marle, Hein J.F., Martinez-Zalacain, Ignacio, Menchon, Jose M., Morgado, Pedro, Narayanaswamy, Janardhanan C., Olivier, Ian S., Picó-Pérez, Maria, Postma, Tjardo S., Rodriguez-Manrique, Daniela, Roessner, Veit, Rus-Oswald, Oana Georgiana, Shivakumar, Venkataram, Soriano-Mas, Carles, Stern, Emily R., Stewart, S. Evelyn, van der Straten, Anouk L., Sun, Bomin, Thomopoulos, Sophia I., Veltman, Dick J., Vetter, Nora C., Visser, Henny, Voon, Valerie, Walter, Henrik, van der Werf, Ysbrand D., van Wingen, Guido, Stein, Dan J., Thompson, Paul M., Veer, Ilya M., and van den Heuvel, Odile A.

Published
2024
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25. Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls

Author

Verdijk, Joey P.A.J., van de Mortel, Laurens A., ten Doesschate, Freek, Pottkämper, Julia C.M., Stuiver, Sven, Bruin, Willem B., Abbott, Christopher C., Argyelan, Miklos, Ousdal, Olga T., Bartsch, Hauke, Narr, Katherine, Tendolkar, Indira, Calhoun, Vince, Lukemire, Joshua, Guo, Ying, Oltedal, Leif, van Wingen, Guido, and van Waarde, Jeroen A.

Published
2024
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26. Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author

Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook

Published
2024
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28. A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study

Author

El-Gazzar, Ahmed, Quaak, Mirjam, Cerliani, Leonardo, Bloem, Peter, van Wingen, Guido, and Thomas, Rajat Mani

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4-Dimensional (3-space + 1-time) tensors. And it is widely believed that the spatio-temporal patterns in fMRI manifests as behaviour and clinical symptoms. Because of the high dimensionality ($\sim$ 1 Million) of fMRI, and the added constraints of limited cardinality of data sets, extracting such patterns are challenging. A standard approach to overcome these hurdles is to reduce the dimensionality of the data by either summarizing activation over time or space at the expense of possible loss of useful information. Here, we introduce an end-to-end algorithm capable of extracting spatiotemporal features from the full 4-D data using 3-D CNNs and 3-D Convolutional LSTMs. We evaluate our proposed model on the publicly available ABIDE dataset to demonstrate the capability of our model to classify Autism Spectrum Disorder (ASD) from resting-state fMRI data. Our results show that the proposed model achieves state of the art results on single sites with F1-scores of 0.78 and 0.7 on NYU and UM sites, respectively., Comment: 8pages

Published
2020

30. Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism

Author

Gazzar, Ahmed El, Cerliani, Leonardo, van Wingen, Guido, and Thomas, Rajat Mani

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning
Abstract

Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-state functional MRI (rsfMRI), which captures the intrinsic connectivity between regions in the brain. Because rsfMRI data points are inherently high-dimensional (~1M), it is impossible to process the entire input in its raw form. In this paper, we propose a very simple transformation of the rsfMRI images that captures all of the temporal dynamics of the signal but sub-samples its spatial extent. As a result, we use a very simple 1-D convolutional network which is fast to train, requires minimal preprocessing and performs at par with the state-of-the-art on the classification of Autism spectrum disorders., Comment: accepted for publication in IJCNN 2019

Published
2019

31. Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters.

Author

Bruin, Willem B, Taylor, Luke, Thomas, Rajat M, Shock, Jonathan P, Zhutovsky, Paul, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Assogna, Francesca, Benedetti, Francesco, Beucke, Jan C, Boedhoe, Premika SW, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Brennan, Brian P, Buitelaar, Jan K, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Dallaspezia, Sara, Denys, Damiaan, Ely, Benjamin A, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Gürsel, Deniz A, Hauser, Tobias U, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Ivanov, Iliyan, James, Anthony, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kuno, Masaru, Kvale, Gerd, Kwon, Jun Soo, Liu, Yanni, Lochner, Christine, Lázaro, Luisa, Marques, Paulo, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Moreira, Pedro S, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, O'Neill, Joseph, Pariente, Jose C, Perriello, Chris, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Rus-Oswald, Oana G, Sakai, Yuki, Sato, João R, Schmaal, Lianne, Shimizu, Eiji, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stern, Emily R, Stevens, Michael C, Stewart, S Evelyn, Szeszko, Philip R, Tolin, David F, Venkatasubramanian, Ganesan, Wang, Zhen, Yun, Je-Yeon, van Rooij, Daan, ENIGMA-OCD Working Group, Thompson, Paul M, van den Heuvel, Odile A, Stein, Dan J, and van Wingen, Guido A

Subjects
ENIGMA-OCD Working Group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

Published
2020

32. Mapping Cortical and Subcortical Asymmetry in Obsessive-Compulsive Disorder: Findings From the ENIGMA Consortium

Author

Kong, Xiang-Zhen, Boedhoe, Premika SW, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Arnold, Paul D, Assogna, Francesca, Baker, Justin T, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Brennan, Brian P, Buitelaar, Jan, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Dallaspezia, Sara, Denys, Damiaan, Ely, Benjamin A, Feusner, Jamie, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Glahn, David C, Gruner, Patricia, Gürsel, Deniz A, Hauser, Tobias U, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, James, Anthony, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kuno, Masaru, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Liu, Yanni, Lochner, Christine, Marques, Paulo, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Medland, Sarah E, Menchón, José M, Minuzzi, Luciano, Moreira, Pedro S, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, O'Neill, Joseph, Pariente, Jose C, Perriello, Chris, Piacentini, John, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, YC Janardhan, Rus-Oswald, Oana Georgiana, Sakai, Yuki, Sato, Joao R, Schmaal, Lianne, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stern, Emily R, Stevens, Michael C, Stewart, S Evelyn, Szeszko, Philip R, Tolin, David F, Tsuchiyagaito, Aki, van Rooij, Daan, van Wingen, Guido A, Venkatasubramanian, Ganesan, Wang, Zhen, Yun, Je-Yeon, Group, ENIGMA OCD Working, Anticevic, Alan, Banaj, Nerisa, and Bargalló, Nuria

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Serious Mental Illness, Mental Health, Neurosciences, Brain Disorders, Clinical Research, Anxiety Disorders, Neurological, Mental health, Adult, Brain, Brain Mapping, Child, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Obsessive-Compulsive Disorder, Thalamus, Brain asymmetry, Laterality, Mega-analysis, Obsessive-compulsive disorder, Pallidum, ENIGMA OCD Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundLateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.MethodsWe studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.ResultsIn the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -0.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.ConclusionsThe results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.

Published
2020

33. Corrigendum

Author

Yun, Je-Yeon, Boedhoe, Premika SW, 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, Gimenez, Monica, 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, Martinez-Zalacain, Ignacio, Mataix-Cols, David, Menchon, Jose 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, YC 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, and Kwon, Jun Soo

Subjects
Biomedical and Clinical Sciences, Health Sciences, Psychology, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Published
2020

34. Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy

Author

ten Doesschate, Freek, Bruin, Willem, Zeidman, Peter, Abbott, Christopher C., Argyelan, Miklos, Dols, Annemieke, Emsell, Louise, van Eijndhoven, Philip F.P., van Exel, Eric, Mulders, Peter C.R., Narr, Katherine, Tendolkar, Indira, Rhebergen, Didi, Sienaert, Pascal, Vandenbulcke, Mathieu, Verdijk, Joey, van Verseveld, Mike, Bartsch, Hauke, Oltedal, Leif, van Waarde, Jeroen A., and van Wingen, Guido A.

Published
2023
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36. Correction: Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression

Author

Argyelan, Miklos, Deng, Zhi-De, Ousdal, Olga Therese, Oltedal, Leif, Angulo, Brian, Baradits, Mate, Spitzberg, Andrew J., Kessler, Ute, Sartorius, Alexander, Dols, Annemiek, Narr, Katherine L., Espinoza, Randall, van Waarde, Jeroen A., Tendolkar, Indira, van Eijndhoven, Philip, van Wingen, Guido A., Takamiya, Akihiro, Kishimoto, Taishiro, Jorgensen, Martin B., Jorgensen, Anders, Paulson, Olaf B., Yrondi, Antoine, Péran, Patrice, Soriano-Mas, Carles, Cardoner, Narcis, Cano, Marta, van Diermen, Linda, Schrijvers, Didier, Belge, Jean-Baptiste, Emsell, Louise, Bouckaert, Filip, Vandenbulcke, Mathieu, Kiebs, Maximilian, Hurlemann, René, Mulders, Peter CR., Redlich, Ronny, Dannlowski, Udo, Kavakbasi, Erhan, Kritzer, Michael D., Ellard, Kristen K., Camprodon, Joan A., Petrides, Georgios, Malhotra, Anil K., and Abbott, Christopher C.

Published
2024
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38. Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study

Author

Voetterl, Helena, van Wingen, Guido, Michelini, Giorgia, Griffiths, Kristi R., Gordon, Evian, DeBeus, Roger, Korgaonkar, Mayuresh S., Loo, Sandra K., Palmer, Donna, Breteler, Rien, Denys, Damiaan, Arnold, L. Eugene, du Jour, Paul, van Ruth, Rosalinde, Jansen, Jeanine, van Dijk, Hanneke, and Arns, Martijn

Published
2023
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40. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

Author

Thompson, Paul M, Jahanshad, Neda, Ching, Christopher RK, Salminen, Lauren E, Thomopoulos, Sophia I, Bright, Joanna, Baune, Bernhard T, Bertolín, Sara, Bralten, Janita, Bruin, Willem B, Bülow, Robin, Chen, Jian, Chye, Yann, Dannlowski, Udo, de Kovel, Carolien GF, Donohoe, Gary, Eyler, Lisa T, Faraone, Stephen V, Favre, Pauline, Filippi, Courtney A, Frodl, Thomas, Garijo, Daniel, Gil, Yolanda, Grabe, Hans J, Grasby, Katrina L, Hajek, Tomas, Han, Laura KM, Hatton, Sean N, Hilbert, Kevin, Ho, Tiffany C, Holleran, Laurena, Homuth, Georg, Hosten, Norbert, Houenou, Josselin, Ivanov, Iliyan, Jia, Tianye, Kelly, Sinead, Klein, Marieke, Kwon, Jun Soo, Laansma, Max A, Leerssen, Jeanne, Lueken, Ulrike, Nunes, Abraham, Neill, Joseph O', Opel, Nils, Piras, Fabrizio, Piras, Federica, Postema, Merel C, Pozzi, Elena, Shatokhina, Natalia, Soriano-Mas, Carles, Spalletta, Gianfranco, Sun, Daqiang, Teumer, Alexander, Tilot, Amanda K, Tozzi, Leonardo, van der Merwe, Celia, Van Someren, Eus JW, van Wingen, Guido A, Völzke, Henry, Walton, Esther, Wang, Lei, Winkler, Anderson M, Wittfeld, Katharina, Wright, Margaret J, Yun, Je-Yeon, Zhang, Guohao, Zhang-James, Yanli, Adhikari, Bhim M, Agartz, Ingrid, Aghajani, Moji, Aleman, André, Althoff, Robert R, Altmann, Andre, Andreassen, Ole A, Baron, David A, Bartnik-Olson, Brenda L, Marie Bas-Hoogendam, Janna, Baskin-Sommers, Arielle R, Bearden, Carrie E, Berner, Laura A, Boedhoe, Premika SW, Brouwer, Rachel M, Buitelaar, Jan K, Caeyenberghs, Karen, Cecil, Charlotte AM, Cohen, Ronald A, Cole, James H, Conrod, Patricia J, De Brito, Stephane A, de Zwarte, Sonja MC, Dennis, Emily L, Desrivieres, Sylvane, Dima, Danai, Ehrlich, Stefan, Esopenko, Carrie, Fairchild, Graeme, Fisher, Simon E, Fouche, Jean-Paul, and Francks, Clyde

Subjects
ENIGMA Consortium, Brain, Humans, Magnetic Resonance Imaging, Reproducibility of Results, Depressive Disorder, Major, Neuroimaging, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Behavioral and Social Science, Genetics, Basic Behavioral and Social Science, Prevention, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Mental health, Neurological, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Published
2020

41. Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium

Author

Yun, Je-Yeon, Boedhoe, Premika SW, 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, YC 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, van den Heuvel, Odile3 A, Stein, Dan J, Thompson, Paul M, Ik, Kang, and Cho, K

Subjects
Mental Health, Brain Disorders, Serious Mental Illness, Clinical Research, Neurosciences, Neurological, Mental health, Adult, Brain, Cerebral Cortex, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Obsessive-Compulsive Disorder, brain structural covariance network, graph theory, obsessive-compulsive disorder, pharmacotherapy, illness duration, ENIGMA-OCD working group, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

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 T1-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

Published
2020

42. Associations of medication with subcortical morphology across the lifespan in OCD: Results from the international ENIGMA Consortium

Author

Poletti, Sara, Fridgeirsson, Egill Axfjord, Ikuta, Toshikazu, de Wit, Stella J., Vriend, Chris, Kasprzak, Selina, Kuno, Masaru, Takahashi, Jumpei, Miguel, Euripedes C., Shavitt, Roseli G., Hough, Morgan, Pariente, Jose C., Ortiz, Ana E., Bertolín, Sara, Real, Eva, Segalàs, Cinto, Moreira, Pedro Silva, Sousa, Nuno, Narumoto, Jin, Yamada, Kei, Tang, Jinsong, Fouche, Jean-Paul, Kim, Taekwan, Choi, Sunah, Ha, Minji, Park, Sunghyun, Ivanov, Iliyan, Boedhoe, Premika S.W., Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Arnold, Paul D., Balachander, Srinivas, Baker, Justin T., Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K., Dallaspezia, Sara, Denys, Damiaan, Diniz, Juliana B., Ely, Benjamin A., Feusner, Jamie D., Ferreira, Sónia, Fitzgerald, Kate D., Fontaine, Martine, Gruner, Patricia, Hanna, Gregory L., Hirano, Yoshiyuki, Hoexter, Marcelo Q., Huyser, Chaim, Ikari, Keisuke, James, Anthony, Jaspers-Fayer, Fern, Jiang, Hongyan, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kwon, Jun Soo, Lázaro, Luisa, Liu, Yanni, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M., Minuzzi, Luciano, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C., Nurmi, Erika L., Oh, Sanghoon, Perriello, Chris, Piacentini, John C., Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Reddy, Y.C. Janardhan, Manrique, Daniela Rodriguez, Sakai, Yuki, Shimizu, Eiji, Simpson, H. Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stern, Emily R., Stevens, Michael C., Stewart, S. Evelyn, Szeszko, Philip R., Tolin, David F., van Rooij, Daan, Veltman, Dick J., van der Werf, Ysbrand D., van Wingen, Guido A., Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij H., Xu, Xiufeng, Yun, Je-Yeon, Zarei, Mojtaba, Zhang, Fengrui, Zhao, Qing, Jahanshad, Neda, Thomopoulos, Sophia I., Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., and O'Neill, Joseph

Published
2022
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44. 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

45. 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

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

Author

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

Subjects
IPD meta-analysis, MRI, linear mixed-effect models, mega-analysis, neuroimaging
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
2018

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