Autor: |
Joaquim Radua, Eduard Vieta, Russell Shinohara, Peter Kochunov, Yann Quidé, Melissa J. Green, Cynthia S. Weickert, Thomas Weickert, Jason Bruggemann, Tilo Kircher, Igor Nenadić, Murray J. Cairns, Marc Seal, Ulrich Schall, Frans Henskens, Janice M. Fullerton, Bryan Mowry, Christos Pantelis, Rhoshel Lenroot, Vanessa Cropley, Carmel Loughland, Rodney Scott, Daniel Wolf, Theodore D. Satterthwaite, Yunlong Tan, Kang Sim, Fabrizio Piras, Gianfranco Spalletta, Nerisa Banaj, Edith Pomarol-Clotet, Aleix Solanes, Anton Albajes-Eizagirre, Erick J. Canales-Rodríguez, Salvador Sarro, Annabella Di Giorgio, Alessandro Bertolino, Michael Stäblein, Viola Oertel, Christian Knöchel, Stefan Borgwardt, Stefan du Plessis, Je-Yeon Yun, Jun Soo Kwon, Udo Dannlowski, Tim Hahn, Dominik Grotegerd, Clara Alloza, Celso Arango, Joost Janssen, Covadonga Díaz-Caneja, Wenhao Jiang, Vince Calhoun, Stefan Ehrlich, Kun Yang, Nicola G. Cascella, Yoichiro Takayanagi, Akira Sawa, Alexander Tomyshev, Irina Lebedeva, Vasily Kaleda, Matthias Kirschner, Cyril Hoschl, David Tomecek, Antonin Skoch, Therese van Amelsvoort, Geor Bakker, Anthony James, Adrian Preda, Andrea Weideman, Dan J. Stein, Fleur Howells, Anne Uhlmann, Henk Temmingh, Carlos López-Jaramillo, Ana Díaz-Zuluaga, Lydia Fortea, Eloy Martinez-Heras, Elisabeth Solana, Sara Llufriu, Neda Jahanshad, Paul Thompson, Jessica Turner, Theo van Erp, David Glahn, Godfrey Pearlson, Elliot Hong, Axel Krug, Vaughan Carr, Paul Tooney, Gavin Cooper, Paul Rasser, Patricia Michie, Stanley Catts, Raquel Gur, Ruben Gur, Fude Yang, Fengmei Fan, Jingxu Chen, Hua Guo, Shuping Tan, Zhiren Wang, Hong Xiang, Federica Piras, Francesca Assogna, Raymond Salvador, Peter McKenna, Aurora Bonvino, Margaret King, Stefan Kaiser, Dana Nguyen, Julian Pineda-Zapata |
Jazyk: |
angličtina |
Rok vydání: |
2020 |
Předmět: |
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Zdroj: |
NeuroImage, Vol 218, Iss , Pp 116956- (2020) |
Druh dokumentu: |
article |
ISSN: |
1095-9572 |
DOI: |
10.1016/j.neuroimage.2020.116956 |
Popis: |
A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning). |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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