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Increased Power by Harmonizing Structural MRI Site Differences with the ComBat Batch Adjustment Method in ENIGMA
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SYSNO ASEP 0531247 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Increased Power by Harmonizing Structural MRI Site Differences with the ComBat Batch Adjustment Method in ENIGMA Author(s) Radua, J. (ES)
Vieta, E. (ES)
Shinohara, R. (US)
Höschl, C. (CZ)
Tomeček, David (UIVT-O) RID, ORCID, SAI
Škoch, A. (CZ)Number of authors 111 Article number 116956 Source Title Neuroimage. - : Elsevier - ISSN 1053-8119
Roč. 218, September 2020 (2020)Number of pages 14 s. Publication form Print - P Language eng - English Country US - United States Keywords Brain ; Cortical thickness ; Gray matter ; Mega-analysis ; Neuroimaging ; Schizophrenia ; Volume Subject RIV FH - Neurology OECD category Neurosciences (including psychophysiology Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 000555460300009 EID SCOPUS 85086881765 DOI 10.1016/j.neuroimage.2020.116956 Annotation 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). Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021 Electronic address http://hdl.handle.net/11104/0309944
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