Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Joaquim Radua*, Eduard Vieta, Russell Shinohara, Peter Kochunov, Yann Quide, Melissa J. Green, Cynthia S. Weickert, Thomas Weickert, Jason Bruggemann, Tilo Kircher, Igor Nenadic, Murray J. Cairns, Marc Seal, Ulrich Schall, Frans Henskens, Janice M. Fullerton, Bryan Mowry, Christos Pantelis, Rhoshel Lenroot, Vanessa CropleyCarmel 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-Rodriguez, Salvador Sarro, Annabella Di Giorgio, Alessandro Bertolino, Michael Staeblein, Viola Oertel, Christian Knoechel, Stefan Borgwardt, Stefan du Plessis, Je-Yeon Yun, Jun Soo Kwon, Udo Dannlowski, Tim Hahn, Dominik Grotegerd, Clara Alloza, Celso Arango, Therese van Amelsvoort, Geor Bakker, ENIGMA Consortium Collaborators

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

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 het-erogeneity 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).

Original languageEnglish
Article number116956
Number of pages14
JournalNeuroimage
Volume218
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Brain
  • Cortical thickness
  • Gray matter
  • Mega-analysis
  • Neuroimaging
  • Schizophrenia
  • Volume
  • SURFACE-BASED ANALYSIS

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