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

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 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).

Thinner cortex is associated with psychosis onset in individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group mega-analysis

“…Prior to conducting the mega-analysis, all neuroimaging data were adjusted for scanner protocol effects using neuroComBat 83 , a modified version of ComBat 84 that increases the statistical power and precision of neuroimaging effect size estimates 83 .…”

A Prodromal Brain‐Clinical Pattern of Cognition in Synucleinopathies

“…Because the 326 participant dataset used here came from different sites, we used ComBat (github.com/ Jfortin1/ComBatHarmonization), a batch-effect correction tool derived from studies in genomics 39 that was shown to accurately model and remove site/scanner effects. [40][41][42] ComBat assumes that imaging features can be measured linearly as a combination of the biological and scanner effects with an error term that includes a multiplicative scanner-specific scaling factor. The batch id vector corresponded to the scanning site (ie, Montreal, Sydney, Strasbourg, and the different sites included in the PPMI cohort) and the design matrix for biological effects of interest included age, sex, education, and disease status.…”

Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

“…All these methods help to overcome the difficulties of MRI in interpreting the intensities between subjects, so that similar intensities will have similar tissue meaning for the standardized images. For harmonization methods working on radiomic features [23][24][25][26][27][28][29][30], a representative and widely used method is ComBat. ComBat [23] was originally proposed for the microarray expression data to remove batch effects, a type of non-biological experimental variation similar to scanner effects but observed across multiple batches in microarray experiments.…”