Dataset Information

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).

SUBMITTER: Radua J

PROVIDER: S-EPMC7524039 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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

Radua Joaquim J Vieta Eduard E Shinohara Russell R Kochunov Peter P Quidé Yann Y Green Melissa J MJ Weickert Cynthia S CS Weickert Thomas T Bruggemann Jason J Kircher Tilo T Nenadić Igor I Cairns Murray J MJ Seal Marc M Schall Ulrich U Henskens Frans F Fullerton Janice M JM Mowry Bryan B Pantelis Christos C Lenroot Rhoshel R Cropley Vanessa V Loughland Carmel C Scott Rodney R Wolf Daniel D Satterthwaite Theodore D TD Tan Yunlong Y Sim Kang K Piras Fabrizio F Spalletta Gianfranco G Banaj Nerisa N Pomarol-Clotet Edith E Solanes Aleix A Albajes-Eizagirre Anton A Canales-Rodríguez Erick J EJ Sarro Salvador S Di Giorgio Annabella A Bertolino Alessandro A Stäblein Michael M Oertel Viola V Knöchel Christian C Borgwardt Stefan S du Plessis Stefan S Yun Je-Yeon JY Kwon Jun Soo JS Dannlowski Udo U Hahn Tim T Grotegerd Dominik D Alloza Clara C Arango Celso C Janssen Joost J Díaz-Caneja Covadonga C Jiang Wenhao W Calhoun Vince V Ehrlich Stefan S Yang Kun K Cascella Nicola G NG Takayanagi Yoichiro Y Sawa Akira A Tomyshev Alexander A Lebedeva Irina I Kaleda Vasily V Kirschner Matthias M Hoschl Cyril C Tomecek David D Skoch Antonin A van Amelsvoort Therese T Bakker Geor G James Anthony A Preda Adrian A Weideman Andrea A Stein Dan J DJ Howells Fleur F Uhlmann Anne A Temmingh Henk H López-Jaramillo Carlos C Díaz-Zuluaga Ana A Fortea Lydia L Martinez-Heras Eloy E Solana Elisabeth E Llufriu Sara S Jahanshad Neda N Thompson Paul P Turner Jessica J van Erp Theo T

NeuroImage 20200526

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, ComB ...[more]

PMID: 32470572

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Project description:The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.

Dataset Information

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

Publications

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

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