Assessment of <scp>ComBat</scp> Harmonization Performance on Structural <scp>Magnetic Resonance Imaging</scp> Measurements

Data aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high-dimensional datasets to form large-scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site-related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi-scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses. In this study, we stand out by using a robust cross-validation approach to assess ComBat performances applied on volume- and surface-based measures acquired across three sites. A machine learning approach based on Multi-Class Gaussian Process Classifier was applied to predict imaging site based on raw and harmonized brain features, providing quantitative insights into ComBat effectiveness, and verifying the association between biological covariates and harmonized brain features. Our findings showed differences in terms of ComBat performances across measures of regional brain morphology, demonstrating tissue specific site effect modeling. ComBat adjustment of site effects also varied across regional level of each specific volume-based and surface-based measures. ComBat effectively eliminates unwanted data site-related variability, by maintaining or even enhancing data association with biological factors. Of note, ComBat has demonstrated flexibility and robustness of application on unseen independent gray matter volume data from the same sites.