Assessment of ComBat Harmonization Performance on Structural Magnetic Resonance Imaging Measurements.

ABSTRACT: 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

SUBMITTER: Tassi E 

PROVIDER: S-EPMC11660414 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature