Brain Age Estimation using Support Vector Regression and Harmonization Across Scanner and Site

Abstract

Brain age estimation based on machine learning from MRI images has been attracting attention as a biomarker of the progression of brain degeneration in such as Alzheimerʼs disease and temporal lobe epilepsy. However, it is known that measurement bias has been existed in the volume due to differences in image quality of MRI obtained from different magnetic field strength, manufacturer and model, which has been reported to significantly reduce the generalizability of machine learning. In this study, we used ComBat harmonization method, which uses a general linear model and empirical Bayesian estimation to correct the measurement bias between scanner and site, which improved the generalizability and accuracy of brain age estimation across different.