Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

ABSTRACT: While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in

SUBMITTER: Beer JC 

PROVIDER: S-EPMC7605103 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature