Title: Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data
Authors: Kristin Linn - University of Pennsylvania (United States) [presenting]
Russell Shinohara - University of Pennsylvania (United States)
Joanne Beer - University of Pennsylvania (United States)
Abstract: Neuroimaging is a major underpinning of modern neuroscience research and the study of brain development, abnormality, and disease. Combining neuroimaging datasets from multiple sites and scanners can increase statistical power for detecting biological effects of interest. However, technical variation due to differences in scanner manufacturer, model, and acquisition protocols may bias estimation of these effects. Originally proposed to address batch effects in genomic datasets, ComBat has been shown to be effective at removing unwanted variation due to scanner in cross-sectional neuroimaging data. We propose an extension of the ComBat model for longitudinal data and demonstrate its performance using simulations as well as longitudinal cortical thickness data from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We demonstrate that longitudinal ComBat controls type I error and has higher power for detecting changes in thickness over time compared to naively applying cross-sectional ComBat to the longitudinal trajectories.