Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data
Joanne C. Beer,
Nicholas J. Tustison,
Philip A. Cook,
Christos Davatzikos,
Yvette I. Sheline,
Russell T. Shinohara,
Kristin A. Linn
Affiliations
Joanne C. Beer
Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Corresponding author.
Nicholas J. Tustison
Department of Radiology and Medical Imaging, University of Virginia, United States
Philip A. Cook
Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
Christos Davatzikos
Center for Biomedical Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
Yvette I. Sheline
Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, United States
Russell T. Shinohara
Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
Kristin A. Linn
Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Corresponding author.
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 cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.