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

TitleLongitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.
Publication TypeJournal Article
Year of Publication2020
AuthorsBeer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, Linn KA
Corporate AuthorsAlzheimer’s Disease Neuroimaging Initiative
Date Published2020 10 15
KeywordsAlzheimer Disease, Brain, Databases, Factual, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuroimaging

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.

Pubmed Link
Alternate JournalNeuroimage
PubMed ID32640273
PubMed Central IDPMC7605103
Grant ListR01 MH123550 / MH / NIMH NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
/ / CIHR / Canada
S10 OD023495 / OD / NIH HHS / United States
U01 MH109991 / MH / NIMH NIH HHS / United States
R01 NS060910 / NS / NINDS NIH HHS / United States
R01 NS085211 / NS / NINDS NIH HHS / United States
T32 MH106442 / MH / NIMH NIH HHS / United States

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