Journal
HUMAN BRAIN MAPPING
Volume 40, Issue 5, Pages 1507-1527Publisher
WILEY
DOI: 10.1002/hbm.24463
Keywords
data harmonization; field strength; LDDMM; magnetic resonance imaging; multi-atlas fusion; total intracranial volume
Funding
- Alzheimer's Disease Neuroimaging Initiative [W81XWH-12-2-001]
- National Institutes of Health [U01 AG024904]
- Canadian Institutes of Health Research (CIHR) [179009, 74580]
- Canada Brain Research Fund (CBRF)
- Brain Canada Foundation
- Michael Smith Foundation for Health Research (MSFHR)
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Pacific Alzheimer Research Foundation (PARF) [C06-01]
- Alzheimer Society Research Program (ASRP)
- Alzheimer Society of Canada
- DOD ADNI [W81XWH-12-2-0012]
- National Institute on Aging [R01 AG055121-01A1]
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When analyzing large multicenter databases, the effects of multiple confounding covariates increase the variability in the data and may reduce the ability to detect changes due to the actual effect of interest, for example, changes due to disease. Efficient ways to evaluate the effect of covariates toward the data harmonization are therefore important. In this article, we showcase techniques to assess the goodness of harmonization of covariates. We analyze 7,656 MR images in the multisite, multiscanner Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We present a comparison of three methods for estimating total intracranial volume to assess their robustness and correct the brain structure volumes using the residual method and the proportional (normalization by division) method. We then evaluated the distribution of brain structure volumes over the entire ADNI database before and after accounting for multiple covariates such as total intracranial volume, scanner field strength, sex, and age using two techniques: (a) Zscapes, a panoramic visualization technique to analyze the entire database and (b) empirical cumulative distributions functions. The results from this study highlight the importance of assessing the goodness of data harmonization as a necessary preprocessing step when pooling large data set with multiple covariates, prior to further statistical data analysis.
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