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Activity Number:
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362
- SPEED: Food, Environment, Biomedical Imaging and Physical System Visualization/Learning, Part 2
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 11:35 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #307796
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Title:
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Harmonization of Multi-Scanner Longitudinal MRI Neuroimaging Data
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Author(s):
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Joanne C Beer* and Russell Shinohara and Kristin Linn
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Companies:
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University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
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Keywords:
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harmonization;
longitudinal;
MRI;
Alzheimer's;
neuroimaging;
multi-site
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Abstract:
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Aggregation of neuroimaging datasets from multiple sites and scanners is becoming increasingly common. While this presents opportunities for increased statistical power, it also presents challenges due to systematic scanner effects. We propose a method for the harmonization of multi-scanner longitudinal MRI data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional MRI data. In simulation studies, we assess the statistical properties of longitudinal ComBat. Using longitudinal cortical thickness data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we demonstrate the presence of scanner-specific location and scale effects. We compare estimates of the association between baseline diagnosis group and change in cortical thickness over time using three versions of the ADNI data: (1) raw data, (2) data harmonized using cross-sectional ComBat, and (3) data harmonized using longitudinal ComBat.
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Authors who are presenting talks have a * after their name.
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