Pooling and integrating diverse imaging data across multiple sites is key to big data analytics in neuroimaging. Data amassed from multiple studies are inevitably heterogeneous due to differences in scanners, acquisition protocols, and post-acquisition image processing pipelines, substantially complicating downstream analyses. Here, we present a harmonization technique for multi-site large-scale longitudinal and cross-sectional data. We demonstrate the utility of our method in removing non-biological variability in cortical thickness measurements of individuals from birth to 100 years of age.
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