Disentangling Site Effects with Cycle-Consistent Adversarial Autoencoder for Multi-site Cortical Data Harmonization

Modern multi-site neuroimaging studies are known to be biased by significant site effects observed in imaging data and their derived structural and functional features. Although many statistical models and deep learning methods have been proposed to eliminate the site effects while maintaining biological characteristics, they have two major drawbacks. First, statistical models are applicable for harmonizing regional-level data but are inherently not suitable to represent the complex non-linear mapping of vertex-wise cortical property maps. Second, existing deep learning methods can only harmonize data between two sites, which are practically less useful in multi-site data harmonization scenario and also ignore the rich information in the whole dataset. To address these issues, we develop a novel, flexible deep learning method to harmonize multi-site cortical surface property maps. Specifically, to detect and remove site effects, we employ a surface-based autoencoder and decompose the encoded cortical features into site-related and site-unrelated components and use an adversarial strategy to encourage the disentanglement. Then decoding the site-unrelated features with other site features can generate mappings across different sites. To learn more controllable and meaningful mappings, we enforce the cycle consistency between forward and backward mappings. Our method can thus efficiently learn rich information from the whole dataset and generate realistic harmonized surface maps at the target site. Experiments on harmonizing infant cortical thickness maps of 2,342 scans from four sites with different scanners and imaging protocols validate the superior performance of our method on both site effects removal and biological variability preservation compared to other methods. To the best of our knowledge, this is the largest validation of different methods on infant cortical data harmonization.