- An, L., Zhang, C., Wulan, N., Zhang, S., Chen, P., Ji, F., Ng, KK., Chen, C.,Zhou, J., Yeo, B.T., 2024. DeepResBat: deep residual batch harmonization accounting for covariate distribution differences, BioRxiv
Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular ComBat method uses a mixed effect regression framework to account for covariate distribution differences. There is growing interest in deep neural network (DNN) approaches, such as conditional variational autoencoders (cVAEs), but these do not explicitly address covariate differences. We propose two covariate-aware DNN-based harmonization methods: covariate VAE (coVAE) and DeepResBat. Our results show that DeepResBat and coVAE outperform ComBat, CovBat, and cVAE in removing dataset differences and enhancing biological effects, though coVAE may produce false positives.
Since the whole Github repository is too big, we provide a stand-alone version of only this project and its dependencies. To download this stand-alone repository, visit this link: https://github.com/ThomasYeoLab/Standalone_An2024_DeepResBat
If you want to use the code from our lab's other stable projects (other than An2024_DeepResBat), you would need to download the whole CBIG repository.
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To download the version of the code that was last tested, you can either
or
- run the following command, if you have Git installed
git checkout -b An2024_DeepResBat v0.33.0-An2024_DeepResBat
- Our code uses Python and R, here is the setup:
- The example of our code is detailed in
examples/README.md
- If you have access to ADNI, AIBL and MACC dataset, you can replicate our result following the instructions detailed in
replication/README.md.
- Release v0.33.0 (05/08/2024): Initial release of An2024_DeepResBat project
Please contact Lijun An at [email protected] and Thomas Yeo at [email protected]