Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

中文翻译：

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DeepResBat：考虑协变量分布差异的深度残差批次协调


汇集来自多个数据集的 MRI 数据需要协调以减少不需要的站点间变异，同时保留生物变量（或协变量）的影响。流行的协调方法 ComBat 使用混合效应回归框架，该框架明确考虑了数据集之间的协变量分布差异。开发基于深度神经网络 （DNN） 的协调方法也引起了人们的浓厚兴趣，例如条件变分自动编码器 （cVAE）。但是，当前的 DNN 方法并未明确考虑数据集之间的协变量分布差异。在这里，我们提供了数学结果，表明不考虑协变量会导致次优协调。我们提出了两种基于 DNN 的协变量感知协调方法：协变量 VAE （coVAE） 和 DeepResBat。coVAE 方法是通过将协变量和站点信息与站点和协变量不变的潜在表示连接起来，是 cVAE 的自然延伸。DeepResBat 采用了受 ComBat 启发的残差框架。DeepResBat 首先使用非线性回归树消除协变量的影响，然后使用 cVAE 消除位点差异。最后，将协变量效应添加回协调残差。使用来自三大洲的三个数据集，共有 2787 名参与者和 10,085 次解剖 T1 扫描，我们发现 DeepResBat 和 coVAE 在消除数据集差异方面优于 ComBat、CovBat 和 cVAE，同时增强了感兴趣的生物学效应。然而，即使不存在关联，coVAE 也会产生解剖 MRI 和协变量之间的虚假关联。未来提出基于 DNN 的协调方法的研究应该意识到这种假阳性陷阱。 总体而言，我们的结果表明 DeepResBat 是 ComBat 的有效深度学习替代方案。DeepResBat 的代码可在此处找到：https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat。