Site effects depth denoising and signal enhancement using dual-projection based ICA model

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realize the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based denoising methods are the two primary methods used to denoise scanner/site-related effects. Unfortunately, there are challenges with both ICA-based and GLM-based denoising methods to remove site effects completely when the signals of interest and scanner/site-related noises are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove the scanner/site-related effects more completely. This method can separate the signal effects correlated with noise variables from the identified noise effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a traveling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the proposed GLM- and ICA-based denoising methods and our DP-based ICA denoising method. Results show that DP-based ICA