Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network.

ABSTRACT: Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influences the reliability and reproducibility of fMRI data analysis and limits its clinical applications. Extensive effort has been devoted to improving fMRI data quality, but in the last two decades, there is no consensus reached which technique is more effective. In this study, we developed a novel deep neural network for denoising fMRI data, named denoising neural network (DeNN). This deep neural network is 1) applicable without requiring externally recorded data to model noise; 2) spatially and temporally adaptive to the variability of noise in different brain regions at different time points; 3) automated to output denoised data without manu

SUBMITTER: Yang Z 

PROVIDER: S-EPMC7792822 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature