DeepCor: denoising fMRI data with contrastive autoencoders

Functional magnetic resonance imaging (fMRI) allows noninvasive measurement of neural activity with high spatial resolution. However, fMRI data are affected by noise. Here we introduce and evaluate a denoising method (DeepCor) that utilizes deep generative models to disentangle and remove noise. The method is applicable to data from single participants. DeepCor outperforms other state-of-the-art denoising approaches on a variety of simulated datasets. In real fMRI data, DeepCor enhances BOLD signal responses to face stimuli, outperforming CompCor by 215%.