Aidas Aglinskas

Abstract 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.

The heterogeneous nature of many psychiatric and neurodevelopmental disorders can impede the diagnosis of patients and the development of targeted support options.1-5 Including neural measures may help identify subtypes that benefit from specific interventions. Recently, there has been an emerging focus on neuroimaging-based subtyping to identify groups of patients sharing similar disorder-related neural alterations.

Abstract Functional magnetic resonance imaging (fMRI) is widely used in neuroscience research. FMRI data is noisy; improving denoising methods could lead to novel discoveries. Here, we introduce and evaluate a denoising method (DeepCor) which utilizes deep generative models to disentangle and remove noise. DeepCor outperforms CompCor (a state-of-the art denoising approach) on a variety of simulated datasets.