fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser

TL;DR

This paper introduces a novel deep learning-based method for imputing missing values in fMRI data, combining spatial and temporal regularization to improve robustness over existing techniques.

Contribution

It proposes a new imputation approach with a specialized layer for chained equations and a recurrent layer for signal tuning, enhancing missing data handling in fMRI analysis.

Findings

Improved robustness compared to state-of-the-art methods

Effective handling of missing values in multivariate fMRI data

Novel layer for deep learning architectures

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. As with any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation method is proposed consisting on two major steps: spatial-dependent signal imputation and time-dependent regularization of the imputed signal. A novel layer, to be used in deep learning architectures, is proposed in this work, bringing back the concept of chained equations for multiple imputation. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives.