Denoising task fMRI data for image reconstructions

Abstract

This study aims to investigate the impact of various denoising algorithms on the quality of visual stimulus reconstructions based on functional magnetic resonance imaging (fMRI) data. While fMRI provides a valuable, noninvasive method for assessing brain activity, the reliability of this data can be impaired by multiple noise types, including thermal, physiological, and scanner-related noise. Numerous denoising methods have been proposed, such as independent component analysis, confound regression and filtering, and GLM denoise. However, their efficiency, especially in the context of limited task fMRI data, remains largely unexplored. Using the Generic Objects Dataset (GOD), our study explores three primary research subquestions: the effectiveness of different denoising algorithms in improving reconstruction quality; the impact of artificially induced noise on these algorithms and whether combining different denoising algorithms can further enhance image reconstruction quality. The primary contributions of this research include the evaluation of various denoising methods with limited task fMRI data, determining the most effective denoising algorithms given a small dataset size, and analyzing how these algorithms perform in the presence of artificially introduced noise. The results of this investigation showed an improvement in performance of reconstruction models given multiple denoising algorithm, the best performer being kurtosis-based PCA used together with nuisanse regression with constant and linear terms bringing a 6.2% increase in score. The noise ceiling is the worst performer, bringing the score down by 4.4%. Denoising algorithms fail to improve reconstructions poisoned with gaussian noise, however, ICA manages to achieve a minor improvement of the quality of reconstructed images given noise sampled from the uniform distribution.