Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.04.368357v1?rss=1 Authors: Vizioli, L., Moeller, S., Dowdle, L., Akcakaya, M., De Martino, F., Yacoub, E., Ugurbil, K. Abstract: Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for investigating the human brain. However, virtually all fMRI studies have relatively poor signal-to-noise ratio (SNR). We introduce a novel fMRI denoising technique, which removes noise that is indistinguishable from zero-mean Gaussian-distributed noise. Thermal noise, falling in this category, is a major noise source in fMRI, particularly, but not exclusively, at high spatial and/or temporal resolutions. Using 7-Tesla high-resolution data, we demonstrate remarkable improvements in temporal-SNR, the detection of stimulus-induced signal changes, and functional maps, while leaving stimulus-induced signal change amplitudes, image spatial resolution, and functional point-spread-function unaltered. We also provide supplementary data demonstrating that the method is equally applicable to supra-millimeter resolution 3- and 7-Tesla fMRI data, different cortical regions, stimulation/task paradigms, and acquisition strategies. The proposed denoising approach is expected to have a transformative impact on the scope and applications of fMRI to study the brain. Copy rights belong to original authors. Visit the link for more info