Exploring Tensor Decomposition as an Alternative to ICA for Denoising Multi-Echo fMRI data

Denoising of the blood oxygen level-dependent signal is critical for the study of brain dynamics with functional MRI data. However, disentangling neurobiological signals from non-neurobiological ones such as head motion-related artifacts, and cardiac-related and respiration-related fluctuations. Multi-echo ICA approaches are often used to denoise the data by exploiting the echo-time dependence of the BOLD signal. Nevertheless, these rely on the optimally combined data and do not employ the information contained in the different echo-time signals. Here, we explore the potential of tensor decomposition techniques, which can simultaneously consider all the information available, as a way to process multi-echo fMRI data.