Keywords: fMRI, spectral analysis, linear transformation, computational neuroscience
TL;DR: We propose a novel linear transformation approach to temporal de-correlate fMRI signals, allowing us to improve their spectral resolution for further analysis.
Abstract: The inherent infra-slow, narrowband signal thwarts the fMRI modality in considering as an optimal neuroimaging modality to its alternatives, e.g., EEG and MEG, in investigating the spectral character of cortical activities. To enhance the spectral resolution of fMRI signal, we put forward a novel linear transformation approach to encourage both the multivariate fMRI time series and their derived temporal derivatives to be temporal de-correlated with each other. Thorough empirical validations of our temporal de-correlation approach on multiple independent fMRI datasets are presented, along with the attached empirical comparison of several alternative methods. Throughout all employed fMRI datasets, we observe a general increment on spectral resolution of temporal de-correlated fMRI signals in terms of wider frequency bandwidth, and more distinctive spectral characters to the original signals.
Track: Original Research Track