New analysis method for functional brain imaging: White noise removed T2* variation mapping using multi-echo EPI

Abstract

BackgroundGenerally, the analysis of functional magnetic resonance imaging (fMRI) using echo-planar imaging (EPI) data is based on independent component analysis (ICA) and the general linear model (GLM). The application of these two approaches is highly independent, like GLM is for task-related activation mapping, and ICA is for resting-state imaging. Herein, we propose white noise-removed T2*-variation mapping as a new analysis method for fMRI that integrates the two conventional mapping approaches. New MethodWe derived the standard deviation to the mean-square ratio of the true T2* signal from the multi-echo EPI (ME-EPI) dataset. For the true T2*-variation-based value, we removed the S0 (initial signal intensity) and white noise component from the variation in the EPI signal using signal-coherence analysis of the echo time (TE) dataset and slope analysis of the TE-variated coefficient of variation of the ME-EPI dataset. ResultsThe activation mapping for a visual task and resting-state imaging by the proposed method showed the reliable activation map in the visual cortex area and area for the typical default mode network, with white noise and the S0 component removed. Comparison with existing methodsConventional analyses for fMRI cannot be applied to both activation mapping and resting-state imaging, with white noise removed, while the proposed method can be applied. ConclusionsWe demonstrated white noise-removed true T2*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.