Abstract
Functional magnetic resonance imaging (fMRI) has become a powerful and influential method to non-invasively study neuronal brain activity. For this purpose, the blood oxygenation level-dependent (BOLD) effect is most widely used. T2* weighted echo planar imaging (EPI) is BOLD sensitive and the prevailing fMRI acquisition technique. Here, we present an alternative to its standard Cartesian recordings, i.e. k-space density weighted EPI, which is expected to increase the signal-to-noise ratio in fMRI data. Based on in vitro and in vivo pilot measurements, we show that fMRI by k-space density weighted EPI is feasible and that this new acquisition technique in fact boosted spatial and temporal SNR as well as the detection of local fMRI activations. Spatial resolution, spatial response function and echo time were identical for density weighted and conventional Cartesian EPI. The signal-to-noise ratio gain of density weighting can improve activation detection and has the potential to further increase the sensitivity of fMRI investigations.
Highlights
Echo planar imaging (EPI) is the first choice for blood oxygenation level-dependent (BOLD, [1]) functional magnetic resonance imaging because it provides a T2*-sensitive contrast
In hypothesis-driven analyses according to the general linear model (GLM), spatial smoothing prepares the data to better meet basic assumptions of Gaussian random field theory (RFT) for statistical thresholding and inference [2,3]
Spatial response functions obtained from those phantom images by deriving the edge spread functions are shown in (D)
Summary
Echo planar imaging (EPI) is the first choice for blood oxygenation level-dependent (BOLD, [1]) functional magnetic resonance imaging (fMRI) because it provides a T2*-sensitive contrast. As part of the (pre-)processing after acquisition of the fMRI time-series and prior to its statistical analysis, the data is often smoothed spatially to a variable degree by a Gaussian filter to improve the signal-tonoise ratio (SNR). In hypothesis-driven analyses according to the general linear model (GLM), spatial smoothing prepares the data to better meet basic assumptions of Gaussian random field theory (RFT) for statistical thresholding and inference [2,3]. Given that anatomical variability across subjects and limits of inter-subject image registration contribute to the variance of fMRI data in common template spaces, spatial smoothing facilitates studying activations at the group level. According to the matched filter theorem, spatial smoothing improves activation detection if the size of activated clusters and the filter applied for smoothing are well matched
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