A novel temporal filtering strategy for functional MRI using UNFOLD

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2012-08, Vol.62 (1), p.59-66
• Main Authors: Domsch, S., Lemke, A., Weingärtner, S., Schad, L.R.
• Format: Article
• Language: English
• Subjects: Algorithms; Autocorrelations; Brain Mapping - methods; Datasets; Evoked Potentials, Motor - physiology; Female; fMRI; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Medical research; Middle Aged; Motor Cortex - physiology; Movement - physiology; Noise; Reproducibility of Results; Sensitivity and Specificity; Studies; Temporal filtering; Time series; UNFOLD
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.03.064

A major challenge for fMRI at high spatial resolution is the limited temporal resolution. The UNFOLD method increases image acquisition speed and potentially enables high acceleration factors in fMRI. Spatial aliasing artifacts due to interleaved k-space sampling are to be removed from the image time series by temporal filtering before statistical mapping in the time domain can be carried out. So far, low-pass filtering and multi-band filtering have been proposed. Particularly at high UNFOLD factors both methods are non-optimal. Low-pass filtering severely degrades temporal resolution and multi-band filtering leads to temporal autocorrelations affecting statistical modelling of activation. In this work, we present a novel temporal filtering strategy that significantly reduces temporal autocorrelations compared to multi-band filtering. Two datasets (finger-tapping and resting state) were post-processed using the proposed and the multi-band filter with varying set-ups (i.e. transition bands). When the proposed filtering strategy was used, a linear regression analysis revealed that the number of false positives was significantly decreased up to 34% whereas the number of activated voxels was not significantly affected for most filter parameters. In total, this led to an effective increase in the number of activated voxels per false positive for each filter set-up. At a significance level of 5%, the number of activated voxels was increased up to 41% by using the proposed filtering strategy. ► Our simulations show that simple multi-band filtering in the context of UNFOLD leads to temporal autocorrelations affecting statistical modeling of fMRI signals. ► The proposed filtering strategy reduces autocorrelated noise compared to multi-band filtering. ► The proposed filtering strategy significantly decreases false positive activations, which effectively increases the statistical inference.