» Articles » PMID: 38328173

Evaluating the Effect of Denoising Submillimeter Auditory FMRI Data with NORDIC

Overview
Journal bioRxiv
Date 2024 Feb 8
PMID 38328173
Authors
Affiliations
Soon will be listed here.
Abstract

Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

References
1.
Olman C, Harel N, Feinberg D, He S, Zhang P, Ugurbil K . Layer-specific fMRI reflects different neuronal computations at different depths in human V1. PLoS One. 2012; 7(3):e32536. PMC: 3308958. DOI: 10.1371/journal.pone.0032536. View

2.
Turner R, Geyer S . Comparing like with like: the power of knowing where you are. Brain Connect. 2014; 4(7):547-57. PMC: 4146387. DOI: 10.1089/brain.2014.0261. View

3.
Schmithorst V, Brown R . Empirical validation of the triple-code model of numerical processing for complex math operations using functional MRI and group Independent Component Analysis of the mental addition and subtraction of fractions. Neuroimage. 2004; 22(3):1414-20. DOI: 10.1016/j.neuroimage.2004.03.021. View

4.
Vaughan J, Garwood M, Collins C, Liu W, Delabarre L, Adriany G . 7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images. Magn Reson Med. 2001; 46(1):24-30. DOI: 10.1002/mrm.1156. View

5.
Setsompop K, Cohen-Adad J, Gagoski B, Raij T, Yendiki A, Keil B . Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage. 2012; 63(1):569-80. PMC: 3429710. DOI: 10.1016/j.neuroimage.2012.06.033. View