Literature DB >> 19369024

Phase vs. magnitude information in functional magnetic resonance imaging time series: toward understanding the noise.

Natalia Petridou1, Andreas Schäfer, Penny Gowland, Richard Bowtell.   

Abstract

Although it has been shown that the phase of the MR signal from the brain is particularly prone to variation due to respiration, the overall physiological information contained in phase time series is not well understood. Here, we explore the different physiological processes contributing to the phase time series noise, identify their spatiotemporal characteristics and examine their relationship to BOLD-related and non-BOLD-related physiological noise in the magnitude time series. This was performed by manipulating the contribution of physiological noise to the total signal variance by modulating the TE and voxel volume, and using a short TR in order to adequately sample physiological signal fluctuations. The phase and magnitude signals were compared both before and after removal of signal fluctuations at the primary respiratory and cardiac frequencies with RETROICOR. We found that the temporal phase noise increased with TE at a faster rate than predicted by 1/TSNR as a result of physiological noise. As suggested by previous studies, the primary contributor to phase physiological noise was respiration-related effects which were manifested at a large scale (>1 cm). Notably, RETROICOR removed respiration-related large-scale artifacts and this resulted in considerable improvements in the temporal phase stability (7-90%). Physiological noise in the magnitude time series after RETROICOR consisted of low-frequency BOLD-related fluctuations (<0.13 Hz) localized to gray matter and the vasculature, and fluctuations in the vasculature correlated with slow (<0.1 Hz) variations in respiration volume and cardiac rhythm. Physiological noise in the phase signal after RETROICOR also occurred in frequencies below 0.13 Hz and was consistent with (1) residual large-scale magneto-mechanical effects correlated with slow variations in respiration volume and cardiac rhythm over time, and (2) local scale (<1 cm) effects localized in gray matter and vasculature most likely due to vascular dephasing mediated by a BOLD susceptibility change. While BOLD-related magnitude noise exhibited a TE dependence similar to BOLD, the 'BOLD-related' noise in the phase data increased with increasing TE and thus caused the overall phase noise to increase at a faster rate with TE than predicted by 1/TSNR. Interestingly, the spatial specificity of this effect was more evident for the higher resolution phase data, as opposed to the magnitude data, suggesting that at a higher spatial resolution the phase signal may contain more information on physiological processes than the magnitude signal.

Mesh:

Year:  2009        PMID: 19369024     DOI: 10.1016/j.mri.2009.02.006

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  15 in total

1.  Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series.

Authors:  Andrew D Hahn; Daniel B Rowe
Journal:  Neuroimage       Date:  2011-10-07       Impact factor: 6.556

2.  Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics.

Authors:  Maarten Mennes; Xi-Nian Zuo; Clare Kelly; Adriana Di Martino; Yu-Feng Zang; Bharat Biswal; F Xavier Castellanos; Michael P Milham
Journal:  Neuroimage       Date:  2010-10-23       Impact factor: 6.556

3.  Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation.

Authors:  Marek Bartoň; Radek Mareček; Lenka Krajčovičová; Tomáš Slavíček; Tomáš Kašpárek; Petra Zemánková; Pavel Říha; Michal Mikl
Journal:  Hum Brain Mapp       Date:  2018-11-07       Impact factor: 5.038

4.  Enhanced phase regression with Savitzky-Golay filtering for high-resolution BOLD fMRI.

Authors:  Robert L Barry; John C Gore
Journal:  Hum Brain Mapp       Date:  2014-01-17       Impact factor: 5.038

5.  Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults.

Authors:  Ali M Golestani; Luxi L Wei; J Jean Chen
Journal:  Neuroimage       Date:  2016-05-11       Impact factor: 6.556

6.  Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI.

Authors:  David A Soltysik; David Thomasson; Sunder Rajan; Nadia Biassou
Journal:  J Neurosci Methods       Date:  2014-12-04       Impact factor: 2.390

7.  The oscillating brain: complex and reliable.

Authors:  Xi-Nian Zuo; Adriana Di Martino; Clare Kelly; Zarrar E Shehzad; Dylan G Gee; Donald F Klein; F Xavier Castellanos; Bharat B Biswal; Michael P Milham
Journal:  Neuroimage       Date:  2009-09-24       Impact factor: 6.556

8.  Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T.

Authors:  Marta Bianciardi; Peter van Gelderen; Jeff H Duyn
Journal:  Hum Brain Mapp       Date:  2013-07-29       Impact factor: 5.038

Review 9.  Methods for cleaning the BOLD fMRI signal.

Authors:  César Caballero-Gaudes; Richard C Reynolds
Journal:  Neuroimage       Date:  2016-12-09       Impact factor: 6.556

10.  COMPLEX-VALUED TIME SERIES MODELING FOR IMPROVED ACTIVATION DETECTION IN FMRI STUDIES.

Authors:  Daniel W Adrian; Ranjan Maitra; Daniel B Rowe
Journal:  Ann Appl Stat       Date:  2018-09-11       Impact factor: 2.083

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