Literature DB >> 18479875

The effect of physiological noise in phase functional magnetic resonance imaging: from blood oxygen level-dependent effects to direct detection of neuronal currents.

Gisela E Hagberg1, Marta Bianciardi, Valentina Brainovich, Antonino Mario Cassarà, Bruno Maraviglia.   

Abstract

Recently, the possibility to use both magnitude and phase image sets for the statistical evaluation of fMRI has been proposed, with the prospective of increasing both statistical power and the spatial specificity. In the present work, several issues that affect the spatial and temporal stability in fMRI phase time series in the presence of physiologic noise processes are reviewed, discussed and illustrated by experiments performed at 3 T. The observed phase value is a fingerprint of the underlying voxel averaged magnetic field variations. Those related to physiological processes can be considered static or dynamic in relation to the temporal scale of a 2D acquisition and will play out on different spatial scales as well: globally across the entire images slice, and locally depending on the constituents and their relative fractions inside the MRI voxel. The 'static' respiration-induced effects lead to magneto-mechanic scan-to-scan variations in the global magnetic field but may also contribute to local BOLD fluctuations due to respiration-related variations in arterial carbon dioxide. Likewise, the 'dynamic' cardiac-related effects will lead to global susceptibility effects caused by pulsatile motion of the brain as well as local blood pressure-related changes in BOLD and changes in blood flow velocity. Finally, subject motion may lead to variations in both local and global tissue susceptibility that will be especially pronounced close to air cavities. Since dissimilar manifestations of physiological processes can be expected in phase and in magnitude images, a direct relationship between phase and magnitude scan-to-scan fluctuations cannot be assumed a priori. Therefore three different models were defined for the phase stability, each dependent on the relation between phase and magnitude variations and the best will depend on the underlying noise processes. By experiments on healthy volunteers at rest, we showed that phase stability depends on the type of post-processing and can be improved by reducing the low-frequency respiration-induced mechano-magnetic effects. Although the manifestations of physiological noise were in general more pronounced in phase than in magnitude images, due to phase wraps and global Bo effects, we suggest that a phase stability similar to that found in magnitude could theoretically be achieved by adequate correction methods. Moreover, as suggested by our experimental data regarding BOLD-related phase effects, phase stability could even supersede magnitude stability in voxels covering dense microvascular networks with BOLD-related fluctuations as the dominant noise contributor. In the interest of the quality of both BOLD-based and nc-MRI methods, future studies are required to find alternative methods that can improve phase stability, designed to match the temporal and spatial scale of the underlying neuronal activity.

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Year:  2008        PMID: 18479875     DOI: 10.1016/j.mri.2008.01.010

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


  12 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.  Enhancing the utility of complex-valued functional magnetic resonance imaging detection of neurobiological processes through postacquisition estimation and correction of dynamic B(0) errors and motion.

Authors:  Andrew D Hahn; Andrew S Nencka; Daniel B Rowe
Journal:  Hum Brain Mapp       Date:  2011-02-08       Impact factor: 5.038

3.  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

4.  Direct detection of neural activity in vitro using magnetic resonance electrical impedance tomography (MREIT).

Authors:  Rosalind J Sadleir; Fanrui Fu; Corey Falgas; Stephen Holland; May Boggess; Samuel C Grant; Eung Je Woo
Journal:  Neuroimage       Date:  2017-08-14       Impact factor: 6.556

5.  Direct neural current imaging in an intact cerebellum with magnetic resonance imaging.

Authors:  Padmavathi Sundaram; Aapo Nummenmaa; William Wells; Darren Orbach; Daniel Orringer; Robert Mulkern; Yoshio Okada
Journal:  Neuroimage       Date:  2016-02-17       Impact factor: 6.556

6.  Modeling magnitude and phase neuronal current MRI signal dependence on echo time.

Authors:  Qingfei Luo; Jia-Hong Gao
Journal:  Magn Reson Med       Date:  2010-12       Impact factor: 4.668

7.  Functional magnetic resonance electrical impedance tomography (fMREIT) sensitivity analysis using an active bidomain finite-element model of neural tissue.

Authors:  Rosalind J Sadleir; Fanrui Fu; Munish Chauhan
Journal:  Magn Reson Med       Date:  2018-05-16       Impact factor: 4.668

Review 8.  Methods for cleaning the BOLD fMRI signal.

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

9.  Inferior frontal gyrus preserves working memory and emotional learning under conditions of impaired noradrenergic signaling.

Authors:  Benjamin Becker; Lucas Androsch; Ralph T Jahn; Therese Alich; Nadine Striepens; Sebastian Markett; Wolfgang Maier; René Hurlemann
Journal:  Front Behav Neurosci       Date:  2013-12-17       Impact factor: 3.558

10.  Optimizing RetroICor and RetroKCor corrections for multi-shot 3D FMRI acquisitions.

Authors:  Rob H N Tijssen; Mark Jenkinson; Jonathan C W Brooks; Peter Jezzard; Karla L Miller
Journal:  Neuroimage       Date:  2013-09-07       Impact factor: 6.556

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