Literature DB >> 15850748

Modeling both the magnitude and phase of complex-valued fMRI data.

Daniel B Rowe1.   

Abstract

In MRI and fMRI, images or voxel measurement are complex valued or bivariate at each time point. Recently, (Rowe, D.B., Logan, B.R., 2004. A complex way to compute fMRI activation. NeuroImage 23 (3), 1078-1092) introduced an fMRI magnitude activation model that utilized both the real and imaginary data in each voxel. This model, following traditional beliefs, specified that the phase time course were fixed unknown quantities which may be estimated voxel-by-voxel. Subsequently, (Rowe, D.B., Logan, B.R., 2005. Complex fMRI analysis with unrestricted phase is equivalent to a magnitude-only model. NeuroImage 24 (2), 603-606) generalized the model to have no restrictions on the phase time course. They showed that this unrestricted phase model was mathematically equivalent to the usual magnitude-only data model including regression coefficients and voxel activation statistic but philosophically different due to it derivation from complex data. Recent findings by (Hoogenrad, F.G., Reichenbach, J.R., Haacke, E.M., Lai, S., Kuppusamy, K., Sprenger, M., 1998. In vivo measurement of changes in venous blood-oxygenation with high resolution functional MRI at .95 Tesla by measuring changes in susceptibility and velocity. Magn. Reson. Med. 39 (1), 97-107) and (Menon, R.S., 2002. Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magn. Reson. Med. 47 (1), 1-9) indicate that the voxel phase time course may exhibit task related changes. In this paper, a general complex fMRI activation model is introduced that describes both the magnitude and phase in complex data which can be used to specifically characterize task related change in both. Hypotheses regarding task related magnitude and/or phase changes are evaluated using derived activation statistics. It was found that the Rowe-Logan complex constant phase model strongly biases against voxels with task related phase changes and that the current very general complex linear phase model can be cast to address several different hypotheses sensitive to different magnitude/phase changes.

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Year:  2005        PMID: 15850748     DOI: 10.1016/j.neuroimage.2005.01.034

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  27 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.  Macrovascular contribution in activation patterns of working memory.

Authors:  Dardo G Tomasi; Elisabeth C Caparelli
Journal:  J Cereb Blood Flow Metab       Date:  2006-04-12       Impact factor: 6.200

3.  Signal and noise of Fourier reconstructed fMRI data.

Authors:  Daniel B Rowe; Andrew S Nencka; Raymond G Hoffmann
Journal:  J Neurosci Methods       Date:  2006-09-01       Impact factor: 2.390

4.  Magnitude and phase signal detection in complex-valued fMRI data.

Authors:  Daniel B Rowe
Journal:  Magn Reson Med       Date:  2009-11       Impact factor: 4.668

5.  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

6.  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

7.  Spatio-Spectral Mixed Effects Model for Functional Magnetic Resonance Imaging Data.

Authors:  Hakmook Kang; Hernando Ombao; Crystal Linkletter; Nicole Long; David Badre
Journal:  J Am Stat Assoc       Date:  2012       Impact factor: 5.033

8.  Improving robustness and reliability of phase-sensitive fMRI analysis using temporal off-resonance alignment of single-echo timeseries (TOAST).

Authors:  Andrew D Hahn; Andrew S Nencka; Daniel B Rowe
Journal:  Neuroimage       Date:  2008-10-18       Impact factor: 6.556

9.  Functional magnetic resonance imaging brain activation directly from k-space.

Authors:  Daniel B Rowe; Andrew D Hahn; Andrew S Nencka
Journal:  Magn Reson Imaging       Date:  2009-07-15       Impact factor: 2.546

10.  Separation of parallel encoded complex-valued slices (SPECS) from a single complex-valued aliased coil image.

Authors:  Daniel B Rowe; Iain P Bruce; Andrew S Nencka; James S Hyde; Mary C Kociuba
Journal:  Magn Reson Imaging       Date:  2015-11-21       Impact factor: 2.546

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