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Phase fMRI reveals sparser function connectivity than magnitude fMRI
Author(s): Zikuan Chen; Vince Calhoun
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Paper Abstract

Phase fMRI refers to a technique of fMRI phase imaging, which acquires the fMRI phase data to accompany the fMRI magnitude data acquisition at no extra cost. Both fMRI phase and magnitude data are generated from the same magnetic field (source), but have different properties. Under a linear phase fMRI approximation, a phase image (unwrapped) represents brain internal magnetic field. Therefore, the fMRI phase data offers, in theory, a more direct and a more accurate depiction of brain functional mapping and functional connectivity, though this comes with additional noise signal as well. In this study, we report on functional connectivity computed from a cohort of fMRI phase data (from 600 subjects). We decomposed the group phase data by independent component analysis (pICA) and calculated the phase functional network connectivity (pFC) matrix by temporal correlations of pICA timecourses. Next, we statistically analyzed the significant connection patterns in pFC. In comparison with conventional magnitude fMRI (denoted by mICA and mFC), our phase fMRI study contributed new information on resting-state brain function connectivity as follows: 1) the thresholded pFC contains a smaller number of significant connections than does the thresholded mFC; and 2) the positive and negative connections in pPNC are more balanced than those in mFC. We seek to justify the phase-inferred brain function connectivity features in the sense of using the phase representation of the brain internal field map.

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530D (15 March 2019); doi: 10.1117/12.2511513
Show Author Affiliations
Zikuan Chen, The Mind Research Network (United States)
Vince Calhoun, The Mind Research Network (United States)
Univ. of New Mexico (United States)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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