Presentation + Paper
15 March 2019 Phase fMRI reveals sparser function connectivity than magnitude fMRI
Zikuan Chen, Vince Calhoun
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zikuan Chen and Vince Calhoun "Phase fMRI reveals sparser function connectivity than magnitude fMRI", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530D (15 March 2019); https://doi.org/10.1117/12.2511513
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Brain

Functional magnetic resonance imaging

Microsoft Foundation Class Library

Matrices

Independent component analysis

Magnetism

Mica

Back to Top