Work

Phase fMRI Reveals More Sparseness and Balance of Rest Brain Functional Connectivity Than Magnitude fMRI

Year: 2019

Type: article

Abstract: Conventionally, brain functions are inferred from the magnitude data of the complex-valued fMRI output. Since the fMRI phase image (unwrapped) provides a representation of brain internal magnetic fieldmap (by a constant scale difference), it can also be used for brain function study from a perspective of direct representation of brain magnetic state. In this study, we collected a cohort of resting-state fMRI magnitude and phase data pairs from 600 subjects (age from 10 to 76, 2615 years, 346 males), decomposed the phase data by group independent component analysis (pICA), calculated the functional network connectivity (pFNC). In comparison with the magnitude-based brain function analysis (mICA and mFNC), we find that the pFNC matrix contains a less number of significant functional connections (with p-value thresholding) than the mFNC matrix, which are sparsely distributed across the whole brain with near/far interconnections and positive/negative correlations in rough balance. Overall, our findings (only a few of brain rest subfunctions are strongly connected during resting state) offer a new picture on brain function connectivity in terms of brain internal magnetic state image.

Source: Frontiers in Neuroscience

DOI: 10.3389/fnins.2019.00204

Authors: Zikuan Chen, Zening Fu, Vince Calhoun

Institutions: Mind Research Network, University of New Mexico

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,950