Phase fMRI Reveals More Sparseness and Balance of Rest Brain Functional Connectivity Than Magnitude fMRI - Chen, Zikuan; Fu, Zening; Calhoun, Vince (Frontiers in Neuroscience, 2019)
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.