Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data
Zhang, C., Lin, Q., Niu, Y., Li, W., Gong, X., Cong, F., Wang, Y., & Calhoun, V. D. (2023). Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data. Human Brain Mapping, 44(17), 5712-5728. https://doi.org/10.1002/hbm.26471
Julkaistu sarjassa
Human Brain MappingTekijät
Päivämäärä
2023Oppiaine
Secure Communications Engineering and Signal ProcessingTietotekniikkaTekniikkaSecure Communications Engineering and Signal ProcessingMathematical Information TechnologyEngineeringTekijänoikeudet
© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex-valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude-only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex-valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex-valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex-valued fMRI, this framework is generalized to work with magnitude-only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex-valued data from University of New Mexico and magnitude-only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude-only fMRI data in terms of retaining more BOLD-related activity and fewer unwanted voxels, compared with amplitude-based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.
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WileyISSN Hae Julkaisufoorumista
1065-9471Asiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/184595889
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This work was supported in part by the National Natural Science Foundation of China under Grant 61871067, Grant 61379012, Grant 61671106, Grant 62071082, and Grant 81471742; in part by the National Science Foundation (NSF) under Grant 2112455; in part by the National Institutes of Health (NIH) under Grant R01MH104680, Grant R01MH107354, Grant R01EB005846; in part by the Fundamental Research Funds for the Central Universities, China, under Grant DUT20ZD220; and in part by the Supercomputing Center of Dalian University of Technology. ...Lisenssi
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