A1 Journal article (refereed)
Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data (2023)

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

JYU authors or editors

Publication details

All authors or editors: Zhang, Chao‐Ying; Lin, Qiu‐Hua; Niu, Yan‐Wei; Li, Wei‐Xing; Gong, Xiao‐Feng; Cong, Fengyu; Wang, Yu‐Ping; Calhoun, Vince D.

Journal or series: Human Brain Mapping

ISSN: 1065-9471

eISSN: 1097-0193

Publication year: 2023

Publication date: 30/08/2023

Volume: 44

Issue number: 17

Pages range: 5712-5728

Publisher: Wiley

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1002/hbm.26471

Research data link: https://www.humanconnectome.org/

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/89050

Abstract

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.

Keywords: independent component analysis; functional magnetic resonance imaging; brain

Free keywords: fMRI; independent component analysis; denoising; mathematical spatial source phase; mapping framework; fixed phase change

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2023

JUFO rating: 2