Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data

• Published in: Human brain mapping Vol. 44; no. 17; pp. 5712 - 5728
• Main Authors: Zhang, Chao-Ying, Lin, Qiu-Hua, Niu, Yan-Wei, Li, Wei-Xing, Gong, Xiao-Feng, Cong, Fengyu, Wang, Yu-Ping, Calhoun, Vince D
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.12.2023
• Subjects: Amplitudes; Brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Decomposition; Functional magnetic resonance imaging; Humans; Independent component analysis; Magnetic Resonance Imaging - methods; Mapping; Mathematical analysis; Networks; Noise reduction; Normal distribution; Phase change; Standard scores; Thresholds; denoising; fMRI; fixed phase change; independent component analysis; mathematical spatial source phase; mapping framework
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.26471

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.