School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Hum Brain Mapp. 2023 Dec 1;44(17):5712-5728. doi: 10.1002/hbm.26471. Epub 2023 Aug 30.
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.
基于独立成分分析 (ICA) 从全幅度 fMRI 数据中提取的脑网络通常使用各种基于幅度的阈值进行去噪。相比之下，从复值 fMRI 数据中提取的脑网络的空间源相位 (SSP) 或相位信息，提供了一种简单而有效的方法，通过固定的相位变化来进行去噪。在这项工作中，我们将该方法扩展到全幅度 fMRI 数据，以避免为 ICA 提取的幅度图进行去噪测试各种幅度阈值，因为大多数研究都没有保存复值数据。主要思想是使用映射框架为幅度图生成数学 SSP 图，映射框架是使用具有已知 SSP 图的复值 fMRI 数据构建的。在这里，我们利用从相位 fMRI 数据导出的相位图具有与 SSP 图相似的相位信息的事实。在验证了使用复值 fMRI 的幅度数据后，该框架被推广到仅使用幅度数据的情况，即使没有相应的相位 fMRI 数据集，也可以使用我们的方法。我们使用模拟和实验 fMRI 数据测试了所提出的方法，包括来自新墨西哥大学的复值数据和来自人类连接组计划的全幅度数据。结果表明，与基于幅度的阈值处理相比，使用固定相位变化的数学 SSP 去噪在保留更多与 BOLD 相关的活动和更少不需要的体素方面，对于从全幅度 fMRI 数据中去除空间图的噪声是有效的。该方法为 fMRI 数据中 ICA 脑网络的去噪提供了一种统一且高效的 SSP 方法。
