An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis

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

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.

SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis

Independent component analysis (ICA) has become a widely used method for functional magnetic resonance imaging (fMRI) data analysis. However, spatial ICA usually performs better than temporal ICA with regard to the stability and accuracy of functional connectivity detection, and temporal ICA is often not feasible when it is applied to the analysis of real fMRI data of the whole brain because of the excessive spatial dimensions. In this paper, to overcome these problems, we propose a sub-packet constrained temporal ICA (SCTICA) method to take advantage of the a priori information using a multi-objective optimization framework with the Newton iterative algorithm. Moreover, a splitting strategy is presented to improve the feasibility of the temporal ICA for whole brain fMRI data analysis. The experimental results of real data show that the splitting strategy improved the ability of the temporal ICA to analyze whole brain fMRI data. Furthermore, the experimental results also demonstrated that the proposed SCTICA method can not only improve the stability of the temporal ICA, but can also improve the functional connectivity detection ability compared with the classical ICA and ICA with a priori information methods. In brief, the proposed SCTICA method overcomes the problem that prevents temporal ICA from being applied to fMRI data of the whole brain, and the functional connectivity detection performance is greatly improved compared with that of traditional methods.