Post-ICA phase de-noising for resting-state complex-valued FMRI data

• Published in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 856 - 860
• Main Authors: Li-Dan Kuang, Qiu-Hua Lin, Xiao-Feng Gong, Fengyu Cong, Calhoun, Vince D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2017
• Subjects: complex-valued fMRI data; Correlation; Correlation coefficient; Data mining; Independent component analysis (ICA); Indexes; Noise measurement; Noise reduction; phase de-noising; phase range detection; resting-state fMRI data; Visualization
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7952277

Magnitude-only resting-state fMRI data have been largely investigated via independent component analysis (ICA) for exacting spatial maps (SMs) and time courses. However, the native complex-valued fMRI data have rarely been studied. Motivated by the significant improvements achieved by ICA of complex-valued task fMRI data than magnitude-only task fMRI data, we present an efficient method for de-noising SM estimates which makes full use of complex-valued resting-state fMRI data. Our two main contributions include: (1) The first application of a post-ICA phase de-noising method, originally proposed for task fMRI data, to resting-state data, which recognizes voxels within a specific phase range as desired voxels. (2) A new phase range detection strategy for a specific SM component based on correlation with its reference. We continuously change the phase range within a larger range, and compute a set of correlation coefficients between each de-noised SM and its reference. The phase range with the maximal correlation determines the final selection. The detected results by the proposed approach confirm the correctness of the post-ICA phase de-noising method in the analysis of resting-state complex-valued fMRI data.