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ICA of full complex-valued fMRI data using phase information of spatial maps
Mou-Chuan Yu1, Qiu-Hua Lin1, Li-Dan Kuang1
1School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China.
Journal of Neuroscience Methods
|April 11, 2015
Summary
This study introduces a novel method for analyzing complex-valued functional MRI data using time courses (TCs) and spatial maps (SMs). The approach enhances Independent Component Analysis (ICA) by effectively handling phase ambiguity, leading to more accurate identification of brain activity.
Area of Science:
- Neuroimaging
- Signal Processing
- Biomedical Engineering
Background:
- Independent Component Analysis (ICA) of complex-valued functional MRI (fMRI) data is challenging due to phase ambiguity and noise.
- Traditional methods often remove noisy regions before ICA, potentially discarding useful information.
- Analyzing the full complex-valued fMRI data is optimal for preserving all relevant information.
Purpose of the Study:
- To develop a novel method for ICA of full complex-valued fMRI data.
- To utilize phase information of spatial maps (SMs) for improved ICA.
- To accurately segment voxels and remove unwanted components without pre-filtering.
Main Methods:
- Developed a time course (TC)-based phase de-ambiguity method for ICA.
- Adjusted SM phases based on estimated TCs to represent spatial phase changes.
- Segmented voxels into BOLD-related and unwanted using TC real-part power maximization.
- Constructed phase masks to remove unwanted voxels from individual and group SM estimates.
Main Results:
- Efficiently estimated both task-related components and the default mode network (DMN).
- Extracted 139-331% more contiguous and reasonable activations compared to magnitude-only ICA.
- Detected more BOLD-related voxels and eliminated more unwanted voxels than pre-ICA de-noising methods.
- Demonstrated higher accuracy and robustness of TC-based phase de-ambiguity over SM-based methods.
Conclusions:
- TC-based phase de-ambiguity is crucial for preparing SM phases.
- SM phases offer a new post-ICA index for reliable identification and suppression of unwanted voxels.
- The proposed method enhances the analysis of complex-valued fMRI data, improving component extraction and noise reduction.
