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A review of decomposition methods for brain states estimation
Guoqiang Hu1,2, Jinxing Wang3, Ziyi Shui3
1College of Artificial Intelligence, Dalian Maritime University, Dalian, China. guoqiang.hu@dlmu.edu.cn.
Biomedical Engineering Online
|February 19, 2026
Summary
This review surveys decomposition methods for analyzing functional magnetic resonance imaging (fMRI) data. It helps researchers disentangle distinct brain states from mixed fMRI signals.
Area of Science:
- Neuroscience
- Neuroimaging
- Data Analysis
Background:
- Functional magnetic resonance imaging (fMRI) is crucial for analyzing brain activity.
- fMRI data often contains mixed signals from distinct brain states.
- Separating these states is essential for in-depth analysis.
Purpose of the Study:
- To provide a comprehensive review of decomposition methods for fMRI data.
- To discuss practical considerations for applying these methods.
- To compare decomposition techniques with other fMRI analysis approaches.
Main Methods:
- Survey of classical, probabilistic, and tensor-based decomposition approaches.
- Discussion of methodological considerations for effective application.
- Comparative analysis of decomposition algorithms against other fMRI techniques.
Main Results:
- Decomposition methods offer a powerful approach to disentangle brain states from fMRI data.
- The review highlights the strengths and limitations of various decomposition algorithms.
- Applicability of these methods for extracting distinct brain states is demonstrated.
Conclusions:
- Decomposition methods are vital for independent analysis of brain states in fMRI.
- Understanding methodological nuances is key to successful application.
- These techniques significantly enhance the extraction of meaningful information from fMRI datasets.
