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Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A
Seongah Jeong1, Xiang Li2, Jiarui Yang3
1School of Electronics Engineering, Kyungpook National University, Daegu 14566, South Korea.
IEEE Access : Practical Innovations, Open Solutions
|May 9, 2022
Summary
This study introduces a novel denoising method for task functional Magnetic Resonance Imaging (tfMRI) data using dictionary learning and sparse coding (DLSC). The DLSC approach enhances brain activation and connectivity patterns, improving analysis accuracy.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Biomedical Engineering
Background:
- Functional Magnetic Resonance Imaging (fMRI) is crucial for studying brain function but suffers from low signal-to-noise ratio (SNR).
- This noise limitation hinders accurate spatial resolution and interpretation of brain activation and connectivity patterns.
Purpose of the Study:
- To develop and implement an advanced denoising method for task fMRI (tfMRI) data.
- To improve the delineation of high-resolution spatial patterns of brain activation and functional connectivity.
Main Methods:
- Dictionary learning and sparse coding (DLSC) were employed, incorporating both data-driven and model-driven terms.
- The method was applied to motor tfMRI data from the Human Connectome Project (HCP).
- Performance was compared against the original data and the temporal non-local means (tNLM) denoising method.
Main Results:
- The DLSC method effectively reduced noise in tfMRI signals.
- Denoising recovered and enhanced disruptive brain activation and functional connectivity patterns.
- DLSC demonstrated superior performance compared to the tNLM method in various settings.
Conclusions:
- The proposed DLSC-based denoising method significantly enhances the interpretability of fMRI results.
- This technique serves as a crucial preprocessing step for high-resolution functional brain analysis.
