Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Functional magnetic resonance imaging (fMRI) is crucial for studying brain dynamics in neurological and psychological conditions.
• High noise levels in fMRI data limit its sensitivity and applicability for detailed neuroscience research.
• Existing noise reduction methods often struggle with the complex, non-linear nature of fMRI signals.

Purpose of the Study:

• To address the limitations of low sensitivity and dynamic signal issues in fMRI.
• To introduce and validate Ensemble Empirical Mode Decomposition (EEMD) as a noise-filtering technique for fMRI data.
• To enhance the analysis of both task-based and resting-state fMRI signals.

Main Methods:

• Utilized Ensemble Empirical Mode Decomposition (EEMD), an adaptive, data-driven method for non-stationary and nonlinear signals.
• Applied EEMD to filter task-irrelevant noise from raw fMRI signals.
• Optimized analytic parameters and identified intrinsic mode functions (IMFs) for noise removal using simulations and real fMRI data.

Main Results:

• EEMD demonstrated high detectability for task engagement in fMRI data.
• Functional sensitivity was significantly enhanced through the removal of task-irrelevant artifacts using EEMD.
• The EEMD-based method showed improved spatial specificity and superior Gaussianity of t-score distributions compared to other methods.

Conclusions:

• EEMD is an effective method for enhancing the functional sensitivity of evoked fMRI.
• The EEMD strategy is applicable to resting-state fMRI signal analysis.
• EEMD offers superior performance over traditional noise-removal techniques like band-pass filtering and independent component analysis.