EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods

Area of Science:

• Neuroscience
• Medical Imaging
• Signal Processing

Background:

• Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer complementary insights into brain activity.
• Combining EEG and fMRI (EEG/fMRI fusion) promises superior spatiotemporal resolution compared to individual modalities.
• Independent component analysis (ICA) is a key technique for EEG/fMRI fusion.

Purpose of the Study:

• To review and discuss ICA-based EEG/fMRI fusion techniques.
• To highlight the potential and limitations of existing fusion approaches.
• To introduce novel hybrid fusion methods and discuss future directions.

Main Methods:

• Review of fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion.
• Outline of hybrid fusion techniques combining ICA with data-/model-driven approaches.
• Specific mention of spatiotemporal EEG/fMRI fusion (STEFF).

Main Results:

• ICA-based fusion methods provide a framework for integrating EEG and fMRI data.
• Hybrid techniques offer advanced capabilities for spatiotemporal analysis.
• Current methods show promise but face limitations in extrapolating neural dynamics.

Conclusions:

• EEG/fMRI fusion, particularly using ICA, significantly advances brain activity analysis.
• Hybrid and novel fusion techniques are crucial for improving spatiotemporal resolution.
• Further methodological development is needed to fully capture and interpret neural dynamics.