Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study.

Area of Science:

• Neuroscience
• Graph Theory
• Biomedical Engineering

Background:

• Brain connectivity is often studied using single-modality imaging, limiting comprehensive analysis.
• Graph theory provides a robust framework for analyzing complex network structures in the brain.

Purpose of the Study:

• To develop a framework for constructing multi-modal brain graphs using concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data.
• To investigate differences in brain connectivity during eyes open and eyes closed resting states using this novel approach.

Main Methods:

• Simultaneous EEG-fMRI data were collected during resting states.
• fMRI data were processed using group independent component analysis (ICA).
• EEG data were segmented into frequency bands, and spectral power time courses were computed.
• Multi-modal brain graphs were constructed by correlating EEG and fMRI time courses, including both static and dynamic analyses.

Main Results:

• Static and dynamic graph measures differed across EEG frequency bands.
• Connectivity measures were generally higher during eyes closed compared to eyes open states.
• Specific brain components showed altered nodal graph measures in certain frequency bands during eyes closed state.

Conclusions:

• The developed framework effectively integrates spatial information from fMRI with frequency-specific insights from EEG.
• This multi-modal graph approach offers a more comprehensive understanding of brain connectivity than single-modality methods.
• The findings highlight distinct brain network characteristics associated with different resting states and frequency bands.