Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomedical Engineering

Background:

• Estimating cortical activity and connectivity from neuroimaging data is crucial for understanding brain function.
• High-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer complementary insights into brain dynamics.
• Integrating EEG and fMRI data presents challenges but holds great potential for a comprehensive view of neural processes.

Purpose of the Study:

• To present a novel computational framework for estimating human cortical activity and connectivity.
• To validate the proposed methods using combined EEG and fMRI data during a cognitive task.
• To demonstrate the utility of the approach in identifying task-dependent functional connectivity patterns.

Main Methods:

• Cortical activity estimation using realistic head volume conductor and distributed source models.
• Cortical connectivity evaluation via Partial Directed Coherence (PDC) between Brodmann areas.
• Analysis of connectivity patterns using graph theory measures across different frequency bands.

Main Results:

• The computational tools successfully estimated cortical activity and connectivity from EEG and fMRI data.
• Distinct functional connectivity patterns were observed and analyzed using graph theory metrics.
• The approach demonstrated sensitivity to changes in connectivity related to the Stroop task.

Conclusions:

• The developed computational methodology provides a robust framework for analyzing brain activity and connectivity.
• This integrated EEG-fMRI approach can effectively identify task-related modulations in functional brain networks.
• The findings highlight the potential of these tools for advancing our understanding of cognitive neuroscience.