Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biophysics

Background:

• Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are key noninvasive brain activity detection methods.
• Combining EEG's temporal resolution with fMRI's spatial precision in multimodal approaches requires refinement.
• Existing multimodal methods face challenges in complexity and source localization accuracy.

Purpose of the Study:

• To present a new spatiotemporal fMRI-constrained EEG source imaging protocol.
• To enhance EEG-fMRI source localization by mitigating biases and improving spatial accuracy.
• To enable more specific multimodal neuroimaging through dynamic fMRI sub-region recruitment.

Main Methods:

• Concurrent EEG and fMRI data acquisition followed by 3D cortical model generation.
• Independent processing of EEG and fMRI data, with fMRI maps segmented into spatial priors.
• Application of a hierarchical Bayesian algorithm using fMRI-derived priors for EEG source localization, optimizing model evidence with dynamic fMRI region selection.

Main Results:

• Generation of cortical activity maps and time-courses with improved specificity.
• Reduction of cross-talk and erroneous activity in multimodal EEG-fMRI source localization.
• The method provides optimized parameterization through dynamic recruitment of fMRI sub-regions.

Conclusions:

• The developed spatiotemporal fMRI-constrained EEG source imaging method offers enhanced localization accuracy.
• This protocol improves multimodal neuroimaging specificity by dynamically integrating fMRI information.
• The method is broadly compatible with standard neuroimaging software and suitable for various studies, despite limitations in detecting EEG-invisible sources.