Assessing interactions in the brain with exact low-resolution electromagnetic tomography.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biophysics

Background:

• Scalp electroencephalogram (EEG) reflects cortical activity, but estimating source current density is an inverse problem.
• Existing methods like exact low-resolution brain electromagnetic tomography (eLORETA) offer precise localization but face challenges with functional connectivity measures.
• Volume conduction and limited spatial resolution inflate measures of brain connectivity, such as coherence and phase synchronization.

Purpose of the Study:

• To present a novel method for improving the physiological accuracy of functional dynamic connectivity assessments derived from EEG.
• To address the overestimation of connectivity measures caused by volume conduction and low spatial resolution in EEG neuroimaging.
• To enhance the interpretation of brain functional connectivity by isolating physiologically relevant components.

Main Methods:

• Utilized exact low-resolution brain electromagnetic tomography (eLORETA) for accurate source localization from scalp EEG.
• Developed a decomposition technique to separate connectivity measures into instantaneous and lagged components.
• Applied the method to EEG data to analyze functional dynamic connectivity.

Main Results:

• The proposed method successfully decomposes connectivity measures into instantaneous and lagged components.
• The lagged component of functional connectivity demonstrates a significantly higher physiological origin.
• This decomposition effectively mitigates the inflated connectivity values caused by volume conduction and spatial limitations.

Conclusions:

• The novel decomposition method enhances the physiological interpretability of EEG-based functional connectivity.
• eLORETA combined with this decomposition technique offers a more accurate assessment of brain dynamics.
• This approach holds promise for more reliable non-invasive brain connectivity analysis.