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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors.

Rodolfo Abreu1, Júlia F Soares1, Ana Cláudia Lima2

  • 1Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied To Health (ICNAS), University of Coimbra, Coimbra, Portugal.

Brain Topography
|February 10, 2022
PubMed
Summary

This study shows that using dynamic functional connectivity from fMRI data improves electroencephalography (EEG) source reconstruction. Incorporating these dynamic priors enhances the accuracy of mapping brain activity from combined EEG-fMRI.

Keywords:
Brain imagingEEG source reconstructionMultiple sclerosisSimultaneous EEG-fMRIfMRI spatial priors

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Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • Electroencephalography (EEG) source reconstruction is challenging due to the ill-posed inverse problem.
  • Prior information, particularly from functional Magnetic Resonance Imaging (fMRI), can improve EEG source localization.
  • Existing methods often overlook the dynamic nature of brain functional connectivity.

Purpose of the Study:

  • To compare four EEG source reconstruction algorithms (MN, LORETA, EBB, MSP) using simultaneous EEG-fMRI data.
  • To evaluate the impact of different spatial priors, including dynamic functional connectivity (dFC) states from fMRI, on reconstruction accuracy.
  • To identify optimal algorithms and prior combinations for more accurate EEG source mapping.

Main Methods:

  • Four inversion algorithms (MN, LORETA, EBB, MSP) were implemented within a Bayesian framework (SPM).
  • Three sets of priors were tested: algorithm-specific, algorithm-specific + fMRI task/RSNs, and algorithm-specific + fMRI task/RSNs + dFC states.
  • Reconstruction quality was assessed using model-based metrics (posterior probability, variance explained) and spatial overlap with known brain regions and RSN templates.

Main Results:

  • Model parsimony favored MSP with algorithm-specific priors based on model-based metrics.
  • Optimal spatial overlap was achieved with EBB (using fMRI task/RSNs) or MSP (using all priors including dFC states).
  • fMRI spatial priors, especially dynamic connectivity modules, significantly improved EEG source component recovery.

Conclusions:

  • Dynamic fMRI spatial priors, reflecting brain network fluctuations, enhance the accuracy of mapping neuronal activity from EEG-fMRI.
  • The choice of algorithm and priors significantly impacts EEG source reconstruction quality.
  • This study provides a framework for selecting optimal EEG source reconstruction approaches, crucial for future neuroimaging research.