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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic
Yohan Attal1, Burkhard Maess, Angela Friederici
1Université Pierre et Marie Curie-Paris 6, Centre de Recherche de l’institut du Cerveau et de la Moelle épinière, UMR-S975, 75651 Paris, France.
Reviews in the Neurosciences
|June 22, 2012
Summary
Estimating deep brain activity using magnetoencephalography (MEG) and electroencephalography (EEG) is challenging due to signal attenuation. This review explores geometric influences and advanced methods for localizing subcortical neural currents.
Area of Science:
- Neuroscience
- Biophysics
- Biomedical Engineering
Background:
- Cognitive functions engage both cortical and subcortical brain structures.
- Subcortical sources yield minimal magnetoencephalographic (MEG) and electroencephalographic (EEG) signals due to distance and electromagnetic properties.
- Accurate estimation of deep neural activity from M/EEG data remains a significant challenge.
Purpose of the Study:
- To review the impact of geometric parameters on M/EEG signals from deep brain structures.
- To discuss methodologies for localizing and quantifying M/EEG contributions from subcortical neural currents.
Main Methods:
- Analysis of geometric influences (location, orientation) on M/EEG signals from deep brain nuclei (amygdalo-hippocampal complex, thalamus, basal ganglia).
- Review of advanced inverse methods incorporating realistic forward models of subcortical regions.
- Exploration of dynamical priors based on biologically plausible neural models, including dynamic causal modeling (DCM) for M/EEG.
Main Results:
- Geometric factors significantly influence the detectability and localization of subcortical M/EEG signals.
- Advanced modeling techniques are crucial for overcoming signal attenuation and electromagnetic silence of deep sources.
- Dynamic causal modeling (DCM) offers a promising framework for integrating neural priors to improve subcortical source estimation.
Conclusions:
- Accurate M/EEG analysis of subcortical structures requires sophisticated modeling that accounts for geometry and neural dynamics.
- Future research should focus on refining these advanced methods to enhance the understanding of deep brain contributions to cognition.
