Mean-Field Models for EEG/MEG: From Oscillations to Waves.

Area of Science:

• Computational Neuroscience
• Theoretical Neuroscience
• Neurodynamics

Background:

• Traditional neural mass models (NMMs) offer simplified representations of large neuronal populations, primarily for understanding brain rhythms.
• Despite their utility, NMMs are phenomenological and cannot fully capture the complex dynamics observed in biological neural tissue.
• Existing models lack detailed mechanisms for synaptic and gap-junction interactions, limiting their biological realism.

Purpose of the Study:

• To introduce and analyze a next-generation neural mass model derived from a simple spiking neuron network.
• To extend this model to a spatially extended planar cortex for simulating large-scale brain activity.
• To demonstrate the model's utility in electroencephalography (EEG) and magnetoencephalography (MEG) research, specifically investigating gap-junction coupling's role in synaptic waves.

Main Methods:

• Developed a spiking neuron network model with both synaptic and gap-junction interactions.
• Derived an exact mean-field description for the spiking neuron network, forming the basis of the new neural mass model.
• Extended the mean-field model to a spatially extended planar cortex and applied it to simulate EEG/MEG data.

Main Results:

• The new mean-field model incorporates an additional dynamical equation for within-population synchrony, enhancing its descriptive power.
• The model successfully captures a richer repertoire of neural responses compared to traditional phenomenological NMMs.
• Simulations revealed the significant role of local gap-junction coupling in shaping large-scale synaptic waves, relevant for EEG/MEG interpretation.

Conclusions:

• The developed spiking neuron network-derived mean-field model provides a more neurobiologically grounded approach to modeling brain activity and rhythms.
• This next-generation mass model offers improved accuracy and a wider range of dynamics compared to conventional NMMs.
• The model serves as a valuable tool for EEG/MEG analysis, aiding in the understanding of neural synchrony and the functional impact of gap junctions.