Theoretical analysis on relationship between the neural activity and the EEG

Area of Science:

• Computational Neuroscience
• Neurophysics
• Biophysics

Background:

• The electroencephalogram (EEG) reflects the collective electrical activity of neurons, but a direct derivation from underlying neural dynamics remains challenging.
• Understanding the relationship between individual neuronal action potentials and macroscopic EEG signals is crucial for interpreting brain function.

Purpose of the Study:

• To derive a collective oscillation mode from neural network dynamics using multicompartment equations and projection operator techniques.
• To simulate time-dependent EEG signals by integrating action potential trains with the derived collective oscillation mode.
• To investigate how small modulations in neuronal activity can influence global EEG patterns.

Main Methods:

• Utilized multicompartment equations and projection operator techniques to model neural network activity.
• Derived a chain structure of circuit loops, each composed of four neurons, representing collective neural oscillations.
• Simulated EEG signals by incorporating action potential trains, postsynaptic potentials, and slow waves, influenced by the collective oscillation mode.

Main Results:

• Identified collective oscillation modes with eigenvalues similar to EEG frequency spectra.
• Successfully simulated characteristic rat EEG features, including theta rhythm, spindle waves, and arousal waves.
• Demonstrated that modulated activity in a small number of neurons can significantly impact global EEG shape.

Conclusions:

• The study provides a comprehensive derivation of EEG signals from fundamental neural activity, specifically action potentials.
• The developed model highlights the significant influence of collective neural oscillations on observable EEG patterns.
• This work offers a computational framework for understanding EEG generation and its relationship to neuronal network dynamics.