Toward a quantitative description of large-scale neocortical dynamic function and EEG.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Large-scale neocortical dynamics involve complex interactions between local neural networks and global brain activity.
• Existing models often struggle to integrate diverse experimental data across different spatial scales and brain states.

Purpose of the Study:

• To present a general conceptual framework for large-scale neocortical dynamics.
• To outline a local/global dynamic theory consistent with electroencephalography (EEG) and magnetoencephalography (MEG) data.
• To propose predictions for EEG measures of traveling and standing wave phenomena.

Main Methods:

• Application of a general conceptual framework to diverse experimental designs, spatial scales, and brain states.
• Development of a local/global dynamic theory describing excitatory and inhibitory synaptic action fields.
• Utilizing EEG and MEG data to estimate modulations of synaptic fields.

Main Results:

• Identified interacting local processes (neural networks) arising from functional segregation and global processes from functional integration.
• Demonstrated that global processes can facilitate synchronous activity in remote cell groups across multiple spatial scales.
• Showcased how local processes can drive macroscopic global dynamics observed in EEG/MEG.
• Proposed that brain states are determined by neuromodulatory control parameters, leading to purely local, purely global, or interactive states.

Conclusions:

• The local/global dynamic theory provides a unified framework for understanding neocortical dynamics across various scales and states.
• The theory makes testable predictions about EEG/MEG wave phenomena and the influence of neuromodulation on brain states.
• Future quantitative theories may incorporate more accurate treatments of local and nonlinear effects for a deeper understanding of brain dynamics.