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Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
Published on: February 15, 2021
Brain dynamics across levels of organization
1Department of Biomedical Engineering, University of Texas, Austin, Engineering Science Building, 1 University Station, C0800 Austin, TX 78712-0238, USA. gwer1@mail.utexas.edu
Journal of Physiology, Paris
|February 13, 2008
Summary
Brain surface electrical activity reflects metastable neuronal states, potentially mapping to the dynamic core (DC) and global neuronal workspace (GNW). This suggests self-organized criticality (SOC) in brain dynamics, analyzable via statistical physics.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Statistical Physics
Background:
- Brain surface electrical activity can reflect underlying neuronal configurations at the mesoscopic level.
- Metastable state transitions are fundamental to brain function.
Purpose of the Study:
- To propose that spatio-temporal patterns in brain electrical activity correspond to the dynamic core (DC) and global neuronal workspace (GNW) models.
- To investigate the role of recursively reentrant activity flow in these models.
- To explore the applicability of self-organized criticality (SOC) to brain dynamics.
Main Methods:
- Analysis of electrical activity recorded from the brain surface.
- Theoretical modeling linking brain activity patterns to DC and GNW.
- Application of statistical physics concepts to understand neuronal avalanche dynamics and phase transitions.
Main Results:
- Electrical activity patterns may represent distinct spatio-temporal dynamics of the DC and GNW.
- Recursively reentrant activity flow in neuronal loops is crucial for both models.
- Temporal characteristics of activity flow suggest self-organized criticality (SOC).
Conclusions:
- Brain electrical activity, particularly EEG and EMG, can be analyzed using statistical physics to understand phase transitions, scaling, and universality in DC and GNW.
- The concept of SOC provides a framework for exploring the dynamics of neuronal avalanches and their relation to macroscopic brain activity.
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