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Discrete spectral eigenmode-resonance network of brain dynamics and connectivity
1School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia.
Physical Review. E
|October 16, 2021
Summary
This study introduces a new method to represent brain dynamics and connectivity using physical spatial eigenmodes and their frequency resonances. This approach simplifies complex brain activity analysis and offers a more natural, data-driven representation than traditional methods.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Control Theory
Background:
- Analyzing complex brain dynamics and connectivity requires natural, compact representations.
- Current methods often rely on artificial discretizations and phenomenological patterns, introducing potential artifacts.
- Bridging physiological theories with experimental data remains a challenge.
Purpose of the Study:
- To develop a novel framework for representing brain dynamics and connectivity using physical spatial eigenmodes and their frequency resonances.
- To demonstrate that this spectral representation enables compact analysis of linear and nonlinear dynamics.
- To link brain activity and connectivity to control-system functions and facilitate inference of dynamic equations from data.
Main Methods:
- Expansion of brain dynamics in terms of physical spatial eigenmodes and their frequency resonances.
- Utilizing the system transfer function for discrete expansion of dynamics.
- Employing system identification methods from control theory to infer dynamic equations.
- Analyzing modal resonances as nodes in a discrete spectral network.
Main Results:
- A compact representation of brain dynamics and connectivity is achieved using natural dynamic "atoms" (modal resonances).
- Modal resonances are determined by system dynamics, offering an advantage over investigator-defined patterns.
- The formulation speeds up numerical calculations of nonlinear interactions and links brain activity to control-system functions like prediction and attention.
- Tracking modal resonance amplitudes provides a more direct and temporally localized dynamic representation.
Conclusions:
- The modal resonance formulation offers a powerful, data-driven approach to understanding brain dynamics and connectivity.
- This framework bridges quantitative measurements, connectivity, dynamics, and function, facilitating integration of diverse research lines.
- It advocates for using standard theoretical-physics and mathematical methods over ad hoc statistical measures for network analysis.
