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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
A linear model of phase-dependent power correlations in neuronal oscillations
David Eriksson1, Raul Vicente, Kerstin Schmidt
1Research Group: Cortical function and dynamics, Max-Planck-Institute for Brain Research Frankfurt, Germany.
Frontiers in Computational Neuroscience
|August 3, 2011
Summary
Communication through coherence (CTC) links neuronal communication to oscillation phase differences. This study models CTC, revealing how phase differences influence power correlations and effective neural interactions.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Effective neuronal communication is hypothesized to depend on phase differences between oscillating neuronal populations, termed communication through coherence (CTC).
- Previous experimental work quantified these interactions using power correlations derived from local field potentials and multi-unit activity.
Purpose of the Study:
- To present a linear model of interacting oscillators to explain the phase dependency of power correlations.
- To provide a reference for detecting non-linearities in neural interactions, such as gain control.
- To analyze how power correlations depend on uncoupled phase difference, connection strength, and network topology.
Main Methods:
- Developed a linear model of interacting oscillators to analyze phase-dependent power correlations.
- Sorted experimental trials based on the coupled phase difference between oscillating neuronal populations.
- Investigated the influence of uncoupled phase difference, connection strength, and topology on power correlations.
Main Results:
- The model accounts for the phase dependency of power correlations between neuronal populations.
- Uncoupled phase difference, the phase relation prior to interaction, was found to be a causal variable influencing power correlations.
- For unidirectional connectivity, the dependency on uncoupled phase difference was broader than on coupled phase difference.
- Bidirectional connectivity altered the characteristics of the phase dependency compared to unidirectional connections.
Conclusions:
- The phase dependency of power correlations provides insights into the mechanisms of communication through coherence.
- The width of the phase dependency can indicate optimal oscillation frequencies for specific connection delays.
- A specific phase dependency width may facilitate stimulus-contrast dependent long-range lateral connections.
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