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Updated: Mar 19, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

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Published on: June 29, 2018

Two generalized algorithms measuring phase-amplitude cross-frequency coupling in neuronal oscillations network.

Qun Li^1,2, Chen-Guang Zheng^1,3, Ning Cheng¹

¹College of Life Sciences and Key Laboratory of Bioactive Materials Ministry of Education, Nankai University, Tianjin, 300071 People's Republic of China.

Cognitive Neurodynamics

|June 9, 2016

Summary

New algorithms effectively measure cross-frequency coupling in brain networks. These methods reveal decreased theta-low gamma phase-amplitude coupling (PAC) in a rat glioma model, offering insights into neural information processing.

Keywords:

Local field potentials (LFPs)Neuronal oscillations Phase–amplitude coupling (PAC)Phase–amplitude coupling-conditional mutual information (PAC-CMI)Phase–amplitude coupling-evolution map approach (PAC-EMA)

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Published on: August 14, 2015

Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Cross-frequency coupling (CFC) is crucial for neural information exchange and integration.
Existing methods for measuring CFC, like phase-amplitude coupling (PAC), are being refined.
Understanding PAC dynamics is key to deciphering complex brain functions.

Purpose of the Study:

To generalize two algorithms for measuring cross-frequency coupling.
To validate the effectiveness of these generalized algorithms using simulation and experimental data.
To assess changes in PAC within specific neural networks, such as the hippocampus.

Main Methods:

Generalization of the phase-amplitude coupling-evolution map approach and phase-amplitude coupling-conditional mutual information.
Quantitative evaluation of coupling strength using simulation data.
Analysis of local field potentials from anesthetized rats using the generalized algorithms.

Main Results:

The generalized algorithms accurately quantify changes in PAC strength, consistent with traditional methods (PAC-PLV, PAC-MI).
Simulation data confirmed the effectiveness of the developed approaches.
Experimental data showed a significant decrease in theta-low gamma PAC in the hippocampal CA3-CA1 network of glioma-bearing rats compared to controls.

Conclusions:

The generalized algorithms provide a robust measure for cross-frequency coupling.
These methods are effective for analyzing PAC in specific neural sub-networks like the hippocampus.
The findings suggest altered neural communication in the glioma model, indicated by reduced theta-low gamma PAC.