A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Neural communication relies on interactions between distant neuronal populations.
• These interactions can be highly nonlinear, posing challenges for analysis.
• Quantifying nonlinear connectivity is crucial for understanding nervous system function.

Purpose of the Study:

• Introduce a novel method, multi-spectral phase coherence (MSPC), to measure nonlinear connectivity.
• Compare MSPC's capabilities against existing phase coupling measures using simulated data.
• Apply MSPC to analyze neural data from a motor control experiment.

Main Methods:

• Developed multi-spectral phase coherence (MSPC) for quantifying nonlinear neural interactions via phase coupling.
• Validated MSPC using simulated data, comparing it with n:m synchronization index and bi-phase locking value.
• Applied MSPC to analyze electroencephalography (EEG) and electromyography (EMG) data during a motor control task.

Main Results:

• MSPC quantifies nonlinearity order, interaction direction, and time delays.
• It reveals harmonic and intermodulation coupling beyond second order, partly missed by other methods.
• Analysis of motor control data showed directional nonlinear connectivity and a perturbation-to-brain response time delay of 43 ± 8 ms.

Conclusions:

• MSPC is a novel and comprehensive approach for assessing high-order nonlinear interactions in the nervous system.
• The method provides insights into the directionality and timing of neural communication.
• MSPC advances the understanding of complex neural dynamics and system timing.