Characterizing instantaneous phase relationships in whole-brain fMRI activation data

Area of Science:

• Neuroimaging
• Complex Systems Analysis
• Signal Processing

Background:

• Functional magnetic resonance imaging (fMRI) data analysis typically employs linear methods in the time domain.
• Existing methods like regression and correlation may not fully capture the complex dynamics of interacting neural systems.

Purpose of the Study:

• To introduce phase synchronization analysis as a novel method for detecting brain activation in fMRI data.
• To investigate the application of phase synchronization for understanding the dynamics of coupled oscillatory systems within the brain.

Main Methods:

• Developed an fMRI-compatible phase synchronization analysis to assess phase locking between voxel time series and task reference functions.
• Quantified phase locking using a synchronization index and determined statistical significance with a nonparametric permutation test.
• Applied the method to fMRI data from five volunteers during an event-related finger-tapping task.

Main Results:

• Generated functional maps illustrating interrelations between instantaneous phases of reference functions and voxel time series.
• Demonstrated the ability of phase synchronization to provide insights into the complex nature of fMRI time series.
• Successfully identified activation patterns by analyzing phase locking in whole-brain fMRI data.

Conclusions:

• Phase synchronization analysis is a valuable tool for uncovering additional information from fMRI data.
• This nonlinear method enhances the understanding of brain dynamics beyond traditional linear approaches.
• The technique offers a novel perspective on fMRI data analysis and activation detection.