A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG

Area of Science:

• Neuroscience
• Brain Imaging
• Computational Neuroscience

Background:

• Understanding brain functional connectivity dynamics is crucial for neuroscience.
• Resting-state functional magnetic resonance imaging (fMRI) reveals intrinsic neural networks via correlations.
• High temporal resolution electrophysiological measures, like magnetoencephalography (MEG), are needed for oscillatory dynamics.

Purpose of the Study:

• To adapt and validate a wavelet-based method for analyzing resting-state MEG data to map functional connectivity.
• To address the lack of consensus on methods for examining connectivity in resting-state MEG.
• To investigate intrinsic interhemispheric connectivity in the human auditory network.

Main Methods:

• Adapted a wavelet-based phase-locking method using anatomical MRI and MEG data with a minimum norm estimate inverse solution.
• Simulated phase-locked oscillations to identify and quantify artifacts from inverse solution imperfections.
• Normalized resting-state MEG data using phase-locking values from empty room data to reduce artifacts.

Main Results:

• The adapted method successfully generated functional connectivity maps across the cortical surface.
• Simulations revealed significant artifacts from inverse solution imperfections.
• Normalization using empty room data effectively reduced inverse solution-related artifacts.
• Revealed intrinsic interhemispheric connectivity in the auditory network in the alpha frequency band in eight subjects.

Conclusions:

• The developed spectral resting-state functional connectivity imaging method provides a robust way to study brain connectivity dynamics.
• This method enhances our understanding of oscillatory dynamics underlying intrinsic functional connectivity.
• The technique is promising for future investigations into the human brain's functional organization.