Estimating functional connectivity using 2D tangential components in MEG sensor space.

Area of Science:

• Neuroscience
• Biophysics
• Signal Processing

Background:

• Magnetoencephalography (MEG) sensor space connectivity analysis is vital for estimating functional relationships between cortical areas.
• The field spread effect significantly complicates the interpretation of these sensor-level connectivity results.
• A novel approach is needed to enhance the interpretability of MEG sensor-level connectivity.

Purpose of the Study:

• To introduce and validate a new connectivity measure based on imaginary coherence for MEG sensor space.
• To improve the interpretability of functional connectivity estimations by mitigating the field spread effect.
• To demonstrate the utility of the proposed measure in analyzing brain activity.

Main Methods:

• MEG signals from axial gradiometers were processed using wavelet transform at a specific frequency.
• 2D tangential magnetometer signals were reconstructed to estimate functional connectivity.
• The Euclidean norm of imaginary coherence between sensor locations was calculated to quantify functional relationships.

Main Results:

• The novel imaginary coherence measure was applied to both measured and simulated auditory evoked MEG data.
• The method successfully eliminated the field spread effect, improving connectivity analysis.
• Increased inter-hemispheric coherence was observed between sensors over the left and right auditory cortex.

Conclusions:

• The proposed imaginary coherence measure offers a more interpretable approach to MEG sensor-level connectivity analysis.
• The method demonstrates robustness in the presence of spontaneous brain activity variability.
• This technique can be extended to various MEG sensor types and signal dimensions.