Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Resting-state electroencephalography (EEG) and magnetoencephalography (MEG) are often analyzed as functional networks.
• Sensor-level network analysis is challenged by mixed source activity in EEG/MEG data.
• Reconstructing source-level network topology from sensor-level data is crucial.

Purpose of the Study:

• To investigate the extent to which network topology can be reconstructed from sensor-level functional connectivity (FC) in MEG data.
• To compare sensor-level and source-level network topologies using realistic simulations.
• To identify optimal methods for analyzing resting-state MEG functional networks.

Main Methods:

• Utilized a diffusion magnetic resonance imaging-constrained whole-brain computational model for resting-state activity simulations.
• Simulated resting-state cortical activity to assess network properties.
• Compared network topologies derived from sensor-level FC measures against source-level activity.

Main Results:

• Field spread significantly impacts network topology, dependent on interaction type (instantaneous vs. lagged).
• Lagged functional connectivity is underestimated at the sensor level due to signal mixing.
• Instantaneous interactions are more susceptible to field spread than lagged interactions.
• Discrepancies between sensor and source topologies are reduced with planar gradiometers compared to axial gradiometers.

Conclusions:

• Sensor-level network analysis of MEG data can misrepresent true source-level network topology.
• Using lagged interaction measures with planar gradiometers is recommended for more accurate resting-state network analysis in MEG.
• Understanding field spread effects is critical for interpreting functional connectivity in MEG studies.