A geometric correction scheme for spatial leakage effects in MEG/EEG seed-based functional connectivity mapping

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Brain Network Analysis

Background:

• Seed-based analyses of brain networks using source-level EEG/MEG are confounded by spatial leakage.
• Existing methods to mitigate spatial leakage rely on specific temporal assumptions.

Purpose of the Study:

• To investigate the efficacy of a model-based geometric correction scheme (GCS) for suppressing spatial leakage in MEG/EEG data.
• To analyze the theoretical properties and practical advantages/limitations of GCS.

Main Methods:

• Theoretical analysis of GCS properties.
• Simulation and experimental MEG data (resting state, auditory-motor task).
• Minimum Norm Estimation (MNE) for source reconstruction, statistical gauging, and comparison with signal orthogonalization.

Main Results:

• GCS effectively suppresses spatial leakage locally, aligning with MNE geometry.
• GCS maps all brain interaction types, including those removed by signal orthogonalization.
• GCS demonstrates robustness against forward model errors but can be affected by overcorrection and seed mislocation.

Conclusions:

• GCS is a valuable tool for correcting spatial leakage in seed-based functional connectivity (FC) analyses of MEG/EEG data.
• GCS complements signal orthogonalization, addressing limitations of each method.
• The GCS offers a robust approach for enhancing the accuracy of brain network investigations.