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Characterization of Second-Order Mixing Effects in Reconstructed Cross-Spectra of Random Neural Fields
1Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. r.hindriks@vu.nl.
Brain Topography
|March 13, 2024
Summary
This study investigates false positives in electroencephalography (EEG) and magnetoencephalography (MEG) functional connectivity analysis. We reveal how second-order mixing, caused by lagged sources, creates spurious interactions, impacting connectivity assessments.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Signal Processing
Background:
- Functional connectivity analysis in EEG/MEG commonly uses measures insensitive to instantaneous source mixing.
- However, these measures are susceptible to false positives from second-order mixing (lagged sources).
- This mixing can lead to numerous inaccurate positive interactions in connectivity assessments.
Purpose of the Study:
- To investigate the impact of first- and second-order mixing on cross-spectra in reconstructed source activity.
- To relate mixing effects to the properties of resolution operators used in source reconstruction.
- To characterize configurations influencing second-order mixing and understand trade-offs.
Main Methods:
- Derived identities linking mixing effects to measurement and source configurations.
- Analyzed properties of resolution operators and their impact on signal mixing.
- Utilized Lagrange's identity for cross-talk functions to analyze mixing trade-offs.
Main Results:
- Identified configurations that maximize/minimize second-order mixing (maximal when measurement locations are distant and sources coincide).
- Described second-order mixing effects near measurement locations using local geometry of point-spread functions.
- Established a trade-off between first- and second-order mixing magnitudes via a generalized cross-product.
Conclusions:
- Second-order mixing poses a significant challenge to accurate functional connectivity estimation in EEG/MEG.
- Understanding mixing effects and their geometric properties is crucial for developing more reliable connectivity measures.
- The study provides a theoretical framework for mitigating false positives in neural source connectivity analysis.
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