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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses.

Adonay S Nunes1, Alexander Moiseev2, Nataliia Kozhemiako1

  • 1Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.

Neuroimage
|December 2, 2019
PubMed
Summary
This summary is machine-generated.

Multiple Constrained Minimum Variance beamformer (MCMV) significantly improves electrophysiological connectivity estimation by reducing signal leakage and cancellation. This novel approach, including Augmented Pairwise MCMV (APW-MCMV), enhances accuracy in both task and resting states.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Functional brain connectivity is crucial for cognitive, perceptual, and motor functions.
  • Magnetoencephalography (MEG) and electroencephalography (EEG) map brain activity but face signal leakage and cancellation issues, biasing connectivity estimates.
  • Accurate electrophysiological connectivity is essential for understanding brain function.

Purpose of the Study:

  • To evaluate if Multiple Constrained Minimum Variance beamformer (MCMV) surpasses Linearly Constrained Minimum Variance beamformer (LCMV) in estimating electrophysiological connectivity.
  • To introduce and assess a novel Augmented Pairwise MCMV (APW-MCMV) method for improved signal leakage suppression in resting-state analyses.

Main Methods:

  • Compared MCMV and LCMV performance using simulated and human MEG data for both task-related (visual steady-state signals) and resting-state conditions (89 subjects).
  • Utilized coherence and phase locking values for task-related connectivity, and envelope correlations for resting-state data.
  • Introduced APW-MCMV and compared its efficacy against LCMV and standard MCMV in reducing spurious connections and controlling false negatives.

Main Results:

  • MCMV significantly reduced signal mixing and coherent source cancellation effects in both task and resting states compared to LCMV.
  • MCMV preserved 0- and short time lag interactions, which are often missed by other methods.
  • APW-MCMV demonstrated superior performance in resting-state analyses, effectively reducing spurious connections while maintaining sensitivity to true connections.

Conclusions:

  • MCMV offers a more accurate and reliable method for estimating functional brain connectivity from MEG/EEG data.
  • The novel APW-MCMV approach provides enhanced precision for resting-state connectivity analysis, crucial for brain research.
  • These findings advance the interpretation of electrophysiological brain connectivity, impacting cognitive neuroscience and clinical applications.