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NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis.

Behrad Soleimani1, Proloy Das2, I M Dushyanthi Karunathilake1

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.

Neuroimage
|July 23, 2022
PubMed
Summary

This study introduces Network Localized Granger Causality (NLGC), a novel method for analyzing brain connectivity using magnetoencephalography (MEG). NLGC improves upon traditional methods by directly estimating neural interactions, offering more accurate insights into sensory processing networks.

Keywords:
Auditory processingFunctional connectivity analysisGranger causalityMEGSource localizationStatistical inference

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Understanding directed cortical connectivity is crucial for deciphering neural mechanisms of sensory processing.
  • Functional magnetic resonance imaging (fMRI) offers limited temporal resolution for millisecond-scale neural interactions.
  • Magnetoencephalography (MEG) provides high temporal resolution but poses challenges for Granger causality (GC) inference due to sensor-level data limitations.

Purpose of the Study:

  • To introduce the Network Localized Granger Causality (NLGC) inference paradigm for enhanced GC analysis directly from MEG data.
  • To address the limitations of conventional two-stage source localization and GC inference methods.
  • To provide a precise statistical characterization of Granger causality links in neural networks.

Main Methods:

  • Developed NLGC, modeling source dynamics as latent sparse multivariate autoregressive processes.
  • Integrated source localization directly within the parameter estimation from MEG measurements.
  • Applied NLGC to simulated data and real MEG data from an auditory tone processing task in younger and older adults.

Main Results:

  • NLGC demonstrated robustness against model mismatch, network size, and low signal-to-noise ratio in simulations.
  • Conventional two-stage methods exhibited high rates of false alarms and missed detections.
  • NLGC successfully characterized cortical network activity during auditory processing and resting state, revealing task- and age-related connectivity changes.

Conclusions:

  • NLGC offers a more accurate and robust approach for inferring directed neural connectivity from MEG data compared to conventional methods.
  • The method enhances the understanding of neural mechanisms underlying sensory processing and age-related cognitive changes.
  • NLGC provides a powerful tool for investigating dynamic brain networks in various cognitive states and populations.