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Related Experiment Video

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Inferring Cortical Connectivity from ECoG Signals Using Graph Signal Processing.

Siddhi Tavildar1,2, Brian Mogen2,3, Stavros Zanos2,4,5

  • 1Computational Science Research Center, San Diego State University, San Diego CA, USA.

IEEE Access : Practical Innovations, Open Solutions
|March 8, 2023
PubMed
Summary

A novel graph inference method accurately characterizes primate cerebral cortex connectivity using Electrocorticographic (ECoG) signals. This approach offers a more precise description of brain connectivity compared to traditional spectral coherence methods.

Keywords:
Brain connectivityCortical ConnectivityElectrocorticography (ECoG)Graph LearningGraph Signal ProcessingNeural Signal Processing

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Graph Theory

Background:

  • Understanding primate cerebral cortex connectivity is crucial for neuroscience.
  • Existing methods for mapping brain networks have limitations.

Purpose of the Study:

  • To introduce a novel graph inference method for characterizing cortical connectivity.
  • To compare the proposed method with spectral coherence using stimulation-based measures.

Main Methods:

  • Inferred connectivity graphs from Electrocorticographic (ECoG) signals in macaque monkeys.
  • Utilized auto-regressive (AR) signal representation and graph smoothness maximization.
  • Compared inferred connectivity maps with cortical evoked potential (CEP) maps and spectral coherence.

Main Results:

  • The proposed graph inference method produced connectivity maps more similar to CEP maps than spectral coherence.
  • Statistical significance was confirmed using surrogate map analysis, demonstrating robust findings.
  • The method effectively describes cortical connectivity patterns.

Conclusions:

  • The novel graph inference method provides a statistically significant and accurate characterization of primate cortical connectivity.
  • This technique offers an improvement over traditional spectral coherence for mapping brain networks.
  • The findings have implications for understanding brain function and dysfunction.