Estimating Brain Connectivity Using Copula Gaussian Graphical Models

@article{Gao2019EstimatingBC,
  title={Estimating Brain Connectivity Using Copula Gaussian Graphical Models},
  author={X. Gao and Weining Shen and Chee Ming Ting and S. Cramer and R. Srinivasan and H. Ombao},
  journal={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
  year={2019},
  pages={108-112}
}
  • X. Gao, Weining Shen, +3 authors H. Ombao
  • Published 2019
  • Computer Science
  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
  • Electroencephalogram (EEG) has been widely used to study cortical connectivity during acquisition of motor skills. Previous studies using graphical models to estimate sparse brain networks focused on time-domain dependency. This paper introduces graphical models in the spectral domain to characterize dependence in oscillatory activity between EEG channels. We first apply a transformation based on a copula Gaussian graphical model to deal with non-Gaussianity in the data. To obtain a simple and… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 24 REFERENCES
    Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models
    • 18
    • PDF
    The generalized shrinkage estimator for the analysis of functional connectivity of brain signals
    • 44
    • PDF
    Resting-state cortical connectivity predicts motor skill acquisition
    • 89
    Inferring Brain Networks through Graphical Models with Hidden Variables
    • 14
    • PDF
    Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network?
    • 503
    Brain graphs: graphical models of the human brain connectome.
    • 766
    • PDF
    Functional connectivity: Shrinkage estimation and randomization test
    • 28
    A hierarchical independent component analysis model for longitudinal neuroimaging studies
    • 11
    • PDF