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Estimating brain connectivity using copula Gaussian graphical models

Gao, Xu and Shen, Weining and Ting, Chee Ming and Cramer, Steven C. and Srinivasan, Ramesh and Ombao, Hernando (2019) Estimating brain connectivity using copula Gaussian graphical models. In: 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, 8 April 2019 through 11 April 2019, Hilton Molino Stucky - Venice, Venice, Italy.

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Official URL: http://dx.doi.org/10.1109/ISBI.2019.8759538


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 robust representation of brain connectivity that explains most variation in the data, we propose a framework based on maximizing penalized likelihood with Lasso regularization utilizing the cross-spectral density matrix to search for a sparse precision matrix. To solve the optimization problem, we developed modified versions of graphical Lasso, Ledoit-Wolf (LW) and the majorize-minimize sparse covariance estimation (SPCOV) algorithms. Simulations show benefits of the proposed algorithms in terms of robustness and accurate estimation under non-Gaussianity and different structures of high-dimensional sparse networks. On EEG data of a motor skill task, the modified graphical Lasso and LW algorithms reveal sparse connectivity pattern among cortices in consistency with previous findings. In addition, our results suggest regions over different frequency bands yield distinct impacts on motor skill learning.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:brain connectivity, copulas, EEG, graphical models, high-dimensional covariance
Subjects:Q Science > QH Natural history > QH301 Biology
T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Biosciences and Medical Engineering
ID Code:89563
Deposited By: Yanti Mohd Shah
Deposited On:22 Feb 2021 14:10
Last Modified:22 Feb 2021 14:10

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