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Identification of oscillatory brain networks with Hidden Gaussian Graphical Spectral models of EEG/MEG

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posted on 2022-07-11, 16:52 authored by Deirel Paz-Linares, Eduardo Gonzalez-Moreira, Ariosky Areces GonzalezAriosky Areces Gonzalez, Ying Wang, Min Li, Eduardo Martínez-Montes, Maria L Bringas-Vega, Mitchell Valdés-Sosa, Pedro Valdés-Sosa

Identifying the connectivity of functional networks underpinning undirectly observed phenomena for neurosciences or other fields poses a Bayesian inverse-problem. Electromagnetic (EEG or MEG) inverse-solutions unveil the cortical oscillatory networks that strongly correlate to brain function with a spectral transparency that no other in vivo neuroimage may provide. Simulations of such an inverse-problem also reveal distortions of the connectivity determined by most common state-of-the-art solutions. We disclose the origin of distortions and remedy them via a Hidden Gaussian Graphical Spectral (HIGGS) model, the Bayesian formalism for the inverse-problem in identifying such networks. In human EEG alpha rhythm simulations, distortions measured as ROC performance do not surpass the 2% in our HIGGS solution and reach 20% in state-of-the-art approaches. Congruence in macaque simultaneous EEG/ECoG recordings provides experimental confirmation for our solution with 1/3 more congruence than state-of-the-art methods.


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Submitting Author's Institution

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China

Submitting Author's Country

  • China