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

Abstract

<p>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.</p>

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“…We opt for a formalization of the inverse problem (Equation 1) and inverse solution (Equation 2) in the frequency domain of the time series which aims to obtain the specific patterns of rhythmic source activity ( 59 – 63 ). A target function (Equations 3, 4) is defined upon cross-spectrum, an essential statistic in the frequency domain of stationary time series ( 64 ). Table 2 summarizes the basic definitions to reproduce our analysis, including quantities ( Table 2 , 1–3), models ( Table 2 , 4–8), method ( Table 2 , 9–11), and functional connectivity ( Table 2 , 12, 13).…”

Source space reduction for eLORETA

“…Brain connectivity variable resolution tomographic analysis (BC-VARETA) is a recent inverse method developed by Gonzales et al [11] that has already been adopted in a few studies [12][13][14]. The method estimates the inverse solution and its precision matrix, which represents the connectivity parameters, by using the frequency domain representation of the stationary time series.…”