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

TL;DR

This paper introduces a Bayesian Hidden Gaussian Graphical Spectral (HIGGS) model for more accurately identifying oscillatory brain networks from EEG/MEG data, significantly reducing estimation errors compared to existing methods.

Contribution

The novel HIGGS model explicitly incorporates functional network structure, improving inverse solution accuracy for EEG/MEG brain network identification.

Findings

HIGGS achieves less than 2% ROC error in simulations

Experimental EEG/ECoG data show 33% higher congruence with HIGGS

HIGGS outperforms state-of-the-art methods in accuracy

Abstract

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflect functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our inverse-solution with 1/3 more congruence according to Riemannian distances than state-of-the-art methods.