Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases

Investigaton of the neuronal efficacy and EEG source power under steady-state visual stimulation

Understanding the nature of the link between neuronal activity and BOLD signal plays a crucial role i) for improving the interpretability of BOLD images and ii) on the design of more realistic models for the integration of EEG and fMRI. The aim of this study is to investigate the neural mechanism underlying hemodynamic behavior in a series of visual stimulation frequencies and explore possible implications for the neurovascular coupling. We studied the relationship between electrophysiological and hemodynamic measures by performing simultaneous steady state electroencephalography (EEG) and fMRI recordings in a healthy human subject during a series of visual stimulation frequencies (6 Hz, 8 Hz, 1 0 Hz, 1 2 Hz). BOLD amplitudes were computed for voxels within an anatomical mask which was obtained by mapping the significantly active voxels using general linear modelling (GLM) on fMRI data. On the same anatomical map, EEG power time series belonging to the fundamental frequency and its harmonics due to the stimulation are estimated using a distributed source imaging technique. The neuronal efficacies which represent the vascular inputs driving the BOLD response are estimated by use of an extended version of Balloon model. A nonlinear relationship is demonstrated between the mean EEG source powers and the neuronal efficacies driving the BOLD response. The result suggests that BOLD signal which is an indicator of the metabolic demand of both synchronized and non-synchronized neuronal activities; changes independent of EEG activity which is a measure sensitive to the synchronicity of neuronal activity