Mapping the dynamic repertoire of the resting brain.

The resting state dynamics of the brain shows robust features of spatiotemporal pattern formation but the actual nature of its time evolution remains unclear. Computational models propose specific state space organization which defines the dynamic repertoire of the resting brain. Nevertheless, methods devoted to the characterization of the organization of brain state space from empirical data still lack and thus preclude comparison of the hypothetical dynamical repertoire of the brain with the actual one. We propose here an algorithm based on set oriented approach of dynamical system to extract a coarse-grained organization of brain state space on the basis of EEG signals. We use it for comparing the organization of the state space of large-scale simulation of brain dynamics with actual brain dynamics of resting activity in healthy subjects. The dynamical skeleton obtained for both simulated brain dynamics and EEG data depicts similar structures. The skeleton comprised chains of macro-states that are compatible with current interpretations of brain functioning as series of metastable states. Moreover, macro-scale dynamics depicts correlation features that differentiate them from random dynamics. We here propose a procedure for the extraction and characterization of brain dynamics at a macro-scale level. It allows for the comparison between models of brain dynamics and empirical measurements and leads to the definition of an effective coarse-grained dynamical skeleton of spatiotemporal brain dynamics.

Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events.

Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique's robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.

Using centrality measures to extract core pattern of brain dynamics during the resting state.

The patterns of brain dynamics were studied during resting state on a macroscopic scale for control subjects and multiple sclerosis patients. Macroscopic brain dynamics is defined after successive coarse-grainings and selection of significant patterns and transitions based on Markov representation of brain activity. The resulting networks show that control dynamics is merely organized according to a single principal pattern whereas patients dynamics depict more variable patterns. Centrality measures are used to extract core dynamical pattern in brain dynamics and classification technique allow to define MS dynamics with relevant error rate.