Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest.

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.

Slow light in ultracompact photonic crystal decoder.

In this study, merging two photonic crystal-based structures, a new design for an all-optical 2-to-4 decoder has been proposed. The switching operation is based on the Kerr effect and refractive index modification. The structure consists of one nonlinear ring resonator and three nonlinear cavities that have been modified for entering the slow-light regime in order to enhance coupling through waveguides. The maximum group index of 94 has been obtained for the proposed slow-light waveguides. With this approach, the maximum and minimum normalized output powers for logic 0 and 1 are 4% and 82%, respectively. The data transfer rate of the decoder is 220 GHz, and the size of the structure is 24×9.5 µm2. The maximum insertion loss and cross talk are -7.45 dB and -16.38 dB, respectively. Considering the above characteristics, the proposed decoder can be qualified as a part of optical integrated circuits.

Ultra-fast all-optical decoder based on nonlinear photonic crystal ring resonators.

In this paper, a 2-to-4 all-optical decoder based on photonic crystal ring resonators is introduced. The photonic crystal structure has a 2D square chalcogenide rod lattice whose maximum response time is 2 ps. Three ring resonators including nonlinear rods with 9×10-17 m2/W for a Kerr coefficient carry out a switching operation at 1550 nm wavelength. The switching speed of the device is 500 GHz, which is more than that in previously presented works. Also, the small size of the structure is sufficient for optical integrated circuits.

Two step transfer entropy - An estimator of delayed directional couplings between multivariate EEG time series.

Quantifying delayed directional couplings between electroencephalographic (EEG) time series requires an efficient method of causal network inference. This is especially due to the limited knowledge about the underlying dynamics of the brain activity. Recent methods based on information theoretic measures such as Transfer Entropy (TE) made significant progress on this issue by providing a model-free framework for causality detection. However, TE estimation from observed data is not a trivial task, especially when the number of variables is large which is the case in a highly complex system like human brain. Here we propose a computationally efficient procedure for TE estimation based on using sets of the Most Informative Variables that effectively contribute to resolving the uncertainty of the destination. In the first step of this method, some conditioning sets are determined through a nonlinear state space reconstruction; then in the second step, optimal estimation of TE is done based on these sets. Validation of the proposed method using synthetic data and neurophysiological signals demonstrates computational efficiency in quantifying delayed directional couplings compared with the common TE analysis.

Inferring time-varying brain connectivity graph based on a new method for link estimation.

Causal interaction estimation among neuronal groups plays an important role in the assessment of brain functions. These directional relations can be best illustrated by means of graphical modeling which is a mathematical representation of a network. Here, we propose an efficient framework to derive a graphical model for the statistical analysis of multivariate processes from observed time series in a data-driven pipeline to explore the interregional brain interactions. A major part of this analysis is devoted to the graph link estimation, which is a measure capable of dealing with the multivariate analysis obstacles. In this paper, we use the Transfer Entropy (TE) measure and focus on its calculation that requires efficient estimation of high dimensional conditional probability distributions. Our method is based on the simplification of high dimensional parts of the conventional TE definition and especially devoted to the reduction of estimation dimension through searching for the most informative contents of the high dimensional parts. To this end, we exploit the causal Markov properties for time series graphs and prove that only a specified subset of involved variables plays an important role in multivariate TE estimation. We demonstrate the performance of our method for stationary processes using some numerical simulated examples as well as real neurophysiological data.

Analysis of the behavior of a seizure neural mass model using describing functions.

Neural mass models are computational nonlinear models that simulate the activity of a population of neurons as an average neuron, in such a way that different inhibitory post-synaptic potential and excitatory post-synaptic potential signals could be reproduced. These models have been developed either to simulate the recognized neural mechanisms or to predict some physiological facts that are not easy to realize naturally. The role of the excitatory and inhibitory activity variation in seizure genesis has been proved, but it is not evident how these activities influence appearance of seizure like signals. In this paper a population model is considered in which the physiological inter-relation of the pyramidal and inter-neurons of the hippocampus has been appropriately modeled. The average neurons of this model have been assumed to act as a linear filter followed by a nonlinear function. By changing the gain of excitatory and inhibitory activities that are modeled by the gain of the filters, seizure-like signals could be generated. In this paper through the analysis of this nonlinear model by means of the describing function concepts, it is theoretically shown that not only the gains of the excitatory and inhibitory activities, but also the time constants may play an efficient role in seizure genesis.

Design and fabrication of anatomical bioreactor systems containing alginate scaffolds for cartilage tissue engineering.

The aim of the present study was to develop a tissue-engineering approach through alginate gel molding to mimic cartilage tissue in a three-dimensional culture system. The perfusion biomimetic bioreactor was designed to mimic natural joint. The shear stresses exerting on the bioreactor chamber were calculated by Computational Fluid Dynamic (CFD). Several alginate/bovine chondrocyte constructs were prepared, and were cultured in the bioreactor. Histochemical and immunohistochemical staining methods for the presence of glycosaminoglycan(GAG), overall matrix production and type II collagen protein were performed, respectively. The dynamic mechanical device applied a linear mechanical displacement of 2 mm to 10 mm. The CFD modeling indicated peak velocity and maximum wall shear stress were 1.706×10(-3)m/s and 0.02407 dyne/cm(2), respectively. Histochemical and immunohistochemical analysis revealed evidence of cartilage-like tissue with lacunas similar to those of natural cartilage and the production of sulfated GAG of matrix by the chondrons, metachromatic territorial matrix-surrounded cells and accumulation of type II collagen around the cells. The present study indicated that when chondrocytes were seeded in alginate hydrogel and cultured in biomimetic cell culture system, cells survived well and secreted newly synthesized matrix led to improvement of chondrogenesis.

A Brief Survey of Computational Models of Normal and Epileptic EEG Signals: A Guideline to Model-based Seizure Prediction.

In recent decades, seizure prediction has caused a lot of research in both signal processing and the neuroscience field. The researches have tried to enhance the conventional seizure prediction algorithms such that the rate of the false alarms be appropriately small, so that seizures can be predicted according to clinical standards. To date, none of the proposed algorithms have been sufficiently adequate. In this article we show that in considering the mechanism of the generation of seizures, the prediction results may be improved. For this purpose, an algorithm based on the identification of the parameters of a physiological model of seizures is introduced. Some models of electroencephalographic (EEG) signals that can also be potentially considered as models of seizure and some developed seizure models are reviewed. As an example the model of depth-EEG signals, proposed by Wendling, is studied and is shown to be a suitable model.

From EEG signals to brain connectivity: a model-based evaluation of interdependence measures.

In the past, considerable effort has been devoted to the development of signal processing techniques aimed at characterizing brain connectivity from signals recorded from spatially-distributed regions during normal or pathological conditions. In this paper, three families of methods (linear and nonlinear regression, phase synchronization, and generalized synchronization) are reviewed. Their performances were evaluated according to a model-based methodology in which a priori knowledge about the underlying relationship between systems that generate output signals is available. This approach allowed us to relate the interdependence measures computed by connectivity methods to the actual values of the coupling parameter explicitly represented in various models of signal generation. Results showed that: (i) some of the methods were insensitive to the coupling parameter; (ii) results were dependent on signal properties (broad band versus narrow band); (iii) there was no "ideal" method, i.e., none of the methods performed better than the other ones in all studied situations. Nevertheless, regression methods showed sensitivity to the coupling parameter in all tested models with average or good performances. Therefore, it is advised to first apply these "robust" methods in order to characterize brain connectivity before using more sophisticated methods that require specific assumptions about the underlying model of relationship. In all cases, it is recommended to compare the results obtained from different connectivity methods to get more reliable interpretation of measured quantities with respect to underlying coupling. In addition, time-frequency methods are also recommended when coupling in specific frequency sub-bands ("frequency-locking") is likely to occur as in epilepsy.

Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals.

Brain functional connectivity can be characterized by the temporal evolution of correlation between signals recorded from spatially-distributed regions. It is aimed at explaining how different brain areas interact within networks involved during normal (as in cognitive tasks) or pathological (as in epilepsy) situations. Numerous techniques were introduced for assessing this connectivity. Recently, some efforts were made to compare methods performances but mainly qualitatively and for a special application. In this paper, we go further and propose a comprehensive comparison of different classes of methods (linear and nonlinear regressions, phase synchronization, and generalized synchronization) based on various simulation models. For this purpose, quantitative criteria are used: in addition to mean square error under null hypothesis (independence between two signals) and mean variance computed over all values of coupling degree in each model, we provide a criterion for comparing performances. Results show that the performances of the compared methods are highly dependent on the hypothesis regarding the underlying model for the generation of the signals. Moreover, none of them outperforms the others in all cases and the performance hierarchy is model dependent.

Time-frequency characterization of interdependencies in nonstationary signals: application to epileptic EEG.

For the past decades, numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between electroencephalographic (EEG) signals. This interdependency parameter, which may be defined in various ways, is often used to characterize a functional coupling between different brain structures or regions during either normal or pathological processes. In this paper, we focus on the time-frequency characterization of the interdependency between signals. Particularly, we propose a novel estimator of the linear relationship between nonstationary signals based on the cross correlation of narrow band filtered signals. This estimator is compared to a more classical estimator based on the coherence function. In a simulation framework, results show that it may exhibit better statistical performances (bias and variance or mean square error) when a priori knowledge about time delay between signals is available. On real data (intracerebral EEG signals), results show that this estimator may also enhance the readability of the time-frequency representation of relationship and, thus, can improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).