Influence of extrinsic inputs and synaptic gains on dynamics of Wendling's neural mass model: A bifurcation analysis.

We analyze the neurodynamics attributed by a model proposed by Wendling and co-workers (2002) [Wendling, F., Bartolomei, F., Bellanger, J.J. & Chauvel, P. (2002) Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur. J. Neurosci., 15, 1499.] to explain several different types of electroencephalographic activities. We could find three principal types of steady states when the system parameters change slowly: (i) the model produce a constant output when it is under a state of stable equilibrium point with a constant input. If a small perturbation is introduced (e.g., noisy input), the output changes into noise without oscillatory components, which is related to the normal background activity or low-voltage rapid activity, (ii) Hopf bifurcations lead to stable limit cycles, which we call Hopf cycles. The model generates a rhythmic oscillating output when it is under a state of Hopf cycles, which is related to slow rhythmic activity or slow quasi-sinusoidal activity, (iii) global bifurcations lead to homoclinic limit cycles that appear suddenly at high amplitude, which we call spike cycles. In general, the spike cycles are not harmonic but they have a spike-like appearance (anharmonic oscillation). The model produces a spike-like output when it is under a state of spike cycles, which is related to the sustained discharge of spikes. Finally, the bifurcation analysis demonstrates the influence of the interaction between the excitatory and inhibitory synaptic gains on the dynamics.

Seizure detection approach using S-transform and singular value decomposition.

Automatic seizure detection plays a significant role in the diagnosis of epilepsy. This paper presents a novel method based on S-transform and singular value decomposition (SVD) for seizure detection. Primarily, S-transform is performed on EEG signals, and the obtained time-frequency matrix is divided into submatrices. Then, the singular values of each submatrix are extracted using singular value decomposition (SVD). Effective features are constructed by adding the largest singular values in the same frequency band together and fed into Bayesian linear discriminant analysis (BLDA) classifier for decision. Finally, postprocessing is applied to obtain higher sensitivity and lower false detection rate. A total of 183.07 hours of intracranial EEG recordings containing 82 seizure events from 20 patients were used to evaluate the system. The proposed method had a sensitivity of 96.40% and a specificity of 99.01%, with a false detection rate of 0.16/h.

Epileptogenic zone localization and seizure control in coupled neural mass models.

Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals. In this study, EZ localization is changed into a driver identification problem, and a nonlinear interdependence measure, the weighted rank interdependence, is proposed and used as a driver indicator because it can detect coupling information, especially directionality, from EEG signals. A proportional integral derivative (PID) controller is then explored, using simulations, to establish its suitability for seizure control. The seizure control we propose rests on identifying the EZ using nonlinear interdependence measures of directed functional connectivity. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters can adjust the sensitivity and completeness of the weighted rank interdependence for different applications, and their effect is discussed in the context of neural mass models. Simulation results demonstrate that use of the weighted rank interdependence for EZ identification can be applied to different EZ types, and the approach achieves an overall identification rate of 98.84 % for several EZ types. Simulations also indicate that PID control can effectively regulate synchronization between neural masses.

Bifurcation and oscillation in a time-delay neural mass model.

The neural mass model developed by Lopes da Silva et al. simulates complex dynamics between cortical areas and is able to describe a limit cycle behavior for alpha rhythms in electroencephalography (EEG). In this work, we propose a modified neural mass model that incorporates a time delay. This time-delay model can be used to simulate several different types of EEG activity including alpha wave, interictal EEG, and ictal EEG. We present a detailed description of the model's behavior with bifurcation diagrams. Through simulation and an analysis of the influence of the time delay on the model's oscillatory behavior, we demonstrate that a time delay in neuronal signal transmission could cause seizure-like activity in the brain. Further study of the bifurcations in this new neural mass model could provide a theoretical reference for the understanding of the neurodynamics in epileptic seizures.

Synchronization regulation in a model of coupled neural masses.

A model of coupled neural masses can generate seizure-like events and dynamics similar to those observed during interictal to ictal transitions and thus can be used for theoretical study of the control of epileptic seizures. In an effort to understand the mechanisms underlying epileptic seizures and how to avoid them, we added a control input to this model. Epileptic seizures are always accompanied by hypersynchronous firing of neurons, so research on synchronization among cortical areas is significant for seizure control. In this study, principal component analysis (PCA) was used to identify synchronization clusters composed of several neural masses. A method for calculating the synchronization cluster strength and participation rate is presented. The synchronization cluster strength can be used to identify synchronization clusters and the participation rate can be employed to identify neural masses that participate in the clusters. Each synchronization cluster is controlled as a whole using a proportional-integral-derivative (PID) controller. We illustrate these points using coupled neural mass models of synchronization to show their responses to increased (between node) coupling with and without control. Experiment results indicated that PID control can effectively regulate synchronization between neural masses and has the potential for seizure prevention.

Feature extraction and recognition of ictal EEG using EMD and SVM.

Automatic seizure detection is significant for long-term monitoring of epilepsy, as well as for diagnostics and rehabilitation, and can decrease the duration of work required when inspecting the EEG signals. In this study we propose a novel method for feature extraction and pattern recognition of ictal EEG, based upon empirical mode decomposition (EMD) and support vector machine (SVM). First the EEG signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and then the coefficient of variation and fluctuation index of IMFs are extracted as features. SVM is then used as the classifier for recognition of ictal EEG. The experimental results show that this algorithm can achieve the sensitivity of 97.00% and specificity of 96.25% for interictal and ictal EEGs, and the sensitivity of 98.00% and specificity of 99.40% for normal and ictal EEGs on Bonn data sets. Besides, the experiment with interictal and ictal EEGs from Qilu Hospital dataset also yields a satisfactory sensitivity of 98.05% and specificity of 100%.

Tissue-specific and cation/anion-specific DNA methylation variations occurred in C. virgata in response to salinity stress.

Salinity is a widespread environmental problem limiting productivity and growth of plants. Halophytes which can adapt and resist certain salt stress have various mechanisms to defend the higher salinity and alkalinity, and epigenetic mechanisms especially DNA methylation may play important roles in plant adaptability and plasticity. In this study, we aimed to investigate the different influences of various single salts (NaCl, Na2SO4, NaHCO3, Na2CO3) and their mixed salts on halophyte Chloris. virgata from the DNA methylation prospective, and discover the underlying relationships between specific DNA methylation variations and specific cations/anions through the methylation-sensitive amplification polymorphism analysis. The results showed that the effects on DNA methylation variations of single salts were ranked as follows: Na2CO3> NaHCO3> Na2SO4> NaCl, and their mixed salts exerted tissue-specific effects on C. virgata seedlings. Eight types of DNA methylation variations were detected and defined in C. virgata according to the specific cations/anions existed in stressful solutions; in addition, mix-specific and higher pH-specific bands were the main type in leaves and roots independently. These findings suggested that mixed salts were not the simple combination of single salts. Furthermore, not only single salts but also mixed salts showed tissue-specific and cations/anions-specific DNA methylation variations.

EEG non-linear feature extraction using correlation dimension and Hurst exponent.

In this work, we evaluated the differences between epileptic electroencephalogram (EEG) and interictal EEG by computing some non-linear features. Correlation dimension (CD) and Hurst exponent (H) were calculated for 100 segments of epileptic EEG and 100 segments of interictal EEG. A comparison was made between epileptic EEG and interictal EEG in those non-linear parameters. Results show that the mean values of CD are 2.64 for epileptic EEG and 4.55 for interictal EEG. We also calculated approximate entropy (ApEn) of those EEG signals. The mean values of ApEn are 0.90 for epileptic EEG and 4.55 for interictal EEG. The values of CD and ApEn of epileptic EEG are generally lower than those of interictal EEG, indicating less complexity of EEG signals during seizures. The mean values of Hurst exponent are 0.19 for epileptic EEG and 0.29 for interictal EEG. Hurst exponents for epileptic EEG and interictal EEG are both <0.5. This indicates that both epileptic and interictal EEGs show long-range anticorrelation. The value of Hurst exponent of epileptic EEG signals is lower than that of interictal EEG signals, showing that the degree of anticorrelation of epileptic EEG signals is larger than that of interictal EEG. Hence, the non-linear parameters such as CD and Hurst exponent can help interpret epileptic and interictal EEGs and their neurodynamics.