On Quantitative Biomarkers of VNS Therapy Using EEG and ECG Signals.

Objective: The goal of this work is to objectively evaluate the effectiveness of neuromodulation therapies, specifically, Vagus nerve stimulation (VNS) in reducing the severity of seizures in patients with medically refractory epilepsy.

Methods: Using novel quantitative features obtained from combination of electroencephalographic (EEG) and electrocardiographic (ECG) signals around seizure events in 16 patients who underwent implantation of closed-loop VNS therapy system, namely AspireSR, we evaluated if automated delivery of VNS at the time of seizure onset reduces the severity of seizures by reducing EEG spatial synchronization as well as the duration and magnitude of heart rate increase. Unsupervised classification was subsequently applied to test the discriminative ability and validity of these features to measure responsiveness to VNS therapy.

L-Type calcium channel blockade reduces network activity in human epileptic hypothalamic hamartoma tissue.

Purpose: Human hypothalamic hamartomas (HHs) are associated with gelastic seizures, intrinsically epileptogenic, and notoriously refractory to medical therapy. We previously reported that the L-type calcium channel antagonist nifedipine blocks spontaneous firing and γ-aminobutyric acid (GABA)(A)-induced depolarization of single cells in HH tissue slices. In this study, we examined whether blocking L-type calcium channels attenuates emergent activity of HH neuronal networks.

Nonlinear dynamics of seizure prediction in a rodent model of epilepsy.

Epilepsy is a dynamical disorder with intermittent crises (seizures) that until recently were considered unpredictable. In this study, we investigated the predictability of epileptic seizures in chronically epileptic rats as a first step towards a subsequent timely intervention for seizure control. We look at the epileptic brain as a nonlinear complex system that undergoes spatio-temporal state transitions and the Lyapunov exponents as indices of its stability.

Control of synchronization of brain dynamics leads to control of epileptic seizures in rodents.

We have designed and implemented an automated, just-in-time stimulation, seizure control method using a seizure prediction method from nonlinear dynamics coupled with deep brain stimulation in the centromedial thalamic nuclei in epileptic rats. A comparison to periodic stimulation, with identical stimulation parameters, was also performed. The two schemes were compared in terms of their efficacy in control of seizures, as well as their effect on synchronization of brain dynamics.

Information flow and application to epileptogenic focus localization from intracranial EEG.

Transfer entropy ( TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. We show the application of the improved TE method to long (in the order of days; approximately a total of 600 h across all patients), continuous, intracranial electroencephalograms (EEG) recorded in two different medical centers from four patients with focal temporal lobe epilepsy (TLE) for localization of their foci.

Homeostasis of brain dynamics in epilepsy: a feedback control systems perspective of seizures.

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce "seizure"-like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations.

Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques.

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL(max)), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures.

Controlling synchronization in a neuron-level population model.

We have studied coupled neural populations in an effort to understand basic mechanisms that maintain their normal synchronization level despite changes in the inter-population coupling levels. Towards this goal, we have incorporated coupling and internal feedback structures in a neuron-level population model from the literature. We study two forms of internal feedback--regulation of excitation, and compensation of excessive excitation with inhibition.

Predictability of epileptic seizures: a comparative study using Lyapunov exponent and entropy based measures.

In this paper, a comparative study involving measures from the theory of chaos, namely the short-term largest Lyapunov exponent, Shannon and Kullback-Leibler entropies from information theory, has been carried out in terms of their predictability of temporal lobe epileptic seizures. These three measures are estimated from electroencephalographic (EEG) recordings with sub-dural and in-depth electrodes from various brain locations in patients with temporal lobe epilepsy. Techniques from optimization theory are applied to select optimal sets of electrodes whose dynamics is then followed over time.