Interictal localization of the epileptogenic zone: Utilizing the observed resonance behavior in the spectral band of surrounding inhibition.

Introduction: The gold standard for identification of the epileptogenic zone (EZ) continues to be the visual inspection of electrographic changes around seizures' onset by experienced electroencephalography (EEG) readers. Development of an epileptogenic focus localization tool that can delineate the EZ from analysis of interictal (seizure-free) periods is still an open question of great significance for improved diagnosis (e.g., presurgical evaluation) and treatment of epilepsy (e.g., surgical outcome). Methods: We developed an EZ interictal localization algorithm (EZILA) based on novel analysis of intracranial EEG (iEEG) using a univariate periodogram-type power measure, a straight-forward ranking approach, a robust dimensional reduction method and a clustering technique. Ten patients with temporal and extra temporal lobe epilepsies, and matching the inclusion criteria of having iEEG recordings at the epilepsy monitoring unit (EMU) and being Engel Class I ≥12 months post-surgery, were recruited in this study. Results: In a nested k-fold cross validation statistical framework, EZILA assigned the highest score to iEEG channels within the EZ in all patients (10/10) during the first hour of the iEEG recordings and up to their first typical clinical seizure in the EMU (i.e., early interictal period). To further validate EZILA's performance, data from two new (Engel Class I) patients were analyzed in a double-blinded fashion; the EZILA successfully localized iEEG channels within the EZ from interictal iEEG in both patients. Discussion: Out of the sampled brain regions, iEEG channels in the EZ were most frequently and maximally active in seizure-free (interictal) periods across patients in specific narrow gamma frequency band (∼60-80 Hz), which we have termed focal frequency band (FFB). These findings are consistent with the hypothesis that the EZ may interictally be regulated (controlled) by surrounding inhibitory neurons with resonance characteristics within this narrow gamma band.

Morphological Changes in Astrocytes by Self-Oxidation of Dopamine to Polydopamine and Quantification of Dopamine through Multivariate Regression Analysis of Polydopamine Images.

Astrocytes, also known as astroglia, are important cells for the structural support of neurons as well as for biochemical balance in the central nervous system (CNS). In this study, the polymerization of dopamine (DA) to polydopamine (PDA) and its effect on astrocytes was investigated. The polymerization of DA, being directly proportional to the DA concentration, raises the prospect of detecting DA concentration from PDA optically using image-processing techniques. It was found here that DA, a naturally occurring neurotransmitter, significantly altered astrocyte cell number, morphology, and metabolism, compared to astrocytes in the absence of DA. Along with these effects on astrocytes, the polymerization of DA to PDA was tracked optically in the same cell culture wells. This polymerization process led to a unique methodology based on multivariate regression analysis that quantified the concentration of DA from optical images of astrocyte cell culture media. Therefore, this developed methodology, combined with conventional imaging equipment, could be used in place of high-end and expensive analytical chemistry instruments, such as spectrophotometry, mass spectrometry, and fluorescence techniques, for quantification of the concentration of DA after polymerization to PDA under in vitro and potentially in vivo conditions.

Predictability and Resetting in a Case of Convulsive Status Epilepticus.

In this case study, we present evidence of resetting of brain dynamics following convulsive status epilepticus (SE) that was treated successfully with antiepileptic medications (AEDs). The measure of effective inflow (EI), a novel measure of network connectivity, was applied to the continuously recorded multichannel intracranial stereoelectroencephalographic (SEEG) signals before, during and after SE. Results from this analysis indicate trends of progressive reduction of EI over hours up to the onset of SE, mainly at sites of the epileptogenic focus with reversal of those trends upon successful treatment of SE by AEDs. The proposed analytical framework is promising for elucidation of the pathology of neuronal network dynamics that could lead to SE, evaluation of the efficacy of SE treatment strategies, as well as the development of biomarkers for susceptibility to SE.

Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy.

Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time-frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures' onset. These trends pointed to a slow decrease of the epileptic brain's global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.

The Concept of Effective Inflow: Application to Interictal Localization of the Epileptogenic Focus From iEEG.

GOAL: Accurate determination of the epileptogenic focus is of paramount diagnostic and therapeutic importance in epilepsy. The current gold standard for focus localization is from ictal (seizure) onset and thus requires the occurrence and recording of multiple typical seizures of a patient. Localization of the focus from seizure-free (interictal) periods remains a challenging problem, especially in the absence of interictal epileptiform activity. METHODS: By exploring the concept of effective inflow, we developed a focus localization algorithm (FLA) based on directed connectivity between brain sites. Subsequently, using the measure of generalized partial directed coherence over a broad frequency band in FLA for the analysis of interictal periods from long-term (days) intracranial electroencephalographic signals, we identified the brain region that is the most frequent receiver of maximal effective inflow from other brain regions. RESULTS: In six out of nine patients with temporal lobe epilepsy, the thus identified brain region was a statistically significant outlier (p < 0.01) and coincided with the clinically assessed epileptogenic focus. In the remaining three patients, the clinically assessed focus still exhibited the highest inflow, but it was not deemed an outlier (p > 0.01). CONCLUSIONS: These findings suggest that the epileptogenic focus is a region of intense influence from other regions interictally, possibly as a mechanism to keep it under control in seizure-free periods. SIGNIFICANCE: The developed framework is expected to assist with the accurate epileptogenic focus localization, reduce hospital stay and healthcare cost, and provide guidance to treatment of epilepsy via resective surgery or neuromodulation.

Resetting of brain dynamics: epileptic versus psychogenic nonepileptic seizures.

We investigated the possibility of differential diagnosis of patients with epileptic seizures (ES) and patients with psychogenic nonepileptic seizures (PNES) through an advanced analysis of the dynamics of the patients' scalp EEGs. The underlying principle was the presence of resetting of brain's preictal spatiotemporal entrainment following onset of ES and the absence of resetting following PNES. Long-term (days) scalp EEGs recorded from five patients with ES and six patients with PNES were analyzed. It was found that: (1) Preictal entrainment of brain sites was reset at ES (P<0.05) in four of the five patients with ES, and not reset (P=0.28) in the fifth patient. (2) Resetting did not occur (p>0.1) in any of the six patients with PNES. These preliminary results in patients with ES are in agreement with our previous findings from intracranial EEG recordings on resetting of brain dynamics by ES and are expected to constitute the basis for the development of a reliable and supporting tool in the differential diagnosis between ES and PNES. Finally, we believe that these results shed light on the electrophysiology of PNES by showing that occurrence of PNES does not assist patients in overcoming a pathological entrainment of brain dynamics. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Seizure prediction and its applications.

Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems.

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. We estimated the spatial synchronization or desynchronization of the maximum short-term Lyapunov exponents (STLmax, approximate measures of chaos) among multiple brain sites over days of electroencephalographic (EEG) recordings from 5 rats that had developed chronic epilepsy according to the lithium pilocarpine rodent model of epilepsy. We utilized this synchronization of EEG dynamics for the construction of a robust seizure prediction algorithm. The parameters of the algorithm were optimized using receiver operator curves (ROCs) on training EEG datasets from each rat for the algorithm to provide maximum sensitivity and specificity in the prediction of their seizures. The performance of the algorithm was then tested on long-term testing EEG datasets per rat. The thus optimized prediction algorithm on the testing datasets over all rats yielded a seizure prediction mean sensitivity of 85.9%, specificity of 0.180 false predictions per hour, and prediction time of 67.6 minutes prior to a seizure onset. This study provides evidence that prediction of seizures is feasible through analysis of the EEG within the framework of nonlinear dynamics, and thus paves the way for just-in-time pharmacological or physiological inter-ventions to abort seizures tens of minutes before their occurrence.

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. The automated just-in-time (JIT) stimulation showed reduction of seizure frequency and duration in 5 of the 6 rats, with significant reduction of seizure frequency (>50%) in 33% of the rats. This constituted a significant improvement over the efficacy of the periodic control scheme in the same animals. Actually, periodic stimulation showed an increase of seizure frequency in 50% of the rats, reduction of seizure frequency in 3 rats and significant reduction in 1 rat. Importantly, successful seizure control was highly correlated with desynchronization of brain dynamics. This study provides initial evidence for the use of closed-loop feedback control systems in epileptic seizures combining methods from seizure prediction and deep brain stimulation.

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. All patients underwent ablative surgery of their clinically assessed foci. Based on a surrogate statistical analysis of the TE results, it is shown that the identified potential focal sites through the suggested analysis were in agreement with the clinically assessed sites of the epileptogenic focus in all patients analyzed. It is noteworthy that the analysis was conducted on the available whole-duration multielectrode EEG, that is, without any subjective prior selection of EEG segments or electrodes for analysis. The above, in conjunction with the use of surrogate data, make the results of this analysis robust. These findings suggest a critical role TE may play in epilepsy research in general, and as a tool for robust localization of the epileptogenic focus/foci in patients with focal epilepsy in particular.

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. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to "seizure"-like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop "modulating" control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities.

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. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion's sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL(max), a measure of phase (ϕ(max)) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α = 0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL(max) synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy.

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. We show that normal feedback actions quickly regulate/compensate an abnormally high coupling between the neural populations, whereas a pathology in these feedback actions can lead to abnormal synchronization and "seizure"-like high amplitude oscillations. We then develop an external control paradigm, termed feedback decoupling, as a robust synchronization control strategy. The external feedback decoupling controller acts to achieve the operational objective of maintaining normal-level synchronous behavior irrespective of the pathology in the internal feedback mechanisms. Results from such an analysis have an interesting physical interpretation and specific implications for the treatment of diseases such as epilepsy. The proposed remedy is consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology.

Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques.

Epileptic seizures occur intermittently as a result of complex dynamical interactions among many regions of the brain. By applying signal processing techniques from the theory of nonlinear dynamics and global optimization to the analysis of long-term (3.6 to 12 days) continuous multichannel electroencephalographic recordings from four epileptic patients, we present evidence that epileptic seizures appear to serve as dynamical resetting mechanisms of the brain, that is the dynamically entrained brain areas before seizures disentrain faster and more frequently (p < 0.05) at epileptic seizures than any other periods. We expect these results to shed light into the mechanisms of epileptogenesis, seizure intervention and control, as well as into investigations of intermittent spatiotemporal state transitions in other complex biological and physical systems.

Epileptic seizure prediction and control.

Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's approximately 50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.

Adaptive epileptic seizure prediction system.

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.