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Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. Goal: We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Methods: Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). Results: When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p 90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (

Objective To investigate dynamic changes in neural activity between the anterior nucleus of the thalamus (ANT) and the seizure onset zone (SOZ) in patients with drug‐resistant temporal lobe epilepsy (TLE) based on anatomic location, seizure subtype, and state of vigilance (SOV). Methods Eleven patients undergoing stereoelectroencephalography for seizure localization were recruited prospectively for local field potential (LFP) recording directly from the ANT. The SOZ was identified using line length and epileptogenicity index. Changes in power spectral density (PSD) were compared between the two anatomic sites as seizures (N = 53) transitioned from interictal baseline to the posttermination stage. Results At baseline, the thalamic LFPs were significantly lower and distinct from the SOZ with the presence of higher power in the fast ripple band (P

Perfusion patterns observed in Subtraction Ictal SPECT Co-registered to MRI (SISCOM) assist in focus localization and surgical planning for patients with medically intractable focal epilepsy. While the localizing value of SISCOM has been widely investigated, its relationship to the underlying electrophysiology has not been extensively studied and is therefore not well understood. In the present study, we set to investigate this relationship in a cohort of 70 consecutive patients who underwent ictal and interictal SPECT studies and subsequent stereo-electroencephalography (SEEG) monitoring for localization of the epileptogenic focus and surgical intervention. Seizures recorded during SEEG evaluation (SEEG seizures) were matched to semiologically-similar seizures during the preoperative ictal SPECT evaluation (SPECT seizures) by comparing the semiological changes in the course of each seizure. The spectral changes of the ictal SEEG with respect to interictal ones over 7 traditional frequency bands (0.1 to 150Hz) were analyzed at each SEEG site. Neurovascular (SEEG/SPECT) relations were assessed by comparing the estimated spectral power density changes of the SEEG at each site with the perfusion changes (SISCOM z-scores) estimated from the acquired SISCOM map at that site. Across patients, a significant correlation (p

Targeted stimulation of white matter has opened newer perspectives in the field of neuromodulation, towards an attempt to improve memory or as a therapy for epilepsy. Stimulation of the fornix, being a part of the Papez circuit, is likely to modulate the limbic network excitability. However, the stimulation-frequency dependent variability in network excitability is unknown. In the case study, which involved stereo electroencephalographic (SEEG) recording of field potentials in a 48-year old left-handed woman with suspected temporal lobe epilepsy, we demonstrated the network effects of acute low (1 and 10 Hz) and high (50 Hz) frequency electrical stimulation of fornix. Mapping the short-latency evoked responses to forniceal stimulation confirmed the SEEG target localization within the Papez circuit. Low and high-frequency stimulation of the fornix produced opposite effects in the post-stimuli excitability, with the latter causing increased excitability in the limbic network that culminated in a clinical seizure. A distinct spectral peak around 8 Hz confirmed that sensing field potentials from the forniceal white matter is feasible. This is the first case study that provided an insight into how the temporal patterning of forniceal stimulation altered the downstream limbic network excitability., Highlights • Stimulation of forniceal white matter engaged the limbic network • High-frequency stimulation increased cortical excitability leading to a clinical seizure • Low-frequency stimulation decreased cortical excitability

People with epilepsy have greatly increased probability of premature mortality due to sudden unexpected death in epilepsy (SUDEP). Identifying which patients are most at risk of SUDEP is hindered by a complex genetic etiology, incomplete understanding of the underlying pathophysiology and lack of prognostic biomarkers. Here we evaluated heterozygous Scn2a gene deletion (Scn2a+/-) as a protective genetic modifier in the Kcna1 knockout mouse (Kcna1-/-) model of SUDEP, while searching for biomarkers of SUDEP risk embedded in electroencephalography (EEG) and electrocardiography (ECG) recordings. The human epilepsy gene Kcna1 encodes voltage-gated Kv1.1 potassium channels that act to dampen neuronal excitability whereas Scn2a encodes voltage-gated Nav1.2 sodium channels important for action potential initiation and conduction. SUDEP-prone Kcna1-/- mice with partial genetic ablation of Nav1.2 channels (i.e. Scn2a+/-; Kcna1-/-) exhibited a two-fold increase in survival. Classical analysis of EEG and ECG recordings separately showed significantly decreased seizure durations in Scn2a+/-; Kcna1-/- mice compared with Kcna1-/- mice, without substantial modification of cardiac abnormalities. Novel analysis of the EEG and ECG together revealed a significant reduction in EEG-ECG association in Kcna1-/- mice compared with wild types, which was partially restored in Scn2a+/-; Kcna1-/- mice. The degree of EEG-ECG association was also proportional to the survival rate of mice across genotypes. These results show that Scn2a gene deletion acts as protective genetic modifier of SUDEP and suggest measures of brain-heart association as potential indices of SUDEP susceptibility.

Accurate epileptogenic focus localization is required prior to surgical resection of brain tissue for the treatment of patients with antiepileptic drug-resistant (intractable) epilepsy. This clinical need is only partially fulfilled through a subjective, and at times inconclusive, the evaluation of the recorded electroencephalogram (EEG) at seizures’ onset (the so-called gold standard for focus localization in epilepsy). We herein present a novel method of multivariate analysis of the EEG that appears to be very promising for an objective and robust localization of the epileptogenic focus at seizures’ onset. Using the measure of generalized partial directed coherence, combined with surrogate data analysis, we first estimated from multichannel intracranial EEG the statistically significant causal interactions between brain regions at the onset of 92 clinical seizures from nine patients with temporal lobe intractable epilepsy. From the networks that were formed based on the thus derived interactions, a set of centrality metrics was estimated per network node (brain site). Brain sites located anatomically within the epileptogenic focus were shown to be associated with greater inward centrality values than non-focal brain regions at high frequencies ( $\mathrm {\gamma }$ band), and particular inward centrality metrics accurately localized the focus in all nine patients. In addition to focus localization from seizure (ictal) onset, the developed novel framework for analysis of EEG could be employed to identify the changes of the focal network over time, peri-ictally and interictally, and thus shed light onto the dynamics of ictogenesis, which could then have a significant impact on automated prediction and closed-loop control of seizures by neuromodulation.

A seizure prediction system has the potential to significantly help patients with epilepsy. For a seizure forecasting system to work effectively, computational algorithms must reliably identify periods with high probability of seizure occurrence. We herein report results of a classification approach based on machine learning of EEG features in the frequency domain and aimed at differentiating between pre-ictal (close to seizure onsets) and interictal (far away from seizures onset) periods in long-term intracranial EEG recordings from the brain of 5 epileptic dogs. Evaluation of performance by the area under the ROC curve ranged from 0.84 to 0.96 in four dogs, while for the fifth dog was considerably less (0.55), resulting to a global value of 0.87 across dogs. These results offer supporting evidence that seizures may be predictable with a proper analysis of the EEG.

A novel measure for analysis of multivariate signals in the time-frequency-space domain, the normalized Gabor entropy (NGE), is introduced and applied to multichannel intracranial EEG (iEEG) recordings hours prior to seizures onset in two patients with focal epilepsy. NGE profiles showed a statistically significant progressive decrease of NGE values at epileptogenic focus-related channels as time for seizures occurrence approached. This result implies the progressive appearance of dominant Gabor atoms in the EEG tens of minutes prior to seizures, the detection and monitoring of which could further assist with improvement of the performance of seizure prediction algorithms.

Motor imagery and motor movement are two distinct tasks with underlying similar neurological mechanisms. We sought to identify the electroencephalographic (EEG) differences between real and imaginary hand movements. Phase Locking Value (PLV) was employed to estimate brain's connectivity and create a network that was then studied on the basis of brain region centrality. We observed that the frontal/prefrontal cortex exhibits higher centrality in the ? band during motor movement than motor imagery tasks. Additionally, difference between left and right hand tasks was observed in the µ band at the somatosensory association cortex post-tasks' completion. These preliminary results indicate that, by proper EEG analysis, it may be possible to distinguish between motor imagery and motor movement.

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality, but its genetic etiology is largely unknown and likely complex involving multiple genes. The Kcna1 gene encodes Kv1.1 potassium channels that act to dampen neuronal excitability whereas the Scn2a gene encodes Nav1.2 sodium channels important for action potential conduction. We tested the hypothesis that subclinical Scn2a heterozygosity reduces SUDEP incidence in Kcna1-null mice of SUDEP, by suppressing neurocardiac dysfunction associated with the absence of Kv1.1 channels. SUDEP-prone Kcna1-null mice exhibited about a 50% increase in lifespan when heterozygous for the Scn2a-null allele, that is Scn2a+/ -- , Kcna1 -- / -- . In simultaneous video electroencephalography (EEG)- electrocardiography (ECG) recordings, Scn2a heterozygosity did not eliminate seizures in Kcna1 -- / -- mice, but seizure burden was partially reduced due to a significant reduction in seizure durations. Analysis of beat-to-beat heart rate variability (HRV) revealed that Kcna1 -- / -- and Scn2a+/ -- , Kcna1 -- / -- mice exhibit similarly high RMSSD (Root Mean Square of the Successive Differences), suggesting the functional interactions between the two mutations do not involve significant alteration of parasympathetic tone. Moreover, analysis of EEG-ECG interaction dynamics revealed a significantly higher degree of association between brain and cardiac activity in double mutant mice compared to Kcna1 -- / -- animals for several different measures, suggesting that Scn2a-null heterozygosity reduces SUDEP risk by altering neural control of the heart. These findings expand our understanding of the complex genetic interactions underlying SUDEP and identify EEG-ECG association as a potential new biomarker of SUDEP susceptibility.

Image analysis of the human eye has provided new insight into ocular disorders and has the potential to assist in their automated diagnosis. We herein report results from analysis of the processed fundus (retina) images from a) healthy subjects and patients diagnosed with b) diabetic retinopathy or c) glaucoma, by means of image fractal analysis and image invariant moments, and linear discriminant analysis (LDA) for classification. Using the fractal dimension and Hu's invariant moments, LDA achieved classification accuracy of 99.2% for the three conditions.

The measure of Generalized Partial Directed Coherence (GPDC) and surrogate data analysis of intracranial electroencephalographic (iEEG) signals can be used to determine the functional connections between brain sites. Characteristics of the nodes of the thus derived network during seizures from 9 patients with temporal lobe epilepsy were studied using centrality measures (Degree, Eigenvector, Katz, PageRank, and Betweenness). The electrode sites with maximum Katz and degree centralities showed close association with the epileptogenic focus during seizures. These results indicate that connectivity analysis of the EEG can contribute to the accurate localization of the epileptogenic focus in patients with focal epilepsy.

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.

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.

In an effort to understand basic functional mechanisms that can produce epileptic seizures, we introduce some key features in a model of coupled neural populations that enable the generation of seizure-like events and similar dynamics with the ones observed during the route of the epileptic brain towards real seizures. In this model, modified from David and Friston’s neural mass model, an internal feedback mechanism is incorporated to maintain synchronous behavior within normal levels despite elevated coupling. Normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to hypersynchronization and the appearance of seizure-like high amplitude oscillations. Feedback decoupling is introduced as a robust seizure control strategy. An external feedback decoupling controller is introduced to maintain normal synchronous behavior. The results from the analysis in this model have an interesting physical interpretation and specific implications for the treatment of epileptic seizures. The proposed model and control scheme 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.

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.

We discuss the key features of theoretical models of networks of coupled chaotic oscillators that produce seizure-like events and bear striking similarities to dynamics of epileptic seizures. Our long-term objective is to understand the basic functional mechanisms that can produce seizures and may ultimately lead to strategies for seizure suppression and control. We show that, from a dynamical systems point of view, a plausible cause of seizures is a pathological feedback in the brain circuitry. This suggests new seizure control approaches, as well as systematic methods to tune existing ones. While the suggested models and control approaches are far from being considered optimal for epileptic seizures, they have interesting physical interpretation and implications for treatments of epilepsy. They also have close ties with a variety of recent practical observations in the human and animal epileptic brain, and with theories from adaptive systems, optimization and chaos.

To understand basic functional mechanisms that cause epileptic seizures, the paper discusses some key features of theoretical brain functioning models. The hypothesis is put forward that a plausible reason for seizures is pathological feedback in brain circuitry. The analysis of such circuitry has an interesting physical interpretation and may be used to cure epilepsy.

During the past decade, several studies have demonstrated experimental evidence that temporal lobe seizures are preceded by changes in dynamical properties (both spatial and temporal) of electroencephalograph (EEG) signals. In this study, we evaluate a method, based on chaos theory and global optimization techniques, for detecting pre-seizure states by monitoring the spatio-temporal changes in the dynamics of the EEG signal. The method employs the estimation of the short-term maximum Lyapunov exponent (STL(max)), a measure of the order (chaoticity) of a dynamical system, to quantify the EEG dynamics per electrode site. A global optimization technique is also employed to identify critical electrode sites that are involved in the seizure development. An important practical result of this study was the development of an automated seizure warning system (ASWS). The algorithm was tested in continuous, long-term EEG recordings, 3-14 days in duration, obtained from 10 patients with refractory temporal lobe epilepsy. In this analysis, for each patient, the EEG recordings were divided into training and testing datasets. We used the first portion of the data that contained half of the seizures to train the algorithm, where the algorithm achieved a sensitivity of 76.12% with an overall false prediction rate of 0.17h(-1). With the optimal parameter setting obtained from the training phase, the prediction performance of the algorithm during the testing phase achieved a sensitivity of 68.75% with an overall false prediction rate of 0.15h(-1). The results of this study confirm our previous observations from a smaller number of patients: the development of automated seizure warning devices for diagnostic and therapeutic purposes is feasible and practically useful.

In this article, we present dynamical approaches and multi-quadratic integer programming techniques to study the problem of seizure prediction. The data used in our studies consist of continuous intracranial electroencephalograms (EEGs) from patients with temporal lobe epilepsy. The results of this study can be used as a criterion to pre-select the critical electrode sites that can be used to predict epileptic seizures.