Dean R. Freestone

CLOSED-LOOP SEIZURE CONTROL WITH VERY HIGH FREQUENCY ELECTRICAL STIMULATION AT SEIZURE ONSET IN THE GAERS MODEL OF ABSENCE EPILEPSY

A closed-loop system for the automated detection and control of epileptic seizures was created and tested in three Genetic Absence Epilepsy Rats from Strasbourg (GAERS) rats. In this preliminary study, a set of four EEG features were used to detect seizures and three different electrical stimulation strategies (standard (130 Hz), very high (500 Hz) and ultra high (1000 Hz)) were delivered to terminate seizures. Seizure durations were significantly shorter with all three stimulation strategies when compared to non-stimulated (control) seizures. We used mean seizure duration of epileptiform discharges persisting beyond the end of electrical stimulation as a measure of stimulus efficacy. When compared to the duration of seizures stimulated in the standard approach (7.0 s ± 10.1), both very high and ultra high frequency stimulation strategies were more effective at shortening seizure durations (1.3 ± 2.2 s and 3.5 ± 6.4 s respectively). Further studies are warranted to further understand the mechanisms by which this therapeutic effect may be conveyed, and which of the novel aspects of the very high and ultra high frequency stimulation strategies may have contributed to the improvement in seizure abatement performance when compared to standard electrical stimulation approaches.

A data-driven framework for neural field modeling.

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch-Tung-Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.

Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons

This paper evaluates the patient-specific seizure prediction performance of pre-ictal changes in bivariate-synchrony between pairs of intracranial electroencephalographic (iEEG) signals within 15min of a seizure in patients with pharmacoresistant focal epilepsy. Prediction horizons under 15min reduce the durations of warning times and should provide adequate time for a seizure control device to intervene. Long-term continuous iEEG was obtained from 6 patients. The seizure prediction performance was evaluated for all possible channel pairs and for different prediction methods to find the best performing channel pairs and methods for both pre-ictal decreases and increases in synchrony. The different prediction methods involved changes in window duration, signal filtering, thresholding approach, and prediction horizon durations. Performance for each patient, for all seizures, was first compared with an analytical-Poisson-based random predictor. The performance of the top 5% of channel pairs for each patient closely matched the top 5% of analytical-Poisson-based random predictor performance indicating that patient-specific, bivariate-synchrony-based seizure prediction could be random in general (under the assumption that channel-pair prediction times are statistically independent). Analysis of the spatial patterns of performance showed no clear relationship to the seizure onset zone. For each patient the best channel pair showed better performance than Poisson-based random prediction for a selected subset of prediction thresholds. Given the caveats of comparing with this form of random prediction, alarm time surrogates were employed to assess statistical significance of a four-fold out-of-sample cross-validation analysis applied to the best channel-pairs. The cross-validation analysis obtained reasonable testing performance for most patients when performance was compared to random prediction based on alarm time surrogates. The most significant case was a patient whose testing set sensitivity and false positive rate were 0.67±0.09 and 3.04±0.29h(-1), respectively, for decreases in synchrony, an intervention time of 15min and a seizure onset period of 5min. For each testing set for this patient, performance was better than that obtained by random prediction at the significance level of 0.05 (average sensitivity of 0.47±0.05). Moreover, there were 9 seizures in each testing set which gives greater power to this cross-validation result, although the cross-validation was performed on the best channel pair selected by within-sample optimization for all seizures of the patient. Further validation with larger datasets from individual patients is needed. Improvements in prediction performance should be achievable through investigations of multivariate synchrony combined with non-linear classification methods.

Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity

We report on a quantitative analysis of electrocorticography data from a study that acquired continuous ambulatory recordings in humans over extended periods of time. The objectives were to examine patterns of seizures and spontaneous interictal spikes, their relationship to each other, and the nature of periodic variation. The recorded data were originally acquired for the purpose of seizure prediction, and were subsequently analysed in further detail. A detection algorithm identified potential seizure activity and a template matched filter was used to locate spikes. Seizure events were confirmed manually and classified as either clinically correlated, electroencephalographically identical but not clinically correlated, or subclinical. We found that spike rate was significantly altered prior to seizure in 9 out of 15 subjects. Increased pre-ictal spike rate was linked to improved predictability; however, spike rate was also shown to decrease before seizure (in 6 out of the 9 subjects). The probability distribution of spikes and seizures were notably similar, i.e. at times of high seizure likelihood the probability of epileptic spiking also increased. Both spikes and seizures showed clear evidence of circadian regulation and, for some subjects, there were also longer term patterns visible over weeks to months. Patterns of spike and seizure occurrence were highly subject-specific. The pre-ictal decrease in spike rate is not consistent with spikes promoting seizures. However, the fact that spikes and seizures demonstrate similar probability distributions suggests they are not wholly independent processes. It is possible spikes actively inhibit seizures, or that a decreased spike rate is a secondary symptom of the brain approaching seizure. If spike rate is modulated by common regulatory factors as seizures then spikes may be useful biomarkers of cortical excitability.

Epilepsy: Ever-changing states of cortical excitability

It has been proposed that the underlying epileptic process is mediated by changes in both excitatory and inhibitory circuits leading to the formation of hyper-excitable seizure networks. In this review we aim to shed light on the many physiological factors that modulate excitability within these networks. These factors have been discussed extensively in many reviews each as a separate entity and cannot be extensively covered in a single manuscript. Thus for the purpose of this work in which we aim to bring those factors together to explain how they interact with epilepsy, we only provide brief descriptions. We present reported evidence supporting the existence of the epileptic brain in several states; interictal, peri-ictal and ictal, each with distinct excitability features. We then provide an overview of how many physiological factors influence the excitatory/inhibitory balance within the interictal state, where the networks are presumed to be functioning normally. We conclude that these changes result in constantly changing states of cortical excitability in patients with epilepsy.

Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle

Significance Dynamic changes of cortical excitability are relevant in both healthy and pathological network dynamics. In epilepsy, pathological changes in excitability commonly underlie the initiation and spread of seizures. Accordingly, the ability to monitor excitability and control its degree is important for adequate clinical care and treatment because classic EEG markers found in epilepsy such as interictal spikes do not reflect seizure propensity and thus excitability. Here, we identify excitability markers and test them on long-term electrocorticogram and EEG recordings. We show that they correlate with more direct excitability measures using external stimulation and allow for real-time excitability monitoring. Our results provide evidence that excitability of cortical networks is reduced by antiepileptic drugs and increases as a function of time awake. Pathological changes in excitability of cortical tissue commonly underlie the initiation and spread of seizure activity in patients suffering from epilepsy. Accordingly, monitoring excitability and controlling its degree using antiepileptic drugs (AEDs) is of prime importance for clinical care and treatment. To date, adequate measures of excitability and action of AEDs have been difficult to identify. Recent insights into ongoing cortical activity have identified global levels of phase synchronization as measures that characterize normal levels of excitability and quantify any deviation therefrom. Here, we explore the usefulness of these intrinsic measures to quantify cortical excitability in humans. First, we observe a correlation of such markers with stimulation-evoked responses suggesting them to be viable excitability measures based on ongoing activity. Second, we report a significant covariation with the level of AED load and a wake-dependent modulation. Our results indicate that excitability in epileptic networks is effectively reduced by AEDs and suggest the proposed markers as useful candidates to quantify excitability in routine clinical conditions overcoming the limitations of electrical or magnetic stimulation. The wake-dependent time course of these metrics suggests a homeostatic role of sleep, to rebalance cortical excitability.

Estimation of effective connectivity via data-driven neural modeling

This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used to track the mechanisms involved in seizure initiation and termination.

Seizure Prediction: Science Fiction or Soon to Become Reality?

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

On synchronization of networks of Wilson-Cowan oscillators with diffusive coupling

We investigate the problem of synchronization in a network of homogeneous Wilson-Cowan oscillators with diffusive coupling. Such networks can be used to model the behavior of populations of neurons in cortical tissue, referred to as neural mass models. A new approach is proposed to address conditions for local synchronization for this type of neural mass models. By analyzing the linearized model around a limit cycle, we study synchronization within a network with direct coupling. We use both analytical and numerical approaches to link the presence or absence of synchronized behavior to the location of eigenvalues of the Laplacian matrix. For the analytical part, we apply two-time scale averaging and the Chetaev theorem, while, for the remaining part, we use a recently proposed numerical approach. Sufficient conditions are established to highlight the effect of network topology on synchronous behavior when the interconnection is undirected. These conditions are utilized to address points that have been previously reported in the literature through simulations: synchronization might persist or vanish in the presence of perturbation in the interconnection gains. Simulation results confirm and illustrate our results.

A forward-looking review of seizure prediction

Purpose of review Seizure prediction has made important advances over the last decade, with the recent demonstration that prospective seizure prediction is possible, though there remain significant obstacles to broader application. In this review, we will describe insights gained from long-term trials, with the aim of identifying research goals for the next decade. Recent findings Unexpected results from these studies, including strong and highly individual relationships between spikes and seizures, diurnal patterns of seizure activity, and the coexistence of different seizure populations within individual patients exhibiting distinctive dynamics, have caused us to re-evaluate many prior assumptions in seizure prediction studies and suggest alternative strategies that could be employed in the search for algorithms providing greater clinical utility. Advances in analytical approaches, particularly deep-learning techniques, harbour great promise and in combination with less-invasive systems with sufficiently power-efficient computational capacity will bring broader clinical application within reach. Summary We conclude the review with an exercise in wishful thinking, which asks what the ideal seizure prediction dataset would look like and how these data should be manipulated to maximize benefits for patients. The motivation for structuring the review in this way is to create a forward-looking, optimistic critique of the existing methodologies.