Hyong C. Lee

Seizure anticipation in pediatric epilepsy: use of kolmogorov entropy

The purpose of this paper is to demonstrate feasibility of using trends in Kolmogorov entropy to anticipate seizures in pediatric patients with intractable epilepsy. Surface and intracranial recordings of preseizure and seizure activity were obtained from five patients and subjected to time series analysis using Kolmogorov entropy. This metric was compared with correlation dimension and power indices, both known to predict seizures in some adult patients. We used alarm levels and introduced regression analysis as a quantitative approach to the analysis of trends. Surrogate time series evaluated data nonlinearity, as a precondition to the use of nonlinear measures. Seizures were anticipated before clinical or electrographic seizure onset for three of the five patients from the intracranial recordings, and in two of five patients from the scalp recordings. Anticipation times varied between 2 and 40 minutes. This is the first report in which simultaneous surface and intracranial recording are used for seizure prediction in children. We conclude that the Kolmogorov entropy and power indices were as effective as the more commonly used correlation dimension in anticipating seizures. Further, regression analysis of the Kolmogorov entropy time series is feasible, making the analysis of data trends more objective.

Emergent epileptiform activity in neural networks with weak excitatory synapses

Brain electrical activity recorded during an epileptic seizure is frequently associated with rhythmic discharges in cortical networks. Current opinion in clinical neurophysiology is that strongly coupled networks and cellular bursting are prerequisites for the generation of epileptiform activity. Contrary to expectations, we found that weakly coupled cortical networks can create synchronized cellular activity and seizure-like bursting. Evaluation of a range of synaptic parameters in a detailed computational model revealed that seizure-like activity occurs when the excitatory synapses are weakened. Guided by this observation, we confirmed experimentally that, in mouse neocortical slices, a pharmacological reduction of excitatory synaptic transmission elicited sudden onset of repetitive network bursting. Our finding provides powerful evidence that onset of seizures can be associated with a reduction in synaptic transmission. These results open a new avenue to explore network synchrony and may ultimately lead to a rational approach to treatment of network pathology in epilepsy.

propagation of seizure-like activity in a model of neocortex.

Summary: Seizures in pediatric epilepsy are often associated with spreading, repetitive bursting activity in neocortex. The authors examined onset and propagation of seizure-like activity using a computational model of cortical circuitry. The model includes two pyramidal cell types and four types of inhibitory interneurons. Each neuron is represented by a multicompartmental model with biophysically realistic ion channels. The authors determined the role of bursting neurons and found that their capability of driving network oscillations is most prominent in networks with either weak or relatively strong excitatory synaptic coupling. Synaptic coupling strength was varied in a separate set of simulations to examine its role in network bursting. Oscillations both between cortical layers (vertical oscillations) and between cortical areas (horizontal oscillations) emerge at moderate excitatory coupling strengths. For horizontal propagation, existence of a fast-conducting fiber system and its properties are critical. Seizure-like oscillatory activity may originate from single neurons or small networks, and that activity may propagate in two principal fashions: one that can be represented by a unidirectional (pacemaker)-type process and the other as multi- or bidirectional propagating waves. The frequency of the bursting patterns relates to underlying propagating activity that can either sustain or disrupt the ongoing oscillation.

Comparison of Seizure Detection Algorithms in Continuously Monitored Pediatric Patients

Summary: Robust, automated seizure detection has long been an important goal in epilepsy research because of both the possibilities for portable intervention devices and the potential to provide prompter, more efficient treatment while in clinic. The authors present results on how well four seizure detection algorithms (based on principal eigenvalue [EI], total power, Kolmogorov entropy [KE], and correlation dimension) discriminated between ictal and interictal EEG and electrocorticoencephalography (ECoG) from four patients (aged 13 months to 21 years). Test data consisted of 46 to 78 hours of continuously acquired EEG/ECoG for each patient (245 hours total), and the detectors’ accuracy was checked against seizures found by a board-certified neurologist and an experienced registered EEG technician. The results were patient-specific: no algorithm performed well on a 13-month-old patient, and no algorithm consistently performed best on the other three patients. One of the metrics (EI) supported the existence of a postictal period of 5 to 15 minutes in the three oldest patients, but no strong evidence of a preictal anticipation was found. Two metrics (EI and KE) cycled continuously with a period of several hours in a 21-year-old patient, highlighting the importance of continuous analysis to differentiate background cycling from anticipation.

Simulation of neocortical epileptiform activity using parallel computing

Abstract A scalable network model intended for study of neocortical epileptiform activity was built on the pGENESIS neural simulator. The model included superficial and deep pyramidal cells plus four types of inhibitory neurons. An electroencephalogram (EEG) simulator was attached to the model to validate model behavior and to determine the contributions of inhibitory and excitatory neuronal populations to the EEG signal. We examined effects of overall excitation and inhibition on activity patterns in the network, and found that the network-bursting patterns occur within a narrow range of the excitation–inhibition space. Further, we evaluated synchronization effects produced by gap junctions during synchronous and asynchronous states.

Comparing epileptiform behavior of mesoscale detailed models and population models of neocortex.

Two models of the neocortex are developed to study normal and pathologic neuronal activity. One model contains a detailed description of a neocortical microcolumn represented by 656 neurons, including superficial and deep pyramidal cells, four types of inhibitory neurons, and realistic synaptic contacts. Simulations show that neurons of a given type exhibit similar, synchronized behavior in this detailed model. This observation is captured by a population model that describes the activity of large neuronal populations with two differential equations with two delays. Both models appear to have similar sensitivity to variations of total network excitation. Analysis of the population model reveals the presence of multistability, which was also observed in various simulations of the detailed model.

Oscillation in a network model of neocortex

A basic understanding of the relationship between activity of individual neurons and macroscopic electrical activity of local field potentials, or electroencephalogram (EEG), may provide guidance for experimental design in neuroscience, improve development of therapeutic approaches in neurology, and offer opportunities for computer-aided design of brain-computer interfaces. We study the relationship between resonant properties of neurons and network oscillations in a computational model of neocortex. Our findings suggest that resonance is associated with subthreshold oscillation of neurons. This subthreshold behavior affects spike timing and plays a significant role in the generation of the networks extracellular currents reflected in the EEG.

Cross Validation for Selection of Cortical Interaction Models From Scalp EEG or MEG

A cross-validation (CV) method based on state-space framework is introduced for comparing the fidelity of different cortical interaction models to the measured scalp electroencephalogram (EEG) or magnetoencephalography (MEG) data being modeled. A state equation models the cortical interaction dynamics and an observation equation represents the scalp measurement of cortical activity and noise. The measured data are partitioned into training and test sets. The training set is used to estimate model parameters and the model quality is evaluated by computing test data innovations for the estimated model. Two CV metrics normalized mean square error and log-likelihood are estimated by averaging over different training/test partitions of the data. The effectiveness of this method of model selection is illustrated by comparing two linear modeling methods and two nonlinear modeling methods on simulated EEG data derived using both known dynamic systems and measured electrocorticography data from an epilepsy patient.

Interaction between cellular voltage-sensitive conductance and network parameters in a model of neocortex can generate epileptiform bursting

We examined the effects of both intrinsic neuronal membrane properties and network parameters on oscillatory activity in a model of neocortex. A scalable network model with six different cell types was built with the pGENESIS neural simulator. The neocortical network consisted of two types of pyramidal cells and four types of inhibitory interneurons. All cell types contained both fast sodium and delayed rectifier potassium channels for generation of action potentials. A subset of the pyramidal neurons contained an additional slow inactivating (persistent) sodium current (NaP). The neurons with the NaP current showed spontaneous bursting activity in the absence of external stimulation. The model also included a routine to calculate a simulated electroencephalogram (EEG) trace from the population activity. This revealed emergent network behavior which ranged from desynchronized activity to different types of seizure-like bursting patterns. At settings with weaker excitatory network effects, the propensity to generate seizure-like behavior increased. Strong excitatory network connectivity destroyed oscillatory behavior, whereas weak connectivity enhanced the relative importance of the spontaneously bursting cells. Our findings are in contradiction with the general opinion that strong excitatory synaptic and/or insufficient inhibition effects are associated with seizure initiation, but are in agreement with previously reported behavior in neocortex.

Developing a petascale neural simulation

Simulations of large neural networks have the potential to contribute uniquely to the study of epilepsy, from the effects of extremely local changes in neuron environment and behavior, to the effects of large scale wiring anomalies. Currently, simulations with sufficient detail in the neuron model, however, are limited to cell counts that are far smaller than scales measured by typical probes. Furthermore, it is likely that future simulations will follow the path that large-scale simulations in other fields have and include hierarchically interacting components covering different scales and different biophysics. The resources needed for problem solving in this domain call for petascale computing - computing with supercomputers capable of 10/sup 15/ operations a second and holding datasets of 10/sup 15/ bytes in memory. We will lay out the structure of our simulation of epileptiform electrical activity in the neocortex, describe experiments and models of its scaling behavior in large cluster supercomputers, identify tight spots in this behavior, and project the performance onto a candidate next generation computing platform.