Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose.

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.

Systematic comparison of the behaviors produced by computational models of epileptic neocortex.

Two existing models of brain dynamics in epilepsy, one detailed (i.e., realistic) and one abstract (i.

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.

Framework for interactive million-neuron simulation.

Large simulations have become increasingly complex in many fields, tending to incorporate scale-dependent modeling and algorithms and wide-ranging physical influences. This scale of simulation sophistication has not yet been matched in neuroscience. The authors describe a framework aimed at enabling natural interaction with complex simulations: their configuration, initial conditions, monitoring, and analysis.

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

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.

Comparison of seizure detection algorithms in continuously monitored pediatric patients.

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.

Role of persistent sodium current in bursting activity of mouse neocortical networks in vitro.

Most types of electrographic epileptiform activity can be characterized by isolated or repetitive bursts in brain electrical activity. This observation is our motivation to determine mechanisms that underlie bursting behavior of neuronal networks. Here we show that the persistent sodium (Na(P)) current in mouse neocortical slices is associated with cellular bursting and our data suggest that these cells are capable of driving networks into a bursting state.

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.

Localization of brainstem auditory evoked potentials in primates: a comparison of localization techniques applied to deep brain sources.

The objective of this study was to evaluate the performance of source localization techniques through localization of deep brain sources. To accomplish this, two replications of a brainstem auditory evoked potential (BAEP, left ear 60 dB nHL clicks) were recorded from five normal rhesus monkeys. We analyzed waves III and IV, as this portion of the BAEP corresponds to the deepest signal.

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.