W. van Drongelen

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.

Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings.

OBJECTIVE The present study aims to accurately estimate the in vivo brain-to-skull conductivity ratio by means of cortical imaging technique. Simultaneous extra- and intra-cranial potential recordings induced by subdural current stimulation were analyzed to get the estimation. METHODS The effective brain-to-skull conductivity ratio was estimated in vivo for 5 epilepsy patients. The estimation was performed using multi-channel simultaneously recorded scalp and cortical electrical potentials during subdural electrical stimulation. The cortical imaging technique was used to compute the inverse cortical potential distribution from the scalp recorded potentials using a 3-shell head volume conductor model. The brain-to-skull conductivity ratio, which leads to the most consistent cortical potential estimates with respect to the direct intra-cranial measurements, is considered to be the effective brain-to-skull conductivity ratio. RESULTS The present estimation provided consistent results in 5 human subjects studied. The in vivo effective brain-to-skull conductivity ratio ranged from 18 to 34 in the 5 epilepsy patients. CONCLUSIONS The effective brain-to-skull conductivity ratio can be estimated from simultaneous intra- and extra-cranial potential recordings and the averaged value/standard deviation is 25+/-7. SIGNIFICANCE The present results provide important experimental data on the brain-to-skull conductivity ratio, which is of significance for accurate brain source localization using piece-wise homogeneous head models.

High-resolution EEG: Cortical potential imaging of interictal spikes

BACKGROUND It is of clinical importance to localize pathologic brain tissue in epilepsy. Noninvasive localization of cortical areas associated with interictal epileptiform spikes may provide important information to facilitate presurgical planning for intractable epilepsy patients. METHODS A cortical potential imaging (CPI) technique was used to deconvolve the smeared scalp potentials into the cortical potentials. A 3-spheres inhomogeneous head model was used to approximately represent the head volume conductor. Five pediatric epilepsy patients were studied. The estimated cortical potential distributions of interictal spikes were compared with the subsequent surgical resections of these same patients. RESULTS The areas of negativity in the reconstructed cortical potentials of interictal spikes in 5 patients were consistent with the areas of surgical resections for these patients. CONCLUSIONS The CPI technique may become a useful alternative for noninvasive mapping of cortical regions displaying epileptiform activity from scalp electroencephalogram recordings.

Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity.

OBJECTIVE In patients with intractable epilepsy, the use of interictal spikes as surrogate markers of the epileptogenic cortex has generated significant interest. Previous studies have suggested that the cortical generators of the interictal spikes are correlated with the epileptogenic cortex as identified from the ictal recordings. We hypothesize that causal analysis of the functional brain networks during interictal spikes are correlated with the clinically-defined epileptogenic zone. METHODS We employed a time-varying causality measure, the adaptive directed transfer function (ADTF), to identify the cortical sources of the interictal spike activity in eight patients with medically intractable neocortical-onset epilepsy. The results were then compared to the foci identified by the epileptologists. RESULTS In all eight patients, the majority of the ADTF-calculated source activity was observed within the clinically-defined SOZs. Furthermore, in three of the five patients with two separate epileptogenic foci, the calculated source activity was correlated with both cortical sites. CONCLUSIONS The ADTF method identified the cortical sources of the interictal spike activity as originating from the same cortical locations as the recorded ictal activity. SIGNIFICANCE Evaluation of the sources of the cortical networks obtained during interictal spikes may provide information as to the generators underlying the ictal activity.

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.

Seizure Detection Using the Phase-Slope Index and Multichannel ECoG

Detection and analysis of epileptic seizures is of clinical and research interest. We propose a novel seizure detection and analysis scheme based on the phase-slope index (PSI) of directed influence applied to multichannel electrocorticogram data. The PSI metric identifies increases in the spatio-temporal interactions between channels that clearly distinguish seizure from interictal activity. We form a global metric of interaction between channels and compare this metric to a threshold to detect the presence of seizures. The threshold is chosen based on a moving average of recent activity to accommodate differences between patients and slow changes within each patient over time. We evaluate detection performance over a challenging population of five patients with different types of epilepsy using a total of 47 seizures in nearly 258 h of recorded data. Using a common threshold procedure, we show that our approach detects all of the seizures in four of the five patients with a false detection rate less than two per hour. A variation on the global metric is proposed to identify which channels are strong drivers of activity in each patient. These metrics are computationally efficient and suitable for real-time application.

Self-organized criticality in a network of interacting neurons

This paper contains an analysis of a simple neural network that exhibits self-organized criticality. Such criticality follows from the combination of a simple neural network with an excitatory feedback loop that generates bistability, in combination with an anti-Hebbian synapse in its input pathway. Using the methods of statistical field theory, we show how one can formulate the stochastic dynamics of such a network as the action of a path integral, which we then investigate using renormalization group methods. The results indicate that the network exhibits hysteresis in switching back and forward between its two stable states, each of which loses its stability at a saddle?node bifurcation. The renormalization group analysis shows that the fluctuations in the neighborhood of such bifurcations have the signature of directed percolation. Thus, the network states undergo the neural analog of a phase transition in the universality class of directed percolation. The network replicates the behavior of the original sand-pile model of Bak, Tang and Wiesenfeld in that the fluctuations about the two states show power-law statistics.

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.

Multiscale Aspects of Generation of High-Gamma Activity during Seizures in Human Neocortex

Visual Abstract High-gamma (HG; 80-150 Hz) activity in macroscopic clinical records is considered a marker for critical brain regions involved in seizure initiation; it is correlated with pathological multiunit firing during neocortical seizures in the seizure core, an area identified by correlated multiunit spiking and low frequency seizure activity. High-gamma (HG; 80-150 Hz) activity in macroscopic clinical records is considered a marker for critical brain regions involved in seizure initiation; it is correlated with pathological multiunit firing during neocortical seizures in the seizure core, an area identified by correlated multiunit spiking and low frequency seizure activity. However, the effects of the spatiotemporal dynamics of seizure on HG power generation are not well understood. Here, we studied HG generation and propagation, using a three-step, multiscale signal analysis and modeling approach. First, we analyzed concurrent neuronal and microscopic network HG activity in neocortical slices from seven intractable epilepsy patients. We found HG activity in these networks, especially when neurons displayed paroxysmal depolarization shifts and network activity was highly synchronized. Second, we examined HG activity acquired with microelectrode arrays recorded during human seizures (n = 8). We confirmed the presence of synchronized HG power across microelectrode records and the macroscale, both specifically associated with the core region of the seizure. Third, we used volume conduction-based modeling to relate HG activity and network synchrony at different network scales. We showed that local HG oscillations require high levels of synchrony to cross scales, and that this requirement is met at the microscopic scale, but not within macroscopic networks. Instead, we present evidence that HG power at the macroscale may result from harmonics of ongoing seizure activity. Ictal HG power marks the seizure core, but the generating mechanism can differ across spatial scales.