Wim van Drongelen

Estimation of in vivo brain-to-skull conductivity ratio in humans.

The electrical conductivity value of the human skull is important for biophysics research of the brain. In the present study, the human brain-to-skull conductivity ratio was estimated through in vivo experiments utilizing intra-cranial electrical stimulation in two epilepsy patients. A realistic geometry inhomogeneous head model including the implanted silastic grids was constructed with the aid of the finite element method, and used to estimate the conductivity ratio. Averaging over 49 sets of measurements, the mean value and standard deviation of the brain-to-skull conductivity ratio were found to be 18.7 and 2.1, respectively.

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.

Neocortical seizure foci localization by means of a directed transfer function method

Purpose:  Determination of the origin of extratemporal neocortical onset seizures is often challenging due to the rapid speed at which they propagate throughout the cortex. Typically, these patients are poor surgical candidates and many times experience recurrences of seizure activity following resection of the assumed seizure focus.

Avalanches in a stochastic model of spiking neurons.

Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.

Convergence in the olfactory system: quantitative aspects of odour sensitivity

Abstract The olfactory organ offers a model of a neural system where a large number of receptor cells converge onto a small number of secondary neurones. A mathematical analysis of the effects of neural convergence in terms of response probability of secondary cells has been carried out. The parameters studied have been the ratio of neural convergence, the individual response probability of primary neurones, and the acceptor distribution over the receptor cells. The results indicate that a neural system with a high convergence ratio can detect stimuli at intensities below the one which is commonly used to demonstrate a conspicuous response in the primary neurones. An analysis of the response probabilities of secondary neurones in a system where the olfactory receptor cells have a multimodal sensitivity v. a unimodal one, shows that the response probabilities remain the same as long as the total number of “acceptors” is the same in the two modalities.

Noise in contemporary neonatal intensive care

Weekly sound surveys (n = 63) were collected, using 5 s sampling intervals, for two modern neonatal intensive care units (NICUs). Median weekly equivalent sound pressure levels (LEQ) for NICU A ranged from 61 to 63 dB (A weighted), depending on the level of care. NICU B L(EQ) measurements ranged from 55 to 60 dB (A weighted). NICU B was recently built with a focus on sound abatement, explaining much of the difference between the two NICUs. Sound levels exceeded 45 dB (A weighted), recommended by the American Academy of Pediatrics, more than 70% of the time for all levels of care. Hourly L(EQ)s below 50 dB (A weighted) and hourly L10s below 55 dB (A weighted), recommended by the Sound Study Group (SSG) of the National Resource Center, were also exceeded in more than 70% of recorded samples. A third SSG recommendation, that the 1 s L(MAX), should not exceed 70 dB (A weighted), was exceeded relatively infrequently (< 11% of the time). Peak impulse measurements exceeded 90 dB for 6.3% of 5 s samples recorded from NICU A and 2.8% of NICU B samples. Twenty-four h periodicities in sound levels as a function of regular staff activities were apparent, but short-term variability was considerable.

A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method

In the present study, we have validated the cortical potential imaging (CPI) technique for estimating cortical potentials from scalp EEG using simultaneously recorded electrocorticogram (ECoG) in the presence of strong local inhomogeneity, i.e., Silastic ECoG grid(s). The finite element method (FEM) was used to model the realistic postoperative head volume conductor, which includes the scalp, skull, cerebrospinal fluid (CSF) and brain, as well as the Silastic ECoG grid(s) implanted during the surgical evaluation in epilepsy patients, from the co-registered magnetic resonance (MR) and computer tomography (CT) images. A series of computer simulations were conducted to evaluate the present FEM-based CPI technique and to assess the effect of the Silastic ECoG grid on the scalp EEG forward solutions. The present simulation results show that the Silastic ECoG grid has substantial influence on the scalp potential forward solution due to the distortion of current pathways in the presence of the extremely low conductive materials. On the other hand, its influence on the estimated cortical potential distribution is much less than that on the scalp potential distribution. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the feasibility of estimating the cortical potentials from the scalp EEG with the implanted Silastic ECoG gird(s), in both computer simulations and in human experimentation. In an epilepsy patient undergoing surgical evaluation, the cortical potentials were reconstructed from the simultaneously recorded scalp EEG, in which main features of spatial patterns during interictal spike were preserved and over 0.75 correlation coefficient value was obtained between the recorded and estimated cortical potentials. The FEM-based CPI technique provides a means of connecting the simultaneous recorded ECoG and the scalp EEG and promises to become an effective tool to evaluate and validate CPI techniques using clinic data.

Three-dimensional Brain Current Source Reconstruction from Intra-cranial ECoG Recordings

We have investigated 3-dimensional brain current density reconstruction (CDR) from intracranial electrocorticogram (ECoG) recordings by means of finite element method (FEM). The brain electrical sources are modeled by a current density distribution and estimated from the ECoG signals with the aid of a weighted minimum norm estimation algorithm. A series of computer simulations were conducted to evaluate the performance of ECoG-CDR by comparing with the scalp EEG based CDR results. The present computer simulation results indicate that the ECoG-CDR provides enhanced performance in localizing single dipole sources which are located in regions underneath the implanted subdural ECoG grids, and in distinguishing and imaging multiple separate dipole sources, in comparison to the CDR results as obtained from the scalp EEG under the same conditions. We have also demonstrated the applicability of the present ECoG-CDR method to estimate 3-dimensional current density distribution from the subdural ECoG recordings in a human epilepsy patient. Eleven interictal epileptiform spikes (seven from the frontal region and four from parietal region) in an epilepsy patient undergoing surgical evaluation were analyzed. The present promising results indicate the feasibility and applicability of the developed ECoG-CDR method of estimating brain sources from intracranial electrical recordings, with detailed forward modeling using FEM.

Emergent Oscillations in Networks of Stochastic Spiking Neurons

Networks of neurons produce diverse patterns of oscillations, arising from the networks global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the networks connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework.

The Source of Afterdischarge Activity in Neocortical Tonic-Clonic Epilepsy

Tonic–clonic seizures represent a common pattern of epileptic discharges, yet the relationship between the various phases of the seizure remains obscure. Here we contrast propagation of the ictal wavefront with the propagation of individual discharges in the clonic phase of the event. In an in vitro model of tonic–clonic epilepsy, the afterdischarges (clonic phase) propagate with relative uniform speed and are independent of the speed of the ictal wavefront (tonic phase). For slowly propagating ictal wavefronts, the source of the afterdischarges, relative to a given recording electrode, switched as the wavefront passed by, indicating that afterdischarges are seeded from wavefront itself. In tissue that has experienced repeated ictal events, the wavefront generalizes rapidly, and the afterdischarges in this case show a different “flip–flop” pattern, with frequent switches in their direction of propagation. This same flip–flop pattern is also seen in subdural EEG recordings in patients suffering intractable focal seizures caused by cortical dysplasias. Thus, in both slowly and rapidly generalizing ictal events, there is not a single source of afterdischarge activity: rather, the source is continuously changing. Our data suggest a complex view of seizures in which the ictal event and its constituent discharges originate from distinct locations.