Detecting seizure origin using basic, multiscale population dynamic measures: preliminary findings

Integration of stationary wavelet transform on a dynamic partial reconfiguration for recognition of pre-ictal gamma oscillations

To define the neural networks responsible of an epileptic seizure, it is useful to perform advanced signal processing techniques. In this context, electrophysiological signals present three types of waves: oscillations, spikes, and a mixture of both. Recent studies show that spikes and oscillations should be separated properly in order to define the accurate neural connectivity during the pre-ictal, seizure and inter-ictal states. Retrieving oscillatory activity is a sensitive task due to the frequency overlap between oscillations and transient activities. Advanced filtering techniques have been proposed to ensure a good separation between oscillations and spikes. It would be interesting to apply them in real time for instantaneous monitoring, seizure warning or neurofeedback systems. This requires improving execution time. This constraint can be overcome using embedded systems that combine hardware and software in an optimized architecture.

We propose here to implement a stationary wavelet transform (SWT) as an adaptive filtering technique retaining only pre-ictal gamma oscillations, as validated in previous work, on a partial dynamic configuration. Then, the same architecture is used with further modifications to integrate spatio temporal mapping for an early recognition of seizure build-up.

Data that contains transient, pre-ictal gamma oscillations and a seizure was simulated. the method on real intracerebral signals was also tested. The SWT was integrated on an embedded architecture. This architecture permits a spatio temporal mapping to detect the accurate time and localization of seizure build-up, while reducing computation time by a factor of around 40. Embedded systems are a promising venue for real-time applications in clinical systems for epilepsy.

Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy

OBJECTIVE

The current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE).

METHODS

Scalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear-Pearson correlation-and non-linear-phase synchronization-measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy.

RESULTS

Changes in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained.

CONCLUSIONS

In TLE patients, the transition to the ictal stage is accompanied by increasing global synchronization and a more ordered spectral content of the signals, indicated by lower spectral entropy. The interictal connectivity imbalance (lower ipsilateral connectivity) is sustained during the seizure, irrespective of any appreciable imbalance in the spectral entropy of the mesial recordings.

Cortical gamma oscillations: the functional key is activation, not cognition

Cortical oscillatory synchrony in the gamma range has been attracting increasing attention in cognitive neuroscience ever since being proposed as a solution to the so-called binding problem. This growing literature is critically reviewed in both its basic neuroscience and cognitive aspects. A physiological "default assumption" regarding these oscillations is introduced, according to which they signal a state of physiological activation of cortical tissue, and the associated need to balance excitation with inhibition in particular. As such these oscillations would belong among a variety of generic neural control operations that enable neural tissue to perform its systems level functions, without implementing those functions themselves. Regional control of cerebral blood flow provides an analogy in this regard, and gamma oscillations are tightly correlated with this even more elementary control operation. As correlates of neural activation they will also covary with cognitive activity, and this typically suffices to account for the covariation between gamma activity and cognitive task variables. A number of specific cases of gamma synchrony are examined in this light, including the original impetus for attributing cognitive significance to gamma activity, namely the experiments interpreted as evidence for "binding by synchrony". This examination finds no compelling reasons to assign functional roles to oscillatory synchrony in the gamma range beyond its generic functions at the level of infrastructural neural control.

Glissandi: transient fast electrocorticographic oscillations of steadily increasing frequency, explained by temporally increasing gap junction conductance

PURPOSE

We describe a form of very fast oscillation (VFO) in patient electrocorticography (ECoG) recordings, that can occur prior to ictal events, in which the frequency increases steadily from ≈ 30-40 to >120 Hz, over a period of seconds. We dub these events "glissandi" and describe a possible model for them.

METHODS

Four patients with epilepsy had presurgical evaluations (with ECoG obtained in two of them), and excised tissue was studied in vitro, from three of the patients. Glissandi were seen spontaneously in vitro, associated with ictal events-using acute slices of rat neocortex-and they were simulated using a network model of 15,000 detailed layer V pyramidal neurons, coupled by gap junctions.

KEY FINDINGS

Glissandi were observed to arise from human temporal neocortex. In vitro, they lasted 0.2-4.1 s, prior to ictal onset. Similar events were observed in the rat in vitro in layer V of frontal neocortex when alkaline solution was pressure-ejected; glissandi persisted when γ-aminobutyric acid A (GABA(A)), GABA(B), and N-methyl-d-aspartate (NMDA), and alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors were blocked. Nonalkaline conditions prevented glissando generation. In network simulations it was found that steadily increasing gap junction conductance would lead to the observed steady increase in VFO field frequency. This occurred because increasing gap junction conductance shortened the time required for an action potential to cross from cell to cell.

SIGNIFICANCE

The in vitro and modeling data are consistent with the hypothesis that glissandi arise when pyramidal cell gap junction conductances rise over time, possibly as a result of an alkaline fluctuation in brain pH.

Application of approximate entropy on dynamic characteristics of epileptic absence seizure

Electroencephalogram signals are time-varying complex electrophysiological signals. Existing studies show that approximate entropy, which is a nonlinear dynamics index, is not an ideal method for electroencephalogram analysis. Clinical electroencephalogram measurements usually contain electrical interference signals, creating additional challenges in terms of maintaining robustness of the analytic methods. There is an urgent need for a novel method of nonlinear dynamical analysis of the electroencephalogram that can characterize seizure-related changes in cerebral dynamics. The aim of this paper was to study the fluctuations of approximate entropy in preictal, ictal, and postictal electroencephalogram signals from a patient with absence seizures, and to improve the algorithm used to calculate the approximate entropy. The approximate entropy algorithm, especially our modified version, could accurately describe the dynamical changes of the brain during absence seizures. We could also demonstrate that the complexity of the brain was greater in the normal state than in the ictal state. The fluctuations of the approximate entropy before epileptic seizures observed in this study can form a good basis for further study on the prediction of seizures with nonlinear dynamics.

Ictal high-gamma oscillation (60-99 Hz) in intracranial electroencephalography and postoperative seizure outcome in neocortical epilepsy

OBJECTIVE

High-gamma oscillations (HGOs) (60-99 Hz) have been suggested to correlate with seizure onset zones and seizure outcomes. We investigated the correlation between the extent of removal of ictal HGO generating areas and postoperative seizure outcome in neocortical epilepsy (NE).

METHODS

Twenty three patients with medically intractable NE underwent chronic intracranial electroencephalography (iEEG) using subdural electrodes. Ictal HGOs and superimposed undersampled ripples within ±3 s of video-iEEG ictal onset were extracted by wavelet clustering and thresholding. Cluster epileptogenicity indices (CEIs) were calculated. The temporal analysis window was locked to the timing of the maximum CEI wavecluster. Root mean square amplitudes, cross-correlation synchronies and the local focus indices within the temporal window were calculated.

RESULTS

Percentages of resected maximum CEI waveclusters and HGO zones with high standardised amplitudes (>3), high cross-correlation synchronies (>0.9) and high local focus indices (>2) were significantly higher in the seizure-free group compared to the not seizure-free group (p=0.036, p=0.018, and p=0.026, respectively).

CONCLUSIONS

The automatic quantitative ictal HGO analysis may be effective in delineating the epileptogenic zone.

SIGNIFICANCE

HGO analysis may be helpful for improving post-resection seizure outcome in NE in the future.

Wave speed in excitable random networks with spatially constrained connections

Very fast oscillations (VFO) in neocortex are widely observed before epileptic seizures, and there is growing evidence that they are caused by networks of pyramidal neurons connected by gap junctions between their axons. We are motivated by the spatio-temporal waves of activity recorded using electrocorticography (ECoG), and study the speed of activity propagation through a network of neurons axonally coupled by gap junctions. We simulate wave propagation by excitable cellular automata (CA) on random (Erdös-Rényi) networks of special type, with spatially constrained connections. From the cellular automaton model, we derive a mean field theory to predict wave propagation. The governing equation resolved by the Fisher-Kolmogorov PDE fails to describe wave speed. A new (hyperbolic) PDE is suggested, which provides adequate wave speed that saturates with network degree , in agreement with intuitive expectations and CA simulations. We further show that the maximum length of connection is a much better predictor of the wave speed than the mean length. When tested in networks with various degree distributions, wave speeds are found to strongly depend on the ratio of network moments rather than on mean degree , which is explained by general network theory. The wave speeds are strikingly similar in a diverse set of networks, including regular, Poisson, exponential and power law distributions, supporting our theory for various network topologies. Our results suggest practical predictions for networks of electrically coupled neurons, and our mean field method can be readily applied for a wide class of similar problems, such as spread of epidemics through spatial networks.

Chemical synaptic and gap junctional interactions between principal neurons: partners in epileptogenesis

Field potential signals, corresponding to electrographic seizures in cortical structures, often contain two components, which sometimes appear to be separable and other times to be superimposed. The first component consists of low-amplitude very fast oscillations (VFO, >70-80 Hz); the second component consists of larger amplitude transients, lasting tens to hundreds of ms, and variously called population spikes, EEG spikes, or bursts--terms chosen in part because of the cellular correlates of the field events. To first approximation, the two components arise because of distinctive types of cellular interactions: gap junctions for VFO (a model of which is reviewed in the following), and recurrent synaptic excitation and/or inhibition for the transients. With in vitro studies of epileptic human neocortical tissue, it is possible to elicit VFO alone, or VFO superimposed on a large transient, but not a large transient without the VFO. If such observations prove to be general, they would imply that gap junction-mediated interactions are the primary factor in epileptogenesis. It appears to be the case then, that in the setting of seizure initiation (but not necessarily under physiological conditions), the gain of gap junction-mediated circuits can actually be larger than the gain in excitatory synaptic circuits.

Coalescence and fragmentation of cortical networks during focal seizures

Epileptic seizures reflect a pathological brain state characterized by specific clinical and electrical manifestations. The proposed mechanisms are heterogeneous but united by the supposition that epileptic activity is hypersynchronous across multiple scales, yet principled and quantitative analyses of seizure dynamics across space and throughout the entire ictal period are rare. To more completely explore spatiotemporal interactions during seizures, we examined electrocorticogram data from a population of male and female human patients with epilepsy and from these data constructed dynamic network representations using statistically robust measures. We found that these networks evolved through a distinct topological progression during the seizure. Surprisingly, the overall synchronization changed only weakly, whereas the topology changed dramatically in organization. A large subnetwork dominated the network architecture at seizure onset and preceding termination but, between, fractured into smaller groups. Common network characteristics appeared consistently for a population of subjects, and, for each subject, similar networks appeared from seizure to seizure. These results suggest that, at the macroscopic spatial scale, epilepsy is not so much a manifestation of hypersynchrony but instead of network reorganization.

Spatiotemporal patterns of electrocorticographic very fast oscillations (> 80 Hz) consistent with a network model based on electrical coupling between principal neurons

PURPOSE

We sought to characterize spatial and temporal patterns of electrocorticography (ECoG) very fast oscillations (> ∼80 Hz, VFOs) prior to seizures in human frontotemporal neocortex, and to develop a testable network model of these patterns.

METHODS

ECoG data were recorded with subdural grids from two preoperative patients with seizures of frontal lobe onset in an epilepsy monitoring unit. VFOs were recorded from rat neocortical slices. A "cellular automaton" model of network oscillations was developed, extending ideas of Traub et al. (Neuroscience, 92, 1999, 407) and Lewis & Rinzel (Network: Comput Neural Syst, 11, 2000, 299); this model is based on postulated electrical coupling between pyramidal cell axons.

RESULTS

Layer 5 of rat neocortex, in vitro, can generate VFOs when chemical synapses are blocked. Human epileptic neocortex, in situ, produces preseizure VFOs characterized by the sudden appearance of "blobs" of activity that evolve into spreading wavefronts. When wavefronts meet, they coalesce and propagate perpendicularly but never pass through each other. This type of pattern has been described by Lewis & Rinzel in cellular automaton models with spatially localized connectivity, and is demonstrated here with 120,000- to 5,760,000-cell models. We provide a formula for estimating VFO period from structural parameters and estimate the spatial scale of the connectivity.

DISCUSSION

These data provide further evidence, albeit indirect, that preseizure VFOs are generated by networks of pyramidal neurons coupled by gap junctions, each predominantly confined to pairs of neurons having somata separated by < ∼1-2 mm. Plausible antiepileptic targets are tissue mechanisms, such as pH regulation, that influence gap-junction conductance.

A nonsynaptic mechanism underlying interictal discharges in human epileptic neocortex

Very fast oscillations (VFOs, >80 Hz) are important for physiological brain processes and, in excess, with certain epilepsies. Putative mechanisms for VFO include interneuron spiking and network activity in coupled pyramidal cell axons. It is not known whether either, or both, of these apply in pathophysiological conditions. Spontaneously occurring interictal discharges occur in human tissue in vitro, resected from neocortical epileptic foci. VFO associated with these discharges was manifest in both field potential and, with phase delay, in excitatory synaptic inputs to fast spiking interneurons. Recruitment of somatic pyramidal cell and interneuron spiking was low, with no correlation between VFO power and synaptic inputs to principal cells. Reducing synaptic inhibition failed to affect VFO occurrence, but they were abolished by reduced gap junction conductance. These data suggest a lack of a causal role for interneurons, and favor a nonsynaptic pyramidal cell network origin for VFO in epileptic human neocortex.

Informational lesions: optical perturbation of spike timing and neural synchrony via microbial opsin gene fusions

Synchronous neural activity occurs throughout the brain in association with normal and pathological brain functions. Despite theoretical work exploring how such neural coordination might facilitate neural computation and be corrupted in disease states, it has proven difficult to test experimentally the causal role of synchrony in such phenomena. Attempts to manipulate neural synchrony often alter other features of neural activity such as firing rate. Here we evaluate a single gene which encodes for the blue-light gated cation channel channelrhodopsin-2 and the yellow-light driven chloride pump halorhodopsin from Natronobacterium pharaonis, linked by a ‘self-cleaving’ 2A peptide. This fusion enables proportional expression of both opsins, sensitizing neurons to being bi-directionally controlled with blue and yellow light, facilitating proportional optical spike insertion and deletion upon delivery of trains of precisely-timed blue and yellow light pulses. Such approaches may enable more detailed explorations of the causal role of specific features of the neural code.