A Proposed Mechanism for Spontaneous Transitions between Interictal and Ictal Activity.

Epileptic networks are characterized by two outputs: brief interictal spikes and rarer, more prolonged seizures. Although either output state is readily modeled in silico and induced experimentally, the transition mechanisms are unknown, in part because no models exhibit both output states spontaneously. In silico small-world neural networks were built using single-compartment neurons whose physiological parameters were derived from dual whole-cell recordings of pyramidal cells in organotypic hippocampal slice cultures that were generating spontaneous seizure-like activity. In silico, neurons were connected by abundant local synapses and rare long-distance synapses. Activity-dependent synaptic depression and gradual recovery delimited synchronous activity. Full synaptic recovery engendered interictal population spikes that spread via long-distance synapses. When synaptic recovery was incomplete, postsynaptic neurons required coincident activation of multiple presynaptic terminals to reach firing threshold. Only local connections were sufficiently dense to spread activity under these conditions. This coalesced network activity into traveling waves whose velocity varied with synaptic recovery. Seizures were comprised of sustained traveling waves that were similar to those recorded during experimental and human neocortical seizures. Sustained traveling waves occurred only when wave velocity, network dimensions, and the rate of synaptic recovery enabled wave reentry into previously depressed areas at precisely ictogenic levels of synaptic recovery. Wide-field, cellular-resolution GCamP7b calcium imaging demonstrated similar initial patterns of activation in the hippocampus, although the anatomical distribution of traveling waves of synaptic activation was altered by the pattern of synaptic connectivity in the organotypic hippocampal cultures.SIGNIFICANCE STATEMENT When computerized distributed neural network models are required to generate both features of epileptic networks (i.e., spontaneous interictal population spikes and seizures), the network structure is substantially constrained. These constraints provide important new hypotheses regarding the nature of epileptic networks and mechanisms of seizure onset.

A candidate mechanism underlying the variance of interictal spike propagation.

Synchronous activation of neural networks is an important physiological mechanism, and dysregulation of synchrony forms the basis of epilepsy. We analyzed the propagation of synchronous activity through chronically epileptic neural networks. Electrocorticographic recordings from epileptic patients demonstrate remarkable variance in the pathways of propagation between sequential interictal spikes (IISs). Calcium imaging in chronically epileptic slice cultures demonstrates that pathway variance depends on the presence of GABAergic inhibition and that spike propagation becomes stereotyped following GABA receptor blockade. Computer modeling suggests that GABAergic quenching of local network activations leaves behind regions of refractory neurons, whose late recruitment forms the anatomical basis of variability during subsequent network activation. Targeted path scanning of slice cultures confirmed local activations, while ex vivo recordings of human epileptic tissue confirmed the dependence of interspike variance on GABA-mediated inhibition. These data support the hypothesis that the paths by which synchronous activity spreads through an epileptic network change with each activation, based on the recent history of localized activity that has been successfully inhibited.

Progressive NKCC1-dependent neuronal chloride accumulation during neonatal seizures.

Seizures induce excitatory shifts in the reversal potential for GABA(A)-receptor-mediated responses, which may contribute to the intractability of electro-encephalographic seizures and preclude the efficacy of widely used GABAergic anticonvulsants such as phenobarbital. We now report that, in intact hippocampi prepared from neonatal rats and transgenic mice expressing Clomeleon, recurrent seizures progressively increase the intracellular chloride concentration ([Cl(-)](i)) assayed by Clomeleon imaging and invert the net effect of GABA(A) receptor activation from inhibition to excitation assayed by the frequency of action potentials and intracellular Ca(2+) transients. These changes correlate with increasing frequency of seizure-like events and reduction in phenobarbital efficacy. The Na(+)-K(+)-2Cl(-) (NKCC1) cotransporter blocker bumetanide inhibited seizure-induced neuronal Cl(-) accumulation and the consequent facilitation of recurrent seizures. Our results demonstrate a novel mechanism by which seizure activity leads to [Cl(-)](i) accumulation, thereby increasing the probability of subsequent seizures. This provides a potential mechanism for the early crescendo phase of neonatal seizures.

Development of spontaneous recurrent seizures after kainate-induced status epilepticus.

Acquired epilepsy (i.e., after an insult to the brain) is often considered to be a progressive disorder, and the nature of this hypothetical progression remains controversial. Antiepileptic drug treatment necessarily confounds analyses of progressive changes in human patients with acquired epilepsy. Here, we describe experiments testing the hypothesis that development of acquired epilepsy begins as a continuous process of increased seizure frequency (i.e., proportional to probability of a spontaneous seizure) that ultimately plateaus. Using nearly continuous surface cortical and bilateral hippocampal recordings with radiotelemetry and semiautomated seizure detection, the frequency of electrographically recorded seizures (both convulsive and nonconvulsive) was analyzed quantitatively for approximately 100 d after kainate-induced status epilepticus in adult rats. The frequency of spontaneous recurrent seizures was not a step function of time (as implied by the "latent period"); rather, seizure frequency increased as a sigmoid function of time. The distribution of interseizure intervals was nonrandom, suggesting that seizure clusters (i.e., short interseizure intervals) obscured the early stages of progression, and may have contributed to the increase in seizure frequency. These data suggest that (1) the latent period is the first of many long interseizure intervals and a poor measure of the time frame of epileptogenesis, (2) epileptogenesis is a continuous process that extends much beyond the first spontaneous recurrent seizure, (3) uneven seizure clustering contributes to the variability in occurrence of epileptic seizures, and (4) the window for antiepileptogenic therapies aimed at suppressing acquired epilepsy probably extends well past the first clinical seizure.

Effects of synaptic depression and recovery on synchronous network activity.

SUMMARY: The output of an artificial neural network of spiking neurons linked by glutamatergic synapses subject to use-dependent depression was compared with physiologic data obtained from rat hippocampal area CA3 in vitro. The authors evaluated how network burst initiation and termination was affected by activity-dependent depression and recovery under a variety of experimental conditions including neuronal membrane depolarization, altered glutamate release probability, the strength of synaptic inhibition, and long-term potentiation and long-term depression of recurrent glutamatergic synapses. The results of computational experiments agreed with the in vitro data and support the idea that synaptic properties, including activity-dependent depression and recovery, play important roles in the timing and duration of spontaneous bursts of network activity. This validated network model is useful for experiments that are not feasible in vitro, and makes possible the investigation of two-dimensional aspects of burst propagation and termination.

A new synaptic plasticity rule for networks of spiking neurons.

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.

Staged anticonvulsant screening for chronic epilepsy.

OBJECTIVE: Current anticonvulsant screening programs are based on seizures evoked in normal animals. One-third of epileptic patients do not respond to the anticonvulsants discovered with these models. We evaluated a tiered program based on chronic epilepsy and spontaneous seizures, with compounds advancing from high-throughput in vitro models to low-throughput in vivo models. METHODS: Epileptogenesis in organotypic hippocampal slice cultures was quantified by lactate production and lactate dehydrogenase release into culture media as rapid assays for seizure-like activity and cell death, respectively. Compounds that reduced these biochemical measures were retested with in vitro electrophysiological confirmation (i.e., second stage). The third stage involved crossover testing in the kainate model of chronic epilepsy, with blinded analysis of spontaneous seizures after continuous electrographic recordings. RESULTS: We screened 407 compound-concentration combinations. The cyclooxygenase inhibitor, celecoxib, had no effect on seizures evoked in normal brain tissue but demonstrated robust antiseizure activity in all tested models of chronic epilepsy. INTERPRETATION: The use of organotypic hippocampal cultures, where epileptogenesis occurs on a compressed time scale, and where seizure-like activity and seizure-induced cell death can be easily quantified with biomarker assays, allowed us to circumvent the throughput limitations of in vivo chronic epilepsy models. Ability to rapidly screen compounds in a chronic model of epilepsy allowed us to find an anticonvulsant that would be missed by screening in acute models.

Automated CT scan scores of bronchiectasis and air trapping in cystic fibrosis.

BACKGROUND: Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lung injury in children with cystic fibrosis (CF). The goal of this cross-sectional pilot study was to validate automated, observer-independent image analysis software to establish objective, simple criteria for bronchiectasis and air trapping. METHODS: HRCT scans of the chest were performed in 35 children with CF and compared with scans from 12 disease control subjects. Automated image analysis software was developed to count visible airways on inspiratory images and to measure a low attenuation density (LAD) index on expiratory images. Among the children with CF, relationships among automated measures, Brody HRCT scanning scores, lung function, and sputum markers of inflammation were assessed. RESULTS: The number of total, central, and peripheral airways on inspiratory images and LAD (%) on expiratory images were significantly higher in children with CF compared with control subjects. Among subjects with CF, peripheral airway counts correlated strongly with Brody bronchiectasis scores by two raters (r=0.86, P<.0001; r=0.91, P<.0001), correlated negatively with lung function, and were positively associated with sputum free neutrophil elastase activity. LAD (%) correlated with Brody air trapping scores (r=0.83, P<.0001; r=0.69, P<.0001) but did not correlate with lung function or sputum inflammatory markers. CONCLUSIONS: Quantitative airway counts and LAD (%) on HRCT scans appear to be useful surrogates for bronchiectasis and air trapping in children with CF. Our automated methodology provides objective quantitative measures of bronchiectasis and air trapping that may serve as end points in CF clinical trials.

Recognition of partially occluded and rotated images with a network of spiking neurons.

In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.