Waldemar Swiercz

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 ∼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.

Progressive NKCC1-Dependent Neuronal Chloride Accumulation during Neonatal Seizures

Seizures induce excitatory shifts in the reversal potential for GABAA-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 GABAA receptor activation from inhibition to excitation assayed by the frequency of action potentials and intracellular Ca2+ 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.

Interictal Spikes Precede Ictal Discharges in an Organotypic Hippocampal Slice Culture Model of Epileptogenesis.

Summary: In organotypic hippocampal slice cultures, principal neurons form aberrant excitatory connections with other principal cells in response to slicing induced deafferentation, similar to mechanisms underlying epileptogenesis in posttraumatic epilepsy. To investigate the consequences of this synaptogenesis, the authors recorded field-potential activity from area CA3 during perfusion with the complete growth medium used during incubation. At 7 days in vitro, slice cultures only displayed multiunit activity. At 14 days in vitro, the majority displayed population bursts reminiscent of interictal-like spikes, but sustained synchronous activity was rare. Band-pass filtering of interictal discharges revealed fast ripple-like complexes, similar to in vivo recordings. Spontaneous ictal-like activity became progressively more prevalent with age: at 21 days in vitro, 50% of organotypic hippocampal slice cultures displayed long-lasting, ictal-like discharges that could be suppressed by phenytoin, whereas interictal activity was not suppressed. The fraction of cultures displaying ictal events continually increased with incubation time. Quantification of population spike activity throughout epileptogenesis using automatic detection and clustering algorithms confirmed the appearance of interictal-like activity before ictal-like discharges and also revealed high-frequency pathologic multiunit activity in slice cultures at 14 to 17 days in vitro. These experiments indicate that interictal-like spikes precede the appearance of ictal-like activity in a reduced in vitro preparation. Epileptiform activity in cultures resembled in vivo epilepsy, including sensitivity to anticonvulsants and steadily increasing seizure incidence over time, although seizure frequency and rate of epileptogenesis were higher in vitro. Organotypic hippocampal slice cultures comprise a useful model system for investigating mechanisms of epileptogenesis as well as developing antiepileptic and antiepileptogenic drugs.

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.

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.

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.

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.

Staged anticonvulsant screening for chronic epilepsy

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.

Network Control Mechanisms-Synaptogenesis and Epilepsy Development

Abstract Epileptogenesis, the process by which brain injury leads to chronic epilepsy, might be considered a model of network plasticity. Understanding the underlying mechanisms could provide fundamental new insights into the means by which the input and output functions of neural networks undergo persistent modification. We outline initial experimental and computational approaches to this problem, emphasizing the specific unknowns that become evident. Chief among these are the strategies that neurons use to develop new synaptic connections. The robust epilepsy that develops in cultured organotypic brain slices, together with the experimental accessibility of this preparation, provides an exciting new tool to explore neural circuit repair and its relation to epileptogenesis.