Occurrence Of Epileptic Seizures Research Articles

Simultaneous occurrence of nonepileptic and epileptic seizures during a single period of in-patient video-electroencephalographic monitoring.

SummaryEpilepsy and psychogenic nonepileptic seizures (PNES) can coexist and may present in two forms: sequential and simultaneous. In sequential presentations, epileptic seizures (ES) are treated and PNES emerge later. Simultaneous recording of ES and PNES by video‐electroencephalogram (vEEG) is less well described. We retrospectively reviewed all patients diagnosed with PNES by vEEG following standard seizure induction practices over a 21‐month period. Within this cohort, we established the prevalence of coexisting epilepsy using clinical and electrographic data acquired from our epilepsy‐specific patient record. We identified patients with simultaneous PNES and ES recorded during a single vEEG admission, establishing the frequency and emergent timing of each type. Of our 262 monitored patients, 59 were diagnosed with PNES. Nineteen of the patients with PNES had coexisting epilepsy (prevalence rate of 7.3% or 32% of those with PNES). Sixteen patients had PNES and ES recorded during the same admission, and the remaining three patients had sequential PNES following successful treatment of ES. PNES occurred earlier (mean, within 1.21 days), with ES occurring later (mean, within 4.86 days). The simultaneous occurrence of PNES and ES recorded during a single admission is more common than previously reported. Identifying this group of patients may require a significantly longer period of vEEG monitoring and a detailed analysis of each individual's historical seizure events.

Circadian Rhythm Sleep-Wake Disorders.

The endogenous circadian rhythms are one of the cardinal processes that control sleep. They are self-sustaining biological rhythms with a periodicity of approximately 24 hours that may be entrained by external zeitgebers (German for time givers), such as light, exercise, and meal times. This article discusses the physiology of the circadian rhythms, their relationship to neurologic disease, and the presentation and treatment of circadian rhythm sleep-wake disorders. Classic examples of circadian rhythms include cortisol and melatonin secretion, body temperature, and urine volume. More recently, the impact of circadian rhythm on several neurologic disorders has been investigated, such as the timing of occurrence of epileptic seizures as well as neurobehavioral functioning in dementia. Further updates include a more in-depth understanding of the symptoms, consequences, and treatment of circadian sleep-wake disorders, which may occur because of extrinsic misalignment with clock time or because of intrinsic dysfunction of the brain. An example of extrinsic misalignment occurs with jet lag during transmeridian travel or with intrinsic circadian rhythm sleep-wake disorders such as advanced or delayed sleep-wake phase disorders. In advanced sleep-wake phase disorder, which is most common in elderly individuals, sleep onset and morning arousal are undesirably early, leading to impaired evening function with excessive sleepiness and sleep-maintenance insomnia with early morning awakening. By contrast, delayed sleep-wake phase disorder is characterized by an inability to initiate sleep before the early morning hours, with subsequent delayed rise time, leading to clinical symptoms of severe sleep-onset insomnia coupled with excessive daytime sleepiness in the morning hours, as patients are unable to "sleep in" to attain sufficient sleep quantity. Irregular sleep-wake rhythm disorder is misentrainment with patches of brief sleep and wakefulness spread throughout the day, leading to unstable sleep and waking behavioral patterns and an entirely idiosyncratic sleep-wake schedule. Familiarity with these major circadian rhythm sleep-wake disorder phenotypes and their overlap with other neurologic disorders is essential for the neurologist so that clinicians may intervene and improve patient functioning and quality of life.

Intracerebral Hemorrhage and Epileptic Seizures: Frequency, Localization and Seizures Types

Abstract Introduction. Among 2.8-18.7% of the patients that suffered from spontaneous intracerebral hemorrhage (ICH) develop seizures. Previous studies suggest that most important contributors to developing subsequent seizures are: volume and localization of hematoma, cortical involvement and age. Aims: To determine the occurrence of new epileptic seizures in patients with spontaneous intracerebral hemorrhage and to analyze it with respect to the patient’s age, gender, presence of premorbid risk factors, localization of the hematoma and the type of the seizures. Methods. This study is retrospective in design, with study population of 308 patients with spontaneous intracerebral hemorrhage admitted to our clinic in the period between 2008 and 2014. Analyzed premorbid risk factors for ICH are: hypertension, smoking, alcohol uptake. According to the computer tomography (CT) of brain findings the patients was divided in two groups: lobar and thalamic (deep). By the time of presents of seizures, they were classified as early (within 1 week of ICH) or late (more than 1 week after ICH). Also we analyzed the seizures type and we divided them in four groups: simple partial, partial complex, secondary generalized and tonic clonic generalized seizures. Results. Arterial hypertension was revealed in 78% of the patients with spontaneous supratentorial ICH. Epileptic seizures developed in 8.2% of analyzed patients, most of them in the first week of brain bleeding. Lobar ICH had 78.6% of the patients, with frontal localization was 44% of patients with lobar ICH, and most of them had simple partial and partial complex seizures. Conclusion. Cortical involvement, large volume of hematoma, may be a factor for provoked seizures, especially in the first days of brain bleeding.

Genome-wide analysis of differential RNA editing in epilepsy.

The recoding of genetic information through RNA editing contributes to proteomic diversity, but the extent and significance of RNA editing in disease is poorly understood. In particular, few studies have investigated the relationship between RNA editing and disease at a genome-wide level. Here, we developed a framework for the genome-wide detection of RNA sites that are differentially edited in disease. Using RNA-sequencing data from 100 hippocampi from mice with epilepsy (pilocarpine–temporal lobe epilepsy model) and 100 healthy control hippocampi, we identified 256 RNA sites (overlapping with 87 genes) that were significantly differentially edited between epileptic cases and controls. The degree of differential RNA editing in epileptic mice correlated with frequency of seizures, and the set of genes differentially RNA-edited between case and control mice were enriched for functional terms highly relevant to epilepsy, including “neuron projection” and “seizures.” Genes with differential RNA editing were preferentially enriched for genes with a genetic association to epilepsy. Indeed, we found that they are significantly enriched for genes that harbor nonsynonymous de novo mutations in patients with epileptic encephalopathy and for common susceptibility variants associated with generalized epilepsy. These analyses reveal a functional convergence between genes that are differentially RNA-edited in acquired symptomatic epilepsy and those that contribute risk for genetic epilepsy. Taken together, our results suggest a potential role for RNA editing in the epileptic hippocampus in the occurrence and severity of epileptic seizures.

Predictors and mechanisms of epilepsy occurrence in cerebral gliomas: What to look for in clinicopathology

Gliomas, especially low-grade gliomas, are highly epileptogenic brain tumors. Histopathological information is valuable in evaluating the diagnosis and/or biologic behavior of various gliomas. Here we explored the clinical data and histopathological predictors of the occurrence of epilepsy in patients with gliomas. A retrospective study examined 310 consecutive patients who had undergone surgical treatment for gliomas in our institution from January 2013 to January 2015. Clinical data and pathological examination results were analyzed. Literatures regarding the predictors and etiology of glioma associated epileptic seizures in the period of 1995–2015 were also reviewed. A total of 234 (75.5%) astrocytic tumors and 76 (24.5%) oligodendrial tumors were included. At diagnosis, 33.6% of patients had epileptic seizures. Multivariate analysis revealed cortex involvement (OR=7.991, 95%CI=1.599–39.926), lower World Health Organization grade (OR=3.584, 95%CI=1.032–12.346) and topoisomerase II (TopoII) positivity (OR=0.943, 95%CI=0.903–0.982) were strong predictors for preoperative epileptic seizures. Gender, disease course, tumor classification, location or volume did not significantly affect epileptic seizure occurrence. Forty-three publications involved glioma-associated epilepsy were found in PubMed online database and key data were extracted and summarized. The present studies on glioma-related epilepsy are relatively limited and inconsistent. Low-grade gliomas, cortex involvement and TopoII positivity were independent predictors of a history of epileptic seizures at diagnosis. Further studies to examine the underlying mechanism of topoisomerase II as well as other molecules in epilepsy occurrence in brain gliomas are needed in the future.

Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping

Epilepsy is a brain disorder that may strike at different stages of life. Patients' lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful information about subject's brain activities. MEG signals are less distorted than electroencephalogram signals by the intervening tissues between the neural source and the sensor (e.g., skull, scalp, and so on), which results in a better spatial accuracy of the MEG. This paper aims to develop a method to detect epileptic spikes from multi-channel MEG signals in a patient-independent setting. Amplitude thresholding is first employed to localize abnormalities and identify the channels where they exist. Then, dynamic time warping is applied to the identified abnormalities to detect the actual epileptic spikes. The sensitivity and specificity of proposed detection algorithm are 92.45% and 95.81%, respectively. These results indicate that the proposed algorithm can help neurologists to analyze MEG data in an automated manner instead of spending considerable time to detect MEG spikes by visual inspection.

Epileptic Angina

PurposeTo investigate the probable ictal origin of unexplained episodic chest pain and if possible to lateralize and localize the epileptic focus.MethodsA 14 year old boy presented with episodic short lasting localized chest pain. His cardiac and other systemic work-up were normal. MRI brain did not reveal any structural pathology. Video telemetry was done for characterization of the paroxysms.ResultsInterictal record showed left fronto-central epileptiform discharges. A left hemispheric, predominantly centroparietal ictal rhythm was identified. The possible localizations of this unusual semiology are somatosensory areas I and II, supplementary sensorimotor area, posterior insula and cingulate cortex. Patient responded remarkably to antiseizure drugs.ConclusionPain is a rare manifestation of epilepsy observed in less than 1% of patients. When present, it is usually accompanied by other focal features. This rare occurrence of epileptic seizures masquerading as angina is a novel observation.

An Adaptive Pattern Learning Framework to Personalize Online Seizure Prediction

The sudden and spontaneous occurrence of epileptic seizures can impose a significant burden on patients with epilepsy. If seizure onset can be prospectively predicted, it could greatly improve the life of patients with epilepsy and also open new therapeutic avenues for epilepsy treatment. However, discovering effective predictive patterns from massive brainwave signals is still a challenging problem. The prediction of epileptic seizures is still in its early stage. Most existing studies actually investigated the predictability of seizures offline instead of a truly prospective online prediction, and also the high inter-individual variability was not fully considered in prediction. In this study, we propose a novel adaptive pattern learning framework with a new online feature extraction approach to achieve personalized online prospective seizure prediction. In particular, a two-level online feature extraction approach is applied to monitor intracranial electroencephalogram (EEG) signals and construct a pattern library incrementally. Three prediction rules were developed and evaluated based on the continuously-updated patient-specific pattern library for each patient, including the adaptive probabilistic prediction (APP), adaptive linear-discriminant-analysis-based prediction (ALP), and adaptive Naive Bayes-based prediction (ANBP). The proposed online pattern learning and prediction system achieved impressive prediction results for 10 patients with epilepsy using long-term EEG recordings. The best testing prediction accuracy averaged over the 10 patients were 79, 78, and 82 percent for the APP, ALP, and ANBP prediction scheme, respectively. The experimental results confirmed that the proposed adaptive prediction framework offers a promising practical tool to solve the challenging seizure prediction problem.

Accelerated cognitive decline in a rodent model for temporal lobe epilepsy.

Cognitive impairment is frequently observed in patients with temporal lobe epilepsy. It is hypothesized that cumulative seizure exposure causes accelerated cognitive decline in patients with epilepsy. We investigated the influence of seizure frequency on cognitive decline in a rodent model for temporal lobe epilepsy. Neurobehavioral assessment was performed before and after surgery, after the induction of self-sustaining limbic status epilepticus (SSLSE), and in the chronic phase in which rats experienced recurrent seizures. Furthermore, we assessed potential confounders of memory performance. Rats showed a deficit in spatial working memory after the induction of the SSLSE, which endured in the chronic phase. A progressive decline in recognition memory developed in SSLSE rats. Confounding factors were absent. Seizure frequency and also the severity of the status epilepticus were not correlated with the severity of cognitive deficits. The effect of the seizure frequency on cognitive comorbidity in epilepsy has long been debated, possibly because of confounders such as antiepileptic medication and the heterogeneity of epileptic etiologies. In an animal model of temporal lobe epilepsy, we showed that a decrease in spatial working memory does not relate to the seizure frequency. This suggests for other mechanisms are responsible for memory decline and potentially a common pathophysiology of cognitive deterioration and the occurrence and development of epileptic seizures. Identifying this common denominator will allow development of more targeted interventions treating cognitive decline in patients with epilepsy. The treatment of interictal symptoms will increase the quality of life of many patients with epilepsy.

41. Epileptiform activity and epileptic seizures in brain injured patients with prolonged disorders of consciousness

Brain injured patients might show “late” seizures beyond one week from injury. In the present observational study we analyzed occurrence of epileptiform activity (EA) and late Epileptic Seizures (ES) during neurorehabilitation stay in 130 traumatic, vascular and anoxic DOC patients (77 males, mean age: 55.8 ± 17.33), with a clinical diagnosis of vegetative state (n = 97) or minimally conscious state (n = 33). We also investigated impact of EA and ES on patients’ long-term (30 months post-onset) clinical outcome. EA resulted to be present in 61/130 (46.9%) DOC patients and ES occurred in 35/130 (26.9%) DOC without significant differences in the clinical diagnosis or etiology. Among patients with EA, only 26/61 (42.6%) develop clinically evident ES. Mortality at 30 months did not significantly differ in DOC patients with or without ES and in DOC patients with of without EA (37.5% vs. 35.8%; p = .86). The proportion of “recovered” patients was higher in patients without ES than in patients with ES (70.5% vs. 48.0%; p = .064), whereas the proportion of recovered patients did not differ between patients without or with EA (64.7% vs. 60.0%; chi-square < 1). Presence of ES, but not of EA, significantly impact on recovery of consciousness and level of responsiveness at long-term outcome.

Epileptic seizures in children with congenital Zika virus syndrome

Abstract Objectives: to describe preliminary data referred to epileptic seizures and the probability of occurring these epileptic seizures in the infants' first months of life with congenital Zika virus (ZIKV) syndrome. Methods: concurrent cohort study including newborns and infants with congenital Zika virus syndrome attended at the specialized outpatient clinic at IMIP, Recife, Pernambuco, from October 2015 to May 2016. Results: data on 106 infants were analyzed with confirmed or suspected association to ZIKV infection. Forty children (38.7%) presented an epileptic seizure, classified at 43.3% of the cases as being spasms, 22.7% as generalized tonic seizures, 20.5% as partial and 4.5% other types of seizures. The median of days until the first report on the occurrence of epileptic seizure was 192 days of life. Conclusions: children with congenital Zika virus syndrome presented a high incidence of epileptic seizures before the end of the first semester of life, and spasm was the epileptic seizure mostly observed.

A Review of The Dynamical Systems Modeling of Epileptic Seizures For Onset Prediction

In this review, we present a critical appraisal of works that consider epilepsy as a dynamic disease and therefore presentable from the perspective of dynamic system theory. Epilepsy is an acute brain disorder characterized by recurrent seizures where parts of the brain elicit abnormally synchronous electrical activity. The most commonly encountered forms of epilepsy are generalized absence epilepsy and temporal lobe epilepsy. The electroencephalography (EEG) which is the recording of the fluctuating electric field of the brain is the major clinical diagnostic tool for epilepsy and also a vital source of data for epilepsy research. In majority of cases accurate diagnosis of the disease can be made and seizures are controlled by the regular use of anti-epileptic drugs (AEDs). However, approximately 30% of epileptic patients suffer from medically refractory epilepsy which has defied all existing treatment protocols. Understanding the mechanisms underlying these forms of epileptic seizures and the development of alternative effective treatment strategies is a fundamental challenge in modern epilepsy research. Experimental researches show that the mechanisms involved in refractory epilepsy are so diverse and complex that it is a formidable task to obtain a single framework that categorizes all the pathophysiological changes in the properties of the epileptic brain involved. There has evolved massive evidence that seizures do not occur abruptly as it has been earlier thought but develop over time even hours before the clinical symptoms, thus raising the hope for predictability of epileptic seizure occurrence. Thus, models of the epileptic brain can be postulated using concepts from deterministic and nondeterministic dynamical systems modelling. The main idea is that since the epileptic brain transitions into and out of seizures we can view it as a dynamical system. The deterministic and non-deterministic models are based on seizure onset detection algorithm for the design of a closed loop seizure warning/intervention system. The major focus being the stimulation of the epileptic brain by sending electrical pulses to it in order to disrupt seizure progression once its onset has been detected. Finally, we considered the essential issues in epileptic seizure prediction including the sceptism expressed in recent publications on the validity of nonlinear dynamical systems modelling to epileptic seizure prediction.

Long-term outcome of patients with disorders of consciousness with and without epileptiform activity and seizures: a prospective single centre cohort study.

Brain-injured patients can experience epileptic seizures beyond 1week from injury (unprovoked remote symptomatic epileptic seizures). In our longitudinal observational study, we analysed occurrence of unprovoked remote epileptic seizures and interictal epileptiform activity in 130 traumatic, vascular or anoxic inpatientswith disorders of consciousness (DOC), with a clinical diagnosis of vegetative state (n=97) or minimally conscious state (n=33). We also investigated impact of epileptic seizures and epileptiform activity on clinical outcome (30months post-onset). Epileptic seizures occurred in 35/130 patients (26.9%), epileptiform activity in 61/130 (46.9%) patients, without significant differences related to clinical diagnosis or aetiology. Among patients with epileptiform activity, only 26/61 (42.6%) developed clinically evident seizures. Mortality at 30months was not significantly influenced by the presence of seizures or epileptiform activity. The proportion of patients who recovered at long-term follow-up was higher in patients without than in patients with epileptic seizures, but was similar in patients with or without epileptiform activity. The presence of epileptic seizures but not of epileptiform activity, significantly affected the level of responsiveness at final outcome. In conclusion, seizures were detected in about one third of the whole sample, and in about a half of patients with epileptiform activity, regardless of clinical diagnosis or aetiology. Although epileptic seizures or epileptiform activity did not significantly affect mortality rate, we demonstrated that epileptic seizures could hamper recovery of consciousness. Epileptic seizures thus qualify as one of the factors largely undetermined at the moment which can influence prognosis in DOC patients.

Understanding and Predicting Epilepsy [Life Sciences

Epilepsy is the second-most prevalent neurological disorder after migraine and affects 1-2% of the world population. Epilepsy is a spectrum of disorders defined by the occurrence of epileptic seizures, which are characterized by the abnormal firing of large populations of neurons. Epilepsies can have a genetic component (as a direct cause or as risk factor) or can be triggered by a brain insult (including stroke, meningitis, and brain trauma). After the initial insult, complex reorganizations occur in neuronal networks (epileptogenesis), ultimately favoring the emergence of spontaneous seizures [1]. Epilepsy is a dynamic process as the reorganization continues during one's life. Other brain regions may get involved, possibly invoking different mechanisms, requiring changes in medication. Seizures can be very difficult to control, and 30% of patients are drug resistant.

Effect of a single dose of retigabine in cortical excitability parameters: A cross-over, double-blind placebo-controlled TMS study

BackgroundAntiepileptic drugs (AEDs) decrease the occurrence of epileptic seizures and modulate cortical excitability through several mechanisms that likely interact. The modulation of brain excitability by AEDs is believed to reflect their antiepileptic action(s) and could be used as a surrogate marker of their efficacy. Transcranial magnetic stimulation (TMS) is one of the best noninvasive methods to study cortical excitability in human subjects. Specific TMS parameters can be used to quantify the various mechanisms of action of AEDs. A new AED called retigabine increases potassium efflux by changing the conformation of KCNQ 2–5 potassium channels, which leads to neuronal hyperpolarisation and a decrease in excitability. HypothesisThe purpose of this study is to investigate the effect of retigabine on cortical excitability. Based on the known mechanisms of action of retigabine, we hypothesized that the oral intake of retigabine would increase the resting motor threshold (RMT). MethodsFifteen healthy individuals participated in a placebo-controlled, double-blind, randomised, clinical trial (RCT). The primary outcome measure was the RMT quantified before and after oral intake of retigabine. Several secondary TMS outcome measures were acquired. ResultsThe mean RMT, active motor threshold (AMT) and intensity to obtain a 1mV peak-to-peak amplitude potential (SI1mV) were significantly increased after retigabine intake compared to placebo (RMT: P=0.039; AMT: P=0.014; SI1mV: P=0.019). No significant differences were found for short-interval intracortical inhibition/intracortical facilitation (SICI/ICF), long-interval intracortical inhibition (LICI) or short-interval intracortical facilitation (SICF). ConclusionA single dose of retigabine increased the RMT, AMT and S1mV in healthy individuals. No modulating intracortical facilitation or inhibition was observed. This study provides the first in vivo demonstration of the modulating effects of retigabine on the excitability of the human brain, and the results are consistent with the data showing that retigabine hyperpolarizes neurons mainly by increasing potassium conductance

Mental disorders in child epilepsy

Introduction. Epilepsy is a chronic condition characterized by the presence of recurrent paroxysmal brain seizures (epileptic seizures), as a result of an excessive discharge of neurons. The prevalence of mental disorders in epilepsy is of 30-40%, and those of psychoses of 1-3%, being higher in patients with temporal lobe epilepsy. Psychoses and personality disorders are 3 times more frequent in those with temporal lobe seizures. Among the mental disorders in child epilepsy we care to mention: cognitive impairment, behavior disorders with aggressiveness and psychomotor agitation, learning disorders, depression, anxiety. Material and methods. There have been evaluated 24 children with epilepsy, admitted in 2014 in the Clinic of Pediatric Neurology and Psychiatry Tg-Mures, with ages between 12 and 18. There have been applied Raven's Matrices, various scales for depression and anxiety (MASC, Hamilton, Beck, Bender). The diagnosis of mental disorders has been established based on the criteria DSM-V and ICD 10. Results. The prevalence of mental disorders in child epilepsy was of 12%, with the highest incidence in the group age between 10 and 12. These mental disorders have been: cognitive disorders (79%), behavior disorders (37,5%), disharmonic personality developments (33%), anxiety (20,8%), psychosis (8,3%), depression (4,2%). Discussions. Child epilepsy evolves having a high risk for the occurrence of cognitive impairment, behavior disorders, language or mood disorders all the more so as the underlying structural disorders are higher. The early onset of the epileptic seizures under the age of 3, in a child with a psychic under development represents a vulnerability factor. Patients with temporal lobe epilepsy present a risk for psychoses, also 1/3 of the epileptics develop personality disorders. Conclusions. The onset of epileptic seizures under the age of 3 constitutes a risk factor for the occurrence of language disorders, cognitive or behavior disorders. The prognostic in mental disorders in the epileptic child is influenced by the underlying structural brain anomalies responsible for the occurrence of epileptic seizures.

Relationship between seizure frequency and number of neuronal and non-neuronal cells in the hippocampus throughout the life of rats with epilepsy

The relationship between seizure frequency and cell death has been a subject of controversy. To tackle this issue, we determined the frequency of seizures and the total number of hippocampal cells throughout the life of rats with epilepsy using the pilocarpine model. Seizure frequency varied in animals with epilepsy according to which period of life they were in, with a progressive increase in the number of seizures until 180 days (sixth months) of epileptic life followed by a decrease (330 days-eleventh month) and subsequently stabilization of seizures. Cell counts by means of isotropic fractionation showed a reduction in the number of hippocampal neuronal cells following 30, 90, 180 and 360 days of spontaneous recurrent seizures (SRS) in rats compared to their controls (about 25%–30% of neuronal cell reduction). In addition, animals with 360 days of SRS showed a reduction in the number of neuronal cells when compared with animals with 90 and 180 days of seizures. The total number of hippocampal non-neuronal cells was reduced in rats with epilepsy after 30 days of SRS, but no significant alteration was observed on the 90th, 180th and 360th days. The total number of neuronal cells was negatively correlated with seizure frequency, indicating an association between occurrence of epileptic seizures throughout life and neuronal loss. In sum, our results add novel data to the literature concerning the time-course of SRS and hippocampal cell number throughout epileptic life.

EPILEPSY IN CHILDREN WITH STROKE

The frequency of detection of stroke in children has increased over the last 25 years (from 1.3 to 13/100 000). The children identified more than 100 risk factors for stroke, but more than a third of cases the cause is not identified. We have analyzed the incidence of epilepsy in children with stroke and risk factors to evaluate the possibility of occurrence of epileptic seizures. It handled 280 case histories of children who had a stroke in the period from 2013 to August 2015 (excluding children with perinatal stroke and bleeding with severe brain injuries) treated at the Morozov Children'sClinicalHospital. All children held in the dynamics of EEG, psychological testing, repeat MRI. The diagnosis of epilepsy had 24 children (8.6%) of the treated patient records. Ischemic stroke was diagnosed in 62%, hemorrhagic – 38%. In HS, prevailed children aged up to 3 years, with ischemic from 6 to 14 years. The most common attacks made their debut from 12 to 24 months after a stroke, some earlier occurrence of attacks observed in hemorrhagic stroke, from 1 month to 12 months (77%). Only 3/24 (12.5%) children seizures were observed in the acute phase stroke. In 71% was a combination of several types of attacks. Two of the children diagnosed with idiopathic forms epilepsy – childhood absence and juvenile myoclonic. Evaluation of the outcome of stroke scale PSOM-SNE and pathopsychological evaluation by the Achenbach revealed more severe neuropsychological problems in children with stroke with epileptic seizures than those without epilepsy.

Identifying excitatory and inhibitory synapses in neuronal networks from dynamics using Transfer Entropy

Measuring the effective connectivity between the elements of a neuronal system promises to give conclusions about brain function and the principles of information processing in the brain. Transfer Entropy [1] (TE) and its extensions have recently become more and more popular for this task and have been applied to various types of neuronal data, such as from EEG, Calcium Imaging and Multi-electrode Array measurements [2]. Being based on information theory, TE can be interpreted as the predictive information transfer between two time series. TE is a model free measurement and quantifies even non-linear interactions and very importantly the directionality. However, due to its information-theoretic nature, TE doesn't distinguish between different types of interactions, for example whether a pre-synaptic neuron drives a post-synaptic neuron via an excitatory or an inhibitory synapse [3]. This distinction is crucial for the understanding of the network dynamics and the exact interplay of excitation and inhibition in neuronal networks plays an important role for network bursts and synchronization. The balance of excitation and inhibition is believed to play a role for the occurrence of epileptic seizures [4]. We propose a method complementary to the TE measurement, to not only measure the effective connectivity from neuronal network dynamics, but also to classify, whether the interactions are excitatory or inhibitory. We achieve this by introducing a new quantity, which is a linear combination of the individual terms that sum up to the Transfer Entropy. This quantity can be computed by applying a Principal Component Analysis (PCA) to the Transfer Entropy terms across different measurements. It assumes positive values for excitatory connections and negative values for inhibitory connections. To verify this method we apply it to the spike trains of noise-driven recurrent neuronal network motifs, simulated with an extended Fitzhugh-Nagumo model. We analyze all possible combinations of three neurons being coupled with either excitatory or inhibitory synapses, which add up to a total of 132 network motifs. The best performance of our reconstruction of these motifs, being measured by the area under the Receiver Operating Characteristic curve, exceeds values of 0.99. Together with the PCA analysis, we are not only able to reconstruct the motif topologies from the neuronal dynamics, but we are also able to distinguish between excitatory and inhibitory synapses.

A signal processing based analysis and prediction of seizure onset in patients with epilepsy.

One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence.