Epilepsy Discharges Research Articles

Is there a rationale for treating interictal epileptic discharges in self-limiting epilepsy with centrotemporal spikes (SeLECTS)?

Is there a rationale for treating interictal epileptic discharges in self-limiting epilepsy with centrotemporal spikes (SeLECTS)?

Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability.

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy-so-called interictal epileptiform activity-with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy.

A machine-learning approach for predicting impaired consciousness in absence epilepsy.

Behavior during 3–4 Hz spike‐wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)‐based machine‐learning method to predict SWD‐related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10‐fold cross‐validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG‐based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

Automatic detection of the spike-and-wave discharges in absence epilepsy for humans and rats using deep learning

Automatic detection of spike-and-wave discharges (SWDs) of absence seizures, is a highly time-consuming process requiring trained technicians or neurologists to categorize thousands of non-overlapping epochs of electroencephalography (EEG) data by visually inspecting several interconnections among different channels. This paper aims to develop an algorithm for a non-invasive real-time detection of SWDs in the EEG recordings of humans with absence epilepsy and a genetic model of absence epilepsy. We develop a SWD detection framework using Convolutional Neural Networks. Our approach utilizes the nature of EEG signals; as the brain signals are dynamics in discrete time, we found that it is more efficient and useful to represent the signal’s power as a function of frequency and time using Thomson’s multitaper power spectral density estimation analysis. Our experiments show that the developed method classified SWDs in humans and rats with high diagnostic performance similar to that of the trained neurologists while using fewer channels, proving that the proposed algorithm can be applied to different domains where the main focus is the detection of SWDs. Although there are different methods to detect SWDs in humans and animals, we showed the need for efficient and more accurate SWD detection. The proposed method, characterized by low computational and memory requirements using non-invasive EEG techniques with fewer channels, offers an efficient multi-purposed deep learning framework to be implemented in wearable or portable devices for accurate real-time detection of SWD patterns in EEG signals. Eventually, the proposed method is a step towards detecting seizures and closed-loop seizure interventions.

Interictal epileptiform discharges in focal epilepsy are preceded by increase in low-frequency oscillations

ObjectiveInterictal epileptiform discharges (IEDs) constitute a diagnostic signature of epilepsy. These events reflect epileptogenic hypersynchronization. Previous studies indicated that IEDs arise from slow neuronal activation accompanied by metabolic and hemodynamic changes. These might induce cortical inhibition followed hypersynchronization at IED onset. As cortical inhibition is mediated by low-frequency oscillations, we aimed to analyze the role of low-frequency oscillations prior the IED using magnetencephalography (MEG). MethodsLow-frequency (1–8 Hz) oscillations pre-IED ([-1000 milliseconds (ms), IED onset]) were analyzed using MEG in 14 focal epilepsy patients (median age = 23 years, range = 7–46 age). Occurrence of local pre-IED oscillations was analyzed using Beamformer Dynamical Imaging of Coherent Sources (DICS) and event-related desynchronization/synchronization (ERD-ERS) maps constructed using cluster-based permutation tests. The development of pre-IED oscillations was characterized using Hilbert transformation. ResultsAll patients exhibited statistically significant increase in delta (1–4 Hz) and/or theta (4–8 Hz) oscillations pre-IED compared to baseline [-2000 ms, −1000 ms]. Furthermore, all patients exhibited low-frequency power increase up to IED onset. ConclusionsWe demonstrated consistently occurring, low-frequency oscillations prior to IED onset. SignificanceAs low-frequency activity mediates cortical inhibition, our study demonstrates that a focal inhibition precedes hypersynchronization at IED onset.

Investigation of dynamic functional connectivity of the source reconstructed epileptiform discharges in focal epilepsy: A graph theory approach

ObjectiveThe aim of the present study is to investigate with noninvasive methods the modulation of dynamic functional connectivity during interictal epileptiform discharge (IED). MethodWe reconstructed the cortical source of the EEG recorded IED of 17 patients with focal epilepsy. We then computed dynamic connectivity using the time resolved phase locking value (PLV). We derived graph theory indices (i.e. degree, strength, local efficiency, clustering coefficient and global efficiency). Finally, we selected the atlas node with the maximum activation as the IED cortical source investigating the graph indices dynamics in theta, alpha, beta and gamma frequency bands. ResultsWe observed IED-locked modulations of the graph indexes depending on the frequency bands. We detected a modulation of the strength, clustering coefficient, local and global efficiency both in theta and in alpha bands, which also displayed modulations of the degree index. In the beta band only the global efficiency was modulated by the IED, while no effects were detected in the gamma band. Finally, we found a correlation between alpha and theta local efficiency, as well as alpha global efficiency, and the epilepsy duration. SignificanceOur findings suggest that the neural synchronization is not limited to the IED cortical source, but implies a phase synchronization across multiple brain areas. We hypothesize that the aberrant electrical activity originating from the IED locus is spread amongst the other network nodes throughout the low frequency bands (i.e. theta and alpha). Moreover, IED-dependent increase in the global efficiency indicates that the IED interfere with the whole network functioning. We finally discussed possible application of this methodology for future investigation.

O-EG003. Can spike voltage topography predict the ictal patterns in temporal lobe epilepsy?

Introduction . To study whether voltage topographic analysis of interictal epileptiform discharges (IEDs) in temporal lobe epilepsy (TLE) help to predict the type of scalp ictal onset pattern. Methods . IEDs from 74 patients with drug resistant TLE and unilateral mesial temporal sclerosis (MTS) who underwent presurgical evaluation were categorized according to dipolar fields into Types A and B temporal IEDs. Type A IEDs were defined as those with dipolar fields (tangential/ vertical) with sharply defined negativity over the infero-lateral temporal scalp and positivity over contra lateral centro-parietal scalp. Type B IEDs included all other IEDs other than Type A. Scalp ictal electrographic patterns were classified into rhythms confined to temporal lobe ipsilateral to MTS (“focal”), lateralized to the hemisphere (“regional”), and diffuse, nonlateralized and non-localized rhythms (“diffuse”) and ictal onset to the contra lateral side of MTS (“discordant”). Results . A total of 5476 IEDs and 377 seizures were analyzed. They were divided into four categories based on the side of MTS: 1) Type A IEDs ipsilateral to MTS (44 patients); 2) bilateral Type A IEDs (16 patients); 3) Type B IEDs ipsilateral and Type A IEDs contra laterally (7 patients); 4) Bilateral type B IEDs (7 patients). In 51 of 60 (85%) patients with Type A IEDs (either ipsilateral and/or bilateral to MTS), the ictal pattern was either focal or regional and only in 9 patients (15%) it was “diffuse”.12 of 14 (86%) patients with either ipsilateral or bilateral Type B IEDs showed a diffuse ictal onset, whereas 2 (14%) showed a contralateral ictal onset. Conclusion . In MTS, Type A IEDs are associated with clearly localized ictal onset and Type B IEDs with diffuse ictal onset. A spikevoltage topographic analysis is sufficient for selection of cases for antero-medial temporal lobe resection. Additional home-video recordings of seizures can rule out psychogenic non-epileptiform events.

Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network

BackgroundEpilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions.MethodsIn this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network.ResultsOur model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly.ConclusionIn this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.

Factors correlated with intracranial interictal epileptiform discharges in refractory epilepsy.

This study was undertaken to evaluate the influence that subject-specific factors have on intracranial interictal epileptiform discharge (IED) rates in persons with refractory epilepsy. One hundred fifty subjects with intracranial electrodes performed multiple sessions of a free recall memory task; this standardized task controlled for subject attention levels. We utilized a dominance analysis to rank the importance of subject-specific factors based on their relative influence on IED rates. Linear mixed-effects models were employed to comprehensively examine factors with highly ranked importance. Antiseizure medication (ASM) status, time of testing, and seizure onset zone (SOZ) location were the highest-ranking factors in terms of their impact on IED rates. The average IED rate of electrodes in SOZs was 34% higher than the average IED rate of electrodes outside of SOZs (non-SOZ; p<.001). However, non-SOZ electrodes had similar IED rates regardless of the subject's SOZ location (p=.99). Subjects on older generation (p<.001) and combined generation (p<.001) ASM regimens had significantly lower IED rates relative to the group taking no ASMs; newer generation ASM regimens demonstrated a nonsignificant association with IED rates (p=.13). Of the ASMs included in this study, the following ASMs were associated with significant reductions in IED rates: levetiracetam (p<.001), carbamazepine (p<.001), lacosamide (p=.03), zonisamide (p=.01), lamotrigine (p=.03), phenytoin (p=.03), and topiramate (p=.01). We observed a nonsignificant association between time of testing and IED rates (morning-afternoon p=.15, morning-evening p=.85, afternoon-evening p=.26). The current study ranks the relative influence that subject-specific factors have on IED rates and highlights the importance of considering certain factors, such as SOZ location and ASM status, when analyzing IEDs for clinical or research purposes.

In 2016, Medicaid and Medicare paid about 65% of all inpatient hospitalization costs for all-age persons hospitalized with epilepsy as the principal diagnosis

The purpose of this study was to examine both the distribution of payers for inpatient hospitalizations (all-ages) by principal diagnosis status (epilepsy versus nonepilepsy) and selected organizational- and community-level factors associated with hospitalizations using the Agency for Healthcare Research and Quality's (AHRQ) Healthcare Utilization Project 2016 National Inpatient Sample (NIS) database. We compared cases with epilepsy (any ICD-10CM diagnostic code beginning with “G40”) as a principal diagnosis (“epilepsy discharges”) versus cases without epilepsy as the principal diagnosis (“nonepilepsy discharges”). Accounting for the complex survey design, we examined how the principal payer source, median income for Zip Code, admission type, hospital location, teaching status, and hospital region varied by principal diagnosis status. For persons of all ages with epilepsy as a principal diagnosis, Medicaid and Medicare public insurance paid for about 65% of inpatient hospitalization costs. The percentage paid by Medicaid among epilepsy discharges (31.6%) significantly exceeded that among nonepilepsy discharges (23.1%). The percentage paid by Medicare among epilepsy discharges (33.9%) was significantly less than that among nonepilepsy discharges (39.7%), as was payment by private insurers (26.1% vs. 30.1%). Median Zip Code income, hospital and admission characteristics, and geographic region differed between hospitalizations with epilepsy versus those with a nonepilepsy discharge. These findings may be used to inform stakeholders’ understanding of epilepsy care-related costs and factors associated with hospitalizations for improved interventions and programs.

Repetitive transcranial photobiomodulation but not long-term omega-3 intake reduces epileptiform discharges in rats with stroke-induced epilepsy.

Epilepsy is a yet under-recognized consequence after a stroke and nearly 30% of cases are pharmacoresistant. There is an unmet need for therapeutic interventions during epileptogenesis for better long-term disease outcomes. Transcranial photobiomodulation (PBM) and omega-3 (Ω-3) dietary supplementation are two approaches that have been shown promising neuroprotective effects after brain injuries. Here, we studied the PBM treatment or Ω-3 diet during epileptogenesis in long-term recurrent spontaneous abnormal electrical discharges after stroke. Wistar rats received repetitive 780 nm-laser in the scalp or oral diet with Ω-3 for 2-months after photothrombotic stroke. EEG recordings were performed 60 days after treatment end. PBM but not Ω-3 reduced both electrographic seizure duration and spikes number in the ipsilateral and contralateral cortices and ventral posteromedial thalamic nucleus. Conclusively, PBM reduced epileptiform discharges in stroke-induced epilepsy. Our results suggest the PBM as a therapeutic approach for stroke-induced epileptogenesis to minimize long-term disease outcomes.

Spike-wave discharges in absence epilepsy: segregation of electrographic components reveals distinct pathways of seizure activity.

The major electrophysiological hallmarks of absence seizures are spike and wave discharges (SWDs), consisting of a sharp spike component and a slow wave component. In a widely accepted scheme, these components are functionally coupled and reflect an iterative progression of neuronal excitation during the spike and post-excitatory silence during the wave. In a genetic rat model of absence epilepsy, local pharmacological inhibition of the centromedian thalamus (CM) selectively suppressed the spike component, leaving self-contained waves in epidural recordings. Thalamic inputs induced activity in cortical microcircuits underlying the spike component, while intracortical oscillations generated the wave component. Based on these findings, we propose a model in which oscillatory waves provide adequate time windows for integration of thalamocortical inputs and feedback responses during generation of a synchronized SWD. Spike and wave discharges (SWDs) are the electrographic hallmark of absence seizures and the major diagnostic criterion for childhood absence epilepsy (CAE). In a widely accepted scheme, the alternating sequence of spikes and waves reflects an iterative progression of neuronal excitation during the spike component and post-excitatory silence during the wave component. Here we challenge this view by showing that these two components are not necessarily coupled. In a genetic rat model of CAE, self-contained waves occurred in motor cortex in synchrony with SWDs in the somatosensory system during blockade of afferent input from the thalamus. Current-source density analyses of multi-site local field potentials (LFPs) revealed layer-specific activity, in which thalamic inputs induced a sequence of cellular-synaptic events underlying the spike component, while intracortical oscillations generated the wave component. These findings indicate novel principles of SWDs, where oscillatory cortical waves provide adequate time windows for integration of thalamocortical inputs and feedback responses during generation of seizure activity.

The Trend of In-Hospital Epilepsy and Its Mortality in the USA: A National Analysis During 1997 - 2014

Background: This study focuses on the trend of prevalence of epilepsy hospital discharges in the USA, and the aim is to find any change in the trend of prevalence of epilepsy in the USA and its in-hospital mortality. Methods: A retrospective cohort study used the Healthcare Cost and Utilization Project (HCUP) national database to analyze trends of epilepsy outcomes between 1997 and 2014. Results: A total of 4,594,213 total epilepsy discharges were documented between 1997 and 2014 in the HCUP database. The prevalence of annual discharges increased significantly during the study period from 209,002 discharges in 1997 to 280,255 in 2014 (P < 0.0001). There were a total of 35,643 in-hospital deaths due to epilepsy within the cohort during the study period. Mortality of epilepsy decreased across the entire cohort and between genders. In 1997, there were 2,256 documented in-hospital deaths, while 1,759 were recorded in 2014 (P = 0.00157). Conclusions: Our data showed that the prevalence of hospital epilepsy discharges is increasing in recent years; however, the in-hospital mortality is decreasing. The increase in the prevalence could be due to better detection or increased population, while the improved mortality could be due to better available treatments in recent years. Focusing on designing more accurate and affordable screening tools, as well as targeting further pharmacology-based treatments are an area of research that requires further investigation. J Neurol Res. 2020;10(1):3-6 doi: https://doi.org/10.14740/jnr563

The Trend of In-Hospital Epilepsy and Its Mortality in the USA: A National Analysis During 1997 - 2014

Background: This study focuses on the trend of prevalence of epilepsy hospital discharges in the USA, and the aim is to find any change in the trend of prevalence of epilepsy in the USA and its in-hospital mortality. Methods: A retrospective cohort study used the Healthcare Cost and Utilization Project (HCUP) national database to analyze trends of epilepsy outcomes between 1997 and 2014. Results: A total of 4,594,213 total epilepsy discharges were documented between 1997 and 2014 in the HCUP database. The prevalence of annual discharges increased significantly during the study period from 209,002 discharges in 1997 to 280,255 in 2014 (P &lt; 0.0001). There were a total of 35,643 in-hospital deaths due to epilepsy within the cohort during the study period. Mortality of epilepsy decreased across the entire cohort and between genders. In 1997, there were 2,256 documented in-hospital deaths, while 1,759 were recorded in 2014 (P = 0.00157). Conclusions: Our data showed that the prevalence of hospital epilepsy discharges is increasing in recent years; however, the in-hospital mortality is decreasing. The increase in the prevalence could be due to better detection or increased population, while the improved mortality could be due to better available treatments in recent years. Focusing on designing more accurate and affordable screening tools, as well as targeting further pharmacology-based treatments are an area of research that requires further investigation. J Neurol Res. 2020;10(1):3-6 doi: https://doi.org/10.14740/jnr563

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning

ObjectiveDevelop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings. MethodsAn epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation. ResultsOn the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation. ConclusionsUsing deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes. SignificanceOur algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

Ongoing intracortical neural activity predicts upcoming interictal epileptiform discharges in human epilepsy.

Interictal epileptiform discharges (IEDs) are a hallmark of focal epilepsies. Most previous studies have focused on whether IED events increase seizure likelihood or, on the contrary, act as a protective mechanism. Here, we study instead whether IED events themselves can be predicted based on measured ongoing neural activity. We examined local field potentials (LFPs) and multi-unit activity (MUA) recorded via intracortical 10 × 10 (4 × 4 mm) arrays implanted in two patients with pharmacologically resistant seizures. Seizures in one patient (P1) were characterized by low-voltage fast-activity (LVFA), and IEDs occurred as isolated (100 - 200 ms) spike-wave events. In the other patient (P2), seizures were characterized by complex spike-wave discharges (2 - 3 Hz) and IEDs consisted of bursts of ~ 2 - 3 spike-wave discharges each lasting ~ 300 - 500 ms. We used extreme gradient boosting (XGBoost) classifiers for IED prediction. Inputs to the classifiers consisted of LFP power spectra; In addition, counts of MUA (1-ms and 100-ms time bins) and envelope, as well as leading eigenvalues/eigenvectors of MUA correlation matrices were used as features. Features were computed from moving short-time windows (1 second) immediately preceding IED events (0.3 - 0.5 preictal gap). Classifiers allowed successful IED prediction in both patients, with better results in the case of IED occurring in the LVFA case (area under ROC curve: 0.86). In comparison, LFP features performed comparatively for P1 datasets, while MUA appeared not predictive in the case of P2. Our preliminary results suggest that features of ongoing activity, predictive of upcoming IED events, can be identified based on intracortical recordings, and warrant further investigation in larger datasets. We expect this type of prediction analyses to contribute to a better understanding of the mechanisms underlying the generation of IED events and their contribution to seizure onset.

Investigation of Generalized EEG Paroxysms Accompanying Focal Epilepsies.

Interictal focal EEG features were frequently observed in generalized, epilepsies, but there is limited information about interictal, epileptiform/nonepileptiform generalized paroxysms in focal epilepsies. We aimed to report the frequency and associated factors of generalized EEG discharges in focal epilepsy with unknown cause (FEUC) and mesial, temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS). A total of 200 patients (FEUC in 90 patients; MTLE-HS in 110 patients) were included. Generalized epileptiform (spike/sharp waves simultaneously in all regions) and nonspecific generalized discharges (paroxysmal slow waves) were investigated. All clinical and laboratory findings of 2 groups were compared with each other and with remaining control group, without generalized paroxysms, statistically. Generalized EEG features were present in 22 (11%; 4 males) patients; 9 in the FEUC group (10%; 2) and 13 in the MTLE-HS group (11.8%). Female gender (P < .021), febrile seizure (P < .034), precipitant factors (P < .025), and parental consanguinity (P < .033) were significantly higher in the group with generalized EEG findings. Monotherapy rates were lower in the MTLE-HS group (P < .05). The relationship of generalized EEG features with female gender and parental consanguinity may point out to a genetic property among focal epilepsies, while the relationship with febrile seizures and precipitant factors may be a clue about mechanisms with more extensive involvement of the neuronal networks.

Epilepsy and sleep

This review focuses on the interrelation between clinical and electrophysiological manifestations of various epilepsy forms and level of wakefulness or sleep. In case of some epileptic syndromes seizures occur predominantly or solely in sleep. Epilepsy can modify normal sleep patterns; on the other hand, sleep influences interictal epileptiform discharges, increasing their number and changing their morphology and spreading depending on sleep stage. Although interictal discharges in focal epilepsy are more common in sleep than in wakefulness, discharge distribution in generalized epilepsy is more variable. For some epileptic syndromes there is a strong correlation between time of onset, frequency, severity of attacks and stage of sleep cycle or wakefulness. Further research in this area can shed light not only on mechanisms of epileptic networks functioning, but also on the sleep-wake cycle regulatory systems.

Predictors and outcome surgery for posterior cortex epilepsies

ObjectiveThe aim of this study is to identify the prognostic role of MRI and EEG in posterior cortex epilepsies (PCES) and to characterize their clinical features. Patient and MethodsWe retrospectively studied 54 consecutive patients (18 females, 36 males) from April 2011 to November 2015, who had undergone PCES surgery. Electro-clinical, pathological and surgical data were evaluated. Of the patients, 18 (33.3%) patients underwent a cortical resection (corticectomy), 10 (18.5%) lobectomy, 6 (11.1%) multilobar resection, 20 (30.1%) disconnection. ResultsThe postoperative follow-up duration was ≥1 year in all patients (12–44 months, mean = 25). Thirty-two patients (59.3%) became seizure free (ILAE 1) and 40 (74.1%) had a good (ILAE 1, 2, 3) outcome. The most common pathological finding was focal cortical dysplasia (in 34 patients). Univariate analysis showed that regional scalp interictal epilepsy discharges (IEDs) (P = 0.031), Regional EEG onset (P = 0.027), a MRI lesion (P = 0.001) and the number of seizures that were recorded by the epilepsy monitor unit (P = 0.016) were significantly associated with freedom from seizures. Multivariate analysis confirmed that MRI positive was statistically significant (HR = 4.284, CI = 1.198–15.315). ConclusionsSurgical treatment is effective for PCES, and MRI and EEG analyses are important in presurgical evaluation of PCES.

Targeting of microRNA-21-5p protects against seizure damage in a kainic acid-induced status epilepticus model via PTEN-mTOR

ObjectiveStudies have shown that microRNAs play a role in the development of epilepsy by regulating downstream target messenger (m)RNA. The present study aims to determine the changes associated with microRNA-21-5p (miR-21-5p) during epileptogenesis in a kainic acid rat model, and to assess whether the PTEN-mTOR pathway is a target of miR-21-5p. MethodReverse transcription polymerase chain reaction (RT-PCR) was used to examine the quantitative expressions of miR-21-5p and PTEN, and Western blotting was used to test the activity of mTOR in the acute, latent, and chronic stages of epileptogenesis. The antagomir of miR-21-5p was injected into the intracerebroventricular space using a microsyringe. Neuronal death and epilepsy discharge were assessed by Nissl staining and electroencephalography (EEG), respectively. The Morris water maze (MWM) was used to assess the cognitive impairment in rats after status epilepticus (SE). ResultsBoth miR-21-5p and mTOR were upregulated and PTEN was downregulated in rats during acute, latent, and chronic stages of epileptogenesis when compared with those of the control. After using antagomir miR-21-5p in vivo, miR-21-5p and mTOR decreased and the expression of PTEN increased compared with that in the SE model. The silencing of miR-21-5p diminished the number of abnormal spikes on EEG and decreased the number of neuron deletions on Nissl staining. The cognitive and memory impairment caused by epilepsy could also be improved after miR-21-5p knockdown in vivo. ConclusionThe results of the present study demonstrate that PTEN-mTOR is the target of miR-21-5p in a kainic acid model of epilepsy. The knockout of miR-21-5p decreases the neuronal damage in stages of epileptogenesis. The miR-21-5p/PTEN/mTOR axis may be a potential target for preventing and treating seizures and epileptic damage.