Ictal State Research Articles

Spreading Depolarization Facilitates the Transition of Neuronal Burst Firing from Interictal to Ictal State

The transition of neuronal burst firing from the interictal to ictal state contributes to seizure initiation in human temporal lobe epilepsy. The low-Mg2+ model of seizure is characterized by initial spontaneous interictal bursting events, which later developed into ictaform discharges. Both experimental and clinical studies point to a complex link between spreading depolarization (SD) and epileptiform field potentials (EFP), including SD-induced epileptic seizures. To investigate the mechanism of SD and EFP interactions, the effect of SD on the transition of interictal to ictal state in low-Mg2+ model of seizure was studied in the rat hippocampus in vitro. After the appearance of interictal activities, SD was elicited by local application of KCl. SD significantly increased the amplitude and duration of action potentials and after-hyperpolarization, and hyperpolarized the membrane potential. Furthermore, SD significantly increased the duration of interictal activities and the threshold potentials of interictal activities. In addition, SD significantly accelerated the transition from interictal to ictal state compared to the control tissues. Ictal activities after induction of SD exhibited a significantly longer duration. This study revealed that SD accelerates interictal-to-ictal transitions and facilitates development of ictaform discharges, possibly via the enhancement of neural synchronization, and points to the potential role of SD in seizure initiation.

Inspection of EEG signals for efficient seizure prediction

Epilepsy seizure prediction has become one of the interesting fields that attract researchers to innovate solutions. For epilepsy patients, Electroencephalography (EEG) signals consist of three activities: normal, pre-ictal and ictal. In order to design a prediction model for the ictal state, it is required to distinguish between the activities of EEG signals. This paper presents efficient seizure prediction approaches from EEG signals based on statistical analysis, digital band-limiting filters and artificial intelligence. Band-limiting filters are used to remove out-of-band noise and spurious effects. Then, statistical analysis is adopted for channel selection and seizure prediction based on a thresholding strategy. This statistical analysis depends on amplitude, median, mean, variance and derivative of the EEG signal. The adopted band-limiting filter affects the seizure prediction metrics such as accuracy, prediction time and false alarm rate. The prediction process consists of two phases: training and testing. Both k-means clustering and Multi-Layer Perceptron (MLP) networks are considered for seizure prediction based on artificial intelligence. The proposed approaches can be implemented in a mobile application to give alerts to patients or care givers. The simulation results reveal that the proposed approaches present high performance in terms of accuracy, prediction time and false alarm rate.

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

Chance and risk in epilepsy.

Epilepsy is a dynamical disorder of the brain characterized by sudden, seemingly unpredictable transitions to the ictal state. When and how these transitions occur remain unresolved questions in neurology. Modelling work based on dynamical systems theory proposed that a slow control parameter is necessary to explain the transition between interictal and ictal states. Recently, converging evidence from chronic EEG datasets unravelled the existence of cycles of epileptic brain activity at multiple timescales - circadian, multidien (over multiple days) and circannual - which could reflect cyclical changes in a slow control parameter. This temporal structure of epilepsy has theoretical implications and argues against the conception of seizures as completely random events. The practical significance of cycles in epilepsy is highlighted by their predictive value in computational models for seizure forecasting. The canonical randomness of seizures is being reconsidered in light of cycles of brain activity discovered through chronic EEG. This paradigm shift motivates development of next-generation devices to track more closely fluctuations in epileptic brain activity that determine time-varying seizure risk.

Paroxysmal Discharges in Tissue Slices From Pediatric Epilepsy Surgery Patients: Critical Role of GABAB Receptors in the Generation of Ictal Activity.

In the present study, we characterized the effects of bath application of the proconvulsant drug 4-aminopyridine (4-AP) alone or in combination with GABAA and/or GABAB receptor antagonists, in cortical dysplasia (CD type I and CD type IIa/b), tuberous sclerosis complex (TSC), and non-CD cortical tissue samples from pediatric epilepsy surgery patients. Whole-cell patch clamp recordings in current and voltage clamp modes were obtained from cortical pyramidal neurons (CPNs), interneurons, and balloon/giant cells. In pyramidal neurons, bath application of 4-AP produced an increase in spontaneous synaptic activity as well as rhythmic membrane oscillations. In current clamp mode, these oscillations were generally depolarizing or biphasic and were accompanied by increased membrane conductance. In interneurons, membrane oscillations were consistently depolarizing and accompanied by bursts of action potentials. In a subset of balloon/giant cells from CD type IIb and TSC cases, respectively, 4-AP induced very low-amplitude, slow membrane oscillations that echoed the rhythmic oscillations from pyramidal neurons and interneurons. Bicuculline reduced the amplitude of membrane oscillations induced by 4-AP, indicating that they were mediated principally by GABAA receptors. 4-AP alone or in combination with bicuculline increased cortical excitability but did not induce seizure-like discharges. Ictal activity was observed in pyramidal neurons and interneurons from CD and TSC cases only when phaclofen, a GABAB receptor antagonist, was added to the 4-AP and bicuculline solution. These results emphasize the critical and permissive role of GABAB receptors in the transition to an ictal state in pediatric CD tissue and highlight the importance of these receptors as a potential therapeutic target in pediatric epilepsy.

Interneuron Desynchronization Precedes Seizures in a Mouse Model of Dravet Syndrome.

Recurrent seizures, which define epilepsy, are transient abnormalities in the electrical activity of the brain. The mechanistic basis of seizure initiation, and the contribution of defined neuronal subtypes to seizure pathophysiology, remains poorly understood. We performed in vivo two-photon calcium imaging in neocortex during temperature-induced seizures in male and female Dravet syndrome (Scn1a+/-) mice, a neurodevelopmental disorder with prominent temperature-sensitive epilepsy. Mean activity of both putative principal cells and parvalbumin-positive interneurons (PV-INs) was higher in Scn1a+/- relative to wild-type controls during quiet wakefulness at baseline and at elevated core body temperature. However, wild-type PV-INs showed a progressive synchronization in response to temperature elevation that was absent in PV-INs from Scn1a+/- mice. Hence, PV-IN activity remains intact interictally in Scn1a+/- mice, yet exhibits decreased synchrony immediately before seizure onset. We suggest that impaired PV-IN synchronization may contribute to the transition to the ictal state during temperature-induced seizures in Dravet syndrome.SIGNIFICANCE STATEMENT Epilepsy is a common neurological disorder defined by recurrent, unprovoked seizures. However, basic mechanisms of seizure initiation and propagation remain poorly understood. We performed in vivo two-photon calcium imaging in an experimental model of Dravet syndrome (Scn1a+/- mice)-a severe neurodevelopmental disorder defined by temperature-sensitive, treatment-resistant epilepsy-and record activity of putative excitatory neurons and parvalbumin-positive GABAergic neocortical interneurons (PV-INs) during naturalistic seizures induced by increased core body temperature. PV-IN activity was higher in Scn1a+/- relative to wild-type controls during quiet wakefulness. However, wild-type PV-INs showed progressive synchronization in response to temperature elevation that was absent in PV-INs from Scn1a+/- mice before seizure onset. Hence, impaired PV-IN synchronization may contribute to transition to seizure in Dravet syndrome.

Plasma Glucose Levels Increase During Spontaneous Attacks of Migraine With and Without Aura.

To investigate plasma glucose changes during the ictal state of migraine compared to the interictal state. Previous studies suggest abnormal glucose metabolism in migraine patients during and outside of attacks. It is not known if plasma glucose levels change during spontaneous migraine attacks. Plasma glucose levels were measured during and outside of spontaneous migraine attacks with and without aura. Plasma glucose values were corrected for diurnal variation of plasma glucose by subtracting the difference between the moving average (intervals of 2 hours) and overall mean from the plasma glucose values. This was a sub-study of a larger study conducted at Rigshospitalet Glostrup in the Capital Region of Denmark. Thirty-one patients (24 F, 7 M, 13 with aura, 18 without aura) were included in the study. Mean time from attack onset to blood sampling was 7.6hours. Mean pain at the time of investigation was 6 on a 0-10 verbal rating scale. Plasma glucose was higher ictally compared to the interictal phase (interictal mean: 88.63mg/dL, SD 11.70mg/dL; ictal mean: 98.83mg/dL, SD 13.16mg/dL, difference 10.20mg/dL, 95% CI=[4.30; 16.10]), P=.0014). The ictal increase was highest in patients investigated early during attacks and decreased linearly with time from onset of migraine (-1.57mg/dL/hour from onset of attack, P=.020). The attack-related increase in blood glucose was not affected by pain intensity or presence of aura symptoms. We demonstrated higher plasma glucose values during spontaneous migraine attacks, independent of the presence of aura symptoms and not related to pain intensity, peaking in the early phase of attacks. Additional studies are necessary to confirm our findings and explore the possible underlying mechanisms.

Epileptic Seizures: Glia-Neuron Interactions For Better or For Worse.

Investigations of the mechanisms generating epileptic seizures have primarily focused on neurons. However, more systemic research of brain circuits has highlighted an important role of non-neuronal cells such as glia in the genesis and spreading of generalized seizures in the brain.

Epileptic seizure detection and prediction using stacked bidirectional long short term memory

Epilepsy is a not a disease but a neurological disorder. But people with epilepsy cannot lead a social life like others. Proper diagnosis and advance prediction of epileptic seizures definitely improves the life of epilepsy patients. In this paper an effort is made to develop a seizure detection and prediction method using stacked bidirectional long short term memory technique. This is the most suitable technique for the analysis of time series datasets as it overcomes the vanishing gradient problem identified in recurrent neural network. The dataset for detection and prediction experiments was taken from Bonn University. Our model could perform the seizure detection with the highest accuracy of 99.08% with 98% precision, 99.5% recall and ROC AUC: 0.984346. A binary classification method with AUC more than 0.9 is considered to be outstanding. Seizure prediction was conducted using the same dataset by classifying preictal states of EEG from interictal and ictal states. For the case of prediction our model could identify preictal states with the overall sensitivity: 89.21% and false prediction rate: 0.06. In future the model could be used to program the wearable devices like wrist watch which can be used by epileptic patients for seizure prediction. The device could be programmed to fire alarm during the detection of the preictal EEG signal before the onset of the seizure.

Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network.

Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial pre-ictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients' data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.

Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity.

The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.

Predictive analytics & modeling for modern health care system for cerebral palsy patients

The time-shifting elements and high between individual changeability make early forecast of a seizure express a difficult errand. Numerous examinations have demonstrated that EEG signals do have profitable data that if effectively broke down, could help in the expectation of seizures in epileptic patients before their event. A few numerical changes have been broke down for its connection with seizure beginning forecast and a progression of analyses were done to ensure their qualities. New calculations are exhibited to help elucidate, screen, and cross-approve the order of EEG signs to foresee the ictal (for example seizure) states, explicitly the preictal, interictal, and postictal states in the mind. These new strategies show promising outcomes in distinguishing the nearness of a preictal stage before the ictal state. Artificial Intelligence and Machine Learning are playing major role in Diagnosis and Predicting the diseases, EEG signal were recorded with 16 channel system from brain scalp and stored in system. From recorded database seizure levels were analyzed from each channel by articrafting with ICA and PCA algorithms and processed through Band pass filter to identify the Delta, Theta, Alpha and Beta levels. After collecting the brain signals from each channel they were bided with FFT to get the data in time domain series. A machine learning model is developed using python programming by sampling on obtained EEG data, sampled data is trained on to the system. Machine Learning algorithms were applied to the data to accuracy and predict the accuracy on developed model neural network is applied to identify the True-False values. The analyzed data is integrated with IOT framework to Diagnosis the Cerebral Palsy patient remotely. Remote monitoring system for the persons with Intellectual disabilities and update the health status to care takers time to time while they are at work. It’s also reduces the patient work load by checking his details staying at home rather going to a hospital. If system identifies any changes in patient heart rate, brain signals, body temperature, the system alerts the doctor and respective relatives about patient’s status over IOT and also stores the patient details in the cloud.

A network approach to investigate the bi-hemispheric synchrony in absence epilepsy

ObjectiveOur objective was to unravel the dynamics underlying spike-and-wave discharges (SWDs) characteristic for childhood absence epilepsy. MethodsSWDs were recorded for a cohort of 28 children using magnetoencephalography. Non-linear association analyses and a graph theoretical metric of local connectedness (LoC) were utilized in a sliding window starting one s before till four s after ictal onset. ResultsA focal pattern of bilateral frontal and parietal areas with high LoC during the spikes alternated by generalized patterns during the waves was found for all children studied during generalization of the SWDs. In the interval preceding the generalization a focal parietal region was most often (16/28) encountered and less often an occipital (4/28), temporal (5/28) or frontal (3/28) region. 55% of the children with a parietal/occipital focal onset became seizure free after the administration of two anti-epileptic drugs, and only 12.5% with a temporal/frontal focal onset. ConclusionsThe transition from the interictal to the ictal state is for some of the children characterized by dominant LoC at either the parietal/occipital and for others at the frontal/temporal region. SignificanceThe focal onset of the SWDs varies in location among the children with a clinical similar profile, who, however, seemingly are differing with regard to seizure control.

Ictal High-Frequency Oscillation for Lateralizing Patients With Suspected Bitemporal Epilepsy Using Wavelet Transform and Granger Causality Analysis.

Identifying lateralization of bilateral temporal lobe epilepsy (TLE) is a challenging issue; scalp electroencephalography (EEG) and routine band electrocorticography (ECoG) fail to reveal the epileptogenic focus for further temporal lobectomy treatment. High-frequency oscillations (HFOs) can be utilized as a biomarker for lateralizing the onset zone in suspected bitemporal epilepsy. Except subjective vision detect the HFOs, objective verification should be performed to raise the accuracy. In the present research, we prospectively studied 10 patients with refractory temporal seizures and who underwent ECoG with wide-band frequency amplifiers (2,048 Hz); all patients had a class I outcome after temporal resection. Pre- and ictal HFOs will be analyzed by wavelet transform (WT) and Granger causality (GC) to objectively verify lateralization of the seizure onset zone (SOZ). WT analysis showed ictal HFOs in 10 patients mainly covered from 80 to 115 Hz (average, 92.59 ± 10.23 Hz), and there was distinct bandpass boundary between pre-ictal HFOs and ictal HFOs. GC analysis showed five patients (2, 4, 5, 6, and 7), no matter the pre-ictal or ictal state, had the highest GC degree in SOZ itself. The remaining patients (1, 3, 8, 9, and 10) had the highest GC degree in SOZ with its adjacent regions in the pre-ictal and ictal stages. GC analysis further confirmed the result of the WT and suggested HFOs are initiated and propagated in the local brain region mainly, afterward, transmitting to adjacent brain regions. These results indicated that the combination of WT and GC analyses significantly contributes to accurate lateralization in patients with suspected bitemporal epilepsy.

Hyperperfusion can identify epileptic stroke mimics

CT perfusion is useful in acute ischaemic stroke to image cerebral tissue at risk (penumbra).1 However, perfusion changes are also common in people with ongoing epileptic seizures or postictal deficits...

Endogenous Na+, K+-ATPase inhibitors and CSF [Na+] contribute to migraine formation.

There is strong evidence that neuronal hyper-excitability underlies migraine, and may or may not be preceded by cortical spreading depression. However, the mechanisms for cortical spreading depression and/or migraine are not established. Previous studies reported that cerebrospinal fluid (CSF) [Na+] is higher during migraine, and that higher extracellular [Na+] leads to hyper-excitability. We raise the hypothesis that altered choroid plexus Na+, K+-ATPase activity can cause both migraine phenomena: inhibition raises CSF [K+] and initiates cortical spreading depression, while activation raises CSF [Na+] and causes migraine. In this study, we examined levels of specific Na+, K+-ATPase inhibitors, endogenous ouabain-like compounds (EOLC), in CSF from migraineurs and controls. CSF EOLC levels were significantly lower during ictal migraine (0.4 nM +/- 0.09) than from either controls (1.8 nM +/- 0.4) or interictal migraineurs (3.1 nM +/- 1.9). Blood plasma EOLC levels were higher in migraineurs than controls, but did not differ between ictal and interictal states. In a Sprague-Dawley rat model of nitroglycerin-triggered central sensitization, we changed the concentrations of EOLC and CSF sodium, and measured aversive mechanical threshold (von Frey hairs), trigeminal nucleus caudalis activation (cFos), and CSF [Na+] (ultra-high field 23Na MRI). Animals were sensitized by three independent treatments: intraperitoneal nitroglycerin, immunodepleting EOLC from cerebral ventricles, or cerebroventricular infusion of higher CSF [Na+]. Conversely, nitroglycerin-triggered sensitization was prevented by either vascular or cerebroventricular delivery of the specific Na+, K+-ATPase inhibitor, ouabain. These results affirm our hypothesis that higher CSF [Na+] is linked to human migraine and to a rodent migraine model, and demonstrate that EOLC regulates them both. Our data suggest that altered choroid plexus Na+, K+-ATPase activity is a common source of these changes, and may be the initiating mechanism in migraine.

Early Detection of Human Epileptic Seizures Based on Intracortical Microelectrode Array Signals.

We examine, for the first time, the use of intracortical microelectrode array (MEA) signals for early detection of human epileptic seizures. 4×4 mm2 96-channel-MEA recordings were obtained during neuro-monitoring preceding resective surgery in five participants. The participant-specific seizure-detection framework consisted of: first, feature extraction from local field potentials (LFPs) and multiunit activity (MUA); second, nonlinear cost-sensitive support vector machine (SVM) classification of ictal and interictal states based on LFP, MUA, and combined LFP-MUA (a SVM was trained for each participant separately); and third, Kalman filter postprocessing of SVM scoring functions. Performance was assessed on data including 17 seizures and 39.0h interictal and preictal recordings. The use of combined LFP-MUA features resulted in 100% sensitivity with short detection latency (average: 2.7 s; median: 2.5 s) and five false alarms (0.13/h). The average detection performance based on the area under the receiver operating characteristic corresponded to 0.97. Importantly, technically false alarms were related to epileptiform activity, subclinical seizures, and recording artifacts. Extreme gradient boosting classifiers ranked features based on LFP spectral coherence or MUA count among the top features for seizures characterized by spike-wave complexes, whereas features related to LFP power spectra were ranked higher for seizures characterized by sustained gamma LFP oscillations. The combination of intracortical LFP and MUA signals may allow reliable detection of human epileptic seizures by improving latency and false alarm rate. Intracortical MEAs provide promising signals for closed-loop seizure-control systems based on seizure early-detection in people with pharmacologically resistant epilepsies.

The role that choice of model plays in predictions for epilepsy surgery

Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.

Dynamic origin of spike and wave discharges in the brain

Spike and wave discharges are the main electrographic characteristic of a number of epileptic brain disorders including childhood absence epilepsy and photosensitive epilepsy. The basic dynamic mechanism that underlies the occurrence of these abnormal electrical patterns in the brain is not well understood. The current paper aims to provide a dynamic explanation for features and generation mechanism of spike and wave discharges in the brain. The main proposition of this study is that epileptic seizures could be interpreted as a resonance phenomenon rather than a limit cycle behavior. To shows this, a revised version of Jansen-Rit neural mass model is employed. The system can switch between monostable and bistable regimes, which are considered in this paper as wake and sleep states of the brain, respectively. In particular, it is shown that, in monostable region, the model can depict the alpha rhythm and alpha rhythm suppression due to mental activity. Frequency responses of the model near the bistable regime demonstrate that high amplitude harmonic excitation may lead to spike and wave like oscillations. Based on the computational results and the concept of stochastic resonance, a model for absence epilepsy is presented which can simulate spontaneous transitions between ictal and interictal states. Finally, it is shown that spike and wave discharges during epileptic seizures can be explained as a resonance phenomenon in a nonlinear system.

A Rare Case of Prolonged Ictal Aphasia

ABSTRACTAphasia is commonly seen in focal brain lesions. Prolonged aphasia from an ictal state is rarely reported. We report the case of a 62-year-old man with focal motor status epilepticus manifested initially as episodic right cheiro-oral clonic movements with preserved awareness and expressive aphasia for 48 hours. EEG showed left frontal and central lateralized periodic discharges (LPDs) without plus features (rhythmicity, overlying fast) and electrographic seizures that correlated with right clonic movements. Treatment with two seizure medications (levetiracetam and lacosamide) resulted in complete electrographic and clinical resolution of his symptoms, including aphasia. In this case, aphasia was determined to be an ictal semiology, as patient had complete resolution of his symptoms supported by a normal EEG after receiving seizure medications. We suggest keeping high suspicion for an ictal process in patients with sudden-onset aphasia supported by EEG findings of LPDs.