Network-motif delay differential analysis of brain activity during seizures.

Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.

The role of subicular VIP-expressing interneurons on seizure dynamics in the intrahippocampal kainic acid model of temporal lobe epilepsy.

The subiculum, a key output region of the hippocampus, is increasingly recognized as playing a crucial role in seizure initiation and spread. The subiculum consists of glutamatergic pyramidal cells, which show alterations in intrinsic excitability in the course of epilepsy, and multiple types of GABAergic interneurons, which exhibit varying characteristics in epilepsy. In this study, we aimed to assess the role of the vasoactive intestinal peptide interneurons (VIP-INs) of the ventral subiculum in the pathophysiology of temporal lobe epilepsy. We observed that an anatomically restricted inhibition of VIP-INs of the ventral subiculum was sufficient to reduce seizures in the intrahippocampal kainic acid model of epilepsy, changing the circadian rhythm of seizures, emphasizing the critical role of this small cell population in modulating TLE. As we expected, permanent unilateral or bilateral silencing of VIP-INs of the ventral subiculum in non-epileptic animals did not induce seizures or epileptiform activity. Interestingly, transient activation of VIP-INs of the ventral subiculum was enough to increase the frequency of seizures in the acute seizure model. Our results offer new perspectives on the crucial involvement of VIP-INs of the ventral subiculum in the pathophysiology of TLE. Given the observed predominant disinhibitory role of the VIP-INs input in subicular microcircuits, modifications of this input could be considered in the development of therapeutic strategies to improve seizure control.

Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes.

Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.

The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex.

Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs' high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.

Evolution of interictal activity in models of mesial temporal lobe epilepsy.

Interictal activity and seizures are the hallmarks of focal epileptic disorders (which include mesial temporal lobe epilepsy, MTLE) in humans and in animal models. Interictal activity, which is recorded with cortical and intracerebral EEG recordings, comprises spikes, sharp waves and high-frequency oscillations, and has been used in clinical practice to identify the epileptic zone. However, its relation with seizures remains debated. Moreover, it is unclear whether specific EEG changes in interictal activity occur during the time preceding the appearance of spontaneous seizures. This period, which is termed "latent", has been studied in rodent models of MTLE in which spontaneous seizures start to occur following an initial insult (most often a status epilepticus induced by convulsive drugs such as kainic acid or pilocarpine) and may mirror epileptogenesis, i.e., the process leading the brain to develop an enduring predisposition to seizure generation. Here, we will address this topic by reviewing experimental studies performed in MTLE models. Specifically, we will review data highlighting the dynamic changes in interictal spiking activity and high-frequency oscillations occurring during the latent period, and how optogenetic stimulation of specific cell populations can modulate them in the pilocarpine model. These findings indicate that interictal activity: (i) is heterogeneous in its EEG patterns and thus, presumably, in its underlying neuronal mechanisms; and (ii) can pinpoint to the epileptogenic processes occurring in focal epileptic disorders in animal models and, perhaps, in epileptic patients.

Classification of Seizure Termination Patterns using Deep Learning on intracranial EEG.

Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient interventional approaches, i.e., it may be critical for the design of treatments that induce or reproduce termination mechanisms that are triggered in self-terminating seizures. In this work, we aim to study temporal and spectral features of intracranial EEG (iEEG) during epileptic seizures to find time-frequency signatures that can predict the termination patterns. We propose a deep learning model for classification of multi channel iEEG epileptic seizure termination pattern into burst suppression and continuous bursting. We decompose the raw time series seizure data into time-frequency maps using Morlet Wavelet Transform. A Convolution Neural Network (CNN) is then trained on cross-patient time-frequency maps to classify the seizure termination patterns. For evaluation of classification performance, we compared the proposed method with k-Nearest Neighbour (k-NN). The CNN is shown to achieve an accuracy of 90 % and precision of 92 % as compared to 70% and 72% accuracy and precision achieved with the k-NN respectively. The proposed model is thus able to capture the temporal and spatial patterns which results in high performance of the classifier. This method of classification can be used to predict how a particular seizure will end and can potentially inform seizure management and treatment. Clinical relevance- This method establishes a model that can be used to classify seizure termination patterns with an accuracy of 90 % which can assist in better treatment of epilepsy patients.

Quantifying seizure termination patterns reveals limited pathways to seizure end.

OBJECTIVE: Despite their possible importance in the design of novel neuromodulatory approaches and in understanding status epilepticus, the dynamics and mechanisms of seizure termination are not well studied. We examined intracranial recordings from patients with epilepsy to differentiate seizure termination patterns and investigated whether these patterns are indicative of different underlying mechanisms. METHODS: Seizures were classified into one of two termination patterns: (a) those that end simultaneously across the brain (synchronous), and (b) those whose termination is piecemeal across the cortex (asynchronous). Both types ended with either a burst suppression pattern, or continuous seizure activity. These patterns were quantified and compared using burst suppression ratio, absolute energy, and network connectivity. RESULTS: Seizures with electrographic generalization showed burst suppression patterns in 90% of cases, compared with only 60% of seizures which remained focal. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous seizure activity were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous seizure activity compared to seizures with burst suppression. SIGNIFICANCE: Based on this spatiotemporal classification scheme, we suggest that there are a limited number of seizure termination patterns and dynamics. If this bears out, it would imply that the number of mechanisms underlying seizure termination is also constrained. Seizures with different termination patterns exhibit different dynamics even before their start. This may provide useful clues about how seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.

Microscale dynamics of electrophysiological markers of epilepsy.

OBJECTIVE: Interictal discharges (IIDs) and high frequency oscillations (HFOs) are established neurophysiologic biomarkers of epilepsy, while microseizures are less well studied. We used custom poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) microelectrodes to better understand these markers' microscale spatial dynamics. METHODS: Electrodes with spatial resolution down to 50 µm were used to record intraoperatively in 30 subjects. IIDs' degree of spread and spatiotemporal paths were generated by peak-tracking followed by clustering. Repeating HFO patterns were delineated by clustering similar time windows. Multi-unit activity (MUA) was analyzed in relation to IID and HFO timing. RESULTS: We detected IIDs encompassing the entire array in 93% of subjects, while localized IIDs, observed across < 50% of channels, were seen in 53%. IIDs traveled along specific paths. HFOs appeared in small, repeated spatiotemporal patterns. Finally, we identified microseizure events that spanned 50-100 µm. HFOs covaried with MUA, but not with IIDs. CONCLUSIONS: Overall, these data suggest that irritable cortex micro-domains may form part of an underlying pathologic architecture which could contribute to the seizure network. SIGNIFICANCE: These results, supporting the possibility that epileptogenic cortex comprises a mosaic of irritable domains, suggests that microscale approaches might be an important perspective in devising novel seizure control therapies.

Distinguishing false and true positive detections of high frequency oscillations.

OBJECTIVE: High frequency oscillations (HFOs) are a promising biomarker of tissue that instigates seizures. However, ambiguous data and random background fluctuations can cause any HFO detector (human or automated) to falsely label non-HFO data as an HFO (a false positive detection). The objective of this paper was to identify quantitative features of HFOs that distinguish between true and false positive detections. APPROACH: Feature selection was performed using background data in multi-day, interictal intracranial recordings from ten patients. We selected the feature most similar between randomly selected segments of background data and HFOs detected in surrogate background data (false positive detections by construction). We then compared these results with fuzzy clustering of detected HFOs in clinical data to verify the feature's applicability. We validated the feature is sensitive to false versus true positive HFO detections by using an independent data set (six subjects) scored for HFOs by three human reviewers. Lastly, we compared the effect of redacting putative false positive HFO detections on the distribution of HFOs across channels and their association with seizure onset zone (SOZ) and resected volume (RV). MAIN RESULTS: Of the 15 analyzed features, the analysis selected only skewness of the curvature (skewCurve). The feature was validated in human scored data to be associated with distinguishing true and false positive HFO detections. Automated HFO detections with higher skewCurve were more focal based on entropy measures and had increased localization to both the SOZ and RV. SIGNIFICANCE: We identified a quantitative feature of HFOs which helps distinguish between true and false positive detections. Redacting putative false positive HFO detections improves the specificity of HFOs as a biomarker of epileptic tissue.

Seizure onset location shapes dynamics of initiation.

OBJECTIVE: Ictal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types. METHODS: We analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset. RESULTS: Seizures of multiple electrographic patterns were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. CONCLUSIONS: Our findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. SIGNIFICANCE: To learn how seizures are initiated, researchers would do well to consider other aspects of their manifestation, in addition to their electrographic patterns. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

Interictal oscillations and focal epileptic disorders.

Neuronal network oscillations represent a main feature of the brain activity recorded in the EEG under normal and pathological conditions such as epilepsy. Specific oscillations occur between seizures in patients and in animal models of focal epilepsy, and thus, they are termed interictal. According to their shape and intrinsic signal frequency, interictal oscillations are classified as spikes and high-frequency oscillations (HFOs). Interictal spikes are recorded in the 'wideband' EEG signal and consist of large-amplitude events that usually last less than 1 s; HFOs, in contrast, are extracted by amplifying the appropriately filtered EEG signal and are usually categorized as ripples (80-200 Hz) and fast ripples (250-500 Hz). Interictal spikes and HFOs are used in clinical practice to localize the seizure onset zone in focal epileptic disorders, which is fundamental for performing successful surgical interventions in epileptic patients not responding to anti-epileptic drug therapy. Both types of interictal oscillations have been widely studied in animal models of focal epilepsy to identify the mechanisms underlying their generation as well as to establish their role in ictogenesis and epileptogenesis. In this review, we will address the cellular mechanisms underlying the generation of interictal spikes and HFOs in animal models of epileptiform synchronization and of focal epilepsy. Moreover, we will highlight in vitro and in vivo evidence indicating that these interictal oscillations mirror specific, dynamic changes in neuronal network excitability causing seizure generation (i.e. ictogenesis) and leading to a chronic epileptic condition (i.e. epileptogenesis).

High frequency oscillations can pinpoint seizures progressing to status epilepticus.

Status epilepticus (SE) is defined as a seizure lasting more than 5min or a period of recurrent seizures without recovery between them. SE is a serious emergency condition that requires immediate intervention; therefore, identifying SE electrophysiological markers may translate in prompt care to stop it. Here, we analyzed the EEG signals recorded from the CA3 region of the hippocampus and the entorhinal cortex in rats that responded to systemic administration of 4-aminopyridine (4AP) by generating either isolated seizures or seizures progressing to SE. We found that high frequency oscillations (HFOs) - which can be categorized as ripples (80-200Hz) and fast ripples (250-500Hz) - had different patterns of occurrence in the two groups (n=5 for each group). Specifically, fast ripples in CA3 and entorhinal cortex of the SE group occurred at higher rates than ripples, both during the ictal and post-ictal periods when compared to the HFOs recorded from the isolated seizure group. Our data reveal that different patterns of HFO occurrence can pinpoint seizures progressing to SE, thus suggesting the involvement of different neuronal networks at the termination of seizure discharges.

Distinct EEG seizure patterns reflect different seizure generation mechanisms.

Low-voltage fast (LVF)- and hypersynchronous (HYP)-seizure onset patterns can be recognized in the EEG of epileptic animals and patients with temporal lobe epilepsy. Ripples (80-200 Hz) and fast ripples (250-500 Hz) have been linked to each pattern, with ripples predominating during LVF seizures and fast ripples predominating during HYP seizures in the rat pilocarpine model. This evidence led us to hypothesize that these two seizure-onset patterns reflect the contribution of neural networks with distinct transmitter signaling characteristics. Here, we tested this hypothesis by analyzing the seizure activity induced with the K(+) channel blocker 4-aminopyridine (4AP, 4-5 mg/kg ip), which enhances both glutamatergic and GABAergic transmission, or the GABAA receptor antagonist picrotoxin (3-5 mg/kg ip); rats were implanted with electrodes in the hippocampus, the entorhinal cortex, and the subiculum. We found that LVF onset occurred in 82% of 4AP-induced seizures whereas seizures after picrotoxin were always HYP. In addition, high-frequency oscillation analysis revealed that 4AP-induced LVF seizures were associated with higher ripple rates compared with fast ripples (P < 0.05), whereas picrotoxin-induced seizures contained higher rates of fast ripples compared with ripples (P < 0.05). These results support the hypothesis that two distinct patterns of seizure onset result from different pathophysiological mechanisms.

Limbic networks and epileptiform synchronization: the view from the experimental side.

In this review, we summarize findings obtained in acute and chronic epilepsy models and in particular experiments that have revealed how neuronal networks in the limbic system-which is closely involved in the pathophysiogenesis of mesial temporal lobe epilepsy (MTLE)-produce hypersynchronous discharges. MTLE is often associated with a typical pattern of brain damage known as mesial temporal sclerosis, and it is one of the most refractory forms of partial epilepsy in adults. Specifically, we will address the cellular and pharmacological features of abnormal electrographic events that, as in MTLE patients, can occur in in vivo and in vitro animal models; these include interictal and ictal discharges along with high-frequency oscillations. In addition, we will consider how different limbic structures made hyperexcitable by acute pharmacological manipulations interact during epileptiform discharge generation. We will also review the electrographic characteristics of two types of seizure onsets that are most commonly seen in human and experimental MTLE as well as in in vitro models of epileptiform synchronization. Finally, we will address the role played by neurosteroids in reducing epileptiform synchronization and in modulating epileptogenesis.

Dynamics of interictal spikes and high-frequency oscillations during epileptogenesis in temporal lobe epilepsy.

Mesial temporal lobe epilepsy (MTLE) is characterized in humans and in animal models by a seizure-free latent phase that follows an initial brain insult; this period is presumably associated to plastic changes in temporal lobe excitability and connectivity. Here, we analyzed the occurrence of interictal spikes and high frequency oscillations (HFOs; ripples: 80-200Hz and fast ripples: 250-500Hz) from 48h before to 96h after the first seizure in the rat pilocarpine model of MTLE. Interictal spikes recorded with depth EEG electrodes from the hippocampus CA3 area and entorhinal cortex (EC) were classified as type 1 (characterized by a spike followed by a wave) or type 2 (characterized by a spike with no wave). We found that: (i) there was a switch in the distribution of both types of interictal spikes before and after the occurrence of the first seizure; during the latent phase both types of interictal spikes predominated in the EC whereas during the chronic phase both types of spikes predominated in CA3; (ii) type 2 spike duration decreased in both regions from the latent to the chronic phase; (iii) type 2 spikes associated to fast ripples occurred at higher rates in EC compared to CA3 during the latent phase while they occurred at similar rates in both regions in the chronic phase; and (iv) rates of fast ripples outside of spikes were higher in EC compared to CA3 during the latent phase. Our findings demonstrate that the transition from the latent to the chronic phase is paralleled by dynamic changes in interictal spike and HFO expression in EC and CA3. We propose that these changes may represent biomarkers of epileptogenicity in MTLE.

Temporal lobe epileptiform activity following systemic administration of 4-aminopyridine in rats.

PURPOSE: The K(+) channel blocker 4-aminopyridine (4AP) induces epileptiform synchronization in brain slices maintained in vitro without interfering with γ-aminobutyric acid (GABA)A receptor-mediated inhibition and, actually, even enhancing it. The hypothesis that similar electrographic epileptiform patterns occur in vivo following systemic 4AP injection was tested here. METHODS: Sprague-Dawley rats (n = 13) were implanted with bipolar electrodes aimed at the hippocampal CA3 region, entorhinal cortex, subiculum, dentate gyrus, and amygdala. They were then injected with a single dose of 4AP (4-5 mg/kg, i.p.), and video-monitoring/electroencephalography (EEG) recordings were performed. KEY FINDINGS: 4AP induced convulsive or nonconvulsive seizures in 12 of 13 rats, along with generalized fascicular twitching, wet-dog shakes, and myoclonic jerks. On EEG, we observed in 7 (58.3%) of 12 animals long-lasting interictal spikes from the subiculum before the occurrence of the first seizure. Once seizures had started, interictal spikes occurred in all areas with no fixed site of origin. Most seizures (41/60, 68.3%) were characterized by a low-voltage fast-activity onset pattern and were convulsive (48/60, 80%). 4AP also induced highly rhythmic theta (6-11 Hz) oscillations in CA3 and entorhinal cortex before seizure occurrence. SIGNIFICANCE: Our study shows that systemic 4AP administration in vivo can enhance theta oscillations and induce slow interictal spikes and low-voltage fast-onset seizures similar to those reported in brain slices. We propose that these effects may reflect, at least in part, enhanced GABAA receptor-mediated inhibition as reported in in vitro studies.

Two seizure-onset types reveal specific patterns of high-frequency oscillations in a model of temporal lobe epilepsy.

High-frequency oscillations (HFOs; 80-500 Hz) are thought to mirror the pathophysiological changes occurring in epileptic brains. However, the distribution of HFOs during seizures remains undefined. Here, we recorded from the hippocampal CA3 subfield, subiculum, entorhinal cortex, and dentate gyrus to quantify the occurrence of ripples (80-200 Hz) and fast ripples (250-500 Hz) during low-voltage fast-onset (LVF) and hypersynchronous-onset (HYP) seizures in the rat pilocarpine model of temporal lobe epilepsy. We discovered in LVF seizures that (1) progression from preictal to ictal activity was characterized in seizure-onset zones by an increase of ripple rates that were higher when compared with fast ripple rates and (2) ripple rates during the ictal period were higher compared with fast ripple rates in seizure-onset zones and later in regions of secondary spread. In contrast, we found in HYP seizures that (1) fast ripple rates increased during the preictal period and were higher compared with ripple rates in both seizure-onset zones and in regions of secondary spread and (2) they were still higher compared with ripple rates in both seizure-onset zones and regions of secondary spread during the ictal period. Our findings demonstrate that ripples and fast ripples show distinct time- and region-specific patterns during LVF and HYP seizures, thus suggesting that they play specific roles in ictogenesis.

A comparison between automated detection methods of high-frequency oscillations (80-500 Hz) during seizures.

High-frequency oscillations (HFOs, ripples: 80-200 Hz, fast ripples: 250-500 Hz) recorded from the epileptic brain are thought to reflect abnormal network-driven activity. They are also better markers of seizure onset zones compared to interictal spikes. There is thus an increasing number of studies analysing HFOs in vitro, in vivo and in the EEG of human patients with refractory epilepsy. However, most of these studies have focused on HFOs during interictal events or at seizure onset, and few have analysed HFOs during seizures. In this study, we are comparing three different automated methods of HFO detection to two methods of visual analysis, during the pre-ictal, ictal and post-ictal periods on multiple channels using the rat pilocarpine model of temporal lobe epilepsy. The first method (method 1) detected HFOs using the average of the normalised period, the second (method 2) detected HFOs using the average of the normalised period in 1s windows and the third (method 3) detected HFOs using the average of a reference period before seizure onset. Overall, methods 2 and 3 showed higher sensitivity compared to method 1. When dividing the analysed traces in pre-, ictal and post-ictal periods, method 3 showed the highest sensitivity during the ictal period compared to method 1, while method 2 was not significantly different from method 1. These findings suggest that method 3 could be used for automated and reliable detection of HFOs on large data sets containing multiple channels during the ictal period.

On the ictogenic properties of the piriform cortex in vitro.

PURPOSE: The piriform cortex (PC) is known to be epileptic-prone and it may be involved in the manifestation of limbic seizures. Herein, we have characterized some electrophysiologic and pharmacologic properties of the spontaneous epileptiform activity generated by PC networks maintained in vitro. METHODS: We performed field potential recordings from the PC in coronal or sagittal rat brain slices along with pharmacologic manipulations of γ-aminobutyric acid (GABA)ergic and glutamatergic signaling during application of the convulsant drug 4-aminopyridine (4AP, 50 µm). KEY FINDINGS: Coronal and sagittal preparations generated interictal-like and ictal-like epileptiform discharges with similar duration and frequency. Ictal-like discharges in sagittal slices were initiated mostly in the PC anterior subregion, whereas interictal activity did not have any preferential site of origin. In sagittal slices, high frequency oscillations (HFOs) at 80-200 Hz were detected mainly at the beginning of the ictal discharge in both posterior and anterior subregions. N-Methyl-d-aspartate (NMDA) receptor antagonism abolished ictal discharges, but failed to influence interictal activity. In the absence of ionotropic glutamatergic transmission, PC networks generated slow, GABA receptor-dependent events. Finally, GABA(A) receptor antagonism during application of 4AP only, abolished ictal discharges and disclosed recurrent interictal activity. SIGNIFICANCE: Our findings demonstrate that PC networks can sustain in vitro epileptiform activity induced by 4AP. HFOs, which emerge at the onset of ictal activity, may be involved in PC ictogenesis. As reported in several cortical structures, ionotropic glutamatergic neurotransmission is necessary but not sufficient for ictal discharge generation, a process that also requires operative GABA(A) receptor-mediated signaling.