Development of spontaneous recurrent seizures accompanied with increased rates of interictal spikes and decreased hippocampal delta and theta activities following extended kindling in mice.

Interictal epileptiform discharges refer to aberrant brain electrographic signals between seizures and feature intermittent interictal spikes (ISs), sharp waves, and/or abnormal rhythms. Recognition of these epileptiform activities by electroencephalographic (EEG) examinations greatly aids epilepsy diagnosis and localization of the seizure onset zone. ISs are a major form of interictal epileptiform discharges recognized in animal models of epilepsy. Progressive changes in IS waveforms, IS rates, and/or associated fast ripple oscillations have been shown to precede the development of spontaneous recurrent seizures (SRS) in various animal models. IS expressions in the kindling model of epilepsy have been demonstrated but IS changes during the course of SRS development in extended kindled animals remain to be detailed. We hence addressed this issue using a mouse model of kindling-induced SRS. Adult C57 black mice received twice daily hippocampal stimulations until SRS occurrence, with 24-h EEG monitoring performed following 50, 80, and ≥ 100 stimulations and after observation of SRS. In the stimulated hippocampus, increases in spontaneous ISs rates, but not in IS waveforms nor IS-associated fast ripples, along with decreased frequencies of hippocampal delta and theta rhythms, were observed before SRS onset. Comparable increases in IS rates were further observed in the unstimulated hippocampus, piriform cortex, and entorhinal cortex, but not in the unstimulated parietal cortex and dorsomedial thalamus. These data provide original evidence suggesting that increases in hippocampal IS rates, together with reductions in hippocampal delta and theta rhythms are closely associated with development of SRS in a rodent kindling model.

Interictal spikes in Alzheimer's disease: Preclinical evidence for dominance of the dentate gyrus and cholinergic control by the medial septum.

Interictal spikes (IIS) are a common type of abnormal electrical activity in Alzheimer's disease (AD) and preclinical models. The brain regions where IIS are largest are not known but are important because such data would suggest sites that contribute to IIS generation. Because hippocampus and cortex exhibit altered excitability in AD models, we asked which areas dominate the activity during IIS along the cortical-CA1-dentate gyrus (DG) dorso-ventral axis. Because medial septal (MS) cholinergic neurons are overactive when IIS typically occur, we also tested the novel hypothesis that silencing the MS cholinergic neurons selectively would reduce IIS. We used mice that simulate aspects of AD: Tg2576 mice, presenilin 2 (PS2) knockout mice and Ts65Dn mice. To selectively silence MS cholinergic neurons, Tg2576 mice were bred with choline-acetyltransferase (ChAT)-Cre mice and offspring were injected in the MS with AAV encoding inhibitory designer receptors exclusively activated by designer drugs (DREADDs). We recorded local field potentials along the cortical-CA1-DG axis using silicon probes during wakefulness, slow-wave sleep (SWS) and rapid eye movement (REM) sleep. We detected IIS in all transgenic or knockout mice but not age-matched controls. IIS were detectable throughout the cortical-CA1-DG axis and occurred primarily during REM sleep. In all 3 mouse lines, IIS amplitudes were significantly greater in the DG granule cell layer vs. CA1 pyramidal layer or overlying cortex. Current source density analysis showed robust and early current sources in the DG, and additional sources in CA1 and the cortex also. Selective chemogenetic silencing of MS cholinergic neurons significantly reduced IIS rate during REM sleep without affecting the overall duration, number of REM bouts, latency to REM sleep, or theta power during REM. Notably, two control interventions showed no effects. Consistent maximal amplitude and strong current sources of IIS in the DG suggest that the DG is remarkably active during IIS. In addition, selectively reducing MS cholinergic tone, at times when MS is hyperactive, could be a new strategy to reduce IIS in AD.

Fast Detection and Quantification of Interictal Spikes and Seizures in a Rodent Model of Epilepsy Using an Automated Algorithm.

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain's abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain's response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate asses sment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.

Passive and active markers of cortical excitability in epilepsy.

Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

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.

Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy.

Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.

High-frequency oscillations (250-500 Hz) in animal models of Alzheimer's disease and two animal models of epilepsy.

OBJECTIVE: To test the hypothesis that high-frequency oscillations (HFOs) between 250 and 500 Hz occur in mouse models of Alzheimer's disease (AD) and thus are not unique to epilepsy. METHODS: Experiments were conducted in three mouse models of AD: Tg2576 mice that simulate a form of familial AD, presenilin 2 knock-out (PS2KO) mice, and the Ts65Dn model of Down's syndrome. We recorded HFOs using wideband (0.1-500 Hz, 2 kHz) intra-hippocampal and cortical surface electroencephalography (EEG) at 1 month until 24 months of age during wakefulness, slow wave sleep (SWS), and rapid eye movement (REM) sleep. In addition, interictal spikes (IISs) and seizures were analyzed for the possible presence of HFOs. Comparisons were made to the intra-hippocampal kainic acid and pilocarpine models of epilepsy. RESULTS: We describe for the first time that hippocampal and cortical HFOs are a new EEG abnormality in AD mouse models. HFOs occurred in all transgenic mice but no controls. They were also detectable as early as 1 month of age and prior to amyloid beta plaque neuropathology. HFOs were most frequent during SWS (vs REM sleep or wakefulness). Notably, HFOs in the AD and epilepsy models were indistinguishable in both spectral frequency and duration. HFOs also occurred during IISs and seizures in the AD models, although with altered spectral properties compared to isolated HFOs. SIGNIFICANCE: Our data demonstrate that HFOs, an epilepsy biomarker with high translational value, are not unique to epilepsy and thus not disease specific. Our findings also strengthen the idea of hyperexcitability in AD and its significant overlap with epilepsy. HFOs in AD mouse models may serve as an EEG biomarker, which is detectable from the scalp and thus amenable to noninvasive detection in people at risk for AD.

Alcohol withdrawal produces changes in excitability, population discharge probability, and seizure threshold.

BACKGROUND: Alcohol withdrawal syndrome (AWS) results from the sudden cessation of chronic alcohol use and is associated with high morbidity and mortality. Alcohol withdrawal-induced central nervous system (CNS) hyperexcitability results from complex, compensatory changes in synaptic efficacy and intrinsic excitability. These changes in excitability counteract the depressing effects of chronic ethanol on neural transmission and underlie symptoms of AWS, which range from mild anxiety to seizures and death. The development of targeted pharmacotherapies for treating AWS has been slow, due in part to the lack of available animal models that capture the key features of human AWS. Using a unique optogenetic method of probing network excitability, we examined electrophysiologic correlates of hyperexcitability sensitive to early changes in CNS excitability. This method is sensitive to pharmacologic treatments that reduce excitability and may represent a platform for AWS drug development. METHODS: We applied a newly developed method, the optogenetic population discharge threshold (oPDT), which uses light intensity response curves to measure network excitability in chronically implanted mice. Excitability was tracked using the oPDT before, during, and after the chronic intermittent exposure (CIE) model of alcohol withdrawal (WD). RESULTS: Alcohol withdrawal produced a dose-dependent leftward shift in the oPDT curve (denoting increased excitability), which was detectable in as few as three exposure cycles. This shift in excitability mirrored an increase in the number of spontaneous interictal spikes during withdrawal. In addition, Withdrawal lowered seizure thresholds and increased seizure severity in optogenetically kindled mice. CONCLUSION: We demonstrate that the oPDT provides a sensitive measure of alcohol withdrawal-induced hyperexcitability. The ability to actively probe the progression of excitability without eliciting potentially confounding seizures promises to be a useful tool in the preclinical development of next-generation pharmacotherapies for AWS.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy.

OBJECTIVE: Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). METHODS: We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. RESULTS: We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. SIGNIFICANCE: The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

GABAA signaling, focal epileptiform synchronization and epileptogenesis.

Under physiological conditions, neuronal network synchronization leads to different oscillatory EEG patterns that are associated with specific behavioral and cognitive functions. Excessive synchronization can, however, lead to focal or generalized epileptiform activities. It is indeed well established that in both epileptic patients and animal models, focal epileptiform EEG patterns are characterized by interictal and ictal (seizure) discharges. Over the last three decades, employing in vitro and in vivo recording techniques, several experimental studies have firmly identified a paradoxical role of GABAA signaling in generating interictal discharges, and in initiating-and perhaps sustaining-focal seizures. Here, we will review these experiments and we will extend our appraisal to evidence suggesting that GABAA signaling may also contribute to epileptogenesis, i.e., the development of plastic changes in brain excitability that leads to the chronic epileptic condition. Overall, we anticipate that this information should provide the rationale for developing new specific pharmacological treatments for patients presenting with focal epileptic disorders such as mesial temporal lobe epilepsy (MTLE).