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).

Robust, long-term video EEG monitoring in a porcine model of post-traumatic epilepsy.

To date, post-traumatic epilepsy (PTE) research in large animal models has been limited. Recent advances in neocortical microscopy have made possible new insights into neocortical PTE. However, it is very difficult to engender convincing neocortical PTE in rodents. Thus, large animal models that develop neocortical PTE may provide useful insights that also can be more comparable to human patients. Because gyrencephalic species have prolonged latent periods, long-term video EEG recording is required. Here, we report a fully subcutaneous EEG implant with synchronized video in freely ambulatory swine for up to 13 months during epileptogenesis following bilateral cortical impact injuries or sham surgery The advantages of this system include the availability of a commercially available system that is simple to install, a low failure rate after surgery for EEG implantation, radiotelemetry that enables continuous monitoring of freely ambulating animals, excellent synchronization to video to EEG, and a robust signal to noise ratio. The disadvantages of this system in this species and age are the accretion of skull bone which entirely embedded a subset of skull screws and EEG electrodes, and the inability to rearrange the EEG electrode array. These disadvantages may be overcome by splicing a subdural electrode strip to the electrode leads so that skull growth is less likely to interfere with long-term signal capture and by placing two implants for a more extensive montage. This commercially available system in this bilateral cortical impact swine model may be useful to a wide range of investigators studying epileptogenesis in PTE.SignificancePost-traumatic epilepsy (PTE) is a cause of significant morbidity after traumatic brain injury (TBI) and is often drug-resistant. Robust, informative animal models would greatly facilitate PTE research. Ideally, this biofidelic model of PTE would utilize a species that approximates human brain anatomy, brain size, glial populations, and inflammatory pathways. An ideal model would also incorporate feasible methods for long-term video EEG recording required to quantify seizure activity. Here, we describe the first model of PTE in swine and describe a method for robust long-term video EEG monitoring for up to 13 months post-TBI. The relatively easy "out-of-the-box" radiotelemetry system and surgical techniques described here will be adaptable by a wide array of investigators studying the pathogenesis and treatment of PTE.

Fast parametric curve matching (FPCM) for automatic spike detection.

Objective. Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers an attractive way to localize epileptogenic cortical structures for surgery planning purposes. Interictal spike detection in lengthy multichannel data is a daunting task that is still often performed manually. This frequently limits such an analysis to a small portion of the data which renders the appropriate risks of missing the potentially epileptogenic region. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, none of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM).Approach. Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to verbalized text-book like descriptions of the expected interictal event morphology allow us to accomplish the spike detection task.Main results. The results of simulations mimicking typical low SNR scenarios show the robustness and high receiver operating characteristic AUC values of the FPCM method as compared to the spike detection performed using more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to rat ECoG data, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist.Significance. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of an arbitrary target morphology, it is likely to complement the existing arsenal of means for analysis of noisy interictal datasets.

Implanting intracranial electrodes does not affect spikes or network connectivity in nearby or connected brain regions.

To determine the effect of implanting electrodes on electrographic features of nearby and connected brain regions in patients with drug-resistant epilepsy, we analyzed intracranial EEG recordings from 10 patients with drug-resistant epilepsy who underwent implant revision (placement of additional electrodes) during their hospitalization. We performed automated spike detection and measured EEG functional networks. We analyzed the original electrodes that remained in place throughout the full EEG recording, and we measured the change in spike rates and network connectivity in these original electrodes in response to implanting new electrodes. There was no change in overall spike rate pre- to post-implant revision (t(9) = 0.1, p = 0.95). The peri-revision change in the distribution of spike rate and connectivity across electrodes was no greater than chance (Monte Carlo method, spikes: p = 0.40, connectivity: p = 0.42). Electrodes closer to or more functionally connected to the revision site had no greater change in spike rate or connectivity than more distant or less connected electrodes. Changes in electrographic features surrounding electrode implantation are no greater than baseline fluctuations occurring throughout the intracranial recording. These findings argue against an implant effect on spikes or network connectivity in nearby or connected brain regions.

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy.

In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help discriminate between similar memories by performing pattern separation, but whether epilepsy leads to a breakdown in this neural computation, and thus to mnemonic discrimination impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by behavioral deficits in mnemonic discrimination tasks, in both humans (females and males) and mice (C57Bl6 males, systemic low-dose kainate model). Using a recently developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the dentate gyrus to perform certain forms of pattern separation. This is because of a subset of granule cells with abnormal bursting that can develop independently of early EEG abnormalities. Overall, our results linking physiology, computation, and cognition in the same mice advance our understanding of episodic memory mechanisms and their dysfunction in epilepsy.SIGNIFICANCE STATEMENT People with temporal lobe epilepsy (TLE) often have learning and memory impairments, sometimes occurring earlier than the first seizure, but those symptoms and their biological underpinnings are poorly understood. We focused on the dentate gyrus, a brain region that is critical to avoid confusion between similar memories and is anatomically disorganized in TLE. We show that both humans and mice with TLE experience confusion between similar situations. This impairment coincides with a failure of the dentate gyrus to disambiguate similar input signals because of pathologic bursting in a subset of neurons. Our work bridges seizure-oriented and memory-oriented views of the dentate gyrus function, suggests a mechanism for cognitive symptoms in TLE, and supports a long-standing hypothesis of episodic memory theories.

Interictal spikes with and without high-frequency oscillation have different single-neuron correlates.

Interictal epileptiform discharges (IEDs) are a widely used biomarker in patients with epilepsy but lack specificity. It has been proposed that there are truly epileptogenic and less pathological or even protective IEDs. Recent studies suggest that highly pathological IEDs are characterized by high-frequency oscillations (HFOs). Here, we aimed to dissect these 'HFO-IEDs' at the single-neuron level, hypothesizing that the underlying mechanisms are distinct from 'non-HFO-IEDs'. Analysing hybrid depth electrode recordings from patients with temporal lobe epilepsy, we found that single-unit firing rates were higher in HFO- than in non-HFO-IEDs. HFO-IEDs were characterized by a pronounced pre-peak increase in firing, which coincided with the preferential occurrence of HFOs, whereas in non-HFO-IEDs, there was only a mild pre-peak increase followed by a post-peak suppression. Comparing each unit's firing during HFO-IEDs to its baseline activity, we found many neurons with a significant increase during the HFO component or ascending part, but almost none with a decrease. No such imbalance was observed during non-HFO-IEDs. Finally, comparing each unit's firing directly between HFO- and non-HFO-IEDs, we found that most cells had higher rates during HFO-IEDs and, moreover, identified a distinct subset of neurons with a significant preference for this IED subtype. In summary, our study reveals that HFO- and non-HFO-IEDs have different single-unit correlates. In HFO-IEDs, many neurons are moderately activated, and some participate selectively, suggesting that both types of increased firing contribute to highly pathological IEDs.

Highly consistent temporal lobe interictal spike networks revealed from foramen ovale electrodes.

OBJECTIVE: A major challenge that limits understanding and treatment of epileptic events from mesial temporal structures comes from our inability to detect and map interictal networks reproducibly using scalp electrodes. Here, we developed a novel approach to map interictal spike networks and demonstrate their relationships to seizure onset and lesions in patients with foramen ovale electrode implantations. METHODS: We applied the direct Directed Transfer Function to reveal interictal spike propagation from bilateral foramen ovale electrodes on 10 consecutive patients and co-registered spatially with both seizure onset zones and temporal lobe lesions. RESULTS: Highly reproducible, yet unique interictal spike networks were seen for each patient (correlation: 0.93 ±â€¯0.13). Interictal spikes spread in both anterior and posterior directions within each temporal lobe, often reverberating between sites. Spikes propagated to the opposite temporal lobe predominantly through posterior pathways. Patients with structural lesions (N = 4), including tumors and sclerosis, developed reproducible spike networks adjacent to their lesions that were highly lateralized compared to patients without lesions. Only 5% of mesial temporal lobe spikes were time-locked with scalp electrode spikes. Our preliminary observation on two lesional patients suggested that along with lesion location, Interictal spike networks also partially co-registered with seizure onset zones suggesting interrelationship between seizure onset and a subset of spike networks. CONCLUSIONS: This is the first demonstration of patient-specific, reproducible interictal spike networks in mesial temporal structures that are closely linked to both temporal lobe lesions and seizure onset zones. SIGNIFICANCE: Interictal spike connectivity is a novel approach to map epileptic networks that could help advance invasive and non-invasive epilepsy treatments.

Delta oscillation underlies the interictal spike changes after repeated transcranial direct current stimulation in a rat model of chronic seizures.

BACKGROUND: Transcranial direct current stimulation (tDCS) provides a noninvasive polarity-specific constant current to treat epilepsy, through a mechanism possibly involving excitability modulation and neural oscillation. OBJECTIVE: To determine whether EEG oscillations underlie the interictal spike changes after tDCS in rats with chronic spontaneous seizures. METHODS: Rats with kainic acid-induced spontaneous seizures were subjected to cathodal tDCS or sham stimulation for 5 consecutive days. Video-EEG recordings were collected immediately pre- and post-stimulation and for the subsequent 2 weeks following stimulation. The acute pre-post stimulation and subacute follow-up changes of interictal spikes and EEG oscillations in tDCS-treated rats were compared with sham. Ictal EEG with seizure behaviors, hippocampal brain-derived neurotrophic factor (BDNF) protein expression, and mossy fiber sprouting were compared between tDCS and sham rats. RESULTS: Interictal spike counts were reduced immediately following tDCS with augmented delta and diminished beta and gamma oscillations compared with sham. Cathodal tDCS also enhanced delta oscillations in normal rats. However, increased numbers of interictal spikes with a decrease of delta and theta oscillations were observed in tDCS-treated rats compared with sham during the following 2 weeks after stimulation. Resuming tDCS suppressed the increase of interictal spike activity. In tDCS rats, hippocampal BDNF protein expression was decreased while mossy fiber sprouting did not change compared with sham. CONCLUSIONS: The inverse relationship between the changes of delta oscillation and interictal spikes during tDCS on and off stimulation periods indicates that an enhanced endogenous delta oscillation underlies the tDCS inhibitory effect on epileptic excitability.

Spatiotemporal dynamics between interictal epileptiform discharges and ripples during associative memory processing.

We describe the spatiotemporal course of cortical high-gamma activity, hippocampal ripple activity and interictal epileptiform discharges during an associative memory task in 15 epilepsy patients undergoing invasive EEG. Successful encoding trials manifested significantly greater high-gamma activity in hippocampus and frontal regions. Successful cued recall trials manifested sustained high-gamma activity in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. Interictal epileptiform discharges during encoding were associated with 15% decreased odds of remembering in hippocampus (95% confidence interval 6-23%). Hippocampal interictal epileptiform discharges during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if interictal epileptiform discharges occurred during the 500-2000 ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal interictal epileptiform discharges were followed by a transient decrease in ripple rate. We hypothesize that interictal epileptiform discharges impair associative memory in a regionally and temporally specific manner by decreasing physiological hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological interictal epileptiform discharge events competing with physiological ripples, interictal epileptiform discharges represent a promising therapeutic target for memory remediation in patients with epilepsy.

Interictal spike localization for epilepsy surgery using magnetoencephalography beamforming.

OBJECTIVE: Magnetoencephalography (MEG) kurtosis beamforming is an automated localization method for focal epilepsy. Visual examination of virtual sensors, which are source activities reconstructed by beamforming, can improve performance but can be time-consuming for neurophysiologists. We propose a framework to automate the method and evaluate its effectiveness against surgical resections and outcomes. METHODS: We retrospectively analyzed MEG recordings of 13 epilepsy surgery patients who had one-year minimum post-operative follow-up. Kurtosis beamforming was applied and manual inspection was confined to morphological clusters. The region with the Maximum Interictal Spike Frequency (MISF) was validated against prospectively modelled sLORETA solutions and surgical resections linked to outcome. RESULTS: Our approach localized spikes in 12 out of 13 patients. In eight patients with Engel I surgical outcomes, beamforming MISF regions were concordant with surgical resection at overlap level for five patients and at lobar level for three patients. The MISF regions localized to spike onset and propagation modelled by sLORETA in two and six patients, respectively. CONCLUSIONS: Automated beamforming using MEG can predict postoperative seizure freedom at the lobar level but tends to localize propagated MEG spikes. SIGNIFICANCE: MEG beamforming may contribute to non-invasive procedures to predict surgical outcome for patients with drug-refractory focal epilepsy.

Evolution of interictal spiking during the latent period in a mouse model of mesial temporal lobe epilepsy.

Interictal spikes and high-frequency oscillations (HFOs, ripples: 80-200 â€‹Hz, fast ripples: 250-500 â€‹Hz) occur in epileptic patients and in animal models of mesial temporal lobe epilepsy (MTLE). In this study, we explored how type 1 and type 2 interictal spikes as well as ripples and fast ripples evolve during the latent period in the hippocampus of pilocarpine-treated mice. Depth EEG recordings were obtained from the hippocampus CA3 subfield of adult male mice (n â€‹= â€‹5, P60-P100) starting one day before pilocarpine-induced status epilepticus up to the first spontaneous seizure, the so-called latent period. We found that rates of type 1 (n â€‹= â€‹1 655) and type 2 (n â€‹= â€‹2 309) interictal spikes were significantly lower during the late phase of the latent period compared to its early and mid phase (p â€‹< â€‹0.001). However, rates of type 1 spikes associated with ripples (n â€‹= â€‹266) or fast ripples (n â€‹= â€‹106), as well as rates of type 2 interictal spikes associated with ripples (n â€‹= â€‹233), were significantly higher during the late phase compared to the early and mid phases (p â€‹< â€‹0.05). Our findings reveal that an increase of type 1 interictal spikes co-occurring with ripples or fast ripples and an increase of type 2 interictal spikes co-occurring with ripples mark the end of the latent period. We propose that changes in the occurrence of interictal spike associated with HFOs represent a biomarker of epileptogenicity in this mouse model of MTLE.