Seizure frequency in intractable partial epilepsy: a statistical analysis

State Space Modeling of Event Count Time Series

This paper proposes a class of algorithms for analyzing event count time series, based on state space modeling and Kalman filtering. While the dynamics of the state space model is kept Gaussian and linear, a nonlinear observation function is chosen. In order to estimate the states, an iterated extended Kalman filter is employed. Positive definiteness of covariance matrices is preserved by a square-root filtering approach, based on singular value decomposition. Non-negativity of the count data is ensured, either by an exponential observation function, or by a newly introduced "affinely distorted hyperbolic" observation function. The resulting algorithm is applied to time series of the daily number of seizures of drug-resistant epilepsy patients. This number may depend on dosages of simultaneously administered anti-epileptic drugs, their superposition effects, delay effects, and unknown factors, making the objective analysis of seizure counts time series arduous. For the purpose of validation, a simulation study is performed. The results of the time series analysis by state space modeling, using the dosages of the anti-epileptic drugs as external control inputs, provide a decision on the effect of the drugs in a particular patient, with respect to reducing or increasing the number of seizures.

Flexible realistic simulation of seizure occurrence recapitulating statistical properties of seizure diaries

OBJECTIVE

A realistic seizure diary simulator is currently unavailable for many research needs, including clinical trial analysis and evaluation of seizure detection and seizure-forecasting tools. In recent years, important statistical features of seizure diaries have been characterized. These include (1) heterogeneity of individual seizure frequencies, (2) the relation between average seizure rate and standard deviation, (3) multiple risk cycles, (4) seizure clusters, and (5) limitations on inter-seizure intervals. The present study unifies these features into a single model.

METHODS

Our approach, Cyclic Heterogeneous Overdispersed Clustered Open-source L-relationship Adjustable Temporally limited E-diary Simulator (CHOCOLATES) is based on a hierarchical model centered on a gamma Poisson generator with several modifiers. This model accounts for the aforementioned statistical properties. The model was validated by simulating 10 000 randomized clinical trials (RCTs) of medication to compare with 23 historical RCTs. Metrics of 50% responder rate (RR50) and median percent change (MPC) were evaluated. We also used CHOCOLATES as input to a seizure-forecasting tool to test the flexibility of the model. We examined the area under the receiver-operating characteristic (ROC) curve (AUC) for test data with and without cycles and clusters.

RESULTS

The model recapitulated typical findings in 23 historical RCTs without the necessity of introducing an additional "placebo effect." The model produced the following RR50 values: placebo: 17 ± 4%; drug 38 ± 5%; and the following MPC values: placebo: 13 ± 6%; drug 40 ± 4%. These values are similar to historical data: for RR50: placebo, 21 ± 10%, drug: 43 ± 13%; and for MPC: placebo: 17 ± 10%, drug: 41 ± 11%. The seizure forecasts achieved an AUC of 0.68 with cycles and clusters, whereas without them the AUC was 0.51.

SIGNIFICANCE

CHOCOLATES represents the most realistic seizure occurrence simulator to date, based on observations from thousands of patients in different contexts. This tool is open source and flexible, and can be used for many applications, including clinical trial simulation and testing of seizure-forecasting tools.

Hippocampal area CA2 controls seizure dynamics, interictal EEG abnormalities and social comorbidity in mouse models of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is characterized by spontaneous recurrent seizures, abnormal activity between seizures, and impaired behavior. CA2 pyramidal neurons (PNs) are potentially important because inhibiting them with a chemogenetic approach reduces seizure frequency in a mouse model of TLE. However, whether seizures could be stopped by timing inhibition just as a seizure begins is unclear. Furthermore, whether inhibition would reduce the cortical and motor manifestations of seizures are not clear. Finally, whether interictal EEG abnormalities and TLE comorbidities would be improved are unknown. Therefore, real-time optogenetic silencing of CA2 PNs during seizures, interictal activity and behavior were studied in 2 mouse models of TLE. CA2 silencing significantly reduced seizure duration and time spent in convulsive behavior. Interictal spikes and high frequency oscillations were significantly reduced, and social behavior was improved. Therefore, brief focal silencing of CA2 PNs reduces seizures, their propagation, and convulsive manifestations, improves interictal EEG, and ameliorates social comorbidities.

HIGHLIGHTS

Real-time CA2 silencing at the onset of seizures reduces seizure durationWhen CA2 silencing reduces seizure activity in hippocampus it also reduces cortical seizure activity and convulsive manifestations of seizuresInterictal spikes and high frequency oscillations are reduced by real-time CA2 silencingReal-time CA2 silencing of high frequency oscillations (>250Hz) rescues social memory deficits of chronic epileptic mice.

Robust chronic convulsive seizures, high frequency oscillations, and human seizure onset patterns in an intrahippocampal kainic acid model in mice

Intrahippocampal kainic acid (IHKA) has been widely implemented to simulate temporal lobe epilepsy (TLE), but evidence of robust seizures is usually limited. To resolve this problem, we slightly modified previous methods and show robust seizures are common and frequent in both male and female mice. We employed continuous wideband video-EEG monitoring from 4 recording sites to best demonstrate the seizures. We found many more convulsive seizures than most studies have reported. Mortality was low. Analysis of convulsive seizures at 2-4 and 10-12 wks post-IHKA showed a robust frequency (2-4 per day on average) and duration (typically 20-30 s) at each time. Comparison of the two timepoints showed that seizure burden became more severe in approximately 50% of the animals. We show that almost all convulsive seizures could be characterized as either low-voltage fast or hypersynchronous onset seizures, which has not been reported in a mouse model of epilepsy and is important because these seizure types are found in humans. In addition, we report that high frequency oscillations (>250 Hz) occur, resembling findings from IHKA in rats and TLE patients. Pathology in the hippocampus at the site of IHKA injection was similar to mesial temporal lobe sclerosis and reduced contralaterally. In summary, our methods produce a model of TLE in mice with robust convulsive seizures, and there is variable progression. HFOs are robust also, and seizures have onset patterns and pathology like human TLE. SIGNIFICANCE: Although the IHKA model has been widely used in mice for epilepsy research, there is variation in outcomes, with many studies showing few robust seizures long-term, especially convulsive seizures. We present an implementation of the IHKA model with frequent convulsive seizures that are robust, meaning they are >10 s and associated with complex high frequency rhythmic activity recorded from 2 hippocampal and 2 cortical sites. Seizure onset patterns usually matched the low-voltage fast and hypersynchronous seizures in TLE. Importantly, there is low mortality, and both sexes can be used. We believe our results will advance the ability to use the IHKA model of TLE in mice. The results also have important implications for our understanding of HFOs, progression, and other topics of broad interest to the epilepsy research community. Finally, the results have implications for preclinical drug screening because seizure frequency increased in approximately half of the mice after a 6 wk interval, suggesting that the typical 2 wk period for monitoring seizure frequency is insufficient.

The Occurrence of Seizure Clusters in Patients With Epilepsy Is Partly Determined by Epilepsy Severity: A Single-Center Retrospective Observational Study

Purpose: This retrospective observational study aimed to investigate the self-reported prevalence of seizure clusters (SCs) in patients with epilepsy (PWE) and its relationship with clinical characteristics.

Methods: We retrospectively analyzed data from consecutive PWE from our hospital in northeastern China. Data were collected from the databank of a tertiary epilepsy center. Logistic regression models were employed to investigate the relationships between the individual patient demographic/clinical variables and the occurrence of SC.

Results: In total, 606 consecutive PWE were included in the final analysis, and 268 (44.2%) patients experienced at least one seizure cluster. In multivariate logistic regression models, age (OR: 1.014; 95% CI: 1.002–1.027; p = 0.02), seizure frequency (OR: 2.08; 95% CI: 1.555–2.783; p < 0.001), multiple seizure types (OR: 5.111; 95% CI: 1.737–15.043; p = 0.003), number of current anti-seizure medications (ASM) (OR: 1.533; 95% CI: 1.15–2.042; p = 0.004), drug-resistant epilepsy (OR: 1.987; 95% CI: 1.159–3.407; p = 0.013), and a history of status epilepticus (OR: 1.903; 95% CI: 1.24–2.922; p = 0.003) were independent variables associated with a history of SC in PWE.

Conclusion: Seizure clusters (SCs) are common occurrences at our study center. The occurrence of SC in individuals with epilepsy, to some extent, is determined by the epilepsy severity.

Good Days and Bad Days: Measuring Health-Related Quality of Life in People With Epilepsy

OBJECTIVES

Cost-effectiveness analyses typically require measurement of health-related quality of life (HRQoL) to estimate quality-adjusted life-years. Challenges with measuring HRQoL arise in the context of episodic conditions if patients are less likely-or even unable-to complete surveys when having disease symptoms. This article explored whether HRQoL measured at regular time intervals adequately reflects the HRQoL of people with epilepsy (PWE).

METHODS

Follow-up data from the Epilepsy Support Dog Evaluation study on the (cost-)effectiveness of seizure dogs were used in which HRQoL is measured in 25 PWE with the EQ-5D at baseline and every 3 months thereafter. Seizure count is recorded daily using a seizure diary. Regression models were employed to explore whether PWE were more likely to complete the HRQoL survey on a good day (ie, when seizures are absent or low in frequency compared with other days) and to provide an estimate of the impact of reporting HRQoL on a good day on EQ-5D utility scores.

RESULTS

A total of 111 HRQoL measurements were included in the analyses. Regression analyses indicated that the day of reporting HRQoL was associated with a lower seizure count (P<.05) and that a lower seizure count was associated with a higher EQ-5D utility score (P<.05).

CONCLUSIONS

When HRQoL is measured at regular time intervals, PWE seem more likely to complete these surveys on good days. Consequently, HRQoL might be overestimated in this population. This could lead to underestimation of the effectiveness of treatment and to biased estimates of cost-effectiveness.

Long-term seizure dynamics are determined by the nature of seizures and the mutual interactions between them

The seemingly random and unpredictable nature of seizures is a major debilitating factor for people with epilepsy. An increasing body of evidence demonstrates that the epileptic brain exhibits long-term fluctuations in seizure susceptibility, and seizure emergence seems to be a consequence of processes operating over multiple temporal scales. A deeper insight into the mechanisms responsible for long-term seizure fluctuations may provide important information for understanding the complex nature of seizure genesis. In this study, we explored the long-term dynamics of seizures in the tetanus toxin model of temporal lobe epilepsy. The results demonstrate the existence of long-term fluctuations in seizure probability, where seizures form clusters in time and are then followed by seizure-free periods. Within each cluster, seizure distribution is non-Poissonian, as demonstrated by the progressively increasing inter-seizure interval (ISI), which marks the approaching cluster termination. The lengthening of ISIs is paralleled by: increasing behavioral seizure severity, the occurrence of convulsive seizures, recruitment of extra-hippocampal structures and the spread of electrographic epileptiform activity outside of the limbic system. The results suggest that repeated non-convulsive seizures obey the 'seizures-beget-seizures' principle, leading to the occurrence of convulsive seizures, which decrease the probability of a subsequent seizure and, thus, increase the following ISI. The cumulative effect of repeated convulsive seizures leads to cluster termination, followed by a long inter-cluster period. We propose that seizures themselves are an endogenous factor that contributes to long-term fluctuations in seizure susceptibility and their mutual interaction determines the future evolution of disease activity.

Natural variability in seizure frequency: Implications for trials and placebo

BACKGROUND

Changes in patient-reported seizure frequencies are the gold standard used to test efficacy of new treatments in randomized controlled trials (RCTs). Recent analyses of patient seizure diary data suggest that the placebo response may be attributable to natural fluctuations in seizure frequency, though the evidence is incomplete. Here we develop a data-driven statistical model and assess the impact of the model on interpretation of placebo response.

METHODS

A synthetic seizure diary generator matching statistical properties seen across multiple epilepsy diary datasets was constructed. The model was used to simulate the placebo arm of 5000 RCTs. A meta-analysis of 23 historical RCTs was compared to the simulations.

RESULTS

The placebo 50 %-responder rate (RR50) was 27.3 ± 3.6 % (simulated) and 21.1 ± 10.0 % (historical). The placebo median percent change (MPC) was 22.0 ± 6.0 % (simulated) and 16.7 ± 10.3 % (historical).

CONCLUSIONS

A statistical model of daily seizure count generation which incorporates quantities related to the natural fluctuations of seizure count data produces a placebo response comparable to those seen in historical RCTs. This model may be useful in better understanding the seizure count fluctuations seen in patients in other clinical settings.

Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy

OBJECTIVE

The purpose of this prospective observational study was to describe the prevalence and adverse outcomes associated with seizure clusters (defined as ≥2 seizures in a 6-hour period) in a large sample of adult patients with a range of epilepsy severities and to identify clinical characteristics predictive of clustering.

METHODS

Patients maintained a seizure diary and were contacted monthly to verify compliance and data accuracy. Logistic regression models were utilized to test associations between individual patient demographic/clinical characteristics and seizure clustering. Fisher's exact test was utilized to test associations between rescue medication use and adverse seizure-related outcomes.

RESULTS

A total of 300 patients were followed prospectively for one year; 247 patients qualified for final analysis. Six-hour seizure clusters occurred in 45.8% of patients with active epilepsy at enrollment, including 62.7% of those with prior day-clusters and 30.0% of those without prior day-clusters. The odds of clustering were markedly greater among patients who reported a higher seizure frequency (>4 seizures per year vs. 1-4 seizures per year) (adjusted odds ratio (OR): 8.9; 95% confidence interval (CI): 3.2-24.6; p < 0.0001) and among patients with prior day-clusters (adjusted OR: 11.0; 95% CI: 1.2-104.2; p = 0.036). Rescue medication use was associated with significantly fewer injuries and emergency department visits, but rescue medication was underutilized.

CONCLUSIONS

Seizure clusters are common, occurring in nearly half of adult patients with active epilepsy followed prospectively over one year, and are more frequent in those with higher seizure frequencies and prior day-clusters. Although underutilized, rescue medication was associated with fewer injuries and emergency department visit.

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

Objective

To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.

Methods

Zero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively.

Results

A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p < 0.004).

Significance

This study explored a large cohort of patients with self‐reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability

Objective

A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.

Methods

Using data from SeizureTracker.com, a patient‐reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed‐effects hidden Markov model for zero‐inflated count data was developed to estimate changes in underlying seizure risk using patient‐reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com.

Results

EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was <12 months, providing a data‐driven estimate of the amount of time a person with TSC would be expected to remain at the same seizure risk level according to the natural course of epilepsy.

Significance

We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient‐reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (AED) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.

Identifying seizure clusters in patients with psychogenic nonepileptic seizures

The present study explored how seizure clusters may be defined for those with psychogenic nonepileptic seizures (PNES), a topic for which there is a paucity of literature. The sample was drawn from a multisite randomized clinical trial for PNES; seizure data are from participants' seizure diaries. Three possible cluster definitions were examined: 1) common clinical definition, where ≥3 seizures in a day is considered a cluster, along with two novel statistical definitions, where ≥3 seizures in a day are considered a cluster if the observed number of seizures statistically exceeds what would be expected relative to a patient's: 1) average seizure rate prior to the trial, 2) observed seizure rate for the previous seven days. Prevalence of clusters was 62-68% depending on cluster definition used, and occurrence rate of clusters was 6-19% depending on cluster definition. Based on these data, clusters seem to be common in patients with PNES, and more research is needed to identify if clusters are related to triggers and outcomes.

A big data approach to the development of mixed-effects models for seizure count data

OBJECTIVE

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.

METHODS

Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies.

RESULTS

For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day.

SIGNIFICANCE

The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

Natural course of treated epilepsy and medico-social outcomes. Turku studies. Part II

Introduction. Population-based data on the prognosis of childhood-onset epilepsy were almost nonexistent in the 1960s. This prompted me to start an epidemiological prospective study on children with epilepsy.

Aim. To study the medical and social outcome of children with epilepsy.

Methods. The most important personal data on the natural course and outcome were reviewed and compared with the relevant data of other investigators.

Results and discussion. The natural course of treated epilepsy is remitting, uninterrupted by relapse (in 48%); a remitting-relapsing course (interrupted by relapses, in terminal remission) (19%); worsening course (early or late remission followed by drug-resistant epilepsy) (14%); and never in ≥5-year remission (drug resistance) (19%) The medical and social outcomes based on my unique, five decades followed cohort show that most subjects are in 10-year remission without medications, which is the definition of resolved epilepsy. Normal or subnormal IQ, non-symptomatic etiology, and low seizure frequency both in the first year of AED treatment and prior to medication appear to be clinical predictors of cure in childhood-onset epilepsy. Subjects with 1-year remission during the first five years form onset of treatment have more than 10-fold chance for entering 5-year terminal remission vs those who have no 1-year remission during the first five years. Even about one fourth of difficult-to-treat subjects become seizure free on medication and more than half of them enter one or more 5-year remissions. Epilepsy has a substantial impact on quality of life even in those who are seizure free off medication for many years and particularly those not in remission or in remission but still on medication.

Conclusions. The prognosis is excellent for medical and social outcome. The successful outcome is confirmed by several longitudinal studies from recent decades. Good response to early drug therapy does not necessarily guarantee a favorable seizure outcome, and even a late good response may still predict a successful prognosis. Our life-cycle study is being continued and targets to answer the question whether or not childhood-onset epilepsy is a risk factor for premature and/or increased incidence of mental impairment and dementia.

Perspectives on seizure clusters: Gaps in lexicon, awareness, and treatment

Seizure clusters in epilepsy can result in serious outcomes such as missed work or school, postictal psychosis, emergency room visits, or hospitalizations, and yet they are often not included in discussions between health-care professionals (HCPs) and their patients. The purpose of this paper was to describe and compare consumer (patient and caregivers) and professional understanding of seizure clusters and to describe how consumers and HCPs communicate regarding seizure clusters. We reviewed social media discussion sites to explore consumers' understanding of seizure clusters. We analyzed professional (medical) literature to explore the HCPs' understanding of seizure clusters. Major themes were revealed in one or both groups, including: communication about diagnosis; frequency, duration, and time frame; seizure type and pattern; severity; and self-management. When comparing discussions of professionals and consumers, both consumers and clinicians discussed the definition of seizure clusters. Discussions of HCPs were understandably clinically focused, and consumer discussions reflected the experience of seizure clusters; however, both groups struggled with a common lexicon. Seizure cluster events remain a problem associated with serious outcomes. Herein, we outline the lack of a common understanding and recommend the development of a common lexicon to improve communication regarding seizure clusters.

Human focal seizures are characterized by populations of fixed duration and interval

OBJECTIVE

We report on a quantitative analysis of data from a study that acquired continuous long-term ambulatory human electroencephalography (EEG) data over extended periods. The objectives were to examine the seizure duration and interseizure interval (ISI), their relationship to each other, and the effect of these features on the clinical manifestation of events.

METHODS

Chronic ambulatory intracranial EEG data acquired for the purpose of seizure prediction were analyzed and annotated. A detection algorithm identified potential seizure activity, which was manually confirmed. Events were classified as clinically corroborated, electroencephalographically identical but not clinically corroborated, or subclinical. K-means cluster analysis supplemented by finite mixture modeling was used to locate groupings of seizure duration and ISI.

RESULTS

Quantitative analyses confirmed well-resolved groups of seizure duration and ISIs, which were either mono-modal or multimodal, and highly subject specific. Subjects with a single population of seizures were linked to improved seizure prediction outcomes. There was a complex relationship between clinically manifest seizures, seizure duration, and interval.

SIGNIFICANCE

These data represent the first opportunity to reliably investigate the statistics of seizure occurrence in a realistic, long-term setting. The presence of distinct duration groups implies that the evolution of seizures follows a predetermined course. Patterns of seizure activity showed considerable variation between individuals, but were highly predictable within individuals. This finding indicates seizure dynamics are characterized by subject-specific time scales; therefore, temporal distributions of seizures should also be interpreted on an individual level. Identification of duration and interval subgroups may provide a new avenue for improving seizure prediction.

Computational modeling of neurostimulation in brain diseases

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.

Use of an online epilepsy diary to characterize repetitive seizures

SIGNIFICANCE

Little is known about patterns of seizures that occur multiple times a day, sometimes called clusters or serial seizures.

OBJECTIVE

The online diary, My Epilepsy Diary (MED), provided self-reported data from community-based patients to describe the characteristics of clusters.

METHODS

We used MED data to define a population of 5098 community outpatients, including 1177 who specified time of multiple seizures in a 24-hour period. Outcomes included cluster prevalence and frequency, distribution of interseizure time intervals, as well as the types of triggers commonly reported.

RESULTS

One-fourth of days with any seizures included clusters for these patients. Most days with clusters included 2 seizures, with >5 events occurring in only 10% of days. One-third of seizures occurred within 3h of the initial event and two-thirds within 6h. When more than 2 seizures occurred, the time to the next seizure decreased from an average of over 2h (to the 3rd event) to a quarter-hour (from the 4th to the 5th event).

CONCLUSION

My Epilepsy Diary data have provided the first overview of cluster seizures in a large community-based population. Treatments with less than 3-hour duration of action would be bioavailable at the time of only one-third of subsequent seizures. Although limited by the self-reported and observational nature of the diary data, some general patterns emerge and can help to focus questions for future studies.

The dynamics of the epileptic brain reveal long-memory processes

The pattern of epileptic seizures is often considered unpredictable and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and man-made systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events. We postulated that human inter-seizure intervals would follow a power-law relationship, and furthermore that evidence for the existence of a long-memory process could be established in this relationship. We performed a post hoc analysis, studying eight patients who had long-term (up to 2 years) ambulatory intracranial EEG data recorded as part of the assessment of a novel seizure prediction device. We demonstrated that a power-law relationship could be established in these patients (β = - 1.5). In five out of the six subjects whose data were sufficiently stationary for analysis, we found evidence of long memory between epileptic events. This memory spans time scales from 30 min to 40 days. The estimated Hurst exponents range from 0.51 to 0.77 ± 0.01. This finding may provide evidence of phase-transitions underlying the dynamics of epilepsy.

Methylphenidate treatment of attention deficit hyperactivity disorder in young people with learning disability and difficult-to-treat epilepsy: evidence of clinical benefit

Purpose

To establish the efficacy and safety of methylphenidate (MPH) treatment for attention deficit hyperactivity disorder (ADHD) in a group of children and young people with learning disability and severe epilepsy.

Methods

This retrospective study systematically reviewed the case notes of all patients treated with methylphenidate (MPH) for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) ADHD at a specialist epilepsy center between 1998 and 2005. Treatment efficacy was ascertained using clinical global impressions (CGI) scores, and safety was indexed by instances of >25% increase in monthly seizure count within 3 months of starting MPH.

Key Findings

Eighteen (18) patients were identified with refractory epilepsies (14 generalized, 4 focal), IQ <70, and ADHD. Male patients predominated (13:5) and ADHD was diagnosed at a median age of 11.5 years (range 6–18 years). With use of a combination of a behavioral management program and MPH 0.3–1 mg/kg/day, ADHD symptoms improved in 61% of patients (11/18; type A intraclass correlation coefficient of CGI 0.85, 95% confidence interval [CI] 0.69–0.94). Daily MPH dose, epilepsy variables, and psychiatric comorbidity did not relate to treatment response across the sample. MPH adverse effects led to treatment cessation in three patients (dysphoria in two, anxiety in one). There was no statistical evidence for a deterioration of seizure control in this group with the use of MPH.

Significance

Methylphenidate with behavioral management was associated with benefit in the management of ADHD in more than half of a group of children with severe epilepsy and additional cognitive impairments. Eighteen percent had significant side effects but no attributable increase in seizures. Methylphenidate is useful in this group and is likely to be under employed.

The effect of STAT3 inhibition on status epilepticus and subsequent spontaneous seizures in the pilocarpine model of acquired epilepsy

Pilocarpine-induced status epilepticus (SE), which results in temporal lobe epilepsy (TLE) in rodents, activates the JAK/STAT pathway. In the current study, we evaluate whether brief exposure to a selective inhibitor of the JAK/STAT pathway (WP1066) early after the onset of SE affects the severity of SE or reduces later spontaneous seizure frequency via inhibition of STAT3-regulated gene transcription. Rats that received systemic WP1066 or vehicle at the onset of SE were continuously video-EEG monitored during SE and for one month to assess seizure frequency over time. Protein and/or mRNA levels for pSTAT3, and STAT3-regulated genes including: ICER, Gabra1, c-myc, mcl-1, cyclin D1, and bcl-xl were evaluated in WP1066 and vehicle-treated rats during stages of epileptogenesis to determine the acute effects of WP1066 administration on SE and chronic epilepsy. WP1066 (two 50mg/kg doses) administered within the first hour after onset of SE results in transient inhibition of pSTAT3 and long-term reduction in spontaneous seizure frequency. WP1066 alters the severity of chronic epilepsy without affecting SE or cell death. Early WP1066 administration reduces known downstream targets of STAT3 transcription 24h after SE including cyclin D1 and mcl-1 levels, known for their roles in cell-cycle progression and cell survival, respectively. These findings uncover a potential effect of the JAK/STAT pathway after brain injury that is physiologically important and may provide a new therapeutic target that can be harnessed for the prevention of epilepsy development and/or progression.

The intrahippocampal kainate model of temporal lobe epilepsy revisited: epileptogenesis, behavioral and cognitive alterations, pharmacological response, and hippoccampal damage in epileptic rats

Systemic or intracerebral (e.g., intrahippocampal or intraamygdalar) administration of kainate, a potent neurotoxic analog of glutamate, is widely used to induce status epilepticus (SE) and subsequent development of epilepsy in rats. However, in apparent contrast to systemic administration, following intracerebral injection the proportion of rats that have been observed to generate spontaneous recurrent seizures (SRS) and the frequency of the SRS are comparatively low. More recently, it has been shown that these problems can be resolved by injecting kainate into the dorsal hippocampus of awake rats, thus avoiding the insult-modifying effects of anesthesia, which had often been used for intracerebral injection of this convulsant in previous studies. For further characterization of this model, we injected kainate (0.4 μg) unilaterally into the CA3 of the posterior hippocampus in awake rats, which induced limbic SE (ranging from 4 to 20 h) in all rats without mortality. Repeated video-EEG monitoring (24h/day, 7 days/week) for periods of 1-2.5 weeks from 1 to 8 months after SE demonstrated that 91% of the rats developed epilepsy, and that seizure frequency significantly increased over the course of the disease. Epilepsy was associated with increased behavioral excitability and impaired learning and memory in a water maze, most likely as a result of hippocampal pathology, which was characterized by extensive neuronal loss in CA3 and dentate hilus and dispersion of granule cells in the ipsilateral hippocampus. A drug trial with phenobarbital showed that all epileptic rats used in this trial responded to treatment with suppression of SRS. The data substantiate that intrahippocampal kainate injection in awake rats offers an excellent model of human temporal lobe epilepsy and indicate that this model may have particular advantages for studying mechanisms of injury-induced epilepsy and comorbidities as targets for antiepileptic and antiepileptogenic therapies.

Computational modeling of epilepsy for an experimental neurologist

Computational modeling can be a powerful tool for an experimentalist, providing a rigorous mathematical model of the system you are studying. This can be valuable in testing your hypotheses and developing experimental protocols prior to experimenting. This paper reviews models of seizures and epilepsy at different scales, including cellular, network, cortical region, and brain scales by looking at how they have been used in conjunction with experimental data. At each scale, models with different levels of abstraction, the extraction of physiological detail, are presented. Varying levels of detail are necessary in different situations. Physiologically realistic models are valuable surrogates for experimental systems because, unlike in an experiment, every parameter can be changed and every variable can be observed. Abstract models are useful in determining essential parameters of a system, allowing the experimentalist to extract principles that explain the relationship between mechanisms and the behavior of the system. Modeling is becoming easier with the emergence of platforms dedicated to neuronal modeling and databases of models that can be downloaded. Modeling will never be a replacement for animal and clinical experiments, but it should be a starting point in designing experiments and understanding their results.

Therapeutic devices for epilepsy

Therapeutic devices provide new options for treating drug-resistant epilepsy. These devices act by a variety of mechanisms to modulate neuronal activity. Only vagus nerve stimulation (VNS), which continues to develop new technology, is approved for use in the United States. Deep brain stimulation of anterior thalamus for partial epilepsy recently was approved in Europe and several other countries. Responsive neurostimulation, which delivers stimuli to 1 or 2 seizure foci in response to a detected seizure, recently completed a successful multicenter trial. Several other trials of brain stimulation are in planning or underway. Transcutaneous magnetic stimulation (TMS) may provide a noninvasive method to stimulate cortex. Controlled studies of TMS are split on efficacy, which may depend on whether a seizure focus is near a possible region for stimulation. Seizure detection devices in the form of shake detectors via portable accelerometers can provide notification of an ongoing tonic-clonic seizure, or peace of mind in the absence of notification. Prediction of seizures from various aspects of electroencephalography (EEG) is in early stages. Prediction appears to be possible in a subpopulation of people with refractory seizures, and a clinical trial of an implantable prediction device is underway. Cooling of neocortex or hippocampus reversibly can attenuate epileptiform EEG activity and seizures, but engineering problems remain in its implementation. Optogenetics is a new technique that can control excitability of specific populations of neurons with light. Inhibition of epileptiform activity has been demonstrated in hippocampal slices, but use in humans will require more work. In general, devices provide useful palliation for otherwise uncontrollable seizures, but with a different risk profile than with most drugs. Optimizing the place of devices in therapy for epilepsy will require further development and clinical experience.

Revising the "Rule of Three" for inferring seizure freedom

PURPOSE

How long after starting a new medication must a patient go without seizures before they can be regarded as seizure-free? A recent International League Against Epilepsy (ILAE) task force proposed using a "Rule of Three" as an operational definition of seizure freedom, according to which a patient should be considered seizure-free following an intervention after a period without seizures has elapsed equal to three times the longest preintervention interseizure interval over the previous year. This rule was motivated in large part by statistical considerations advanced in a classic 1983 paper by Hanley and Lippman-Hand. However, strict adherence to the statistical logic of this rule generally requires waiting much longer than recommended by the ILAE task force. Therefore, we set out to determine whether an alternative approach to the Rule of Three might be possible, and under what conditions the rule may be expected to hold or would need to be extended.

METHODS

Probabilistic modeling and application of Bayes' rule.

KEY FINDINGS

We find that an alternative approach to the problem of inferring seizure freedom supports using the Rule of Three in the way proposed by the ILAE in many cases, particularly in evaluating responses to a first trial of antiseizure medication, and to favorably-selected epilepsy surgical candidates. In cases where the a priori odds of success are less favorable, our analysis requires longer seizure-free observation periods before declaring seizure freedom, up to six times the average preintervention interseizure interval. The key to our approach is to take into account not only the time elapsed without seizures but also empirical data regarding the a priori probability of achieving seizure freedom conferred by a particular intervention.

SIGNIFICANCE

In many cases it may be reasonable to consider a patient seizure-free after they have gone without seizures for a period equal to three times the preintervention interseizure interval, as proposed on pragmatic grounds in a recent ILAE position paper, although in other commonly encountered cases a waiting time up to six times this interval is required. In this work we have provided a coherent theoretical basis for modified criterion for seizure freedom, which we call the "Rule of Three-To-Six."

Modeling longitudinal daily seizure frequency data from pregabalin add-on treatment

The purpose of this study was to describe longitudinal daily seizure count data with respect to the effects of time and pregabalin add-on therapy. Models were developed in a stepwise manner: base model, time effect model, and time and drug effect (final) model, using a negative binomial distribution with Markovian features. Mean daily seizure count (λ) was estimated to be 0.385 (relative standard error [RSE] 3.09%) and was further increased depending on the seizure count on the previous day. An overdispersion parameter (OVDP), representing extra-Poisson variation, was estimated to be 0.330 (RSE 11.7%). Interindividual variances on λ and OVDP were 84.7% and 210%, respectively. Over time, λ tended to increase exponentially with a rate constant of 0.272 year⁻¹ (RSE 26.8%). A mixture model was applied to classify responders/nonresponders to pregabalin treatment. Within the responders, λ decreased exponentially with respect to dose with a constant of 0.00108 mg⁻¹ (RSE 11.9%). The estimated responder rate was 66% (RSE 27.6%). Simulation-based diagnostics showed the model reasonably reproduced the characteristics of observed data. Highly variable daily seizure frequency was successfully characterized incorporating baseline characteristics, time effect, and the effect of pregabalin with classification of responders/nonresponders, all of which are necessary to adequately assess the efficacy of antiepileptic drugs.

ECoG studies of valproate, carbamazepine and halothane in frontal-lobe epilepsy induced by head injury in the rat

The use of electrocorticography (ECoG) with etiologically realistic epilepsy models promises to facilitate the discovery of better anti-epileptic drugs (AEDs). However, this novel approach is labor intensive, and must be optimized. To this end, we employed rostral parasagittal fluid percussion injury (rpFPI) in the adolescent rat, which closely replicates human contusive closed head injury and results in posttraumatic epilepsy (PTE). We systematically examined variables affecting the power to detect anti-epileptic effects by ECoG and used a non-parametric bootstrap strategy to test several different statistics, study designs, statistical tests, and impact of non-responders. We found that logarithmically transformed data acquired in repeated-measures experiments provided the greatest statistical power to detect decreases in seizure frequencies of preclinical interest with just 8 subjects and with up to approximately 40% non-responders. We then used this optimized design to study the anti-epileptic effects of acute exposure to halothane, and chronic (1 week) exposures to carbamazepine (CBZ) and valproate (VPA) 1 month post-injury. While CBZ was ineffective in all animals, VPA induced, during treatment, a progressive decrease in seizure frequency in animals primarily suffering from non-spreading neocortical seizures, but was ineffective in animals with a high frequency of spreading seizures. Halothane powerfully blocked all seizure activity. The data show that rpFPI and chronic ECoG can conveniently be employed for the evaluation of AEDs, suggest that VPA may be more effective than CBZ to treat some forms of PTE, and support the theory that pharmacoresistance may depend on the severity of epilepsy. The data also demonstrate the utility of chronic exposures to experimental drugs in preclinical studies and highlight the need for greater attention to etiology in clinical studies of AEDs.

Predicting seizures: a behavioral approach

This article reviews the epilepsy cycle, distinguishing the interictal, preictal, ictal, and postictal phases. Evidence suggesting that the preictal phase can sometimes be identified based on neurophysiologic signals, premonitory features, the presence of trigger factors, or self-report is also reviewed. Diary studies have shown that seizures are not randomly distributed in time and that a subgroup of persons with epilepsy can predict an impending seizure. Paper diary data and preliminary analysis of electronic diary data suggest that seizure prediction is feasible. Whereas all of this evidence sets the stage for seizure prediction and preemptive therapy, several questions remain unanswered. First, what proportion of persons with epilepsy can predict their seizures? Second, within and among individuals, how accurate is prediction? Third, can prediction be improved through education about group level or individual predictors? And finally, in a group that can make robust predictions what are the most effective interventions for reducing seizure probability at times of high risk? The answers to these questions could reduce the burden of epilepsy by making seizures predictable and setting the stage for preemptive therapy. This work could improve the understanding of epilepsy by providing a context for studying the transitions from the interictal to preictal and ictal states. More prospective studies are needed; challenges certainly exist, but as the studies discussed here demonstrate, the field is rich with promise for improving the lives of patients with epilepsy.

Toward rational design of electrical stimulation strategies for epilepsy control

Electrical stimulation is emerging as a viable alternative for patients with epilepsy whose seizures are not alleviated by drugs or surgery. Its attractions are temporal and spatial specificity of action, flexibility of waveform parameters and timing, and the perception that its effects are reversible unlike resective surgery. However, despite significant advances in our understanding of mechanisms of neural electrical stimulation, clinical electrotherapy for seizures relies heavily on empirical tuning of parameters and protocols. We highlight concurrent treatment goals with potentially conflicting design constraints that must be resolved when formulating rational strategies for epilepsy electrotherapy, namely, seizure reduction versus cognitive impairment, stimulation efficacy versus tissue safety, and mechanistic insight versus clinical pragmatism. First, treatment markers, objectives, and metrics relevant to electrical stimulation for epilepsy are discussed from a clinical perspective. Then the experimental perspective is presented, with the biophysical mechanisms and modalities of open-loop electrical stimulation, and the potential benefits of closed-loop control for epilepsy.

The development of recurrent seizures after continuous intrahippocampal infusion of methionine sulfoximine in rats: a video-intracranial electroencephalographic study

Glutamine synthetase is deficient in astrocytes in the epileptogenic hippocampus in human mesial temporal lobe epilepsy (MTLE). To explore the role of this deficiency in the pathophysiology of MTLE, rats were continuously infused with the glutamine synthetase inhibitor methionine sulfoximine (MSO, 0.625 microg/h) or 0.9% NaCl (saline control) unilaterally into the hippocampus. The seizures caused by MSO were assessed by video-intracranial electroencephalogram (EEG) monitoring. All (28 of 28) of the MSO-treated animals and none (0 of 12) of the saline-treated animals developed recurrent seizures. Most recurrent seizures appeared in clusters of 2 days' duration (median; range, 1 to 12 days). The first cluster was characterized by frequent, predominantly stage I seizures, which presented after the first 9.5 h of infusion (median; range, 5.5 to 31.7 h). Subsequent clusters of less-frequent, mainly partial seizures occurred after a clinically silent interval of 7.1 days (median; range, 1.8 to 16.2 days). The ictal intracranial EEGs shared several characteristics with recordings of partial seizures in humans, such as a distinct evolution of the amplitude and frequency of the EEG signal. The neuropathology caused by MSO had similarities to hippocampal sclerosis in 23.1% of cases, whereas 26.9% of the animals had minimal neuronal loss in the hippocampus. Moderate to severe diffuse neuronal loss was observed in 50% of the animals. In conclusion, the model of intrahippocampal MSO infusion replicates key features of human MTLE and may represent a useful tool for further studies of the cellular, molecular and electrophysiological mechanisms of this disorder.

Epileptic seizures are temporally interdependent under certain conditions

PURPOSE

The possibility that seizures may be intercorrelated has not been sufficiently investigated. A handful of studies, the majority based on patient seizure diaries, provide disparate results: some claim that seizures are serially correlated and others that they are random events. This study investigates the effect that a seizure may have on the time of occurrence and severity of subsequent ones in subjects undergoing invasive surgical evaluation.

METHODS

The Savit-Green statistic, a measure of time series lag dependency, was applied to seizure sequences derived from the ECoGs of 26 epilepsy surgery candidates. Seizure onset times, intensities and durations were obtained using a validated seizure detection algorithm, and from these, inter-seizure intervals (ISI) and severities were computed and their lag dependencies were compared to suitably randomized and amplitude-scaled linear surrogate sets.

RESULTS

The null hypothesis (seizures are uncorrelated) was rejected (p<0.05) for ISI in 12/26 subjects and for seizure severity in 13/26. The temporal correlations spanned up to three preceding seizures and were nonlinear in 7/12 subjects for ISI and in 8/13 for severity. An important finding is that dependencies may be related to the frequency of seizures in the sample.

CONCLUSIONS

This study demonstrates that under certain conditions, there are linear and nonlinear seizure dependencies of low order and at small time scales (minutes to hours), for ISI and seizure severity. This observation has important implications for studies of seizure predictability, which de facto treat seizures as independent occurrences. Given the study subjects' conditions, it is not clear if the dependencies reflect innate brain dynamics, drug withdrawal, local trauma or a combination of these.

Cyclicity of spontaneous recurrent seizures in pilocarpine model of temporal lobe epilepsy in rat

Pilocarpine administration to rats results in status epilepticus (SE) and after a latency period to the occurrence of spontaneous seizures. The model is commonly used to investigate mechanisms of epileptogenesis as well as the antiepileptic effects of novel compounds. Surprisingly, there have been no video-EEG studies determining the duration of latency period from SE to the appearance of the first spontaneous seizures or the type and frequency of spontaneous seizures at early phase of pilocarpine-induced epilepsy even though such information is critical for design of such studies. To address these questions, we induced SE with pilocarpine in 29 adult male Wistar rats with cortical electrodes. Rats were continuously video-EEG monitored during SE and up to 23 days thereafter. The first spontaneous seizures occurred 7.2+/-3.6 days after SE. During the follow-up, the mean daily seizure frequency was 2.6+/-1.9, the mean seizure duration 47+/-7 s, and the mean behavioral seizure score 3.2+/-0.9. Typically first seizures were partial (score 1-2). Interestingly, spontaneous seizures occurred in clusters with cyclicity, peaking every 5 to 8 days. These data show that in the pilocarpine model of temporal lobe epilepsy the latency period is short. Because many of the early seizures are partial and the seizures occur in clusters, the true phenotype of epilepsy triggered by pilocarpine-induced SE may be difficult to characterize without continuous long-term video-EEG monitoring. Finally, our data suggest that the model can be used for studies aiming at identifying the mechanisms of seizure clustering.

Measuring clusters of spontaneous spike-wave discharges in absence epileptic rats

Spike-wave discharges (SWDs) characterizing absence epilepsy appear in closely packed aggregated sequences, which gave rise to the name "pyknolepsy" for this disease. In WAG/Rij rats, genetically prone to absence epilepsy, spontaneous SWDs seem to occur in clusters as well. Here, we aimed to quantify the seizures' clusters. SWDs sequences were extracted from long-term (complete estrous cycle) EEG recordings of adult female WAG/Rij rats. Spectral characteristics and half-decay time of autocorrelation functions (AC-tau) were calculated for time series of i(SWD) (proportion of time occupied by spike-wave activity), measured for subsequent periods. The clusters were characterized by means of AC-tau calculated for time series of i(SWD). The absence seizures were indeed clustered in a minute range. The clustering had a non-periodical character, since no significant and consistent periodicity was found in the minute range. AC-tau correlated positively with propensity of SWDs: i.e. the aggravation of absence epilepsy led to longer sequences of paroxysms and thus to a less random distribution. AC-tau was not sensitive to various phases of the estrous cycle, but was larger in the dark than in the light periods. We suggest that AC-tau can be used to quantify aggregation of epileptic events in the search for physiological basis of its temporal clustering.

Identifying seizure clusters in patients with epilepsy

Clinicians often encounter patients whose neurologic attacks appear to cluster. In a daily diary study, the authors explored whether clustering is a true phenomenon in epilepsy and can be identified in the clinical setting. Nearly half the subjects experienced at least one episode of three or more seizures in 24 hours; 20% also met a statistical clustering criterion. Utilizing the clinical definition of clustering should identify all seizure clusterers, and false positives can be determined with diary data.

Seizure clustering

Seizure clusters, also known as repetitive or serial seizures, occur commonly in epilepsy. Clustering implies that the occurrence of one seizure may influence the probability of a subsequent seizure; thus, the investigation of the clustering phenomenon yields insights into both specific mechanisms of seizure clustering and more general concepts of seizure occurrence. Seizure clustering has been defined clinically as a number of seizures per unit time and, statistically, as a deviation from a random distribution, or interseizure interval dependence. This review explores the pathophysiology, epidemiology, and clinical implications of clustering, as well as other periodic patterns of seizure occurrence. Risk factors for experiencing clusters and potential precipitants of clustering are also addressed.

Use of chronic epilepsy models in antiepileptic drug discovery: the effect of topiramate on spontaneous motor seizures in rats with kainate-induced epilepsy

PURPOSE

Potential antiepileptic drugs (AEDs) are typically screened on acute seizures in normal animals, such as those induced in the maximal electroshock and pentylenetet-razole models. As a proof-of-principle test, the present experiments used spontaneous epileptic seizures in kainate-treated rats to examine the efficacy of topiramate (TPM) with a repeated-measures, crossover protocol.

METHODS

Kainic acid was administered in repeated low doses (5 mg/kg) every hour until each Sprague-Dawley rat experienced convulsive status epilepticus for >3 h. Six 1-month trials (n = 6-10 rats) assessed the effects of 0.3-100 mg/kg TPM on spontaneous seizures. Each trial involved six pairs of TPM and saline-control treatments administered as intraperitoneal injections on alternate days with a recovery day between each treatment day. Data analysis included a log transformation to compensate for the asymmetric distribution of values and the heterogeneous variances, which appeared to arise from clustering of seizures.

RESULTS

A significant effect of TPM was observed for 12 h (i.e., two 6-h periods) after a 30-mg/kg injection, and full recovery from the drug effect was complete within 43 h. TPM exerted a significant effect at doses of 10, 30, and 100 mg/kg, and the effects of TPM (0.3-100 mg/kg) were dose dependent.

CONCLUSIONS

These data suggest that animal models with spontaneous seizures, such as kainate- and pilocarpine-treated rats, can be used efficiently for rapid testing of AEDs with a repeated-measures, crossover protocol. Furthermore, the results indicate that this design allows both dose-effect and time-course-of-recovery studies.

Seizure Clustering: Risks and Outcomes

PURPOSE

Patients with epilepsy often experience seizures in clusters. In this preliminary study, we examined the prevalence of self-reported clustering and identified potential risk factors and outcomes associated with reported clustering for further analysis in an ongoing prospective diary study.

METHODS

Detailed intake questionnaires were administered. Subjects reporting their typical seizure pattern to be three or more seizures within a 24-h period were classified as clusterers. Magnetic resonance imaging (MRI) and EEG data were obtained, and epilepsy was classified by International League Against Epilepsy (ILAE) criteria. All subjects completed Beck Anxiety and Depression Inventories, and Quality of Life in Epilepsy testing.

RESULTS

Overall, 29% of subjects reported typical seizure clustering. Extratemporal lobe epilepsy [p = 0.02; OR, 3.0 (1.1-7.8)] was significantly associated with reported seizure clustering. Remote symptomatic epilepsy was associated with clustering [p = 0.03; OR, 2.3 (1.1-4.3)], particularly in association with a history of head trauma with loss of consciousness before epilepsy onset [p = 0.003; OR, 4.2 (1.6-11.1)], although this analysis was limited. Reported clustering was significantly associated with a history of convulsive status epilepticus (SE) [p = 0.029; OR, 3.0 (1.1-8.3)], other seizure-related hospitalization [p = 0.006; OR, 5.3 (1.5-17.6)], and worse seizure control (p = 0.004). Quality-of-life measures were not significantly associated with reported clustering.

CONCLUSIONS

These preliminary results identify extratemporal epilepsy and possibly head trauma as potential risk factors for reported seizure clustering and indicate a significant association between reported clustering and convulsive SE. Additionally, seizure clustering appears to be a marker for more intractable epilepsy. The ongoing diary study will refine the definition of seizure clustering and further evaluate specific predictors of poor and favorable outcomes of clustering.

Amygdala amino acid and monoamine levels in genetically Fast and Slow kindling rat strains during massed amygdala kindling: a microdialysis study

We investigated the neurochemistry of epileptic seizures in rats selectively bred to be seizure-prone (Fast) vs. seizure-resistant (Slow) to amygdala kindling. Microdialysis was used to measure levels of amino acids [glutamate, aspartate and gamma-aminobutyric acid (GABA)] and monoamines (noradrenaline, dopamine and serotonin) during 'massed' stimulation (MS) (every 6 min) of the ipsilateral amygdala for a total of 40 stimulation trials. Behavioral seizure profiles together with their afterdischarge thresholds (ADTs) and associated durations were assessed during the procedure, and subsequently were redetermined 1, 7 and 14 days later. Then normal 'daily' kindling commenced and continued until the animal reached the fully kindled state. During MS, several generalized seizures were triggered in Fast rats that were associated with long afterdischarge (AD) durations and intermittent periods of elevated thresholds, but in Slow rats, most stimulations were associated with stable ADTs and short ADs. Progressively increasing extracellular glutamate and decreasing GABA was observed in Fast rats during the MS, whereas Slow rats showed levels similar to baseline values. Levels of noradrenaline and dopamine, but not of serotonin, were also increased in both strains throughout the MS treatment. In Fast rats, a dramatic lengthening of AD durations occurred 7 and 14 days following MS, as well as subsequent strong positive transfer to daily kindling, all of which were not seen in Slow rats. Together, these results show that repeated, closely spaced stimulations of the amygdala can differentially alter excitatory and/or inhibitory transmitter levels in a seizure network, and that sensitivity to this manipulation is genetically determined.

Seizure clustering during epilepsy monitoring

PURPOSE

To identify risk factors associated with seizure clustering during epilepsy monitoring and to assess the effect of clustering on localization of the epileptogenic zone.

METHODS

Patients undergoing presurgical epilepsy monitoring at Montefiore Medical Center or Yale-New Haven Hospital were recruited. Seizure clustering was defined as three or more seizures within 24 h. Risk factors for seizure clustering were examined by using regression analysis. The effect of clustering on localization was examined by Student's t test evaluation of mean interseizure interval for consecutive concordant and discordant seizures.

RESULTS

Of 91 patients, clustering was present in 56 (61.5%). Variables significantly associated with clustering included a history of seizure clustering at home (p = 0.0003) and the presence of mesial temporal sclerosis (MTS) on magnetic resonance imaging (MRI; p = 0.0172). Clustering was present in nine of 10 patients with more than one seizure-onset zone. Ictal EEG localization was not associated with clustering, nor was rapid medication withdrawal. Mean interseizure intervals were not significantly different for concordant and discordant seizures.

CONCLUSIONS

Seizure clustering during epilepsy monitoring is common. Risk factors include history of clustering at home, MTS on MRI, and possibly more than one seizure focus. In this study, seizures that occurred in clusters had as important a role in localization as did nonclustered seizures.

Prediction of epileptic seizures

For almost 40 years, neuroscientists thought that epileptic seizures began abruptly, just a few seconds before clinical attacks. There is now mounting evidence that seizures develop minutes to hours before clinical onset. This change in thinking is based on quantitative studies of long digital intracranial electroencephalographic (EEG) recordings from patients being evaluated for epilepsy surgery. Evidence that seizures can be predicted is spread over diverse sources in medical, engineering, and patent publications. Techniques used to forecast seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent engineered systems. Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks. Treatments such as electrical stimulation or focal drug infusion could be given on demand and might eliminate side-effects in some patients taking antiepileptic drugs long term. Whether closed-loop seizure-prediction and treatment devices will have the profound clinical effect of their cardiological predecessors will depend on our ability to perfect these techniques. Their clinical efficacy must be validated in large-scale, prospective, controlled trials.

[Eletroencephalographic ambulatory monitoring in refractory epilepsies in childhood]

The objective of our study was, by means of continuous prolonged ambulatory electroencephalographic monitoring, to analyze the temporal distribution of paroxysmal discharges during sleep and awake in children and adolescents with refractory epilepsies. Twenty-one patients in the 4-to-17 year age bracket with refractory epilepsies, with 52.3% (n=11) male and 47.6% (n=10) female from the Discipline of Neurology of the Universidade Federal de São Paulo (Federal University of São Paulo). Cerebral Holter was carried out with Bioware EEG-2008 of prolonged ambulatory electroencephalographic monitoring equipment. We observed greater frequency of isolated and grouped epileptic discharges in day and in night sleep in relation to awake; day and night sleep led to activation of epileptic discharges, both isolated and grouped. The cerebral Holter was more effective in detecting epileptiform discharges than the routine EEG in 33.33% of the patients. The cerebral Holter proved a useful and precise method in detecting epileptic discharges, as an aid in the assessment of the fluctuations in frequency of paroxysmal activity in children with refractory epilepsies, both in relation to activities in daily life, and to the relation to the biological cycle of sleep and awake.

An introduction to contingent (closed-loop) brain electrical stimulation for seizure blockage, to ultra-short-term clinical trials, and to multidimensional statistical analysis of therapeutic efficacy

Automated seizure blockage is a top research priority of the American Epilepsy Society. This delivery modality (referred to herein as contingent or closed loop) requires for implementation a seizure detection algorithm for control of delivery of therapy via a suitable device. The authors address the many potential advantages of this modality over conventional alternatives (periodic or continuous), and the challenges it poses in the design and analysis of trials to assess efficacy and safety-in the particular context of direct delivery of electrical stimulation to brain tissue. The experimental designs of closed-loop therapies are currently limited by ethical, technical, medical, and practical considerations. One type of design that has been used successfully in an in-hospital "closed-loop" trial using subjects undergoing epilepsy surgery evaluation as their own controls is discussed in detail. This design performs a two-way comparison of seizure intensity, duration, and extent of spread between the control (surgery evaluation) versus the experimental phase, and, within the experimental phase, between treated versus untreated seizures. The proposed statistical analysis is based on a linear model that accounts for possible circadian effects, changes in treatment protocols, and other important factors such as change in seizure probability. The analysis is illustrated using seizure intensity as one of several possible end points from one of the subjects who participated in this trial. In-hospital ultra-short-term trials to assess safety and efficacy of closed-loop delivery of electrical stimulation for seizure blockage are both feasible and valuable.

Course of chronic focal epilepsy resistant to anticonvulsant treatment

Sixty-three patients (32 women, 31 men), chronically treated with carbamazepine, phenytoin, valproate and/or phenobarbital, were investigated. Mean age at manifestation of epilepsy was 16.4 years; mean age at onset of documentation of seizure frequency was 33.7 years; mean duration of follow-up was 104.8 months; mean number of documented seizures per patient was 313.2. A statistical analysis of seizure diaries was performed. For each day the entries were the number of seizures per day. Linear trends, the amount of variance of the seizure frequency compared to randomly distributed events, were computed with the resulting coefficients fitting a general linear regression model. Non-random aggregations (clusters) were searched for as well as gaps of seizure manifestation. A similar number of patients exhibited a decrease (30%) or increase (29%) of seizure frequency during the course of epilepsy. The variance of the daily seizure frequency was in most patients (78%) significantly higher or lower (5%) than corresponding random distributions. The coupling coefficient to the day before was significantly larger than zero (indicating seizure clustering) in 57% of patients. Significant rhythmic components in seizure profiles (quasi-weekly or quasi-monthly) were found in 31 patients (both male and female). Periods of increased seizure frequency (clusters) manifested in 62% of patients; seizure gaps of at least 1 year occurred in 29 patients followed by seizure relapses in 20 patients. In conclusion, there is no evidence for a unique type of course in chronic focal epilepsy in patients on antiepileptic drugs.