No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Seizure Frequency in Intractable Partial Epilepsy: A Statistical Analysis
Abstract
We examined the seizure records of 13 patients (nine men and four women, ages 27-50 years) with intractable partial epilepsy, maintained with steady anti-epileptic drug dosages. Patients recorded daily seizure frequency on calendars. Periods of outpatient observation ranged from 99 to 1,710 days and the number of observed seizures ranged from 18 to over 400, with daily seizure rates of 0.1-4.3 per day. We used the quasi-likelihood regression model to examine the following four departures of the daily seizure counts from a Poisson (random) model: (1) linear increasing or decreasing time trends in expected seizure rates; (2) clustering, where the expected seizure rate on a given day depends on the number of seizures observed on the immediate prior days; (3) monthly cyclicity; and (4) increased variability (overdispersion). Linear time trends were seen in six patients (four increasing and two decreasing), clustering was seen in 10 patients, and a near-monthly cycle appeared in four patients (two of nine men and two of four women). A significant amount of extra variation (overdispersion) relative to a Poisson distribution was observed in all but one of the 13 patients. Departures from a Poisson (random) model appear more common in this population of patients with medically intractable epilepsy than is commonly recognized, and have clinical importance as well as implications for the design of clinical studies.
... The unpredictability of seizures is a major debilitating factor that decreases the quality of life for people with epilepsy (Cook et al., 2013;Fisher et al., 2000). Statistical analyses of patients' seizure diaries demonstrated non-random patterns in seizure distribution (Binnie et al., 1984;Milton et al., 1987;Balish et al., 1991;Tauboll et al., 1991;Bauer and Burr, 2001;Sunderam et al., 2007). One of the most important factors contributing to a non-random seizure occurrence is seizure clustering. ...
... Clinically, a cluster is defined as a closely grouped series of seizures or an overall increase in the patient's typical seizure frequency (Haut, 2006). In patients with pharmacoresistant epilepsy, the prevalence of clustering ranges from 13 to 78% (Binnie et al., 1984;Milton et al., 1987;Balish et al., 1991;Tauboll et al., 1991;Bauer and Burr, 2001;Sunderam et al., 2007;Haut, 2006). Recently, the existence of a non-random seizure distribution was confirmed in studies in which intracranial EEG was recorded for months or years (Baud et al., 2018;Cook et al., 2014;Karoly et al., 2016). ...
... Long-term fluctuations in seizure probability or seizure clustering are present in both humans and animal models of chronic epilepsy, respectively. The period of fluctuations can range from a few days to weeks, months (Binnie et al., 1984;Balish et al., 1991;Bauer and Burr, 2001;Baud et al., 2018;Karoly et al., 2016;Maturana et al., 2020;Karoly et al., 2020) or even years (Griffiths and Fox, 1938). In humans, the analyses of seizure diaries demonstrate that seizure clustering is a common phenomenon (Milton et al., 1987;Balish et al., 1991;Tauboll et al., 1991;Bauer and Burr, 2001;Baud et al., 2018;Cook et al., 2014;Griffiths and Fox, 1938;Osorio et al., 2009;Fisher et al., 2015;Haut et al., 2005;Sillanpaa and Schmidt, 2008). ...
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.
... There have been previous statistical models of daily seizure count data: several models incorporated clusters and memory (Tharayil et al., 2017;Trocóniz et al., 2009;Balish et al., 1991;Ahn et al., 2012;Delattre et al., 2012;Albert, 1991), cycles (Balish et al., 1991), and other features (e.g., linear time trends and dropout) (Hougaard et al., 1997;Balish et al., 1991;Ahn et al., 2012;Nielsen et al., 2015;Deng et al., 2016;Alosh, 2009). Six of these models were fitted to seizure diaries from less than 100 patients (Balish et al., 1991;Alosh, 2009;Albert, 2000;Thall and Vail, 1990;Molenberghs et al., 2007), 5 models were fitted to seizure diaries from over 100 patients who were eligible for RCT participation (Trocóniz et al., 2009;Ahn et al., 2012;Delattre et al., 2012;Nielsen et al., 2015;Deng et al., 2016), and 1 model was based on seizure diaries from 1526 users of SeizureTracker (Tharayil et al., 2017). ...
... There have been previous statistical models of daily seizure count data: several models incorporated clusters and memory (Tharayil et al., 2017;Trocóniz et al., 2009;Balish et al., 1991;Ahn et al., 2012;Delattre et al., 2012;Albert, 1991), cycles (Balish et al., 1991), and other features (e.g., linear time trends and dropout) (Hougaard et al., 1997;Balish et al., 1991;Ahn et al., 2012;Nielsen et al., 2015;Deng et al., 2016;Alosh, 2009). Six of these models were fitted to seizure diaries from less than 100 patients (Balish et al., 1991;Alosh, 2009;Albert, 2000;Thall and Vail, 1990;Molenberghs et al., 2007), 5 models were fitted to seizure diaries from over 100 patients who were eligible for RCT participation (Trocóniz et al., 2009;Ahn et al., 2012;Delattre et al., 2012;Nielsen et al., 2015;Deng et al., 2016), and 1 model was based on seizure diaries from 1526 users of SeizureTracker (Tharayil et al., 2017). ...
... There have been previous statistical models of daily seizure count data: several models incorporated clusters and memory (Tharayil et al., 2017;Trocóniz et al., 2009;Balish et al., 1991;Ahn et al., 2012;Delattre et al., 2012;Albert, 1991), cycles (Balish et al., 1991), and other features (e.g., linear time trends and dropout) (Hougaard et al., 1997;Balish et al., 1991;Ahn et al., 2012;Nielsen et al., 2015;Deng et al., 2016;Alosh, 2009). Six of these models were fitted to seizure diaries from less than 100 patients (Balish et al., 1991;Alosh, 2009;Albert, 2000;Thall and Vail, 1990;Molenberghs et al., 2007), 5 models were fitted to seizure diaries from over 100 patients who were eligible for RCT participation (Trocóniz et al., 2009;Ahn et al., 2012;Delattre et al., 2012;Nielsen et al., 2015;Deng et al., 2016), and 1 model was based on seizure diaries from 1526 users of SeizureTracker (Tharayil et al., 2017). ...
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.
... The available evidence indicates that the temporal profile of seizures is not random. [25][26][27][28][29][30][31] Using Markov models, the probability of seizure on a particular day can be assessed based on the probability of seizures on the previous day or days. These approaches show that seizure occurrence is a nonrandom event. ...
... Negative dependence, or a lower probability of seizure events following a day of high seizure activity, has also been noted. 27 An alternative approach tests the hypothesis that seizures are randomly distributed in time by looking for deviation from a Poisson process. A Poisson model describes a stochastic (random) system whereby the number of events in disjointed (nonoverlapping) time intervals are independent random variables, and the number of events within each time variable occurs as random variables with a Poisson distribution. ...
... Seizure clustering has been reviewed elsewhere 32 and seizure clusters have been shown to significantly deviate from a Poisson process. 25,27,29,30 Clinical examples of seizures as periodic events are well recognized, and seizure regularity is seen largely in patients who experience daily seizures. ...
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.
... 2 Nonrandom distributions can be evaluated further, using any or several of a large number of mathematical algorithms to determine patterns and periodicities of seizure occurrence. 1,[3][4][5][6][7] These analyses have demonstrated circadian, ultradian, lunar, annual, and other patterns and periodicities of seizure occurrence and have provided statistical validation of cyclic seizure exacerbation in relation to the menstrual cycle, that is, catamenial epilepsy. 1,[3][4][5][6][7] Statistical analysis lends mathematical coherence to the definition of clusters. ...
... 1,[3][4][5][6][7] These analyses have demonstrated circadian, ultradian, lunar, annual, and other patterns and periodicities of seizure occurrence and have provided statistical validation of cyclic seizure exacerbation in relation to the menstrual cycle, that is, catamenial epilepsy. 1,[3][4][5][6][7] Statistical analysis lends mathematical coherence to the definition of clusters. ...
Objective
We assessed whether (1) women with statistical clustering of daily seizure counts (DSCs) or seizure intervals (SIs) also showed clinical clustering, defined separately by ≥2 (≥2‐SC) and ≥3 (≥3‐SC) seizures on any single day; and (2) how these classifiers might apply to catamenial epilepsy.
Methods
This is a retrospective case–control analysis of data from 50 women with epilepsy (WWE). We assessed the relationships of the four classifiers to each other and to catamenial versus noncatamenial epilepsy using chi‐squared, correlation, logistic regression, and receiver operating characteristic (ROC) analyses.
Results
≥3‐SC, not ≥2‐SC, was more frequent in WWE who had statistical DSC clustering versus those who did not (21/25 [84.0%] vs. 11/25 [44.0%], p = .007). Logistic regression (p = .006) and ROC (p = .015) identified ≥3‐SC, not ≥2‐SC, as a predictor of statistical DSC clustering, but ≥4‐SC was more accurate. ≥3‐SC correlated with the average daily seizure frequencies (ADSFs) of the subjects (p = .01). ROC optimal sensitivity–specificity cut‐point for ADSF prediction of ≥3‐SC (.372) was 64.6% higher than for ≥2‐SC (.226). SI clustering was more common in WWE who had catamenial versus noncatamenial epilepsy (p = .013). Logistic regression identified statistical SI clustering as the only significant classifier (p = .043). ROC analysis offered only marginal support (p = .056), because specificity was low (42.1%).
Significance
The findings lend statistical support for (1) the utility of clinical ≥3‐SC as a predictor of convulsive status epilepticus, (2) consideration of ADSFs in defining clustering, and (3) ≥4‐SC as a more accurate clinical predictor of statistical DSC clustering. Statistical SI clustering occurred more frequently in women with catamenial than noncatamenial epilepsy (90.3% vs. 57.9%, p = .013). Although sensitivity was high (90.3%, 28/31), specificity was only 42.1% (8/19). Algorithms that test patterns and periodicities of clusters are more applicable.
... An electrographic seizure was also defined by high frequency rhythmic activity (>5 Hz) which consisted of an abnormal pattern (large amplitude spikes and clusters of spikes lasting for at least 10 s). Ten seconds was chosen because seizures in TLE typically last at least 10 s and often are 20-60 s (Balish et al., 1991). In addition, most seizures recorded in a standard Epilepsy Monitoring Unit typically last more than 10 s (Jenssen et al., 2006). ...
... One element is seizure duration. We provide evidence of prolonged convulsive seizures similar to seizure durations reported in human TLE (Balish et al., 1991) and data from an Epilepsy Monitoring Unit (Jenssen et al., 2006). The longer-lasting seizures contrast with the 3-7 s epileptiform abnormalities reported by some past studies of IHKA in mice Sandau et al., 2019;Lai et al., 2020). ...
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.
... A deviation from this model can reflect seizure clusters, and also indicate periodic patterns or regularity [8]. Deviations could include negative dependence of seizures (having too many seizures in the prior day decreases the chance of having seizure in the following day), and positive dependence (occurrence of seizure in the prior day increases the chance of having seizure) [24]. Departure from a random model can be observed in many patients with refractory epilepsy. ...
... In a study of 24 patients with epilepsy who maintained a seizure diary, 10 out of 22 had seizures that were not randomly distributed [25]. In another study of 13 patients with intractable epilepsy who self-reported their seizures using a seizure diary, in almost all the patients studied, seizures did not follow a random pattern [24]. ...
Purpose:
To summarize definitions, prevalence, risk factors, consequences, and acute management of seizure clusters using rescue medications.
Methods:
We searched MEDLINE for studies that assessed definitions, clinical characteristics, outcomes, and use of rescue medication for aborting seizure clusters.
Results:
Different clinical and statistical definitions for seizure clusters have been proposed, including: ≥3 seizures in 24 h, ≥2 seizures in 24 h, and ≥2 seizures in 6 h. Most studies of seizure clusters have been conducted in tertiary epilepsy centers, with refractory epilepsy patients. Patients with severe and poorly controlled epilepsy are more likely to experience seizure clusters. Seizure clusters can result in increased health care utilization and have negative impact on the quality of life of patients and caregivers. Use of benzodiazepine rescue medications in acute management of seizure clusters can help avoid progression to status epilepticus and reduce emergency room visits. Rescue medications are underutilized in seizure clusters. Currently, rectal diazepam gel is the only FDA approved rescue medication for seizure clusters. In addition, buccal midazolam is approved in European countries for treatment of prolonged seizures. However, various non-rectal non-IV benzodiazepines are safe and effective in treating acute seizures and clusters. Most patients and caregivers preferred non-rectal routes.
Conclusion:
Identifying patients that are at high risk for seizure clusters, providing them with formal action plans and educating them about use of rescue medication for seizure clusters can help ameliorate the outcomes in this group of epilepsy patients.
... 11,12 Other models, unsuited to clinical trial simulators, do not allow generation of simulated data. 13,14 Most existing models have been developed using adult data (Table 1). Most others exclude younger children, developing only one model for childhood and adult epilepsy. ...
... Several groups have reported clustered seizure counts, finding models with memory (where past results affect probability of future results) perform better than those without. 13,14,24 Improvements over the NB model include an NB generalized linear (A) Data selection procedure. Data were recorded from patients meeting common clinical trial eligibility criteria. ...
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.
... Some authors have applied concepts from dynamic systems theory, including that of self-organizing criticality to study inter-seizure intervals 5 . Other authors have noted more variability in seizure frequency than would be expected if their distribution followed a simple Poisson model, with overdispersion in series of seizure counts [6][7][8][9][10][11] . ...
... The underlying distributions of seizures by subject were explored using histograms and kernel density plots, which are shown in Figure 2A and 2B. Figure 2A shows clear patterns portraying both unimodal and multi-modal underlying distributions of seizure durations. Subjects 3,8,9,11,and 13, show, at least bimodal density patterns. On the other hand subjects 1, 2, 6, 12, and 15 each show strong unimodal seizure duration distributions. ...
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.
... Statistical definitions of clusters describe a significant increase (e.g., 3-or 4-fold or 3 standard deviations) in seizure frequency compared with earlier times. This earlier time can be taken as the frequency for the prior day [8], prior 3 days [2], or some other time interval before the cluster. The precluster interval can be defined in relation to the average seizure-free interval for that individual. ...
... Paper or electronic diaries are mainstays of clinical epilepsy management and of therapeutic trials [12]. One small diary study of patients with intractable epilepsy [8] found clustering in 10 of 13 patients. Milton and Gotman [21] evaluated 24 patients keeping a seizure diary for a mean of 237 days. ...
... It is well established that in some epilepsies, certain normal states of vigilance are more conducive to seizure than others343536. Seizure recurrence is also correlated with physiological rhythms [24,37] and drug taper [38]. As outlined in Section 3.3, if an identifiable state can reliably predict seizures, it could serve as a treatment marker for closed-loop stimulation designed to avoid this preictal or seizure-permissive state, or to force a transition back to baseline to achieve a therapeutic effect. ...
... But without knowledge of the biophysical mechanisms, there is no rational basis for assuming that waveforms optimized for other diseases/anatomical targets would be effective in epilepsy control. 2. Confounding factors: Drug taper [38], physiological rhythms [37], sleep stage [34], stress, and various other factors are known to influence seizure rate and/or severity and thereby interfere with the quantitative assessment of treatment effects. ...
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 mentioned algorithms have played a prominent role in applications in biomedical research and adjacent fields, such as public health [14]. Examples of corresponding count time series include epidemiological data (such as the famous U.S. poliomyelitis incidence time series, consisting of monthly counts starting in 1970) [15], sleep stage sequences, erythrocyte counts, infectious disease data [16], and epileptic seizure counts [17,18]. While most epilepsies respond well to anti-epileptic treatment, modeling the effects of antiepileptic drugs (AEDs) on seizure frequency is essential for patients with difficult-to-treat or treatment-resistant epilepsies [19,20]. ...
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.
... The threshold, 2 times the SD of the baseline mean, was chosen because it was adequate to differentiate seizures from normal EEG. Ten seconds was chosen because seizures in TLE typically last at least 10 sec and often are 20-60 sec 21 As seizure onset we defined the time when the baseline of the left 25 hippocampal lead exceeded 2 times the SD of the baseline mean. The end of a seizure (seizure termination) was defined as the time when high amplitude activity declined to less than 2 times the SD of the baseline mean. ...
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 duration
When CA2 silencing reduces seizure activity in hippocampus it also reduces cortical seizure activity and convulsive manifestations of seizures
Interictal spikes and high frequency oscillations are reduced by real-time CA2 silencing
Real-time CA2 silencing of high frequency oscillations (>250Hz) rescues social memory deficits of chronic epileptic mice
... Therefore, there have been several attempts to model seizure diaries over the years. 17,18 Most models have been based on a single small data set, or even no data at all. Often a Poisson model was assumed, which although easy to compute, is known to poorly represent 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.
... Among people with medically refractory epilepsy, seizure frequencies may range from less than one per month to several seizures per day. 13,14 A change in seizure frequency has traditionally been the main measure of efficacy for epilepsy treatments. To incorporate effects of treatment in cost-effectiveness analyses, changes in seizure frequency should be accompanied by changes in HRQoL, for treatment benefits to be captured in QALYs. ...
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.
... An electrographic seizure was also defined by high frequency rhythmic activity 433 (>5 Hz) which consisted of an abnormal pattern (large amplitude spikes and clusters of 434 spikes lasting for at least 10 sec). Ten seconds was chosen because seizures in TLE 435 typically last at least 10 sec and often are 20-60 sec (Balish et al., 1991). In addition, 436 most seizures recorded in a standard Epilepsy Monitoring Unit typically last more than 437 10 sec (Jenssen et al., 2006). ...
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 ambiguity, we slightly modified previous methods and employed continuous wideband video-EEG monitoring from 4 recording sites to best detect and characterize chronic epilepsy outcomes in both male and female mice. 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 sec) 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 STATEMENT
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 sec 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.
HIGHLIGHTS
Our implementation of the IHKA model led to robust chronic spontaneous convulsive seizures in mice
Convulsive seizures were synchronized in both hippocampi and two cortical sites
Seizure frequency increased from 2-4 wks to 10-12 wks in 50% of mice and declined in others
Convulsive seizures fit LVF and HYP types found in human temporal lobe epilepsy
HFOs (>250 Hz) were common, at >1 location, and were both ictal and interictal
... We model zero seizures as an observed manifestation of a low seizure risk state rather than a separate no-risk state, as people with epilepsy are presumed to always have at least some probability of having a seizure. 20 As the true underlying conditional probability distribution is unknown, our specification of emission distribution depends on several considerations: (1) seizure count data is empirically overdispersed relative to that expected under a generic Poisson process, with the variance exceeding the mean; 21,22 and (2) seizure occurrence patterns exhibit dependence over time. 23,24 To account for these considerations, we employ a zero-inflated Poisson (ZIP) process for the seizure emission distribution. ...
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.
... Haut et al. [3] concluded that seizure clustering is common among people with epilepsy, but the incidence is likely lower if a statistical definition as opposed to patient-reported data is used. Others indicate that the rates can range from 13% to 76% in outpatient studies [29,[35][36][37] and from 18% to 61% in epilepsy monitoring unit studies [38][39][40][41][42]. Fisher et al. [26] reviewed persons who utilized My Epilepsy Diary. Information was downloaded from 28,697 patients with 546,768 patient days for an average of 70 diary days per unique user. ...
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.
... Our study is the first population study on seizure clustering and first to present pretreatment cases of clustering. We may confirm the previous ob-servation about poor seizure outcome associated with seizure clusters (Balish et al., 1991;Haut et al., 1999). ...
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.
... Clustering patterns, in which one seizure appears to increase the likelihood of subsequent seizures, are also encountered frequently in clinical practice. Despite these observations, analyses of long-term seizure patterns based on patient-reported seizure counts (seizure diary) have yielded inconsistent findings (Binnie et al., 1984;Milton et al., 1987;Albert, 1991;Balish et al., 1991;Tauboll et al., 1991;Iasemidis et al., 1994;Bauer and Burr, 2001;Lee and No, 2005;Hall et al., 2009). Although some authors conclude that the timing of seizure recurrence is random, others hypothesize that seizures occur in a probabilistic nonlinear fashion. ...
This resource addresses the disorders presenting in children, adolescents and adults which may be mistaken for epilepsy or which are associated with epilepsy and can develop into or out of epileptic seizures. It features case reports and tables (especially those which address the differential diagnosis of epilepsy and the disorders discussed), and covers anxiety/hyperventilation attacks, psychogenic nonepileptic seizures, epileptic and nonepileptic encephalopathies, autism, autoimmune encephalopathies, Tourette's Syndrome, transient ischemic attacks, transient global amnesia, myoclonus, alcohol-related seizures, hyperekplexia and dyskinesia, stereotypical behaviors, organic personality disorder and episodic dyscontrol syndrome.
... Though cycles of seizure activity associated with biological rhythms (circadian and menstrual) have long been recognized, Poisson processes have been felt to describe the pattern of seizure occurrence, with departures perhaps explained by external factors (Milton et al., 1987). Many authors have noted more variability in seizure frequency than would be expected if their distribution followed a simple Poisson model, with overdispersion in series of seizure counts (Balish et al., 1991;Greenwood and Yule, 1920;Hopkins et al., 1985;Iasemidis et al., 1994;Taubøll et al., 1991). ...
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 manmade 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 8 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 5 out of the 6 subjects whose data was sufficiently stationary for analysis, we found evidence of long memory between epileptic events. This memory spans time scales from 30 minutes to 40 days. The estimated Hurst exponents range from 0.51-0.77±0.01. This finding may provide evidence of phasetransitions underlying the dynamics of epilepsy. © 2014 Cook, Varsavsky, Himes, Leyde, Berkovic, O_brien and Mareels.
... Though cycles of seizure activity associated with biological rhythms (circadian and menstrual) have long been recognized, Poisson processes have been felt to describe the pattern of seizure occurrence, with departures perhaps explained by external factors (1). Many authors have noted more variability in seizure frequency than would be expected if their distribution followed a simple Poisson model, with overdispersion in series of seizure counts (2)(3)(4)(5)(6). ...
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.
... From the statistical point of view, serial seizures are those without random distribution, or with a dependence pattern of interseizure interval (1). Some studies have applied Poisson distribution test to seizure frequency, to evaluate the randomness of seizures (8,9). The prevalence of clustering varies widely between studies because there is no definitive clinical definition for serial seizures. ...
Serial seizures occur commonly in inpatient epileptic children. This type of seizure due to its characteristics has a significant impact on the patient's health. Untreated serial seizures lead to status epilepticus; therefore, finding a more effective treatment for such patients is essential. This study was performed to compare the outcome of intermittent intravenous diazepam in the pediatric neurology clinic and intravenous midazolam in the pediatric intensive care unit (PICU), in order to introduce an alternative treatment for serail seizures.
In this study, 38 inpatient children aged 6 mo-15 years with refractory serial seizures were treated by first line antiepileptic drugs and then randomly treated with either intermittent intravenous diazepam in the neurology ward or intravenous midazolam in PICU.
Fourteen (70%) diazepam group patients and 13 (72.2%) midazolam group patients had good response to treatment, there was no significant difference between the two groups. Four midazolam group patients and two diazepam group patients needed mechanical ventilation and were intubated during treatment, with no significant difference between the two groups. Durations of mechanical ventilation and PICU and hospital stay were not significantly different between the two groups.
Intermittent intravenous diazepam is an effective alternative therapy for midazolam drip in the treatment of serial seizures due to similar therapeutic effects and fewer side effects.
... This simple index provides a summary view of the relative change in seizure frequency over a 3-month period from initiating MPH. Time-series modeling methods have been applied for seizure frequency analysis in refractory epilepsy to permit more sensitive analyses (Balish et al., 1991;Pujar et al., 2010). Given the potential for recording bias that exists with retrospective data, these methods would, however, perform suboptimally in this context, in contrast to the relatively simple method used in this study. ...
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.
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.
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.
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.
... One American group who studied 13 patients with intractable drug resistant epilepsy analysed seizure frequency by a quasi-likehood regression model and found that ten showed mathematically proven clustering, and a near-monthly cycle appeared in four (two of nine men and two of four women). 17 The authors of this paper make the interesting point that the more severe the epilepsy the more likely the patient was to manifest clustering. Nearly all the studies in this area come from specialist epilepsy centres. ...
Since ancient times the menstrual cycle has been thought to influence the occurrence and frequency of seizures. The phenomenon of catamenial epilepsy seizures, which either occur at or around the time of menstruation, or become more frequent at this time, has been the subject of much research, although the literature is beset by methodological problems. In contrast, epilepsy at the menopause has not attracted the same attention and in the last 15 years only three studies have been published. None of them has produced conclusive results on the effect of the menopause on the course of epilepsy or the effects, if any, of hormone replacement therapy on seizure control.
... Overall, seizure frequencies often progressively increase over time in chemoconvulsant-induced animal models of TLE, although in some models this increase is overlaid on a cyclic pattern with periods of increasing and decreasing seizure frequencies over time often referred to as "clustering" (Goffin et al., 2007;Williams et al., 2009). Similarly, a subset of patients with TLE experience a progressive worsening of their epilepsy, with an increase in seizure frequency, severity, and/or clustering over time from disease onset (Balish et al., 1991;Berg et al., 2003;Blume, 2006;Haut et al., 2002Haut et al., , 2006Shukla and Prasad, 2012). WP1066 treatment after onset of SE did not prevent the development of electrographic spontaneous seizures in the pilocarpine model (Fig. 5A), and the latency to first spontaneous seizure was similar for WP1066-treated and vehicle-treated rats. ...
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 effects 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 24hours 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.
... Clustering patterns, where one seizure appears to increase or decrease the likelihood of subsequent seizures, are a common clinical observation. Analyses of long-term seizure patterns are usually based on seizure diaries [47][48][49][50][51][52]. While some authors have concluded that the timing of seizure recurrence is random and follows a homogeneous Poisson distribution, others have observed significant deviations from a homogeneous Poisson process and hypothesized that seizures occur in a probabilistically nonlinear fashion. ...
The analysis of Xevents arising in dynamical systems with many degrees of freedom represents a challenge for many scientific
fields. This is especially true for the open, dissipative, and adaptive system known as the human brain. Due to its complex structure, its immense functionality, and — as in the case of epilepsy — due to the coexistence of normal
and abnormal functions, the brain can be regarded as one of the most complex and fascinating systems in nature. Data gathered
so far show that the epileptic process exhibits a high spatial and temporal variability. Small, specific, regions of the brain
are responsible for the generation of focal epileptic seizures, and the amount of time a patient spends actually having seizures
is only a small fraction of his/her lifetime. In between these Xevents large parts of the brain exhibit normal functioning.
Since the occurrence of seizures usually can not be explained by exogenous factors, and since the brain recovers its normal
state after a seizure in the majority of cases, this might indicate that endogenous nonlinear (deterministic and/or stochastic)
properties are involved in the control of these Xevents. In fact, converging evidence now indicates that (particularly) nonlinear
approaches to the analysis of brain activity allow us to define precursors which, provided sufficient sensitivity and specificity
can be obtained, might lead to the development of patient-specific seizure anticipation and seizure prevention strategies.
... Many processes involving recurrent random events can be reasonably approximated by Poisson process models, though it must be realized that such models treat the intervals between events as statistically independent, and hence cannot describe more complex temporal features characteristic of some cases of epilepsy such as systematic clustering or diurnal variation, which may play important roles in the management of some patients' seizures ( Balish et al., 1991;Haut et al., 2005). Expected effects of deviations from this simple model on the results of the following analysis are addressed in the Discussion section and Supplemental Material. ...
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."
... Finally, the drug testing protocol was optimized on the basis of extensive nonparametric power analyses. Because FPI-induced epilepsy displays high rat-to-rat variability in frequency of CSRSs and seizure clustering, as expected for a clinically relevant model of human PTE (Haut et al., 2005; Balish et al., 1991; Bauer and Burr, 2001), and because human PTE is often pharmacoresistant (Semah et al., 1998), detailed optimization of the study protocols and of the analytical methods was required before attempting AED studies. We demonstrate that, despite the variability of CSRS frequency and the existence of non-responders, an optimized testing protocol can reliably detect antiepileptic effects of preclinical interest with small numbers of animals. ...
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.
... Results reported by Gottman [45], Sherwin [46], and Lange [47] showed evidence of increased interictal spiking prior to onset in two studies, but not the third [45]. In two publications ([48, 49]), an examination of the predictability of interictal interval times yielded inconclusive results. More recently, Litt et al. [50] studied long-term energy bursts, subclinical seizure-like bursts (chirps), and accumulated energy features in five patients. ...
A Novelty Detection Approach to Seizure Analysis from Intracranial EEG Andrew B. Gardner 146 pages Directed by Dr. George Vachtsevanos and Dr. Brian Litt A framework for support vector machine classification of time series events is proposed and applied to analyze physiological signals recorded from epileptic patients. In contrast to previous works, this research formulates seizure analysis as a novelty detection problem which allows seizure detection and prediction to be treated uniformly, in a way that is capable of accommodating multichannel and/or multimodal measurements. Theoretical properties of the support vector machine algorithm employed provide a straightforward means for controlling the false alarm rate of the detector. The resulting novelty detection system was evaluated both offline and online on a corpus of 1077 hours of intracranial electroencephalogram (IEEG) recordings from 12 patients diagnosed with medically resistant temporal lobe epilepsy during evaluation for epilepsy surgery. These patients collectively had 118 seizures during the recording period. The performance of the novelty detection framework was assessed with an emphasis on four key metrics: (1) sensitivity (probability of correct detection), (2) mean detection latency, (3) early-detection fraction (prediction or detection of seizure prior to electrographic onset), and (4) false positive rate. Both the offline and online novelty detectors achieved state-of-the-art seizure detection performance. In particular, the online detector achieved 97.85% sensitivity, -13.3 second latency, and 40% early-detection fraction at an average of 1.74 false positive predictions per hour (Fph). These results demonstrate that a novelty detection approach is not only feasible for seizure analysis, but it improves upon the state-of-the-art as an effective, robust technique. Additionally, an extension of the basic novelty detection framework demonstrated its use as a simple, effective tool for examining the spread of seizure onsets. This may be useful for automatically identifying seizure focus channels in patients with focal epilepsies. It is anticipated that this research will aid in localizing seizure onsets, and provide more efficient algorithms for use in a real device. Ph.D. Committee Chair: Lanterman, Aaron; Committee Member: Butera, Robert; Committee Member: Esteller, Rosana; Committee Member: Koblasz, Arthur; Committee Member: Litt, Brian; Committee Member: Vachtsevanos, George
... There is a striking tendency for the seizures in the present model to occur in clusters that are separated by longer, seizure-free intervals. Clustering of seizures has been reported in other animal models of temporal lobe epilepsy (Dudek, et al., 2006;Goffin, et al., 2007) and in human partial epilepsies (Balish, et al., 1991;Bauer and Burr, 2001). Perhaps one of the most common types of seizure clustering is seen in catamenial epilepsy (Penovich and Helmers, 2008). ...
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.
This chapter discusses a case of pharmacoresistant non-dominant temporal lobe epilepsy due to dual pathology (hippocampal sclerosis and temporal encephalocele). We present the success rates and the predictors of post-operative seizure outcome in patients with pharmacoresistant epilepsy due to dual pathology. We discuss the incidence and factors leading to the post-operative development of de novo psychogenic non-epileptic events. We analyze semiologies such as experiential or psychic phenomena, forced thinking, automatisms, and urinary incontinence. We provide a brief overview of the definition of cluster seizures.
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.
Seizure frequency has until recently been the usual measure of efficacy of epilepsy treatment. The aims of this thesis were to develop and implement two new outcome measures of epilepsy therapy. A new seizure severity scale and a measure of the handicap associated with epilepsy were designed and evaluated. The psychosocial burden of epilepsy was assessed in an unselected population using the new measure of handicap. The benefits of epilepsy surgery and programs of comprehensive epilepsy assessment were investigated in patients with intractable seizures. The new seizure severity scale was found to be reliable and to have construct validity. It is now in use in international antiepileptic drug trials. The Subjective Handicap of Epilepsy scale (SHE) was found to be a reliable and valid measure of the impact of epilepsy on the life of an individual with epilepsy. In a unselected community-based sample of persons with epilepsy, the severity of subjective handicap was related to seizure frequency and to the duration of remission of epilepsy. A third of persons with active epilepsy were found to be significantly handicapped by their condition. Between a third and a half of subjects had psychiatric symptoms. Scores on a measure of general health indicated that active seizures and drug treatment both had detrimental effects on well-being. In a longitudinal observational study, significant improvements in seizure control, subjective handicap, quality of life and psychiatric status were seen in 42 surgically treated patients compared with 82 subjects assessed for surgery but not operated upon. Compared with control groups, 67 patients who underwent a program of comprehensive assessment improved on some measures of quality of life and handicap. Remission of seizures had a primary role in achieving a major reduction in handicap and gains in quality of life.
A cronobiologia 6 o estudo dos ritmos biológicos e de seus mecanismos subjacentes. A cronobiologia medics, em particular, tem interesse com o ritmo circadiano. 0 momento exato e as circunstancias em que ocorre a crise epileptics, como também a recorrência de crises, são motivos de apreensão para o paciente e para o neurologista. Em alguns pacientes, as crises epilépticas tam uma recorrência regular; em outros, ha um quadro periódico sem crise e, apes alguns dies, surge um surto de crises epilépticas. Este trabalho discute a cronobiologia médica e sua aplicação nas epilepsias.
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.
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 (IRR) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed effects models, respectively.
Results
A total of 1037909 seizures were logged by 10186 subjects (56.7% children) from 12/2007 to 1/2016. Children had more frequent seizures than adults (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 AM and 10:00 AM and lower overnight) and seizures were reported differentially across the week (seizure rates higher Monday to Friday than Saturday or Sunday). Longer seizures (>5 or >30 minutes) 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 prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results but also generation of novel hypotheses.
This article is protected by copyright. All rights reserved.
Objectives:
To characterize the burden of seizure clusters (SC) on patients and caregivers, a large internet-based survey was conducted.
Methods:
The Seizure Cluster Burden of Illness US Survey was conducted online by Harris Poll on behalf of The Epilepsy Foundation in September 2014. Respondents included adult patients 18 years and above with epilepsy or a seizure disorder who had experienced SC in the past year (defined as ≥2 seizures within 24 h outside the patient's typical seizure pattern), caregivers providing current care for a patient with SC (adult or child), and clinicians (neurologists, epileptologists) who treat adult or pediatric patients. Responses to a wide range of topics, including emotional well-being, daily function, productivity, and approach to clinical practice, were collected.
Results:
There were 861 respondents (259 adult SC patients, 263 caregivers, and 339 clinicians). A majority of all respondent groups felt SC have a moderate/major negative impact on patient and caregiver quality of life, including emotional, financial, and social components. Responses indicated possible overutilization of emergency room services and underutilization of rescue treatment. Only 30% of patients reported having a seizure emergency plan. Some responses showed discrepancies between clinicians and patients/caregivers in the perceived degree of negative impact of SC and management practices for SC.
Conclusions:
These results suggest the need for increased education on managing SC. Clinicians need to develop seizure emergency plans and discuss rescue therapies, whereas patients and caregivers need to ask for and utilize these management strategies.
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.
Objectives
This study elucidated the temporal recurrence patterns of syncope in patients with frequent vasovagal syncope (VVS).
Background
Understanding the temporal distribution of fainting spells in syncope patients may illuminate biological processes and inform decision making.
Methods
Patients from the POST 2 (Prevention of Syncope Trial 2) were included; all had VVS and fainted ≥4 times in the study year, providing ≥3 interevent intervals (IEIs). Only fainting spells separated by ≥1 day were included. IEI distributions were analyzed using Poisson modeling and cumulative sum distributions.
Results
Twenty-four patients (5 males, 19 females; mean 33 years of age) had a total of 286 syncopal events and 262 IEIs, with a median 6 IEI. They resembled excluded subjects in age and sex but fainted more often in their lives (median: 57 vs. 13 fainting spells, respectively; p < 0.0001) and in the previous year (median: 23 vs. 3 fainting spells, respectively; p < 0.0001). Subjects had a median IEI duration of 8 (interquartile range: 4 to 19) days. The IEI distributions were fit well by Poisson models with a median r² of 0.94 (95% confidence interval: 0.91 to 0.97). The patients’ Poisson rate constant frequencies were 7 to 263 fainting spells/year with a median rate of 19 fainting spells/year. The modal syncope frequency was 10 to 15 fainting spells per year. Seven patients had biexponential distributions, and many patients fainted in clusters.
Conclusions
Patients with frequent VVS have fainting spells that occur randomly in time. Clusters of syncope occur, and in this population, there is a central tendency to 10 to 15 fainting spells per year. This provides a quantitative measure of frequency and predictability that may afford individualized treatment goals.
Human beings are anything but homogeneous experimental units. No matter how rigorous the effort to restrict a pool of patient candidates for a controlled clinical trial, even an effective treatment accounts for only a small portion of the resulting range and variability of clinical measurements. In other words, there is usually a lot of “noise” in the data. Ultimately, a mass of data must be condensed into quantities (statistics) which can be used to determine whether or not there is a signal of efficacy amongst the noise. Often the statistical challenge is to design a trial so that a chosen statistic will produce the greatest chance of detecting a true difference between treatments (power) while simultaneously limiting the chance of falsely concluding that there is a difference when there is none (type I error). For any given trial, the “p value” of a statistical test estimates the probability of a statistic at least as large as that found in the trial under the assumption that the treatment and control are indistinguishable as measured (null hypothesis). If the p value falls below a given threshold of improbability (α-level) under the assumption of no treatment effect, we are led to conclude that the lack of a treatment effect is implausible. For example, the finding of a large difference between the sample means in the trial would suggest that the original assumption of no difference between the populations was wrong. If the difference is large enough compared with a measure of noise in the trial (i.e., the p value is less than α), we then say that the test is “statistically significant”. Conventionally, a is set at 5%, or a 1 in 20 chance (over repeated similarly designed trials) of arriving at the false conclusion that the treatment is effective when, in fact, it is not. Rather than depend upon a probability model of the actual state of nature, an alternative strategy is to gather evidence which may serve to contradict an assumed state of nature, e.g., no treatment effect.
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.
Little is known about patterns of seizures that occur multiple times a day, sometimes called clusters or serial seizures.
The online diary, My Epilepsy Diary (MED), provided self-reported data from community-based patients to describe the characteristics of clusters.
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.
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).
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.
Copyright © 2015 Elsevier Inc. All rights reserved.
Many patients with epilepsy experience 'clusters' or flurries of seizures, also termed acute repetitive seizures (ARS). Seizure clustering has a significant impact on health and quality of life. This review summarizes recent advances in the definition and neurophysiologic understanding of clustering, the epidemiology and risk factors for clustering and both inpatient and outpatient clinical implications. New treatments for seizure clustering/ARS are perhaps the area of greatest recent progress.
Efforts have focused on creating a uniform definition of a seizure cluster. In neurophysiologic studies of refractory epilepsy, seizures within a cluster appear to be self-triggering. Clinical progress has been achieved towards a more precise prevalence of clustering, and consensus guidelines for epilepsy monitoring unit safety. The greatest recent advances are in the study of nonintravenous route of benzodiazepines as rescue medications for seizure clusters/ARS. Rectal benzodiazepines have been very effective but barriers to use exist. New data on buccal, intramuscular and intranasal preparations are anticipated to lead to a greater number of approved treatments. Progesterone may be effective for women who experience catamenial clusters.
Seizure clustering is common, particularly in the setting of medically refractory epilepsy. Clustering worsens health and quality of life, and the field requires greater focus on clarifying of definition and clinical implications. Progress towards the development of nonintravenous routes of benzodiazepines has the potential to improve care in this area.
Long-term video-EEG monitoring (LTM) is the gold standard for initial lateralization and localization of seizures in the workup for neurosurgical treatment of medically intractable epilepsy. Previous studies have yielded contradictory results as to whether seizures that occur in clusters tend to arise from the same brain region and may lead to the incorrect conclusion that seizures arise from a single focus. To determine whether seizure clustering affects localization in an LTM setting, the authors performed an observational study over 6 years at a large regional epilepsy center on those undergoing LTM for seizure diagnosis, characterization, or presurgical workup. Excluding repeat studies and LTMs with generalized or nonepileptic seizures resulted in 479 monitorings with 2,774 focal seizures for analysis. Sequential pairs of consecutive focal seizures were classed as “concordant”, “discordant,” or “other,” based on EEG localization. ANOVA analysis on the logarithm of the interseizure interval (LISI) among the three seizure pair groups showed no significant difference, p = 0.47, nor did analysis defining concordance as lateralization to the same hemisphere (p = 0.34). Analyses on subgroups with multifocal seizures, bilateral seizures, and extratemporal seizures all failed to show a significant difference. In conclusion, seizures have the same localizing value whether occurring in a cluster over a few hours or sporadically over a few days. This could potentially lead to shorter monitoring times.
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 (0.4μg) unilaterally into the CA3 of the posterior hippocampus in awake rats, which induced limbic SE (ranging from 4 to 20h) 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 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 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.
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.
To define a likelihood one has to specify the form of distribution of the observations, but to define a quasi likelihood function one need only specify a relation between the mean and variance of the observations and the quasi likelihood can then be used for estimation. For a one parameter exponential family the log likelihood is the same as the quasi likelihood and it follows that assuming a one parameter exponential family is the weakest sort of distributional assumption that can be made. The Gauss Newton method for calculating nonlinear least squares estimates generalizes easily to deal with maximum quasi likelihood estimates, and a rearrangement of this produces a generalization of the method described by Nelder and Wedderburn (1972).
• The analysis of the results of many well-designed, double-blind trials of anticonvulsant drugs has been unsophisticated. We draw attention to the nonrandom occurrence of seizures, which negates the simple comparison of average seizure frequency. We propose a method of taking into account clustering of seizures when deciding on the appropriate length of follow-up after introducing a new treatment. Deterministic and nondeterministic models were used to show why there may be reasons for sometimes using more than one drug in the treatment of epilepsy.
The distribution of interictal spikes and the frequency and time of occurrence of seizures were examined from twenty 17 to 72-hour continuous electroencephalograms recorded by radiotelemetry from five patients with epilepsy and behavior disorders. Power spectral analysis of spike incidence over time indicates that there is waxing and waning of interictal spike or spike-wave activity both day and night at periods harmonically related to 90 minutes. Seizure occurrence was noted to be systematically related to this spike cycle. Spontaneous and induced decrements in the mean interictal spike rate were followed by rebound in the abundance and duration of spike bursts. The time of occurrence of clinical seizures appears to be determined by the interaction of interictal spike rate, a 90-minute cyclic suppression of same and sensory, including affective, stimuli.
Poisson regression models are widely used in analyzing count data. This article develops tests for detecting extra-Poisson variation in such situations. The tests can be obtained as score tests against arbitrary mixed Poisson alternatives and are generalizations of tests of Fisher (1950) and Collings and Margolin (1985). Accurate approximations for computing significance levels are given, and the power of the tests against negative binomial alternatives is compared with those of the Pearson and deviance statistics. One way to test for extra-Poisson variation is to fit models that parametrically incorporate and then test for the absence of such variation within the models; for example, negative binomial models can be used in this way (Cameron and Trivedi 1986; Lawless 1987a). The tests in this article require only the Poisson model to be fitted. Two test statistics are developed that are motivated partly by a desire to have good distributional approximations for computing significance levels. Simulations suggest that one of the statistics should be satisfactory for testing extra-Poisson variation in most practical situations involving Poisson regression models.
In a growing class of neurophysiological experiments, the train of impulses (“spikes”) produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains.
In representing a realationship between a response and a number of independent variables, it is preferable when possible to work with a simple functional form in transformed variables rather than with a more complicated form in the original variables. This paper describes and illustrates a procedure to estimate appropriate transformations in this context.
Nineteen patients, aged 4 to 44 years, with generalized epilepsy and generalized 3 Hz spike-and-wave activity, were monitored for 24 or 36 hours on one to nine occasions to determine the time distribution of ictal and interictal electroencephalographic discharges and the consistency of the time patterns observed. Two patients with a low incidence of spike-and-wave activity had a random pattern of occurrence; the other 17 showed one of three types of time modulation. The latter can be explained by the interaction of two modulative processes, one with a period of about 100 minutes and the other of approximately 24 hours. The pattern observed depends on the phase angle of the two cyclic processes. Treatment with ethosuximide altered the pattern of time modulation, and eventually, when the spike-and-wave activity was reduced to a low level, the time distribution became random.
Die statistische Kommunikationstheorie hat Methoden entwickelt, die es auch in der Neuropsychiatrie erlauben, periodische Komponenten in Zeitfunktionen aufzudecken. Eine dieser Methoden, die Bestimmung des Varianz-Spektrums von zeitabhngigen Daten, wird in ihren Voraussetzungen und Grundzgen erlutert. Am Beispiel der zeitlichen Verteilung cerebraler Anflle wird in drei langfristigen Einzelverlufen die Bedeutung periodischer Komponenten fr die Anfallsmanifestation nachgewiesen.Diese periodischen Komponenten bei Epilepsiekranken zeigen folgende Eigenschaften:
1.
Die Periodenlnge betrgt bei den zwei untersuchten Mnnern 20 bzw. 22 Tage, bei der untersuchten Frau 25 Tage.
2.
Die Periodenform weicht in allen drei Fllen von der einer Sinusfunktion deutlich ab.
3.
Bei der untersuchten Frau findet sich eine Bindung des Maximums der Anfallshufigkeit an einen umgrenzten Abschnitt des Menstruationscyclus, der wahrscheinlich dem Follikelsprung entspricht.
4.
Diese Bindung wird dadurch verdeutlicht, da die antikonvulsive Therapie vorwiegend die Zahl der nicht periodisch auftretenden, zufallsgebundenen Anflle vermindert.
Die dargestellte Methode erffnet somit neue Mglichkeiten zur Bearbeitung weiterer klinischer Probleme und berechtigt durch ihre Ergebnisse, im epileptischen Anfall eine pathologische Vernderung eines biologischen Regelvorganges zu sehen.
To define a likelihood we have to specify the form of distribution of the observations, but to define a quasi-likelihood function
we need only specify a relation between the mean and variance of the observations and the quasi-likelihood can then be used
for estimation. For a one-parameter exponential family the log likelihood is the same as the quasi-likelihood and it follows
that assuming a one-parameter exponential family is the weakest sort of distributional assumption that can be made. The Gauss-Newton
method for calculating nonlinear least squares estimates generalizes easily to deal with maximum quasi-likelihood estimates,
and a rearrangement of this produces a generalization of the method described by Nelder & Wedderburn (1972).
This paper discusses a quasi-likelihood (QL) approach to regression analysis with time series data. We consider a class of Markov models, referred to by Cox (1981, Scandinavian Journal of Statistics 8, 93-115) as "observation-driven" models in which the conditional means and variances given the past are explicit functions of past outcomes. The class includes autoregressive and Markov chain models for continuous and categorical observations as well as models for counts (e.g., Poisson) and continuous outcomes with constant coefficient of variation (e.g., gamma). We focus on Poisson and gamma data for illustration. Analogous to QL for independent observations, large-sample properties of the regression coefficients depend only on correct specification of the first conditional moment.
Seizure diaries were maintained prospectively in 24 epileptic patients (19 with partial complex, three with partial simple, and three with primary generalized seizures) who were selected consecutively, had stable seizure patterns, were reliable historians, and were known to be compliant with medications. Diaries were maintained for an average of 237 days (range, 61–365), and an average of 18 seizures were recorded per patient (range, 5–76). Seizure patterns were analyzed by using the methods appropriate for a time series of events (point process). Two patients had a decreasing trend in seizure frequency. For 12 patients, seizure occurrence was indistinguishable from that of a Poisson process. The remaining 10 patients had an exponential distribution of seizure intervals, but did not fit other criteria for a Poisson process; 3 of these showed evidence for seizure clustering; none showed evidence for a seizure cycle. It is concluded that the pattern of seizure occurrence in most epileptic people is random, but in approximately 50%, it is not occurring according to a Poisson process. These observations indicate that seizure cycling and/or clustering are not common in epileptic patients, but do not exclude the possibility that seizures have been precipitated by some randomly occurring event, such as sleep deprivation or increased stress.
RÉSUMÉ
Un relevé quotidien des crises a été effectué de façon prospective chez 24 patients épileptiques (19 présentant des crises par‐tielles complexes, 3 des crises partielles élémentaires et 3 des crises généralisées). Ces patients ont été sélectionnés consécu‐tivement, ont été jugés fiables dans le relevé des crises et dans l'observance thérapeutique. Les relevés ont été effectués pendant une moyenne de 237 jours (61 à 365) et une moyenne de 18 crises par patient a été enregistrde (de 5 à 76). La distribution des crises a étéétudiée par les moyens adaptés aux phénomènes répétés dans le temps. Chez 2 patients nous avons constaté une tendance à l'espacement des crises. Chez 12 patients, la fréquence des crises n'a pas été différente d'une répartition suivant la loi de Poisson. Les 10 patients restants présentaient une distribution exponentielle des intervalles entre les crises mais ne rgpondaient pas aux autres critères de la loi de Poisson. 3 d'entre eux présentaient des crises en séries; aucun ne présentait de cyclicité des crises. Nous concluons que la distribution des crises est, chez la plupart des patients, le fait du hasard, mais que, chez environ 50% des patients, cette répartition ne suit pas une loi de Poisson. Ces observations indiquent qu'une distribution cy‐clique ou en série des crises n'est pas fréquente chez les épilep‐tiques, mais n'excluent pas la possibilityé que les crises peuvent avoir été provoquées par quelque évènement survenant au hasard, comme un manque de sommeil ou un stress particulier.
RESUMEN
Se nan elaborado diarios con respecto al número de ataques, de modo prospectivo, en 24 enfermos epilépticos (19 con ataques complejos parciales, 3 con ataques simples parciales y 3 con ataques generalizados primarios) que se seleccionaron con‐secutivamente; Tenfan patrones de ataques estables, eran histor‐iadores Cables y eran conocidos por su nivel de confianza en la administration de medicamentos. Los diarios se mantuvieron durante un promedio de 237 días (rango de 61 a 365) registrán‐dose un promedio de 18 ataques por paciente (rango de 5 a 76). Los patrones de los ataques fueron analizados utilizando meto‐dología apropiada para la serie de acontecimientos en el tiempo (proceso puntual). Dos pacientes mostraron una reducción de la tendencia a la frecuentia de ataques. En 12 pacientes la aparición de los episodios fue indistinguible de la del proceso de Poisson. Los restantes 10 pacientes tenian una distribución exponential de los intervalos de ataques pero no encajó en otros criterios para un proceso Poisson; 3 de ellos mostraron evi‐dencia de “ataques acumulados”. Ninguno presentó un ciclo de ataques. Se concluye que el partón aparición de ataques en la mayor parte de las personas epilépticas es aleatorio pero que en, aproximadamente el 50%, no ocurre de acuerdo con un procese Poisson. Estas observaciones indican que los ciclos de los ataques y/o su “acumulo” no son comunes en enfermos epilép‐ticos pero no excluyen la posibilidad de que los ataques hayan sido precipitados por un acontecimiento que ocurra aleatoria‐ mente tales como la privacyón del sueno o el incremenlo de stress.
The analysis of the results of many well-designed, double-blind trials of anticonvulsant drugs has been unsophisticated. We draw attention to the nonrandom occurrence of seizures, which negates the simple comparison of average seizure frequency. We propose a method of taking into account clustering of seizures when deciding on the appropriate length of follow-up after introducing a new treatment. Deterministic and nondeterministic models were used to show why there may be reasons for sometimes using more than one drug in the treatment of epilepsy.
Twenty-four hour EEG records were recorded by radiotelemetry during free behavior from five patients with seizures for a total of 18 days and nights. Quantitative measurements of duration of epileptiform bursts, frank seizures and inter-spike and inter-seizure intervals were made from magnetic tape and ink polygraph records. Total consecutive spikes per 4 min over 24 h were enumerated automatically by a specially designed spike recognition unit. Proportional relationships were demonstrated between seizure length and pre- and post-seizure intervals. A broad or quasi normal distribution was found for diurnal seizure and interval durations in contrast to a random (Poisson) distribution for corresponding nocturnal ictal events. A relatively constant inter-ictal spike rate, interrupted every 80 to 120 min was shown for most patients during both day and night. Nocturnally, the period of spike interruption coincided with rapid eye movement (REM) sleep, and spike recurrence with slow wave sleep. A tendency for clinical ictus to occur in relation to the REM periods was apparent.
(1) A stochastic analysis was carried out on the inter-ictal events in order to unveil their epileptiform properties and to assess their epileptogenicity. (2) Inter-ictally occurring "B" bursts, consisting of a sharp high voltage wave followed by a polyspike discharge, were found to produce a depression lasting 3 or more seconds. This was not manifested by the A bursts, the wave repetition rate of which is less than half of that of the B burst. (3) AB events consisting of A immediately followed by B were observed inter-ictally and also at the start of each seizure. Evidence was obtained that these could only partly arise by B cutting randomly into A. The majority of AB's must be due to a triggering mechanism by which some A's induce the appearance of B's. (4) The distributions of the inter-event intervals and of the duration of A bursts fitted incomplete gamma functions. The durations of B's showed J shaped distributions.
A method for microphysiological study of the brain for experimental or diagnostic purposes by means of multiple microelecrodes is described and discussed. An improved model of the multiple microelectrode manipulator is presented. Means for remote control orautomation of the micro-driving are described. Application of these methods in experimental electroencephalography especially in correlation of single neurone activity and EEG are discussed.
The time relations of epileptic events have been studied in 3 sets of data: (I) counts of individual epileptiform discharges in twelve 48 h EEG recordings, (IIa) seizure calendars of 30 therapy-resistant outpatients participating in a drug trial, (IIb) seizure calendars of 10 mentally subnormal epileptic patients resident in a long-stay unit. The EEG data I were characterized most often by a Poisson distribution of intervals between discharges and the occurrence of marked periodicities, particularly at night. The periods of rhythmic nocturnal events ranged from 13 to 142 min and did not appear to correspond to the REM/non-REM cycle. In the seizure data IIa and b a Poisson distribution of intervals between events was found in half the patients. Periodicities occurred only in group IIa and did not correspond to weekly or monthly cycles. A stochastic process is considered to be the model which best fits these data.
The possibility of connections between weather and the onset of epileptic seizures has long been suggested (see, for example, the Hammurabi Codex 1600 BC). Work in the 20th Century points to a probability that the onset of both local and generalised epilepsy is significantly influenced by an interaction between genetic and extrinsic factors. In an attempt to clarify the situation a detailed study of the history of 315 attacks from 1 Jan. to 31 July 1981 suffered by a small number of patients in Munich has been undertaken. Although linkages between “classical” meteorological parameters and the onset of seizures are very weak, links with more generalised indexes (e.g. passage of fronts and disturbances) are more promising. However, the correlation between onsets and “atmospherics” of 28 KHz (positive) and 10 KHz (negative) impulses, are significant and call for urgent study.
This study examines the effects of major life events, daily hassles and uplifts, and daily stress levels as they increase or decrease the risks of having seizures and estimates risk ratios for specific stressors and perceived stress levels. Utilizing a prospective design, 12 adults with severe epilepsy monitored the occurrence of seizures, stressors, and stress levels over a 3‐month period. In within‐individual analyses, high stress levels and stressful events were associated with more frequent seizures for most participants. The association between higher stress levels and increased seizures was confirmed in group analyses. This study provides empirical evidence of the association between stress and seizures and describes the use of a statistical model that is useful for investigating risk factors as they influence physical and mental illness.
RESUMEN
Este estudio examina los efectos de los acontecimientos importantes de la vida, los esfuerzos y recompensas diarias y los nivelés de tensión emocional que puedan aumentar o reducir los riesgos de padecer ataques y también estima la proporción de riesgos de ciertos esfuerzos así como los nivelés de percepción de los mismos. Utilizando un diseño prospectivo, 12 adultos con epilepsía severa se monitorizaron ellos mismos la ocurrencia de ataques, factores que condicionaban tensión emocional y los nivelés de tensión durante un periodo de 3 meses. En los análisis intra‐individuales se encontró una asociación entre los altos nivelés de tension, y los acontecimientos que producian tension, con un incremento de la frecuencia de ataques en la mayoría de los participantes. La asociación entre los nivelés altos de tensión emocional y el incremento de ataques fué confirmada en los análisis de grupo. El estudio proporciona evidencia empírìca de que existe una asociación entre tension emocional (stress) y ataques y también describe la utilización de un modelo estadístico que tiene la utilidad de investigar los factores de riesgo que pueden influenciar enfermedades físicas y mentales.
This review of 126 reports on catamenial epilepsy describes seizure exacerbations associated with the menses. The importance of hormonal measurements, the influence of antiepileptic drugs and oral contraceptives, and the significance of hormonal changes on epilepsy are evaluated. The EEG changes during menses are discussed. Explanations for conflicting data are offered, and potential future investigations on catamenial epilepsy are suggested.
Esta revisión de 126 casos de “epilepsyía catamenial” describe la exacerbación de las crisis durante los periodos menstruales. La importancia de las mediciones hormonales, de la influencia entre anticomiciales y contraceptivos orales y la significación de los cambios hormonales en la epilepsyía son tratadas en este estudio. Se dan explicaciones para los datos contradictories existentes y se sugieren vias de investigación potencial sobre la “epilepsyía catamenial”.
Periodicity of seizure activity in persons with complex partial seizures
- Bowman T.
L'epilepsie cyclique
- Denys WJ
The influence of lunar and seasonal periodicity on epileptic seizures
- Pasternak M.