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Background: The placebo response in epilepsy randomized clinical trials (RCTs) has recently been shown to largely reflect underlying natural variability in seizure frequency. Based on this observation, we sought to explore the parameter space of RCT design to optimize trial efficiency and cost., Methods: We used one of the world's largest patient reported seizure diary databases, SeizureTracker.com to derive virtual patients for simulated RCTs. We ran 1000 randomly generated simulated trials using bootstrapping (sampling with replacement) for each unique combination of trial parameters, sweeping a large set of parameters in durations of the baseline and test periods, number of patients, eligibility criteria, drug effect size, and patient dropout. We studied the resulting trial efficiency and cost., Results: A total of 6,732,000 trials were simulated, drawing from 5097 patients in the database. We found that the strongest regression predictors of placebo response were durations of baseline and test periods. Drug effect size had a major impact on trial efficiency and cost. Dropout did not have a major impact on trial efficiency or cost. Eligibility requirements impacted trial efficiency to a limited extent. Cost was minimized while maintaining statistical integrity with very short RCT durations., Discussion: This study suggests that RCT parameters can be improved over current practice to reduce costs while maintaining statistical power. In addition, use of a large-scale population dataset in a massively parallel computing analysis allows exploration of the wider parameter space of RCT design prior to running a trial, which could help accelerate drug discovery and approval.

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., (Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.)

Objective: Sudden unexplained death in epilepsy (SUDEP) during inpatient electroencephalography (EEG) monitoring has been a rare but potentially preventable event, with associated cardiopulmonary markers. To date, no systematic evaluation of alarm settings for a continuous pulse oximeter (SpO 2 ) has been performed. In addition, evaluation of the interrelationship between the ictal and interictal states for cardiopulmonary measures has not been reported., Methods: Patients with epilepsy were monitored using video-EEG, SpO 2 , and electrocardiography (ECG). Alarm thresholds were tested systematically, balancing the number of false alarms with true seizure detections. Additional cardiopulmonary patterns were explored using automated ECG analysis software., Results: One hundred ninety-three seizures (32 generalized) were evaluated from 45 patients (7,104 h recorded). Alarm thresholds of 80-86% SpO 2 detected 63-73% of all generalized convulsions and 20-28% of all focal seizures (81-94% of generalized and 25-36% of focal seizures when considering only evaluable data). These same thresholds resulted in 25-146 min between false alarms. The sequential probability of ictal SpO 2 revealed a potential common seizure termination pathway of desaturation. A statistical model of corrected QT intervals (QTc), heart rate (HR), and SpO 2 revealed close cardiopulmonary coupling ictally. Joint probability maps of QTc and SpO 2 demonstrated that many patients had baseline dysfunction in either cardiac, pulmonary, or both domains, and that ictally there was dissociation-some patients exhibited further dysfunction in one or both domains., Significance: Optimal selection of continuous pulse oximetry thresholds involves a tradeoff between seizure detection accuracy and false alarm frequency. Alarming at 86% for patients that tend to have fewer false alarms and at 80% for those who have more, would likely result in a reasonable tradeoff. The cardiopulmonary findings may lead to SUDEP biomarkers and early seizure termination therapies., (Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.)

Purpose: There is controversy about relative contributions of ictal scalp video EEG recording (vEEG), routine scalp outpatient interictal EEG (rEEG), intracranial EEG (iEEG) and MRI for predicting seizure-free outcomes after temporal lobectomy. We reviewed NIH experience to determine contributions at specific time points as well as long-term predictive value of standard pre-surgical investigations., Methods: Raw data was obtained via retrospective chart review of 151 patients. After exclusions, 118 remained (median 5 years follow-up). MRI-proven mesial temporal sclerosis (MTSr) was considered a separate category for analysis. Logistic regression estimated odds ratios at 6-months, 1-year, and 2 years; proportional hazard models estimated long-term comparisons. Subset analysis of the proportional hazard model was performed including only patients with commonly encountered situations in each of the modalities, to maximize statistical inference., Results: Any MRI finding, MRI proven MTS, rEEG, vEEG and iEEG did not predict two-year seizure-free outcome. MTSr was predictive at six months (OR=2.894, p=0. 0466), as were MRI and MTSr at one year (OR=10.4231, p=0. 0144 and OR=3.576, p=0. 0091). Correcting for rEEG and MRI, vEEG failed to predict outcome at 6 months, 1year and 2 years. Proportional hazard analysis including all available follow-up failed to achieve significance for any modality. In the subset analysis of 83 patients with commonly encountered results, vEEG modestly predicted long-term seizure-free outcomes with a proportional hazard ratio of 1.936 (p=0.0304)., Conclusions: In this study, presurgical tools did not provide unambiguous long-term outcome predictions. Multicenter prospective studies are needed to determine optimal presurgical epilepsy evaluation., (Published by Elsevier B.V.)

Randomized placebo-controlled trials are a mainstay of modern clinical epilepsy research; the success or failure of innovative therapies depends on proving superiority to a placebo. Consequently, understanding what drives response to placebo (including the "placebo effect") may facilitate evaluation of new therapies. In this review, part one will explore observations about placebos specific to epilepsy, including the relatively higher placebo response in children, apparent increase in placebo response over the past several decades, geographic variation in placebo effect, relationship to baseline epilepsy characteristics, influence of nocebo on clinical trials, the possible increase in (SUDEP) in placebo arms of trials, and patterns that placebo responses appear to follow in individual patients. Part two will discuss the principal causes of placebo responses, including regression to the mean, anticipation, classical conditioning, the Hawthorne effect, expectations from symbols, and the natural history of disease. Included in part two will be a brief overview of recent advances using simulations from large datasets that have afforded new insights into causes of epilepsy-related placebo responses. In part three, new developments in study design will be explored, including sequential parallel comparison, two-way enriched design, time to pre-randomization, delayed start, and cohort reduction techniques., (Published by Elsevier B.V.)

For unknown reasons, placebos reduce seizures in clinical trials in many patients. It is also unclear why some drugs showing statistical superiority to placebo in one trial may fail to do so in another. Using Seizuretracker.com, a patient-centered database of 684,825 seizures, we simulated "placebo" and "drug" trials. These simulations were employed to clarify the sources of placebo effects in epilepsy, and to identify methods of diminishing placebo effects. Simulation 1 included 9 trials with a 6-week baseline and 6-week test period, starting at time 0, 3, 6…24 months. Here, "placebo" reduced seizures regardless of study start time. Regression-to-the-mean persisted only for 3 to 6 months. Simulation 2 comprised a 6-week baseline and then 2 years of follow-up. Seizure frequencies continued to improve throughout follow-up. Although the group improved, individuals switched from improvement to worsening and back. Simulation 3 involved a placebo-controlled "drug" trial, to explore methods of placebo response reduction. An efficacious "drug" failed to demonstrate a significant effect compared with "placebo" (p = 0.12), although modifications either in study start time (p = 0.025) or baseline population reduction (p = 0.0028) allowed the drug to achieve a statistically significant effect compared with placebo. In epilepsy clinical trials, some seizure reduction traditionally attributed to placebo effect may reflect the natural course of the disease itself. Understanding these dynamics will allow future investigations into optimal clinical trial design and may lead to identification of more effective therapies. Ann Neurol 2015;78:329-336., (© 2015 American Neurological Association.)

Background: Autoimmune encephalitis is increasingly identified as a cause of nonviral, idiopathic encephalitis. Present treatment algorithms recommend costly immune-modulating treatments and do not identify a role for oral corticosteroids., Objective: To present a patient with γ-aminobutyric acid(B) receptor-antibody encephalitis before and after treatment with oral corticosteroids., Design: Case report., Setting: The inpatient course as well as outpatient follow-up is discussed., Patient: A 43-year-old man with initial presentation of seizures and altered mental status., Intervention: Our patient was treated with an extended course of oral corticosteroids as an outpatient., Results: After treatment with oral corticosteroids, our patient had steady clinical improvement, achieved seizure freedom, and experienced improved mental status to within normal limits., Conclusions: This case supports the use of low-cost oral corticosteroids in treating patients with γ-aminobutyric acid(B) receptor-antibody encephalitis.

Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Although magnetoencephalography (MEG) and electroencephalography (EEG) have been available for decades, their relative merits are still debated. We examined regional differences in signal-to-noise-ratios (SNRs) of cortical sources in MEG and EEG. Data from four subjects were used to simulate focal and extended sources located on the cortical surface reconstructed from high-resolution magnetic resonance images. The SNR maps for MEG and EEG were found to be complementary. The SNR of deep sources was larger in EEG than in MEG, whereas the opposite was typically the case for superficial sources. Overall, the SNR maps were more uniform for EEG than for MEG. When using a noise model based on uniformly distributed random sources on the cortex, the SNR in MEG was found to be underestimated, compared with the maps obtained with noise estimated from actual recorded MEG and EEG data. With extended sources, the total area of cortex in which the SNR was higher in EEG than in MEG was larger than with focal sources. Clinically, SNR maps in a patient explained differential sensitivity of MEG and EEG in detecting epileptic activity. Our results emphasize the benefits of recording MEG and EEG simultaneously., (2008 Wiley-Liss, Inc.)