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Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts.A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead.One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained50% for forecast horizons of up to 7 days. Superior performance (mean error = .99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day.This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.

OBJECTIVE: This study was undertaken to measure the duration of chronic electrocorticography (ECoG) needed to attain stable estimates of the seizure laterality ratio in patients with drug-resistant bilateral temporal lobe epilepsy (BTLE). METHODS: We studied 13 patients with drug-resistant BTLE who were implanted for at least 1 year with a responsive neurostimulation device (RNS System) that provides chronic ambulatory ECoG. Bootstrap analysis and nonlinear regression were applied to model the relationship between chronic ECoG duration and the probability of capturing at least one seizure. Laterality of electrographic seizures in chronic ECoG was compared with the seizure laterality ratio from Phase 1 scalp video-electroencephalographic (vEEG) monitoring. The Kaplan–Meier estimator was used to evaluate time to seizure laterality ratio convergence. RESULTS: Seizure laterality ratios from Phase 1 scalp vEEG monitoring correlated poorly with those from RNS chronic ECoG (r = .31, p = .30). Across the 13 patients, average electrographic seizure frequencies ranged from 1.4 seizures/month to 5.1 seizures/day. A 50% probability of recording at least one electrographic seizure required 9.1 days of chronic ECoG, and 90% probability required 44.3 days of chronic ECoG. A median recording duration of 150.9 days (5 months), corresponding to a median of 16 seizures, was needed before confidence intervals for the seizure laterality ratio reliably contained the long-term value. The median recording duration before the point estimate of the seizure laterality ratio converged to a stationary value was 236.8 days (7.9 months). SIGNIFICANCE: RNS chronic ECoG overcomes temporal sampling limitations intrinsic to inpatient Phase 1 vEEG evaluations. In patients with drug-resistant BTLE, approximately 8 months of chronic RNS ECoG are needed to precisely estimate the seizure laterality ratio, with 75% of people with BTLE achieving convergence after 1 year of RNS recording. For individuals who are candidates for unilateral resection based on seizure laterality, optimized recording duration may help avert morbidity associated with delay to definitive treatment.

Purpose Recently a realistic simulator of patient seizure diaries was developed that can reproduce effects seen in randomized clinical trials (RCTs). RCTs suffer from high costs and statistical inefficiencies. Using realistic simulation and machine learning this study aimed to identify a more statistically efficient outcome metric. Methods Five candidate deep learning architectures with 54 permutations of hyperparameters were compared to the traditional standard, median percent change (MPC). Each were also tested for type 1 error. All models had similar outcomes, with appropriate low levels of type 1 error. Results The simplest model was equivalent to a logistic regression of a histogram of individual percentage changes in seizure rate, requiring 21-22% less patients to discriminate drug from placebo at 90% power. This model was referred to as LPC. Conclusion Future studies to validate LPC may enable faster, cheaper and more efficient clinical trials.

In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy.A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria.A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n = 95, 87.2%; caregivers, n = 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n = 43, 78.2%; caregivers, n = 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity.Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

Objective A major source of disability for people with epilepsy involves uncertainty surrounding seizure timing and severity. Although patients often report that long seizure‐free intervals are followed by more severe seizures, there is little experimental evidence supporting this observation. Optimal characterization of seizure severity is debated; however, seizure duration is associated with seizure type and can be quantified in electrographic recordings as a limited proxy of clinical seizure severity. Here, using chronic intracranial electroencephalography (cEEG), we investigate the relationship between interseizure interval (ISI) and duration of the subsequent seizure. Methods We performed a retrospective analysis of 14 subjects implanted with a responsive neurostimulation device (RNS System) that provides cEEG, including timestamps of electrographic seizures. We determined seizure durations for isolated seizures and for representative seizures from clusters determined through unsupervised methods. For each subject, the median ISI preceding long‐duration seizures, defined as the top quintile of seizure durations, was compared with the median ISI preceding seizures with durations in the residual quintiles. In a group analysis, the mean seizure duration and the proportion of long‐duration seizures were compared across ISI categories representing different lengths. Results For 5 out of 14 subjects (36%), the median ISI preceding long‐duration seizures was significantly greater than the median ISI preceding shorter‐duration seizures. In the group analysis, when ISI was categorized by length, the proportion of long‐duration seizures within the high ISI category was significantly higher than that of the low ISI category (P

PURPOSE: This study aims to characterize the natural history of generalized motor seizures through longitudinal stratification of patient-reported clinical seizures into high, medium and low rates of generalized motor seizures (also known as generalized tonic-clonic seizures or GTCs). METHODS: From 2007–2018, 1.4 million seizures were recorded by 12,402 SeizureTracker.com users that met inclusion/exclusion criteria. The number of GTCs per year since the first seizure diary entry was calculated for each user and categorized as: Low (0 GTCs/year), Medium (1–2 GTCs/year), or High (≥ 3 GTCs/year) GTC rates. RESULTS: Kaplan-Meier survival curves for the time until exiting the initial category were computed. There was a global difference between risk groups (p

Preemptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of preempting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm.

Objective Adults living with intellectual and developmental disability (IDD) and epilepsy (IDD-E) face challenges in addition to those faced by the general population of adults with epilepsy, which may be associated with distinct priorities for improving health-related quality of life (HR-QOL). This study sought to (1) conduct a survey of HR-QOL priorities identified by adults with IDD-E and caregivers, and (2) perform an exploratory cross-sectional comparison to adults with epilepsy who do not have IDD. Methods This cross-sectional study recruited 65 adults with IDD-E and 134 adults with epilepsy without IDD and caregivers. Using a three-step development process, 256 items from existing quality-of-life scales recommended by the American Academy of Neurology (AAN) were rated by patients/caregivers for their importance as HR-QOL priorities. HR-QOL items identified as critical to the majority of the sample of adults with IDD-E were reported. Health-related quality of life priorities were compared between adults with IDD-E and adults with epilepsy without IDD. Results Health-related quality of life was significantly lower in adults with IDD-E. Health-related quality of life domains identified as critical priorities by adults with IDD-E included seizure burden, anti-seizure medication side effects, seizure unpredictability, and family impact. Priorities for improving HR-QOL differed between adults with and without IDD-E, with concerns about family impact, difficulty finding appropriate living conditions, inadequate assistance, and difficulty transitioning from pediatric-to-adult care valued significantly more among those with IDD-E. Significance Intellectual and developmental disability is an important determinant of HR-QOL among adults with epilepsy. We report HR-QOL priorities identified by adults with IDD-E and their caregivers. These results may help epilepsy clinicians and researchers develop tailored strategies to address priorities of the patient with IDD-E/caregiver community.

Summary Objective To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers. Methods Zero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively. Results A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p 5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p

Author(s): Chiang, Sharon; Vannucci, Marina; Goldenholz, Daniel M; Moss, Robert; Stern, John M | Abstract: ObjectiveA 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.MethodsUsing 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.ResultsEpiSAT 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 l12 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.SignificanceWe 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.

ObjectiveWe conducted clinical testing of an automated Bayesian machine learningalgorithm (Epilepsy Seizure Assessment Tool [EpiSAT]) for outpatient seizure risk assessment using seizure counting data, and validated performance against specialized epilepsy clinicianexperts.MethodsWe conducted a prospective longitudinal study of EpiSAT performance against 24 specialized clinician expertsat three tertiary referral epilepsy centers in the United States. Accuracy, interrater reliability, and intra-rater reliability of EpiSATfor correctly identifyingchanges inseizure risk (improvements, worsening, or no change) were evaluated using 120 seizures from four synthetic seizure diaries (seizure risk known) and 120 seizures from four real seizure diaries (seizure risk unknown). The proportion of observed agreement between EpiSAT and clinicians was evaluated to assess compatibility of EpiSAT with clinical decision patterns by epilepsy experts.ResultsEpiSAT exhibited substantial observed agreement (75.4%) with clinicians for assessing seizure risk. The mean accuracy of epilepsy providers for correctly assessing seizure risk was 74.7%. EpiSAT accurately identified seizure risk in 87.5% of seizure diary entries, corresponding to a significant improvement of 17.4% (P=.002). Clinicians exhibited low-to-moderate interrater reliability for seizure risk assessment (Krippendorff's α=0.46) with good intrarater reliability across a 4- to 12-week evaluation period (Scott's π=0.89).SignificanceThese results validate the ability of EpiSAT to yield objective clinical recommendations on seizure risk which follow decision patterns similar to those from specialized epilepsy providers, but with improved accuracy and reproducibility. This algorithm may serve as a useful clinical decision support system for quantitative analysis of clinical seizure frequency in clinical epilepsy practice.

In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

Introduction The unpredictability of epilepsy has a severe impact on health-related quality of life (HR-QOL) for people with epilepsy. Seizure detection devices have the potential to improve HR-QOL by improving seizure safety, reducing caregiver hypervigilance, and reducing seizure anxiety. Emerging data have led to an improved understanding of characteristics that promote acceptability of detection devices for people with epilepsy and caregivers. However, whether usage of seizure detection devices is associated with clinically meaningful improvement in anxiety and HR-QOL remains poorly understood. Methods We analyzed cross-sectional survey data collected first-hand from 371 people with epilepsy and caregivers on seizure detection device and HR-QOL using an enriched population of electronic seizure diary users. Metrics related to quality of life and anxiety reduction were compared between users and nonusers of seizure detection devices. Results Compared with nonusers of seizure detection devices, device users were significantly more likely to have been impacted by epilepsy in multiple HR-QOL domains, including anxiety, mood, emotional regulation/aggression, speech/language, sleep quality, social life, activities of daily living, independence, and education/academic potential. The majority (80.2%) of people using seizure detection devices experienced moderate or greater anxiety reduction from seizure detection device usage, while 11.1% reported that detection devices did not help at all with anxiety. Despite potential benefit, seizure detection devices were used only by a minority (21.8%) of people with epilepsy surveyed, and usage tended to be skewed toward younger patient age, higher income, and caregivers. There was no significant difference in overall HR-QOL between users and nonusers. Conclusions Seizure detection devices provide moderate or greater anxiety reduction among the majority of people with epilepsy and their caregivers, but current translatability into improvements in overall HR-QOL may be limited. Affordability and technological support are potential barriers to maximizing benefit equally among the epilepsy community. These considerations may be useful to help guide future device development and inform patient–clinician discussions on device usage and HR-QOL.

Purpose Temporal lobe epilepsy (TLE) is thought to be a network disease and structural changes using diffusion tensor imaging (DTI) have been shown. However, lateralized differences in the structural integrity of TLE, as well as changes in structural integrity with longer disease duration, have not been well defined. Methods We examined the fractional anisotropy (FA) and mean diffusivity (MD) in the hippocampus, as well as its primary (cingulum and fornix) and remote (uncinate and external capsule) connections in both right and left TLE. Changes in diffusion measures over the disease course were examined by correlating FA and MD in the various structures with epilepsy duration. The potential for each measure of anisotropy and diffusivity as a marker of TLE laterality was investigated using random forest (RF) analysis. Results MD was increased in the bilateral hippocampus, cingulum, fornix and the right external capsule in both left and right TLE compared to controls. In addition, left TLE exhibited an increased MD in the ipsilateral uncinate fasciculus and bilateral external capsules. A decrease in FA was seen in the left cingulum in left TLE. RF analysis demonstrated that MD of the right hippocampus and FA of the left external capsule were important predictors of TLE laterality. An association of increased MD with epilepsy duration was seen in the left hippocampus in left TLE. Conclusion Evidence of disrupted white matter architecture in the hippocampus and its primary and remote connections were demonstrated in TLE. While changes in the hippocampus and cingulum were more prominent in right TLE, remote changes were more prominent in left TLE. MD of the right hippocampus and FA of the left external capsule were found to be the strongest structural predictors of TLE laterality. Changes associated with duration of epilepsy indicated that changes in structural integrity may be progressive over the disease course. This study illustrates the potential of structural diffusion tensor imaging in elucidating pathophysiology, enhancing diagnosis and assisting prognostication.

Patients with refractory epilepsy undergo video-electroencephalography for seizure characterization, among whom approximately 10-30% will be discharged with the diagnosis of psychogenic nonepileptic seizures (PNESs). Clinical PNES predictors have been described but in general are not sensitive or specific. We evaluated whether multiple complaints in a routine review-of-system (ROS) questionnaire could serve as a sensitive and specific marker of PNESs. We performed a retrospective analysis of a standardized ROS questionnaire completed by patients with definite PNESs and epileptic seizures (ESs) diagnosed in our adult epilepsy monitoring unit. A multivariate analysis of covariance (MANCOVA) was used to determine whether groups with PNES and ES differed with respect to the percentage of complaints in the ROS questionnaire. Tenfold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on the percentage of positive complaints in the ROS questionnaire. A total of 44 patients were included for analysis. Patients with PNESs had a significantly higher number of complaints in the ROS questionnaire compared to patients with epilepsy. A threshold of 17% positive complaints achieved a 78% specificity and 85% sensitivity for discriminating between PNESs and ESs. We conclude that the routine ROS questionnaire may be a sensitive and specific predictive tool for discriminating between PNESs and ESs.

SummaryObjective 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.

Summary Purpose To investigate lateralized differences in limbic system functional connectivity between left and right temporal lobe epilepsy (TLE) using graph theory. Methods Interictal resting state fMRI was performed in 14 left TLE patients, 11 right TLE patients, and 12 controls. Graph theory analysis of 10 bilateral limbic regions of interest was conducted. Changes in edgewise functional connectivity, network topology, and regional topology were quantified, and then left and right TLE were compared. Results Limbic edgewise functional connectivity was predominantly reduced in both left and right TLE. More regional connections were reduced in right TLE, most prominently involving reduced interhemispheric connectivity between the bilateral insula and bilateral hippocampi. A smaller number of limbic connections were increased in TLE, more so in left than in right TLE. Topologically, the most pronounced change was a reduction in average network betweenness centrality and concurrent increase in left hippocampal betweenness centrality in right TLE. In contrast, left TLE exhibited a weak trend toward increased right hippocampal betweenness centrality, with no change in average network betweenness centrality. Conclusion Limbic functional connectivity is predominantly reduced in both left and right TLE, with more pronounced reductions in right TLE. In contrast, left TLE exhibits both edgewise and topological changes that suggest a tendency toward reorganization. Network changes in TLE and lateralized differences thereof may have important diagnostic and prognostic implications.

PurposeTemporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30–50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes.MethodsWe performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE.ResultsAlthough different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed.ConclusionsFuture studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility.

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE.

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.

Until the present day, U.S. health care systems have adopted various measures to maximize cost savings. It has been well known to encourage or have mandatory requirements to use cheaper, generic drugs instead of their branded counterparts (Andermann et al., 2007). The U.S. Food and Drug Administration (FDA) has allowed generic medications that have shown short-term bioequivalence to be granted product license. However, bioavailability may be different from branded counterparts because of the use of other chemicals that are used to make generic medications (Borgheini, 2003). Generic medications must have the same dose and form as the brand name and must have been studied and determined to have equivalent bioavailability of the brand name (Andermann et al., 2007). Notwithstanding, variation of generic antiepileptic drugs (AEDs) versus name brand AEDs can be highly problematic among people with epilepsy (PWE). This has led to a debate on the appropriateness of generic substitution of AEDs to generic compounds. Because of the potential for poor clinical outcomes with generic AEDs, several agencies (UK National Institute for Health and Clinical Excellence (Andermann et al., 2007) and the American Academy of Neurology (AAN) (Borgheini, 2003) have instituted policies to discourage the substitution of generic AEDs from brand name due to the potentially negative consequences such as increased monitoring costs and loss of seizure control. Other countries have implemented stricter guidelines in dealing with generic AEDs. In Sweden the Medical Products Agency instituted a policy that certain AEDs (lamotrigine, carbamazepine, phenytoin, valproic acid, gabapentin) cannot be switched from brand name to generic (Borgheini, 2003; Andermann et al., 2007). The Italian Chapter of the International League Against Epilepsy instituted a policy that patients who are in seizure remission or with well-controlled seizure activity on a brand name AED, should not be switched from branded to generic medication (Perucca et al., 2006). Confirmatory observations lend support to the concerns involving generic AEDs. Recent studies have noted high switch back rates, loss of seizure control, increased toxicity, and increased health care utilization costs associated with generic AEDs (Andermann et al., 2007; LeLorier et al., 2008; Kesselheim 2010; Labiner et al., 2010; Sethi et al., 2010). Given these findings of poor outcomes, the AAN has instituted guidelines in 2006 opposing generic substitution of AEDs without attending/physician approval (Liow et al., 2007). Nevertheless, other organizations such as the FDA promote interchangeability with generic and branded AEDs. To our knowledge, none of the recent studies specifically investigated the potential consequences associated with Keppra or generic levetiracetam (LEV). The purpose of our study is to measure the proportion of PWE who switch back from generic to branded LEV required due to poor clinical outcomes directly associated with the generic compound.

© 2015. Temporal lobe epilepsy (TLE) is often associated with progressive changes to seizures, memory, and mood during its clinical course. However, the cerebral changes related to this progression are not well understood. Because the changes may be related to changes in brain networks, we used functional connectivity MRI (fcMRI) to determine whether brain network parameters relate to the duration of TLE. Graph theory-based analysis of the sites of reported regions of TLE abnormality was performed on resting-state fMRI data in 48 subjects: 24 controls, 13 patients with left TLE, and 11 patients with right TLE. Various network parameters were analyzed including betweenness centrality (BC), clustering coefficient (CC), path length (PL), small-world index (SWI), global efficiency (GE), connectivity strength (CS), and connectivity diversity (CD). These were compared for patients with TLE as a group, compared to controls, and for patients with left and right TLE separately. The association of changes in network parameters with epilepsy duration was also evaluated. We found that CC, CS, and CD decreased in subjects with TLE compared to control subjects. Analyzed according to epilepsy duration, patients with TLE showed a progressive reduction in CD. In conclusion, we found that several network parameters decreased in patients with TLE compared to controls, which suggested reduced connectivity in TLE. Reduction in CD associated with epilepsy duration suggests a homogenization of connections over time in TLE, indicating a reduction of the normal repertoire of stronger and weaker connections to other brain regions.

Seizure-related head injuries (SRHIs) are among the most commonly encountered injuries in people with epilepsy (PWE). Whether head injury has an effect on preexisting epilepsy is not known. The purpose of this study was to systematically assess for any possible effects of SRHIs on seizure frequency and seizure semiology over a 2-year period. We identified 204 patients who have been followed at the Baylor Comprehensive Epilepsy Center from 2008 to 2010. SRHI occurred in 18.1% of the cohort. Most injuries (91%) were classified as mild. Though seizure frequency varied following head injury, overall seizure frequency was not significantly impacted by presence or absence of SRHI over the 2-year study period. Changes in seizure semiology were not observed in those with SRHIs. Although mild SRHI is common among PWE, it does not appear to have an effect on seizure characteristics over a relatively short period.

The molecular determinants and signaling pathways responsible for hematogenous leukocyte trafficking during peripheral neuroinflammation are incompletely elucidated. Chemokine ligand/receptor pair CCL2/CCR2 has been pathogenically implicated in the acute inflammatory demyelinating polyradiculoneuropathy variant of Guillain-Barré syndrome (GBS). We evaluated the role of CCR2 in peripheral neuroinflammation utilizing a severe murine experimental autoimmune neuritis (sm-EAN) model. Sm-EAN was induced in 8-12 week old female SJL CCR2 knockout (CCR2KO), heterozygote (CCR2HT) and wild type (CCR2WT) mice, and daily neuromuscular severity scores and weights recorded. In vitro and in vivo splenocyte proliferation and cytokine expression assays, and sciatic nerve Toll-like receptor (TLR) 2, TLR4 and CCL2 expression assays were performed to evaluate systemic and local innate immune activation at disease onset. Motor nerve electrophysiology and sciatic nerve histology were also performed to characterize the inflammatory neuropathy at expected peak severity. To further determine the functional relevance of CCR2 in sm-EAN, 20 mg/kg CCR2 antagonist, RS 102895 was administered daily for 5 days to a cohort of CCR2WT mice following sm-EAN disease onset, with efficacy compared to 400 mg/kg human intravenous immunoglobulin (IVIg). CCR2KO mice were relatively resistant to sm-EAN compared to CCR2WT and CCR2HT mice, associated with attenuated peripheral nerve demyelinating neuritis. Partial CCR2 gene deletion did not confer any protection against sm-EAN. CCR2KO mice demonstrated similar splenocyte activation or proliferation profiles, as well as TLR2, TLR4 and CCL2 expression to CCR2WT or CCR2HT mice, implying a direct role for CCR2 in sm-EAN pathogenesis. CCR2 signaling blockade resulted in rapid, near complete recovery from sm-EAN following disease onset. RS 102895 was significantly more efficacious than IVIg. CCR2 mediates pathogenic hematogenous monocyte trafficking into peripheral nerves, with consequential demyelination in sm-EAN. CCR2 is amenable to pharmacologic blockade, making it a plausible drug target for GBS.