Increasing challenges to trial recruitment and conduct over time.

OBJECTIVE: This study was undertaken to evaluate how the challenges in the recruitment and retention of participants in clinical trials for focal onset epilepsy have changed over time. METHODS: In this systematic analysis of randomized clinical trials of adjunct antiseizure medications for medication-resistant focal onset epilepsy, we evaluated how the numbers of participants, sites, and countries have changed since the first such trial in 1990. We also evaluated the proportion of participants who completed each trial phase and their reasons for early trial exit. We analyzed these trends using mixed effects generalized linear models accounting for the influence of the number of trial sites and trial-specific variability. RESULTS: The number of participants per site has steadily decreased over decades, with recent trials recruiting fewer than five participants per site (reduction by .16 participants/site/year, p < .0001). Fewer participants also progressed from recruitment to randomization over time (odds ratio = .94/year, p = .014). Concurrently, there has been an increase in the placebo response over time (increase in median percent reduction of .4%/year, p = .02; odds ratio of increase in 50% responder rate of 1.03/year, p = .02), which was not directly associated with the number of sites per trial (p > .20). SIGNIFICANCE: This historical analysis highlights the increasing challenges with participant recruitment and retention, as well as increasing placebo response. It serves as a call to action to change clinical trial design to address these challenges.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. SIGNIFICANCE: 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.

Clinical presentation and evaluation of epilepsy with eyelid myoclonia: Results of an international expert consensus panel.

OBJECTIVE: The objective of this study was to determine areas of consensus among an international panel of experts for the clinical presentation and diagnosis of epilepsy with eyelid myoclonia (EEM; formerly known as Jeavons syndrome) to improve a timely diagnosis. METHODS: An international steering committee was convened of physicians and patients/caregivers with expertise in EEM. This committee summarized the current literature and identified an international panel of experts (comprising 25 physicians and five patients/caregivers). This international expert panel participated in a modified Delphi process, including three rounds of surveys to determine areas of consensus for the diagnosis of EEM. RESULTS: There was a strong consensus that EEM is a female predominant generalized epilepsy syndrome with onset between 3 and 12 years of age and that eyelid myoclonia must be present to make the diagnosis. There was a strong consensus that eyelid myoclonia may go unrecognized for years prior to an epilepsy diagnosis. There was consensus that generalized tonic-clonic and absence seizures are typically or occasionally seen in patients. There was a consensus that atonic or focal seizures should lead to the consideration of reclassification or alternate diagnoses. There was a strong consensus that electroencephalography is required, whereas magnetic resonance imaging is not required for diagnosis. There was a strong consensus to perform genetic testing (either epilepsy gene panel or whole exome sequencing) when one or a combination of factors was present: family history of epilepsy, intellectual disability, or drug-resistant epilepsy. SIGNIFICANCE: This international expert panel identified multiple areas of consensus regarding the presentation and evaluation of EEM. These areas of consensus may be used to inform clinical practice to shorten the time to the appropriate diagnosis.

Management of epilepsy with eyelid myoclonia: Results of an international expert consensus panel.

OBJECTIVE: There are limited data about the treatment and management of epilepsy with eyelid myoclonia (EEM). The objective of this study was to determine areas of consensus among an international panel of experts for the management of EEM (formerly known as Jeavons syndrome). METHODS: An international steering committee was convened of physicians and patients/caregivers with expertise in EEM. This committee summarized the current literature and identified an international panel of experts (comprising 25 physicians and five patients/caregivers). This panel participated in a modified Delphi process, including three rounds of surveys to determine areas of consensus for the treatment, other areas of management, and prognosis for EEM. RESULTS: There was a strong consensus for valproic acid as the first-line treatment, with levetiracetam or lamotrigine as preferable alternatives for women of childbearing age. There was a moderate consensus that ethosuximide and clobazam are also efficacious. There was a strong consensus to avoid sodium channel-blocking medications, except for lamotrigine, as they may worsen seizure control. There was consensus that seizures typically persist into adulthood, with remission occurring in <50% of patients. There was less agreement about other areas of management, including dietary therapy, lens therapy, candidacy for driving, and outcome. SIGNIFICANCE: This international expert panel identified multiple areas of consensus regarding the optimal management of EEM. These areas of consensus may inform clinical practice to improve the management of EEM. In addition, multiple areas with less agreement were identified, which highlight topics for further research.

Written seizure action plans for adult patients with epilepsy: Distilling insights from emergency action plans for other chronic conditions.

Seizure emergencies and potential emergencies, ranging from seizure clusters to prolonged seizure and status epilepticus, may affect adults with epilepsy despite stable antiseizure therapy. Seizure action plans (SAPs) are designed for patients and their caregivers/care partners to provide guidance on the individualized treatment plan, including response to potential seizure emergencies and appropriate use of rescue therapy. The use of pediatric SAPs is common (typically required by schools), however, most adults with epilepsy do not have a plan. Patient-centered action plans are integral to care for other chronic conditions and may offer insights applicable to the care of adults with epilepsy. This review analyzes the potential benefits of action plans for medical conditions by exploring their utility in conditions such as asthma, diabetes, chronic obstructive pulmonary disease, heart disease, and opioid overdose. Evidence across these conditions substantiates the value of action plans for patients, and the benefits of adult SAPs in epilepsy are emerging. Because wide implementation of SAPs has faced barriers, other conditions may provide insights that are relevant to implementing SAPs in epilepsy. Based on these analyses, we propose concrete steps to improve the use of SAPs among adults. A recent consensus statement promoting the use of formal SAPs in epilepsy and advances in rescue therapy delivery methods provides support to engage patients around the value of SAPs. The precedent for use of SAPs for pediatric epilepsy patients serves as the foundation to support increased usage in adults. Seizure action plans in the context of improved clinical outcomes are expected to reduce healthcare utilization, improve patient quality of life, and optimize epilepsy management.

Amygdala subfield and prefrontal cortex abnormalities in patients with functional seizures.

BACKGROUND: Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology. In the present study, we specifically investigated the neuroanatomic changes in subregions of the amygdala and hippocampus in FS. METHODS: T1 MRI scans of 37 female patients with FS and 37 age-matched female seizure naïve controls (SNC) were analyzed retrospectively in FreeSurfer version 7.1. Seizure naïve controls included patients with depression and anxiety disorders. The analysis included whole-brain cortical thickness, subcortical volumes, and subfields of the amygdala and hippocampus. Group comparisons were carried out using multivariable linear models. RESULTS: The FS and SNC groups did not differ in the whole hippocampus and amygdala volumes. However, patients had a significant reduction of the right lateral amygdala volume (p = 0.00041), an increase of the right central amygdala, (p = 0.037), and thinning of the left superior frontal gyrus (p = 0.024). Additional findings in patients were increased volumes of the right medial amygdala (p = 0.031), left anterior amygdala (p = 0.017), and left dentate gyrus of the hippocampus (p = 0.035). CONCLUSIONS: The observations from the amygdala and hippocampus segmentation affirm that there are neuroanatomic associations of FS. The pattern of these changes aligned with some of the cerebral changes described in chronic stress conditions and depression. The pattern of detected changes further study, and may, after validation, provide biomarkers for diagnosis and treatment.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls.

PURPOSE: Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations. METHODS: Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners. RESULTS: After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm3, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS. CONCLUSIONS: These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Variant Intestinal-Cell Kinase in Juvenile Myoclonic Epilepsy.

BACKGROUND: In juvenile myoclonic epilepsy, data are limited on the genetic basis of networks promoting convulsions with diffuse polyspikes on electroencephalography (EEG) and the subtle microscopic brain dysplasia called microdysgenesis. METHODS: Using Sanger sequencing, we sequenced the exomes of six members of a large family affected with juvenile myoclonic epilepsy and confirmed cosegregation in all 37 family members. We screened an additional 310 patients with this disorder for variants on DNA melting-curve analysis and targeted real-time DNA sequencing of the gene encoding intestinal-cell kinase ( ICK). We calculated Bayesian logarithm of the odds (LOD) scores for cosegregating variants, odds ratios in case-control associations, and allele frequencies in the Genome Aggregation Database. We performed functional tests of the effects of variants on mitosis, apoptosis, and radial neuroblast migration in vitro and conducted video-EEG studies in mice lacking a copy of Ick. RESULTS: A variant, K305T (c.914A→C), cosegregated with epilepsy or polyspikes on EEG in 12 members of the family affected with juvenile myoclonic epilepsy. We identified 21 pathogenic ICK variants in 22 of 310 additional patients (7%). Four strongly linked variants (K220E, K305T, A615T, and R632X) impaired mitosis, cell-cycle exit, and radial neuroblast migration while promoting apoptosis. Tonic-clonic convulsions and polyspikes on EEG resembling seizures in human juvenile myoclonic epilepsy occurred more often in knockout heterozygous mice than in wild-type mice (P=0.02) during light sleep with isoflurane anesthesia. CONCLUSIONS: Our data provide evidence that heterozygous variants in ICK caused juvenile myoclonic epilepsy in 7% of the patients included in our analysis. Variant ICK affects cell processes that help explain microdysgenesis and polyspike networks observed on EEG in juvenile myoclonic epilepsy. (Funded by the National Institutes of Health and others.).

Behavioral adverse events with brivaracetam, levetiracetam, perampanel, and topiramate: A systematic review.

PURPOSE: To understand the currently available post-marketing real-world evidence of the incidences of and discontinuations due to the BAEs of irritability, anger, and aggression in people with epilepsy (PWE) treated with the anti-seizure medications (ASMs) brivaracetam (BRV), levetiracetam (LEV), perampanel (PER), and topiramate (TPM), as well as behavioral adverse events (BAEs) in PWE switching from LEV to BRV. METHODS: A systematic review of published literature using the Cochrane Library, PubMed/MEDLINE, and Embase was performed to identify retrospective and prospective observational studies reporting the incidence of irritability, anger, or aggression with BRV, LEV, PER, or TPM in PWE. The incidences of these BAEs and the rates of discontinuation due to each were categorized by ASM, and where possible, weighted means were calculated but not statistically assessed. Behavioral and psychiatric adverse events in PWE switching from LEV to BRV were summarized descriptively. RESULTS: A total of 1500 records were identified in the searches. Of these, 44 published articles reporting 42 studies met the study criteria and were included in the data synthesis, 7 studies were identified in the clinical trial database, and 5 studies included PWE switching from LEV to BRV. Studies included a variety of methods, study populations, and definitions of BAEs. While a wide range of results was reported across studies, weighted mean incidences were 5.6% for BRV, 9.9% for LEV, 12.3% for PER, and 3.1% for TPM for irritability; 3.3%* for BRV, 2.5% for LEV, 2.0% for PER, and 0.2%* for TPM for anger; and 2.5% for BRV, 2.6% for LEV, 4.4% for PER, and 0.5%* for TPM for aggression. Weighted mean discontinuation rates were 0.8%* for BRV, 3.4% for LEV, 3.0% for PER, and 2.2% for TPM for irritability and 0.8%* for BRV, 2.4% for LEV, 9.2% for PER, and 1.2%* for TPM for aggression. There were no discontinuations for anger. Switching from LEV to BRV led to improvement in BAEs in 33.3% to 83.0% of patients (weighted mean, 66.6%). *Denotes only 1 study. CONCLUSIONS: This systematic review characterizes the incidences of irritability, anger, and aggression with BRV, LEV, PER, and TPM, and it provides robust real-world evidence demonstrating that switching from LEV to BRV may improve BAEs. While additional data remain valuable due to differences in methodology (which make comparisons difficult), these results improve understanding of the real-world incidences of discontinuations due to these BAEs in clinical practice and can aid in discussions and treatment decision-making with PWE.

Reliability of additional reported seizure manifestations to identify dissociative seizures.

PURPOSE: Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews. METHODS: Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing. RESULTS: In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of déjà vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS. CONCLUSIONS: Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.

Prospective validation study of an epilepsy seizure risk system for outpatient evaluation.

OBJECTIVE: We conducted clinical testing of an automated Bayesian machine learning algorithm (Epilepsy Seizure Assessment Tool [EpiSAT]) for outpatient seizure risk assessment using seizure counting data, and validated performance against specialized epilepsy clinician experts. METHODS: We conducted a prospective longitudinal study of EpiSAT performance against 24 specialized clinician experts at three tertiary referral epilepsy centers in the United States. Accuracy, interrater reliability, and intra-rater reliability of EpiSAT for correctly identifying changes in seizure 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. RESULTS: EpiSAT 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). SIGNIFICANCE: These 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.

Objective score from initial interview identifies patients with probable dissociative seizures.

OBJECTIVE: To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment. METHODS: Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews. RESULTS: The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%). SIGNIFICANCE: The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.

WONOEP appraisal: Network concept from an imaging perspective.

Neuroimaging techniques applied to a variety of organisms-from zebrafish, to rodents to humans-can offer valuable insights into neuronal network properties and their dysfunction in epilepsy. A wide range of imaging methods used to monitor neuronal circuits and networks during evoked seizures in animal models and advances in functional magnetic resonance imaging (fMRI) applied to patients with epilepsy were discussed during the XIV Workshop on Neurobiology of Epilepsy (XIV WONOEP) organized in 2017 by the Neurobiology Commission of the International League Against Epilepsy (ILAE). We review the growing number of technological approaches developed, as well as the current state of knowledge gained from studies applying these advanced imaging approaches to epilepsy research.

An objective score to identify psychogenic seizures based on age of onset and history.

OBJECTIVE: Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic. METHODS: Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews. RESULTS: The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES. DISCUSSION: Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.

Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.

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.

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

OBJECTIVE: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS: For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE: The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

Identifying psychogenic seizures through comorbidities and medication history.

OBJECTIVE: Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost. METHODS: Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database. RESULTS: Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone. SIGNIFICANCE: An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.

Diagnostic implications of review-of-systems questionnaires to differentiate epileptic seizures from psychogenic seizures.

OBJECTIVE: Early and accurate diagnosis of patients with psychogenic nonepileptic seizures (PNES) leads to appropriate treatment and improves long-term seizure prognosis. However, this is complicated by the need to record seizures to make a definitive diagnosis. Suspicion for PNES can be raised through knowledge that patients with PNES have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (RoSQs) than patients with epileptic seizures. If the responses on the RoSQ can differentiate PNES from other seizure types, then these forms could be an early screening tool. METHODS: Our dataset included all patients admitted from January 2006 to June 2016 for video-electroencephalography at UCLA. RoSQs prior to May 2015 were acquired through retrospective chart review (n=405), whereas RoSQs from subsequent patients were acquired prospectively (n=190). Controlling for sex and number of comorbidities, we used binomial regression to compare the total number of symptoms and the frequency of specific symptoms between five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of RoSQs to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and the number of medical comorbidities. RESULTS: On average, patients with PNES or mixed PNES and ES reported more than twice as many symptoms than patients with isolated ES or PSLE (p<0.001). The prospective accuracy to differentiate PNES from ES was not significantly higher than naïve assumption that all patients had ES (76% vs 70%, p>0.1). DISCUSSION: This analysis of RoSQs confirms that patients with PNES with and without comorbid ES report more symptoms on a population level than patients with epilepsy or PSLE. While these differences help describe the population of patients with PNES, the consistency of RoSQ responses was neither accurate nor specific enough to be used solely as an early screening tool for PNES. Our results suggest that the RoSQ may help differentiate PNES from ES only when, based on other information, the pre-test probability of PNES is at least 50%.

Use of resting-state fMRI in planning epilepsy surgery.

Epileptic seizures result from abnormal neuronal excitability and synchronization, affecting 0.5-1% of the population worldwide. Although anti-seizure drugs are often effective, a significant number of patients with epilepsy continue to experience refractory seizures and are candidates for surgical resection. Whereas standard presurgical evaluation has relied on intracranial electroencephalography (icEEG) and direct cortical stimulation to identify epileptogenic tissue and areas of cortex for which resection would produce clinical deficits, the invasive nature and limited spatial extent of icEEG has led to the investigation of less invasive imaging modalities as adjunctive tools in the presurgical workup. In the past few decades, functional connectivity MRI has emerged as a promising approach for presurgical mapping, leading to a surge in the number of proposed methods and biomarkers for identifying epileptogenic tissue. This review focuses on recent advances in the use of functional connectivity MRI toward its application for presurgical planning, including epilepsy localization and eloquent cortex mapping.

Time-dependence of graph theory metrics in functional connectivity analysis.

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.