Neurology residents' education in functional seizures.

We report a survey of neurology residency program directors (PDs) and recent neurology residency graduates about the education provided during residency on functional seizures (FS), a subtype of functional neurological disorder (FND). The purpose of our study was to assess the education gap for neurology residents about FS since patients with FS are frequently seen by neurologists, who typically conduct the evaluation and share the findings with the patient. A survey was sent to 93 Neurology residency program directors and 71 recent graduates. We obtained a low response rate of 17%. Results of the survey revealed that the most frequent settings for education on FS were within a clinical rotation in the Epilepsy Monitoring Unit (68.8% of PDs and 88.7% of recent graduate respondents) and via a single didactic lecture (81.3% of PDs and 80.3% of recent graduate respondents). The majority of programs did not provide a curriculum for training and feedback on best practices in communicating the diagnosis or on evidence-based treatments. Eighteen percent of neurology residents reported not learning how to communicate the diagnosis of FS to patients, while 77% responded that they were not taught about treatment. These results illustrate a curriculum gap in what neurology residents are taught about diagnosis and management of FS (and FND). We propose a standardized model that can be adapted in residencies.

Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures.

Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is commonly based on electroencephalogram and concurrent video recordings (vEEG). Here, we demonstrate that these two types of seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors. We used Empatica E4 Wristband sensors worn on both arms in vEEG confirmed seizures, and machine learning methods to train classifiers, specifically, extreme gradient boosting (XGBoost). Classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic, which is 6 standard deviations above the baseline of 68% accuracy. Our dataset contained altogether 35 seizures from 18 patients out of which 8 patients had 13 convulsive seizures. Prediction of seizure type was based on simple features derived from the segments of autonomic activity measurements (electrodermal activity, body temperature, blood volume pulse, and heart rate) and forearm acceleration. Features related to heart rate and electrodermal activity were ranked as the top predictors in XGBoost classifiers. We found that patients with PNES had a higher ictal heart rate and electrodermal activity than patients with ES. In contrast to existing published studies of mainly convulsive seizures, our classifier focuses on autonomic signals to differentiate convulsive or nonconvulsive semiology ES from PNES. Our results show that autonomic activity recorded via wearable sensors provides promising signals for detection and discrimination of psychogenic and epileptic seizures, but more work is necessary to improve the predictive power of the model.

White matter integrity correlates with depressive symptomatology in temporal lobe epilepsy.

RATIONALE: White matter abnormalities occur in both temporal lobe epilepsy (TLE) and depression, but there is limited research examining the depression-white matter association in depressed individuals with TLE. This study examined the relationship between white matter integrity (WMI) and depression including the influence of age at seizure onset, in adults with TLE, TLE and depression, and depression only. METHODS: Thirty-one adults were in one of three groups: TLE without depression (TLE; n=11), TLE with depression (TLE+DEP; n=9), and depression without TLE (DEP; n=11). Participants completed structured interviews for depression diagnosis and severity. White matter integrity was estimated based on fractional anisotropy (FA) calculated in frontotemporolimbic (FTL) and non-FTL regions in the JHU DTI atlas. RESULTS: In adults with TLE (n=20), depressive symptomology was significantly correlated with FA in non-FTL regions and trended toward significance in FTL regions. These associations were found in FTL (statistically significant) and non-FTL (trended toward significance) regions in participants with childhood seizure onset but not in those with adolescent/adult seizure onset. CONCLUSIONS: Current results suggest that WMI, within FTL and non-FTL regions, are associated with depressive symptomology in adults with TLE. This association may be most notable in those with childhood-onset epilepsy. These findings could have important implications for the conceptualization and clinical care of neuropsychiatric comorbidities in TLE.

Cluster reduction in patients in a pilot treatment trial for psychogenic nonepileptic seizures.

INTRODUCTION: The present study examined seizure clusters as a primary outcome in patients receiving treatment for PNES. Cluster reduction is examined longitudinally using frequency threshold and statistical definitions of seizure cluster for patients. Possible risk factors for clustering will be examined along with clustering as a risk factor for poorer secondary outcomes. METHODS: Participants were from a pilot randomized treatment trial for PNES where they received cognitive behavioral therapy-informed psychotherapy (CBT-ip), sertraline, combination therapy, or treatment as usual. Seizure data are from patients' seizure dairies. RESULTS: Cluster reduction was observed for those receiving CBT-ip or combination treatment using all definitions of daily clusters and weekly clusters. No risk factors of clustering were observed. Those who were identified as having clusters during the trial had poorer secondary outcomes on several measures at baseline relative to those who were not identified as having clusters. DISCUSSION: This is the first study known to the authors to not only examined seizure clusters as a primary outcome for those with PNES, but also the first study to suggest that CBT-ip and combination therapy may be effective in reducing the frequency of clusters.

Identifying seizure clusters in patients with psychogenic nonepileptic seizures.

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

Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research.

RATIONALE: As electronic health record (EHR) systems become more available, they will serve as an important resource for collecting epidemiologic data in epilepsy research. However, since clinicians do not have a systematic method for coding psychogenic nonepileptic seizures (PNES), patients with PNES are often misclassified as having epilepsy, leading to sampling error. This study validates a natural language processing (NLP) tool that uses linguistic information to help identify patients with PNES. METHODS: Using the VA national clinical database, 2200 notes of Iraq and Afghanistan veterans who completed video electroencephalograph (VEEG) monitoring were reviewed manually, and the veterans were identified as having documented PNES or not. Reviewers identified PNES-related vocabulary to inform a NLP tool called Yale cTakes Extension (YTEX). Using NLP techniques, YTEX annotates syntactic constructs, named entities, and their negation context in the EHR. These annotations are passed to a classifier to detect patients without PNES. The classifier was evaluated by calculating positive predictive values (PPVs), sensitivity, and F-score. RESULTS: Of the 742 Iraq and Afghanistan veterans who received a diagnosis of epilepsy or seizure disorder by VEEG, 44 had documented events on VEEG: 22 veterans (3.0%) had definite PNES only, 20 (2.7%) had probable PNES, and 2 (0.3%) had both PNES and epilepsy documented. The remaining 698 veterans did not have events captured during the VEEG admission and/or did not have a definitive diagnosis. Our classifier achieved a PPV of 93%, a sensitivity of 99%, and a F-score of 96%. CONCLUSION: Our study demonstrates that the YTEX NLP tool and classifier is highly accurate in excluding PNES, diagnosed with VEEG, in EHR systems. The tool may be very valuable in preventing false positive identification of patients with epilepsy in EHR-based epidemiologic research.

Decreased serum BDNF levels in patients with epileptic and psychogenic nonepileptic seizures.

OBJECTIVE: Neurotrophins promote neurogenesis and help regulate synaptic reorganization. Their dysregulation has been implicated in a number of neurologic and psychiatric disorders. Previous studies have shown decreased levels of brain-derived neurotrophic factor (BDNF) in the serum of patients with psychiatric disorders such as major depressive disorder (MDD) and conversion disorder (CD). In human patients with temporal lobe epilepsy, there is an increase in both BDNF mRNA and protein levels in surgically resected hippocampi compared to controls. One study of children with epilepsy has found normal to increased serum BDNF levels compared to controls. Plasma [corrected] BDNF levels have not been investigated in adult patients with epileptic seizures (ES). We hypothesized that BDNF would differentiate between ES and psychogenic nonepileptic seizures (PNES). METHODS: We assessed plasma [corrected] BDNF immunoreactivity in 15 patients with ES, 12 patients with PNES, and 17 healthy volunteers. Plasma [corrected] BDNF levels were measured using an enzyme-linked immunoassay. RESULTS: Healthy controls showed higher BDNF levels (4,289 ± 1,810 pg/mL) compared to patients with PNES (1,033 ± 435 pg/mL) (p < 0.001). However, unexpectedly, healthy controls also showed higher levels of BDNF compared to patients with ES without comorbid MDD (977 ± 565 pg/mL) (p < 0.001). CONCLUSIONS: Unlike children, adults with epilepsy appear to have decreased levels of plasma [corrected] BDNF. Reduced plasma [corrected] BDNF levels can be used to differentiate adult patients with ES or PNES from healthy controls. Further human studies are needed to better understand the pathophysiology explaining the decreased plasma [corrected] BDNF levels found in epilepsy and in PNES.

Pilot pharmacologic randomized controlled trial for psychogenic nonepileptic seizures.

OBJECTIVE: There have been few treatment trials for psychogenic nonepileptic seizures (PNES). Some psychotherapies have been shown to improve PNES and comorbid symptom outcomes. We evaluated a pharmacologic intervention to test the hypothesis that sertraline would reduce PNES. METHODS: We conducted a pilot, double-blind, randomized, placebo-controlled trial in an academic medical hospital with epilepsy center outpatients. Subjects aged 18 to 65 years diagnosed with video-EEG-confirmed PNES were treated with flexible-dose sertraline or placebo over 12 weeks. Seizure calendars and symptom scales were charted prospectively. Secondary outcome measures included psychiatric symptom scales and psychosocial variables. RESULTS: Thirty-eight subjects enrolled, and 26 (68%) completed the trial. Thirty-three subjects with nonzero nonepileptic seizure rates at baseline were included in intent-to-treat analysis of the primary outcome. Subjects assigned to the sertraline arm experienced a 45% reduction in seizure rates from baseline to final visit (p = 0.03) vs an 8% increase in placebo (p = 0.78). Secondary outcome scales revealed no significant between-group differences in change scores from baseline to final visit, after adjustment for differences at baseline. CONCLUSIONS: PNES were reduced in patients treated with a serotonin selective reuptake inhibitor, whereas those treated with placebo slightly increased. This study provides feasibility data for a larger-scale study. LEVEL OF EVIDENCE: This study provides Class II evidence that flexible-dose sertraline up to a maximum dose of 200 mg is associated with a nonsignificant reduction in PNES rate compared with a placebo control arm (risk ratio 0.51, 95% confidence interval 0.25-1.05, p = 0.29), adjusting for differences at baseline.

Interrater reliability of EEG-video monitoring.

OBJECTIVE: The diagnosis of psychogenic nonepileptic seizures (PNES) can be challenging. In the absence of a gold standard to verify the reliability of the diagnosis by EEG-video, we sought to assess the interrater reliability of the diagnosis using EEG-video recordings. METHODS: Patient samples consisted of 22 unselected consecutive patients who underwent EEG-video monitoring and had at least an episode recorded. Other test results and histories were not provided because the goal was to assess the reliability of the EEG-video. Data were sent to 22 reviewers, who were board-certified neurologists and practicing epileptologists at epilepsy centers. Choices were 1) PNES, 2) epilepsy, and 3) nonepileptic but not psychogenic ("physiologic") events. Interrater agreement was measured using a kappa coefficient for each diagnostic category. We used generalized kappa coefficients, which measure the overall level of between-method agreement beyond that which can be ascribed to chance. We also report category-specific kappa values. RESULTS: For the diagnosis of PNES, there was moderate agreement (kappa = 0.57, 95% confidence interval [CI] 0.39-0.76). For the diagnosis of epilepsy, there was substantial agreement (kappa = 0.69, 95% CI 0.51-0.86). For physiologic nonepileptic episodes, the agreement was low (kappa = 0.09, 95% CI 0.02-0.27). The overall kappa statistic across all 3 diagnostic categories was moderate at 0.56 (95% CI 0.41-0.73). CONCLUSIONS: Interrater reliability for the diagnosis of psychogenic nonepileptic seizures by EEG-video monitoring was only moderate. Although this may be related to limitations of the study (diagnosis based on EEG-video alone, artificial nature of the forced choice paradigm, single episode), it highlights the difficulties and subjective components inherent to this diagnosis.

The use of herbal alternative medicines in neuropsychiatry. A report of the ANPA Committee on Research.

Growing numbers of people throughout the United States (40% in 1998) are using various forms of alternative therapies. A MEDLINE literature search of journals from the past three decades and an Internet database query were performed to determine the types and frequency of alternative therapies used, with special attention given to the herbal medicines used in neuropsychiatric disorders. Clinical effects, mechanisms of action, interactions, and adverse reactions of the herbal treatments are detailed. Objective controlled trials will be needed to establish safety and efficacy of herbal supplements. Knowledge of the properties of these therapies can improve the care of neuropsychiatric patients.