A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker TM system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.
Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEGTM SubQ, NeuroviewTM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.
Periventricular nodular heterotopia (PNH) is a common structural malformation of cortical development. Mutations in the filamin A gene are frequent in familial cases with X-linked PNH. However, many cases with sporadic PNH remain genetically unexplained. Although medically refractory epilepsy often brings attention to the underlying PNH, patients are often not candidates for surgical resection. This limits access to neuronal tissue harboring causal mutations. We evaluated a patient with PNH and medically refractory focal epilepsy who underwent a presurgical evaluation with stereotactically placed electroencephalographic (SEEG) depth electrodes. Following SEEG explantation, we collected trace tissue adherent to the electrodes and extracted the DNA. Whole-exome sequencing performed in a Clinical Laboratory Improvement Amendments-approved genetic diagnostic laboratory uncovered a de novo heterozygous pathogenic variant in novel candidate PNH gene MEN1 (multiple endocrine neoplasia type 1; c.1546dupC, p.R516PfsX15). The variant was absent in an earlier exome profiling of the venous blood-derived DNA. The MEN1 gene encodes the ubiquitously expressed, nuclear scaffold protein menin, a known tumor suppressor gene with an established role in the regulation of transcription, proliferation, differentiation, and genomic integrity. Our study contributes a novel candidate gene in PNH generation and a novel practical approach that integrates electrophysiological and genetic explorations of epilepsy.
OBJECTIVE: To estimate the dynamic nature of graph theory measures of whole-brain functional connectivity (FC), in order to (1) improve sensitivity of connectomic investigations in epilepsy and (2) improve discriminatory power of imaging biomarkers based on graph theory methods. BACKGROUND: Connectomic analysis of temporal lobe epilepsy (TLE) using graph-theoretical methods is increasingly found to be a powerful quantitative method for investigating epileptic brain networks. Studies increasingly demonstrate the utility of graph measures of FC for identifying network abnormalities and serving as diagnostic markers for localization or disease extent. The majority of graph theory investigations of FC have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that FC fluctuates dynamically over time. METHODS: Interictal resting-state fMRI was performed in 32 TLE patients and 24 healthy controls. Sliding window analysis was used to extract time-varying graph metrics across the length of the scan. Dynamic changes in graph metrics was quantified through Bayesian hidden Markov modeling. Temporal stability was estimated for graph measures of network connectivity, including small-world index, global integration measures (global efficiency, characteristic path length), local segregation measures (clustering coefficient, local efficiency), and centrality measures (betweenness centrality, eigenvector centrality). RESULTS: Small-world index, betweenness centrality, and global integration measures exhibited greater temporal stationarity than other network characteristics. The exception was clustering coefficient for TLE patients, which exhibited the least temporal stationarity for healthy controls but greatest for TLE. We further show that imaging markers that account for subject-level differences in network dynamics obtain better discriminatory power as a marker for TLE. CONCLUSIONS: Our results suggest that the robustness of static FC analysis depends on the graph measure investigated. Temporal stability of network topology may itself serve as a marker for TLE. Incorporating network dynamics into imaging biomarkers may improve the sensitivity of connectomic investigations in TLE.
OBJECTIVE: Determine if a routine review of system (ROS) questionnaire may serve as a predictive tool for psychogenic non-epileptic seizures. BACKGROUND: Psychogenic non-epileptic seizures (PNES) represent a subset of pharmaco-resistant epilepsy. PNES is often misdiagnosed as epileptic seizures (ES) leading to unnecessary and potentially harmful treatment. Certain characteristics have been described that increase the likelihood for PNES including ictal stuttering and the "teddy bear sign", which are specific, but not sensitive, markers. We observed that patients clinically suspected to have PNES tend to report more somatic complaints in our ROS. DESIGN/METHODS: A retrospective analysis of standardized ROS questionnaire completed by patients with definite PNES and ES diagnosis. A multivariate analysis of covariance was used to determine whether PNES and ES groups differed. Ten-fold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on percentage of positive complaints. RESULTS: A total of 44 patients were included. PNES and ES groups were similar with respect to gender, age at evaluation, psychiatric history, and history of abuse. Compared to the ES group, the PNES group had a significantly later age of epilepsy onset. From MANCOVA, PNES patients were found to have a larger percentage of positive complaints on ROS than epilepsy patients (F=20.78, p<0.0001). With a cutoff of 17[percnt] of the ROS items reported as positive, there is 78.3[percnt] specificity and 85.7[percnt] sensitivity of the diagnosis being PNES, with higher specificity of diagnosis at higher cutoffs. CONCLUSIONS: Our study demonstrates that multiple somatic complaints in a standard review of system questionnaire exhibits great sensitivity and specificity for the diagnosis of PNES, and may be a useful aid in developing a pretest probability for its diagnosis during evaluation of seizures. A prospective study is underway to validate our current findings. Study Supported by: No support.
Ocular compression (OC) is a maneuver performed during EEG to demonstrate increased vagal reactivity in children with suspected syncope including breath-holding spells. We examined the relationship between the simulated OC pressure exerted by different physicians and the cardiac slowing responses that they had historically obtained as per EEG records. Simulated OC was performed by each physician using a sphygmomanometer. EEGs were reviewed for the rate of positive cardiac slowing per physician. Among three physicians who performed a total of 73 OC, the mean +/- SD of applied pressure were 29.0 +/- 2.4, 60.7 +/- 3.5 and 42.4 +/- 2.5 mmHg, respectively. There was good intra-physician consistency for the OC pressures exerted. The mean pressure exerted was significantly different between physicians (p < 0.001, ANOVA). The positive response rate for cardiac slowing among these physicians was 11/37 (29.7%), 10/21 (47.6%) and 8/15 (53.3%) respectively. The difference in positive OC responses between physicians was not significant (p = 0.127, chi-square). Higher OC pressures did not translate into more positive responses. A pressure of 30 mmHg is as good as 60 mmHg in demonstrating cardiac slowing during OC.
