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Objective: Scalp electroencephalographic (EEG)–functional magnetic resonance imaging (fMRI) studies suggest that the maximum blood oxygen level‐dependent (BOLD) response to an interictal epileptiform discharge (IED) identifies the area of IED generation. However, the maximum BOLD response has also been reported in distant, seemingly irrelevant areas. Given the poor postoperative outcomes associated with extra‐temporal lobe epilepsy, we hypothesized this finding is more common when analyzing extratemporal IEDs as compared to temporal IEDs. We further hypothesized that a subjective, holistic assessment of other significant BOLD clusters to identify the most clinically relevant cluster could be used to overcome this limitation and therefore better identify the likely origin of an IED. Specifically, we also considered the second maximum cluster and the cluster closest to the electrode contacts where the IED was observed. Methods: Maps of significant IED‐related BOLD activation were generated for 48 different IEDs recorded from 33 patients who underwent intracranial EEG‐fMRI. The locations of the maximum, second maximum, and closest clusters were identified for each IED. An epileptologist, blinded to these cluster assignments, selected the most clinically relevant BOLD cluster, taking into account all available clinical information. The distances between these BOLD clusters and their corresponding IEDs were then measured. Results: The most clinically relevant cluster was the maximum cluster for 56% (27/48) of IEDs, the second maximum cluster for 13% (6/48) of IEDs, and the closest cluster for 31% (15/48) of IEDs. The maximum clusters were closer to IED contacts for temporal than for extratemporal IEDs (p =.022), whereas the most clinically relevant clusters were not significantly different (p =.056). Significance: The maximum BOLD response to IEDs may not always be the most indicative of IED origin. We propose that available clinical information should be used in conjunction with EEG‐fMRI data to identify a BOLD cluster representative of the IED origin. [ABSTRACT FROM AUTHOR]

Objective: To use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy. Methods: This was a population‐based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000‐2012 were included. A modified Delphi process identified 30 potential risk factors. Outcome was early (within 4 years of epilepsy diagnosis) and late (4 years or more from diagnosis) mortality. We used regularized logistic regression, support vector machines, Gaussian naive Bayes, and random forest classifiers to predict outcomes. We assessed model calibration, discrimination, and generalizability using the Brier score, mean area under the receiver operating characteristic curve (AUC) derived from stratified fivefold cross‐validation, plotted calibration curves, and extracted measures of association where possible. Results: We identified 10 499 presumed incident cases from 11 194 182 patients. All models performed comparably well following stratified fivefold cross‐validation, with AUCs ranging from 0.73 to 0.81 and from 0.71 to 0.79 for early and late death, respectively. In addition to comorbid disease, social habits (alcoholism odds ratio [OR] for early death = 1.54, 95% confidence interval [CI] = 1.12‐2.11 and OR for late death = 2.62, 95% CI = 1.66‐4.16) and treatment patterns (OR for early death when no antiseizure medication [ASM] was prescribed at baseline = 1.33, 95% CI = 1.07‐1.64 and OR for late death after receipt of enzyme‐inducing ASM at baseline = 1.32, 95% CI = 1.04‐1.66) were significantly associated with increased risk of premature death. Baseline ASM polytherapy (OR = 0.55, 95% CI = 0.36‐0.85) was associated with reduced risk of early death. Significance: Clinically informed models using routine electronic medical records can be used to predict early and late mortality in epilepsy, with moderate to high accuracy and evidence of generalizability. Medical, social, and treatment‐related risk factors, such as delayed ASM prescription and baseline prescription of enzyme‐inducing ASMs, were important predictors. [ABSTRACT FROM AUTHOR]

Objectives: Epilepsy surgery is offered in resistant focal epilepsy. Non‐invasive investigations like scalp video EEG monitoring (SVEM) help delineate epileptogenic zone. Complex cases may require intracranial video EEG monitoring (IVEM). Stereoelectroencephalography (SEEG)‐based intracerebral electrode implantation has better spatial resolution, lower morbidity, better tolerance, and superiority in sampling deep structures. Our objectives were to assess IVEM using SEEG with regard to reasoning behind implantation, course, surgical interventions, and outcomes. Materials and methods: Seventy‐two admissions for SEEG from January 2014 to December 2018 were included in the study. Demographic and clinical data were retrospectively collected. Results: The cohort comprised of 69 adults of which 34 (47%) had lesional MRI. Reasons for SEEG considering all cases included non‐localizing ictal onset (76%), ictal‐interictal discordance (21%), discordant semiology (17%), proximity to eloquent cortex (33%), nuclear imaging discordance (34%), and discordance with neuropsychology (19%). Among lesional cases, additional reasons included SVEM discordance (68%) and dual or multiple pathology (47%). Forty‐eight patients (67%) were offered resective surgery, and 41 underwent it. Twenty‐three (56%) had at least one year post‐surgical follow‐up of which 14 (61%) had Engels class I outcome. Of the remaining 23 who were continued on medical management, 4 (17%) became seizure‐free and 12 (51%) had reduction in seizure frequency. Conclusion: SEEG monitoring is an important and safe tool for presurgical evaluation with good surgical and non‐surgical outcomes. Whether seizure freedom following non‐surgical management could be related to SEEG implantation, medication change, or natural course needs to be determined. [ABSTRACT FROM AUTHOR]