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Intracranial EEG Validation of Single-Channel Subgaleal EEG for Seizure Identification

Pacia, Steven V; Doyle, Werner K; Friedman, Daniel; H Bacher, Daniel; Kuzniecky, Ruben I
PURPOSE/OBJECTIVE:A device that provides continuous, long-term, accurate seizure detection information to providers and patients could fundamentally alter epilepsy care. Subgaleal (SG) EEG is a promising modality that offers a minimally invasive, safe, and accurate means of long-term seizure monitoring. METHODS:Subgaleal EEG electrodes were placed, at or near the cranial vertex, simultaneously with intracranial EEG electrodes in 21 epilepsy patients undergoing intracranial EEG studies for up to 13 days. A total of 219, 10-minute single-channel SGEEG samples, including 138 interictal awake or sleep segments and 81 seizures (36 temporal lobe, 32 extra-temporal, and 13 simultaneous temporal/extra-emporal onsets) were reviewed by 3 expert readers blinded to the intracranial EEG results, then analyzed for accuracy and interrater reliability. RESULTS:Using a single-channel of SGEEG, reviewers accurately identified 98% of temporal and extratemporal onset, intracranial, EEG-verified seizures with a sensitivity of 98% and specificity of 99%. All focal to bilateral tonic--clonic seizures were correctly identified. CONCLUSIONS:Single-channel SGEEG, placed at or near the vertex, reliably identifies focal and secondarily generalized seizures. These findings demonstrate that the SG space at the cranial vertex may be an appropriate site for long-term ambulatory seizure monitoring.
PMID: 32925251
ISSN: 1537-1603
CID: 4592552

N-Tools-Browser: Web-Based Visualization of Electrocorticography Data for Epilepsy Surgery

Burkhardt, Jay; Sharma, Aaryaman; Tan, Jack; Franke, Loraine; Leburu, Jahnavi; Jeschke, Jay; Devore, Sasha; Friedman, Daniel; Chen, Jingyun; Haehn, Daniel
ORIGINAL:0015540
ISSN: 2673-7647
CID: 5192342

Sudden unexpected death in epilepsy

Friedman, Daniel
PURPOSE OF REVIEW/OBJECTIVE:Sudden unexpected death in epilepsy (SUDEP) is a major contributor to premature mortality in people with epilepsy. This review provides an update on recent findings on the epidemiology of SUDEP, clinical risk factors and potential mechanisms. RECENT FINDINGS/RESULTS:The overall risk rate of SUDEP is approximately 1 per 1000 patients per year in the general epilepsy population and that children and older adults have a similar incidence. Generalized convulsive seizures (GCS), perhaps through their effects on brainstem cardiopulmonary networks, can cause significant postictal respiratory and autonomic dysfunction though other mechanisms likely exist as well. Work in animal models of SUDEP has identified multiple neurotransmitter systems, which may be future targets for pharmacological intervention. There are also chronic functional and structural changes in autonomic function in patients who subsequently die from SUDEP suggesting that some SUDEP risk is dynamic. Modifiable risks for SUDEP include GCS seizure frequency, medication adherence and nighttime supervision. SUMMARY/CONCLUSIONS:Current knowledge of SUDEP risk factors has identified multiple targets for SUDEP prevention today as we await more specific therapeutic targets that are emerging from translational research studies.
PMID: 35102124
ISSN: 1473-6551
CID: 5182232

Observational study of medical marijuana as a treatment for treatment-resistant epilepsies

Devinsky, Orrin; Marmanillo, Angelica; Hamlin, Theresa; Wilken, Philip; Ryan, Daniel; Anderson, Conor; Friedman, Daniel; Todd, George
OBJECTIVES/OBJECTIVE:Medical cannabis formulations with cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) are widely used to treat epilepsy. We studied the safety and efficacy of two formulations. METHODS:We prospectively observed 29 subjects (12 to 46 years old) with treatment-resistant epilepsies (11 Lennox-Gastaut syndrome; 15 with focal or multifocal epilepsy; three generalized epilepsy) were treated with medical cannabis (1THC:20CBD and/or 1THC:50CBD; maximum of 6 mg THC/day) for ≥24 weeks. The primary outcome was change in convulsive seizure frequency from the pre-treatment baseline to the stable optimal dose phase. RESULTS:There were no significant differences during treatment on stable maximal doses for convulsive seizure frequency, seizure duration, postictal duration, or use of rescue medications compared to baseline. No benefits were seen for behavioral disorders or sleep duration; there was a trend for more frequent bowel movements compared to baseline. Ten adverse events occurred in 6/29 patients, all were transient and most unrelated to study medication. No serious adverse events were related to study medication. INTERPRETATION/CONCLUSIONS:Our prospective observational study of two high-CBD/low-THC formulations found no evidence of efficacy in reducing seizures, seizure duration, postictal duration, or rescue medication use. Behavioral disorders or sleep duration was unchanged. Study medication was generally well tolerated. The doses of CBD used were lower than prior studies. Randomized trials with larger cohorts are needed, but we found no evidence of efficacy for two CBD:THC products in treating epilepsy, sleep, or behavior in our population.
PMID: 35267245
ISSN: 2328-9503
CID: 5182322

Neurostimulation in people with drug-resistant epilepsy: Systematic review and meta-analysis from the ILAE Surgical Therapies Commission

Touma, Lahoud; Dansereau, Bénédicte; Chan, Alvin Y; Jetté, Nathalie; Kwon, Churl-Su; Braun, Kees P J; Friedman, Daniel; Jehi, Lara; Rolston, John D; Vadera, Sumeet; Wong-Kisiel, Lily C; Englot, Dario J; Keezer, Mark R
OBJECTIVE:Summarize the current evidence on efficacy and tolerability of vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS) through a systematic review and meta-analysis. METHODS:We followed the Preferred Reporting Items of Systematic reviews and Meta-Analyses reporting standards and searched Ovid Medline, Ovid Embase, and the Cochrane Central Register of Controlled Trials. We included published randomized controlled trials (RCTs) and their corresponding open-label extension studies, as well as prospective case series, with ≥20 participants (excluding studies limited to children). Our primary outcome was the mean (or median, when unavailable) percentage decrease in frequency, as compared to baseline, of all epileptic seizures at last follow-up. Secondary outcomes included the proportion of treatment responders and proportion with seizure freedom. RESULTS:We identified 30 eligible studies, six of which were RCTs. At long-term follow-up (mean 1.3 years), five observational studies for VNS reported a pooled mean percentage decrease in seizure frequency of 34.7% (95% confidence interval [CI]: -5.1, 74.5). In the open-label extension studies for RNS, the median seizure reduction was 53%, 66%, and 75% at 2, 5, and 9 years of follow-up, respectively. For DBS, the median reduction was 56%, 65%, and 75% at 2, 5, and 7 years, respectively. The proportion of individuals with seizure freedom at last follow-up increased significantly over time for DBS and RNS, whereas a positive trend was observed for VNS. Quality of life was improved in all modalities. The most common complications included hoarseness, and cough and throat pain for VNS and implant site pain, headache, and dysesthesia for DBS and RNS. SIGNIFICANCE/CONCLUSIONS:Neurostimulation modalities are an effective treatment option for drug-resistant epilepsy, with improving outcomes over time and few major complications. Seizure-reduction rates among the three therapies were similar during the initial blinded phase. Recent long-term follow-up studies are encouraging for RNS and DBS but are lacking for VNS.
PMID: 35352349
ISSN: 1528-1167
CID: 5201112

Intracranial electroencephalographic biomarker predicts effective responsive neurostimulation for epilepsy prior to treatment

Scheid, Brittany H; Bernabei, John M; Khambhati, Ankit N; Mouchtaris, Sofia; Jeschke, Jay; Bassett, Dani S; Becker, Danielle; Davis, Kathryn A; Lucas, Timothy; Doyle, Werner; Chang, Edward F; Friedman, Daniel; Rao, Vikram R; Litt, Brian
OBJECTIVE:Despite the overall success of responsive neurostimulation (RNS) therapy for drug-resistant focal epilepsy, clinical outcomes in individuals vary significantly and are hard to predict. Biomarkers that indicate the clinical efficacy of RNS-ideally before device implantation-are critically needed, but challenges include the intrinsic heterogeneity of the RNS patient population and variability in clinical management across epilepsy centers. The aim of this study is to use a multicenter dataset to evaluate a candidate biomarker from intracranial electroencephalographic (iEEG) recordings that predicts clinical outcome with subsequent RNS therapy. METHODS:We assembled a federated dataset of iEEG recordings, collected prior to RNS implantation, from a retrospective cohort of 30 patients across three major epilepsy centers. Using ictal iEEG recordings, each center independently calculated network synchronizability, a candidate biomarker indicating the susceptibility of epileptic brain networks to RNS therapy. RESULTS:Ictal measures of synchronizability in the high-γ band (95-105 Hz) significantly distinguish between good and poor RNS responders after at least 3 years of therapy under the current RNS therapy guidelines (area under the curve = .83). Additionally, ictal high-γ synchronizability is inversely associated with the degree of therapeutic response. SIGNIFICANCE/CONCLUSIONS:This study provides a proof-of-concept roadmap for collaborative biomarker evaluation in federated data, where practical considerations impede full data sharing across centers. Our results suggest that network synchronizability can help predict therapeutic response to RNS therapy. With further validation, this biomarker could facilitate patient selection and help avert a costly, invasive intervention in patients who are unlikely to benefit.
PMID: 34997577
ISSN: 1528-1167
CID: 5107542

Shared computational principles for language processing in humans and deep language models

Goldstein, Ariel; Zada, Zaid; Buchnik, Eliav; Schain, Mariano; Price, Amy; Aubrey, Bobbi; Nastase, Samuel A; Feder, Amir; Emanuel, Dotan; Cohen, Alon; Jansen, Aren; Gazula, Harshvardhan; Choe, Gina; Rao, Aditi; Kim, Catherine; Casto, Colton; Fanda, Lora; Doyle, Werner; Friedman, Daniel; Dugan, Patricia; Melloni, Lucia; Reichart, Roi; Devore, Sasha; Flinker, Adeen; Hasenfratz, Liat; Levy, Omer; Hassidim, Avinatan; Brenner, Michael; Matias, Yossi; Norman, Kenneth A; Devinsky, Orrin; Hasson, Uri
Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.
PMCID:8904253
PMID: 35260860
ISSN: 1546-1726
CID: 5190382

Vascular risk factors as predictors of epilepsy in older age: The Framingham Heart Study

Stefanidou, Maria; Himali, Jayandra J; Devinsky, Orrin; Romero, Jose R; Ikram, Mohammad Arfan; Beiser, Alexa S; Seshadri, Sudha; Friedman, Daniel
OBJECTIVE:Stroke is the most common cause of epilepsy in older age. Subclinical cerebrovascular disease is believed to underlie some of the 30%-50% of late-onset epilepsy without a known cause (Li et al. Epilepsia. 1997;38:1216; Cleary et al. Lancet. 2004;363:1184). We studied the role of modifiable vascular risk factors in predicting subsequent epilepsy among participants ages 45 or older in the Framingham Heart Study (FHS), a longitudinal, community-based study. METHODS:Participants of the Offspring Cohort who attended FHS exam 5 (1991-1995) were included who were at least 45-years-old at that time, had available vascular risk factor data, and epilepsy follow-up (n = 2986, mean age 58, 48% male). Adjudication of epilepsy cases included review of medical charts to exclude seizure mimics and acute symptomatic seizures. The vascular risk factors studied included hypertension, diabetes mellitus, smoking, and hyperlipidemia. The role of the Framingham Stroke Risk Profile score was also investigated. Cox proportional hazards regression models were used for the analyses. RESULTS:Fifty-five incident epilepsy cases were identified during a mean of 19 years of follow-up. Hypertension was associated with a near 2-fold risk (hazard ratio [HR]: 1.93, 95% confidence interval [CI]: 1.10-3.37, p = .022) of developing epilepsy, even after adjustment for prevalent and interim stroke. In secondary analysis, excluding patients with normal blood pressure who were receiving anti-HTN (anti-hypertensive) treatment (n = 2613, 50 incident epilepsy cases) the association was (HR: 2.44, 95% CI: 1.36-4.35, p = .003). SIGNIFICANCE/CONCLUSIONS:Our results offer further evidence that hypertension, a potentially modifiable and highly prevalent vascular risk factor in the general population, increases 2- to 2.5-fold the risk of developing late-onset epilepsy.
PMID: 34786697
ISSN: 1528-1167
CID: 5049142

Dietary Transitions and Health Outcomes in Four Populations - Systematic Review

Pressler, Mariel; Devinsky, Julie; Duster, Miranda; Lee, Joyce H; Glick, Courtney S; Wiener, Samson; Laze, Juliana; Friedman, Daniel; Roberts, Timothy; Devinsky, Orrin
Importance/UNASSIGNED:Non-communicable chronic diseases (NCDs) such as obesity, type 2 diabetes, heart disease, and cancer were rare among non-western populations with traditional diets and lifestyles. As populations transitioned toward industrialized diets and lifestyles, NCDs developed. Objective/UNASSIGNED:We performed a systematic literature review to examine the effects of diet and lifestyle transitions on NCDs. Evidence Review/UNASSIGNED:We identified 22 populations that underwent a nutrition transition, eleven of which had sufficient data. Of these, we chose four populations with diverse geographies, diets and lifestyles who underwent a dietary and lifestyle transition and explored the relationship between dietary changes and health outcomes. We excluded populations with features overlapping with selected populations or with complicating factors such as inadequate data, subgroups, and different study methodologies over different periods. The selected populations were Yemenite Jews, Tokelauans, Tanushimaru Japanese, and Maasai. We also review transition data from seven excluded populations (Pima, Navajo, Aboriginal Australians, South African Natal Indians and Zulu speakers, Inuit, and Hadza) to assess for bias. Findings/UNASSIGNED:The three groups that replaced saturated fats (SFA) from animal (Yemenite Jews, Maasai) or plants (Tokelau) with refined carbohydrates had negative health outcomes (e.g., increased obesity, diabetes, heart disease). Yemenites reduced SFA consumption by >40% post-transition but men's BMI increased 19% and diabetes increased ~40-fold. Tokelauans reduced fat, dramatically reduced SFA, and increased sugar intake: obesity and diabetes rose. The Tanushimaruans transitioned to more fats and less carbohydrates and used more anti-hypertensive medications; stroke and breast cancer declined while heart disease was stable. The Maasai transitioned to lower fat, SFA and higher carbohydrates and had increased BMI and diabetes. Similar patterns were observed in the seven other populations. Conclusion/UNASSIGNED:The nutrient category most strongly associated with negative health outcomes - especially obesity and diabetes - was sugar (increased 600-650% in Yemenite Jews and Tokelauans) and refined carbohydrates (among Maasai, total carbohydrates increased 39% in men and 362% in women), while increased calories was less strongly associated with these disorders. Across 11 populations, NCDs were associated with increased refined carbohydrates more than increased calories, reduced activity or other factors, but cannot be attributed to SFA or total fat consumption.
PMCID:8892920
PMID: 35252289
ISSN: 2296-861x
CID: 5190802

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Chen, Zhe Sage; Hsieh, Aaron; Sun, Guanghao; Bergey, Gregory K; Berkovic, Samuel F; Perucca, Piero; D'Souza, Wendyl; Elder, Christopher J; Farooque, Pue; Johnson, Emily L; Barnard, Sarah; Nightscales, Russell; Kwan, Patrick; Moseley, Brian; O'Brien, Terence J; Sivathamboo, Shobi; Laze, Juliana; Friedman, Daniel; Devinsky, Orrin
Objective/UNASSIGNED:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods/UNASSIGNED:This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results/UNASSIGNED:The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions/UNASSIGNED:Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.
PMCID:8973318
PMID: 35370908
ISSN: 1664-2295
CID: 5191502