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Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.

Epilepsy surgery is the treatment of choice for patients with drug-resistant seizures. A timely evaluation for surgical candidacy can be life-saving for patients who are identified as appropriate surgical candidates, and may also enhance the care of nonsurgical candidates through improvement in diagnosis, optimization of therapy, and treatment of comorbidities. Yet, referral for surgical evaluations is often delayed while palliative options are pursued, with significant adverse consequences due to increased morbidity and mortality associated with intractable epilepsy. The Surgical Therapies Commission of the International League Against Epilepsy (ILAE) sought to address these clinical gaps and clarify when to initiate a surgical evaluation. We conducted a Delphi consensus process with 61 epileptologists, epilepsy neurosurgeons, neurologists, neuropsychiatrists, and neuropsychologists with a median of 22 years in practice, from 28 countries in all six ILAE world regions. After three rounds of Delphi surveys, evaluating 51 unique scenarios, we reached the following Expert Consensus Recommendations: (1) Referral for a surgical evaluation should be offered to every patient with drug-resistant epilepsy (up to 70 years of age), as soon as drug resistance is ascertained, regardless of epilepsy duration, sex, socioeconomic status, seizure type, epilepsy type (including epileptic encephalopathies), localization, and comorbidities (including severe psychiatric comorbidity like psychogenic nonepileptic seizures [PNES] or substance abuse) if patients are cooperative with management; (2) A surgical referral should be considered for older patients with drug-resistant epilepsy who have no surgical contraindication, and for patients (adults and children) who are seizure-free on 1-2 antiseizure medications (ASMs) but have a brain lesion in noneloquent cortex; and (3) referral for surgery should not be offered to patients with active substance abuse who are noncooperative with management. We present the Delphi consensus results leading up to these Expert Consensus Recommendations and discuss the data supporting our conclusions. High level evidence will be required to permit creation of clinical practice guidelines.

Clinical practice guidelines (CPGs) are statements that provide evidence-based recommendations aimed at optimizing patient care. However, many other documents are often published as "guidelines" when they are not; these documents, although also important in clinical practice, are usually not systematically produced following rigorous processes linking the evidence to the recommendations. Specifically, the International League Against Epilepsy (ILAE) guideline development toolkit aims to ensure that high-quality CPGs are developed to fill knowledge gaps and optimize the management of epilepsy. In addition to adhering to key methodological processes, guideline developers need to consider that effective CPGs should lead to improvements in clinical processes of care and health care outcomes. This requires monitoring the effectiveness of epilepsy-related CPGs and interventions to remove the barriers to epilepsy CPG implementation. This article provides an overview of what distinguishes quality CPGs from other documents and discusses their benefits and limitations. We summarize the recently revised ILAE CPG development process and elaborate on the barriers and facilitators to guideline dissemination, implementation, and adaptation.

Almost 20% of patients with COVID-19 experience long-term effects, known as post-COVID condition or long COVID. Among many lingering neurologic symptoms, chronic headache is the most common. Despite this health concern, the etiology of long COVID headache is still not well characterized. Here, we present a longitudinal multi-omics analysis of blood leukocyte transcriptomics, plasma proteomics and metabolomics of long COVID patients with chronic headache. Long COVID patients experienced a state of hyper-inflammation prior to chronic headache onset and maintained persistent inflammatory activation throughout the progression of chronic headache. Metabolomic analysis also revealed augmented arginine and lipid metabolisms, skewing towards a nitric oxide-based pro-inflammation. Furthermore, metabolisms of neurotransmitters including serotonin, dopamine, glutamate, and GABA were markedly dysregulated during the progression of long COVID headache. Overall, these findings illustrate the immuno-metabolomics landscape of long COVID patients with chronic headache, which may provide insights to potential therapeutic interventions.

Three neuromodulation therapies have been appropriately tested and approved in refractory focal epilepsies: vagus nerve stimulation (VNS), deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS), and closed-loop responsive neurostimulation of the epileptogenic zone or zones. These therapies are primarily palliative. Only a few individuals have achieved complete freedom from seizures for more than 12 months with these therapies, whereas more than half have benefited from long-term reduction in seizure frequency of more than 50%. Implantation-related adverse events primarily include infection and pain at the implant site. Intracranial haemorrhage is a frequent adverse event for ANT-DBS and responsive neurostimulation. Other stimulation-specific side-effects are observed with VNS and ANT-DBS. Biomarkers to predict response to neuromodulation therapies are not available, and high-level evidence to aid decision making about when and for whom these therapies should be preferred over other antiepileptic treatments is scant. Future studies are thus needed to address these shortfalls in knowledge, approve other forms of neuromodulation, and develop personalised closed-loop therapies with embedded machine learning. Until then, neuromodulation could be considered for individuals with intractable seizures, ideally after the possibility of curative surgical treatment has been carefully assessed and ruled out or judged less appropriate.

Objective To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients. Methods A MATLAB‐based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections. Results The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71‐0.81). Significance Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user‐friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine‐learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at https://www.lniunicamp.com/resectvol.

This study was undertaken to better understand the long-term palliative and disease-modifying effects of surgical resection beyond seizure freedom, including frequency reduction and both late recurrence and remission, in patients with drug-resistant epilepsy.This retrospective database-driven cohort study included all patients with9 years of follow-up at a single high-volume epilepsy center. We included patients who underwent lobectomy, multilobar resection, or lesionectomies for drug-resistant epilepsy; we excluded patients who underwent hemispherectomies. Our main outcomes were (1) reduction in frequency of disabling seizures (at 6 months, each year up to 9 years postoperatively, and at last follow-up), (2) achievement of seizure remission (6 months,1 year, and longest duration), and (3) seizure freedom at last follow-up.We included 251 patients; 234 (93.2%) achieved 6 months and 232 (92.4%) experienced 1 year of seizure freedom. Of these, the average period of seizure freedom was 10.3 years. A total of 182 (72.5%) patients were seizure-free at last follow-up (defined as1 year without seizures), with a median 11.9 years since remission. For patients not completely seizure-free, the mean seizure frequency reduction at each time point was 76.2%, and ranged from 66.6% to 85.0%. Patients decreased their number of antiseizure medications on average by .58, and 53 (21.2%) patients were on no antiseizure medication at last follow-up. Nearly half (47.1%) of those seizure-free at last follow-up were not seizure-free immediately postoperatively.Patients who continue to have seizures after resection often have considerable reductions in seizure frequency, and many are able to achieve seizure freedom in a delayed manner.

BACKGROUND: Since coronavirus disease 2019 (COVID-19) was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? STUDY DESIGN AND METHODS: We included adult patients (≥ 18 years) positive for laboratory-confirmed severe acute respiratory syndrome-coronavirus 2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. RESULTS: We analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91). INTERPRETATION: Hypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7–11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9–2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8–0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9–1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63–0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64–0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients.

Background Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited. Method Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes. Results In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ2 = 12.8, P < .001; χ2 = 21.6, P < .002; χ2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2. Conclusions Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host’s immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC50) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

ObjectiveTo develop and externally validate models to predict the probability of postoperative verbal memory decline in adults after temporal lobe resection (TLR) for epilepsy using easily accessible preoperative clinical predictors.MethodsMultivariable models were developed to predict delayed verbal memory outcome on 3 commonly used measures: Rey Auditory Verbal Learning Test (RAVLT) and Logical Memory (LM) and Verbal Paired Associates (VPA) subtests from Wechsler Memory Scale–Third Edition. With the use of the Harrell step-down procedure for variable selection, models were developed in 359 adults who underwent TLR at the Cleveland Clinic and validated in 290 adults at 1 of 5 epilepsy surgery centers in the United States or Canada.ResultsTwenty-nine percent of the development cohort and 26% of the validation cohort demonstrated significant decline on at least 1 verbal memory measure. Initial models had good to excellent predictive accuracy (calibration [c] statistic range 0.77–0.80) in identifying patients with memory decline; however, models slightly underestimated decline in the validation cohort. Model coefficients were updated with data from both cohorts to improve stability. The model for RAVLT included surgery side, baseline memory score, and hippocampal resection. The models for LM and VPA included surgery side, baseline score, and education. Updated model performance was good to excellent (RAVLT c = 0.81, LM c = 0.76, VPA c = 0.78). Model calibration was very good, indicating no systematic overestimation or underestimation of risk.ConclusionsNomograms are provided in 2 easy-to-use formats to assist clinicians in estimating the probability of verbal memory decline in adults considering TLR for treatment of epilepsy.Classification of EvidenceThis study provides Class II evidence that multivariable prediction models accurately predict verbal memory decline after TLR for epilepsy in adults.

Objective Patients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy. Methods Ninety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom. Results Forty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non-seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897. Significance This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.

Objective To develop a model to predict the probability of mood decline in adults following temporal lobe resection for the treatment of pharmacoresistant epilepsy. Methods Variable selection was performed on 492 patients from the Cleveland Clinic using best subsets regression. After completing variable selection, a subset of variables was requested from four epilepsy surgery centers across North America (n = 100). All data were combined to develop a final model to predict postoperative mood decline (N = 592). Internal validation with bootstrap resampling was performed. A clinically significant increase in depressive symptoms was defined as a 15% increase in Beck Depression Inventory-Second Edition score and a postoperative raw score > 11. Results Fourteen percent of patients in the Cleveland Clinic cohort and 22% of patients in the external cohort experienced clinically significant increases in depressive symptoms following surgery. The final prediction model included six predictor variables: psychiatric history, resection side, relationship status, verbal fluency score, age at preoperative testing, and presence/absence of malformation of cortical development on magnetic resonance imaging. The model had an optimism-adjusted c-statistic of .70 and good calibration, with slight probability overestimation in higher risk patients. Significance Clinicians can utilize our nomogram via a paper tool or online calculator to estimate the risk of postoperative mood decline for individual patients prior to temporal lobe epilepsy surgery.

Stereoelectroencephalography (SEEG) has gained popularity as an invasive monitoring modality for epileptogenic zone (EZ) localization. The need and indications for SEEG in patients with evident brain lesions or associated abnormalities on imaging is debated. We report our experience with SEEG as a presurgical evaluation tool for patients with lesional epilepsy.A retrospective cohort study was performed of 131 patients with lesional or magnetic resonance imaging abnormality-associated medically refractory focal epilepsy who underwent resections from 2010 to 2017. Seventy-one patients had SEEG followed by resection, and 60 had no invasive recordings. Volumetric analysis of resection cavities from 3T magnetic resonance imaging was performed.Mean lesion and resection volumes for SEEG and non-SEEG were 16.2 (standard deviation [SD] = 29) versus 23.7 cmSeizure outcomes did not correlate with final resection volume after SEEG evaluation. SEEG evaluation presurgically can be used to maintain the efficacy of resection and decrease the volume and subsequent risk of extensive tissue removal. We believe that this technology allows resective surgery to proceed in a subpopulation of patients with lesional epilepsy who may otherwise not have been considered surgical candidates.

Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug-resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure-free and non-seizure-free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave-one-patient-out cross-validation scheme within the MC data set and then tested using the out-of-sample CC data set. Finally, we compared the predictive performance of normal scalp EEG-derived features against MRI abnormalities.We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10-25 Hz range. The Naïve Bayes classification based on those features predicted 1-year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10-25 Hz range provided better predictability than other combinations and (b) normal scalp EEG-derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (10% higher F1 score).These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug-resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

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.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.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.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.

The aim of this study was to conduct item reduction of the Memory Assessment Clinics Self-Rating Scale (MAC-S) to create a briefer measure that can be used to quickly evaluate subjective memory com...

OBJECTIVE: To characterize seizure and cognitive outcomes of sparing vs removing an magnetic resonance imaging (MRI)–normal hippocampus in patients with temporal lobe epilepsy. METHODS: In this retrospective cohort study, we reviewed clinical, imaging, surgical, and histopathological data on 152 individuals with temporal lobe epilepsy and non-lesional hippocampi categorized into hippocampus-spared (n = 74) or hippocampus-resected (n = 78). Extra-hippocampal lesions were allowed. Pre- and postoperative cognitive data were available on 86 patients. Predictors of seizure and cognitive outcomes were identified using Cox-proportional hazard modeling followed by treatment-specific model reduction according to Akaike information criterion, and built into an online risk calculator. RESULTS: Seizures recurred in 40% within one postoperative year, and in 63% within six postoperative years. Male gender (P = .03), longer epilepsy duration (P < .01), normal MRI (P = .04), invasive evaluation (P = .02), and acute postoperative seizures (P < .01) were associated with a higher risk of recurrence. We found no significant difference in postoperative seizure freedom rates at 5 years between those whose hippocampus was spared and those whose hippocampus was resected (P = .17). Seizure outcome models built with pre- and postoperative data had bootstrap validated concordance indices of 0.65 and 0.72. The dominant hippocampus-spared group had lower rates of decline in verbal memory (39% vs 70%; P = .03) and naming (41% vs 79%; P = .01) compared to the hippocampus-resected group. Partial hippocampus sparing had the same risk of verbal memory decline as for complete removal. SIGNIFICANCE: Sparing or removing an MRI-normal hippocampus yielded similar long-term seizure outcome. A more conservative approach, sparing the hippocampus, only partially shields patients from postoperative cognitive deficits. Risk calculators are provided to facilitate clinical counseling.

Objective Postencephalitic epilepsy is often resistant to antiseizure medications, leading to evaluation for epilepsy surgery. Characterizing its localization carries implications for optimal surgical approach. We aimed to determine whether a prior history of encephalitis is associated with specific epileptogenic networks among patients with drug resistant epilepsy undergoing stereotactic EEG (SEEG). Methods We conducted a retrospective cohort study of drug resistant epilepsy, with and without a prior history of encephalitis. We analyzed SEEG recordings to identify patterns of seizure onset and organization. Seventeen patients with a history of encephalitis (of infectious etiology in two subjects) were identified from a database of patients undergoing SEEG and were compared to seventeen drug-resistant epilepsy controls without a history of encephalitis matched for confounding variables including pre-implantation hypotheses, epilepsy duration, age, and sex. Results Independent bilateral seizures were noted in 65% of the postencephalitic epilepsy cohort. We identified four SEEG-ictal patterns in patients with a prior history of encephalitis: (1) anteromesial temporal onset (24%), (2) anteromesial temporal onset with early spread to the perisylvian region (29%), (3) perisylvian (59%) and (4) synchronized anteromesial temporal and perisylvian (29%) onsets. Patterns 3 and 4, with perisylvian involvement at onset, were unique to the encephalitis group (p = 0.0003 and 0.04 respectively) and exhibited a “patchwork” organization. None of the encephalitis patients vs 5/7 matched controls had Engel I outcome (p = 0.0048). Conclusions Postencephalitic epilepsies involve anteromesial temporal and perisylvian networks, often in a bilateral independent manner. Unique ictal patterns involving the perisylvian regions was identified in the encephalitis group, but not in the matched control group. Significance: These findings may reflect a selective vulnerability of the perisylvian regions to epilepsy resulting from encephalitis, significantly mitigating the chances of success with SEEG-guided temporal resections.

ObjectivesTo provide information on the effect of the coronavirus disease of 2019 (COVID-19) pandemic on people with epilepsy and provide consensus recommendations on how to provide the best possible care for people with epilepsy while avoiding visits to urgent care facilities and hospitalizations during the novel coronavirus pandemic.MethodsThe authors developed consensus statements in 2 sections. The first was “How should we/clinicians modify our clinical care pathway for people with epilepsy during the COVID-19 pandemic?” The second was “What general advice should we give to people with epilepsy during this crisis? The authors individually scored statements on a scale of −10 (strongly disagree) to +10 (strongly agree). Five of 11 recommendations for physicians and 3/5 recommendations for individuals/families were rated by all the authors as 7 or above (strongly agree) on the first round of rating. Subsequently, a teleconference was held where statements for which there was a lack of strong consensus were revised.ResultsAfter revision, all consensus recommendations received a score of 7 or above. The recommendations focus on administration of as much care as possible at home to keep people with epilepsy out of health care facilities, where they are likely to encounter COVID-19 (including strategies for rescue therapy), as well as minimization of risk of seizure exacerbation through adherence, and through ensuring a regular supply of medication. We also provide helpful links to additional helpful information for people with epilepsy and health providers.ConclusionThese recommendations may help health care professionals provide optimal care to people with epilepsy during the coronavirus pandemic.

BACKGROUND AND PURPOSE: NeuroQuant is an FDA-approved software that performs automated MR imaging quantitative volumetric analysis. This study aimed to compare the accuracy of NeuroQuant analysis with visual MR imaging analysis by neuroradiologists with expertise in epilepsy in identifying hippocampal sclerosis. MATERIALS AND METHODS: We reviewed 144 adult patients who underwent presurgical evaluation for temporal lobe epilepsy. The reference standard for hippocampal sclerosis was defined by having hippocampal sclerosis on pathology (n = 61) or not having hippocampal sclerosis on pathology (n = 83). Sensitivities, specificities, positive predictive values, and negative predictive values were compared between NeuroQuant analysis and visual MR imaging analysis by using a McNemar paired test of proportions and the Bayes theorem. RESULTS: NeuroQuant analysis had a similar specificity to neuroradiologist visual MR imaging analysis (90.4% versus 91.6%; P = .99) but a lower sensitivity (69.0% versus 93.0%, P

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host’s immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest enrichment for genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate one of the first examples of a viral factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 21 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line (EC50 value of 4.1 µM), suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%

Coronavirus disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ~3.4 million individuals, large-scale deep immune cell profiling data, and single-cell RNA-sequencing data from aged COVID-19 patients across diverse populations. We found that decreased lymphocyte count and elevated inflammatory markers (C-reactive protein, D-dimer, and neutrophil-lymphocyte ratio) are significantly associated with age-specific COVID-19 severities. We identified the reduced abundance of naïve CD8 T cells with decreased expression of antiviral defense genes (i.e., IFITM3 and TRIM22) in aged severe COVID-19 patients. Older individuals with severe COVID-19 displayed type I and II interferon deficiencies, which is correlated with SARS-CoV-2 viral load. Elevated expression of SARS-CoV-2 entry factors and reduced expression of antiviral defense genes (LY6E and IFNAR1) in the secretory cells are associated with critical COVID-19 in aged individuals. Mechanistically, we identified strong TGF-beta-mediated immune-epithelial cell interactions (i.e., secretory-non-resident macrophages) in aged individuals with critical COVID-19. Taken together, our findings point to immuno-inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID-19 patients.

ObjectiveThe aim was to compare the outcomes of subdural electrode (SDE) implantations versus stereotactic electroencephalography (SEEG), the 2 predominant methods of intracranial electroencephalography (iEEG) performed in difficult-to-localize drug-resistant focal epilepsy.MethodsThe Surgical Therapies Commission of the International League Against Epilepsy created an international registry of iEEG patients implanted between 2005 and 2019 with ≥1 year of follow-up. We used propensity score matching to control exposure selection bias and generate comparable cohorts. Study endpoints were: (1) likelihood of resection after iEEG; (2) seizure freedom at last follow-up; and (3) complications (composite of postoperative infection, symptomatic intracranial hemorrhage, or permanent neurological deficit).ResultsTen study sites from 7 countries and 3 continents contributed 2,012 patients, including 1,468 (73%) eligible for analysis (526 SDE and 942 SEEG), of whom 988 (67%) underwent subsequent resection. Propensity score matching improved covariate balance between exposure groups for all analyses. Propensity-matched patients who underwent SDE had higher odds of subsequent resective surgery (odds ratio [OR]=1.4, 95% confidence interval [CI] 1.05, 1.84) and higher odds of complications (OR=2.24, 95% CI 1.34, 3.74; unadjusted: 9.6% after SDE vs 3.3% after SEEG). Odds of seizure freedom in propensity-matched resected patients were 1.66 times higher (95% CI 1.21, 2.26) for SEEG compared with SDE (unadjusted: 55% seizure free after SEEG-guided resections vs 41% after SDE).InterpretationIn comparison to SEEG, SDE evaluations are more likely to lead to brain surgery in patients with drug-resistant epilepsy but have more surgical complications and lower probability of seizure freedom. This comparative-effectiveness study provides the highest feasible evidence level to guide decisions on iEEG. ANN NEUROL 2021;90:927-939.

We highlight an under-recognized epileptic pathology in a 56-year-old left-handed female with progressive right facial numbness and weekly focal seizures characterized by episodic aphasia. She was found to have a left frontoparietal intradiploic meningoencephalocele (IDME). Her only epilepsy risk factor was minor head trauma 10 years prior to presentation. She underwent craniotomy for encephalocele resection and mesh cranioplasty without residual neurological deficits and excellent seizure outcome: at 3-year follow-up, she was still seizure-free since surgery, except for an isolated breakthrough seizure at 7 postoperative months when she discontinued her preoperative regimen of Lacosamide monotherapy. Traumatic IDME is a rare condition and rarely presents with seizures. Symptoms may arise up to decades following minor head trauma and are progressive in nature. The likely definitive treatment is cranioplasty and dural repair with or without resecting the protruding parenchyma.

ObjectiveTo determine whether a pocket card treatment algorithm improves the early treatment of status epilepticus and to assess its utilization and retention in clinical practice.MethodsMultidisciplinary care teams participated in video-recorded status epilepticus simulation sessions from 2015 to 2019. In this longitudinal cohort study, we examined the sessions recorded before and after introducing an internally developed, guideline-derived pocket card to determine differences in the adequacy or timeliness of rescue benzodiazepine. Simulation participants were queried 9 months later for submission of a differentiating identification number on each card to assess ongoing availability and utilization.ResultsForty-four teams were included (22 before and 22 after the introduction of the pocket card). The time to rescue therapy was shorter for teams with the pocket card available (84 seconds [64–132]) compared with teams before introduction (144 seconds [100–162]) (U = 94; median difference = −46.9, 95% confidence interval [CI]: −75.9 to −21.9). The adequate dosing did not differ with card availability (odds ratio 1.48, 95% CI: 0.43–5.1). At the 9-month follow-up, 32 participants (65%) completed the survey, with 26 (81%) self-reporting having the pocket card available and 11 (34%) confirming ready access with the identification number. All identification numbers submitted corresponded to the hard copy laminated pocket card, and none to the electronic version.ConclusionsA pocket card is a feasible, effective, and worthwhile educational tool to improve the implementation of updated guidelines for the treatment of status epilepticus.

Coronavirus Disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ∼3.4 million individuals, large-scale deep immune cell profiling data, and single-cell RNA-sequencing data from aged COVID-19 patients across diverse populations. To begin, we confirmed a significantly increased rate of severe outcomes in aged COVID-19 patients. Furthermore, we identified increased inflammatory markers (C-reactive protein, D-dimer, and neutrophil-lymphocyte ratio), viral entry factors in secretory cells, and TGFβ-mediated immune-epithelial cell interactions, as well as reduction in both naïve CD8 T cells and expression of interferon antiviral defense genes (i.e.,IFITM3andTRIM22), along with strong TGF-beta mediated immune-epithelial cell interactions (i.e., secretory - T regulatory cells), in aged severe COVID-19 patients. Taken together, our findings point to immuno-inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID-19 patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a disproportionately severe effect on men, suggesting that the androgen pathway plays a role in the disease. Studies on the effect of castration and androgen receptor blockade have been mixed, while 5α-reductase inhibitor (5ARI) use in men with COVID-19 (2019 novel coronavirus) have shown potential benefits. We assessed the association of 5ARI use on risk of community acquired SARS-CoV-2 infection.A total of 60,474 males in a prospective registry of people tested for SARS-CoV-2 between March 8, 2020 and February 15, 2021 were included. Using a matched cohort design, men using 5ARIs were matched 1:1 to non5ARI users. Independent analysis using unconditional multivariable logistic regression on the entire unmatched data set was completed for validation. Primary outcome measures were the association of 5ARI use on rates of SARS-Cov-2 positivity and disease severity.Of the men 1,079 (1.8%) reported 5ARI use and 55,100 were available for matching. The final matched cohorts included 944 men each. Mean duration of use was 60.4 months (IQR 17-84 months). Absolute risk for infection was significantly lower in 5ARI users compared to nonusers, 42.3% (399/944) vs 47.2% (446/944), respectively (absolute risk reduction [ARR] 4.9%, OR 0.81, 95% CI 0.67-0.97, p=0.026). Unconditional multivariable logistic regression analysis of the entire study cohort of 55,100 men confirmed the protective association of 5ARI use (ARR 5.3%, OR=0.877, 95% CI 0.774-0.995, p=0.042). Use of 5ARIs was not associated with disease severity.Use of 5ARIs in men without prostate cancer was associated with a reduction in community acquired SARS-CoV-2 infection.

Objective This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. Methods We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction. Results Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. Significance This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Background Prior studies in the field of epilepsy surgical disparities have examined barriers in undergoing epilepsy surgical resections in disadvantaged populations involving trust in health providers, education level, social support, and fear of treatment. Few studies have analyzed the geographical locations of specialized epilepsy centers and their role in nationwide epilepsy surgical access and disparities. Objective To better visualize the locations of epilepsy level IV centers in the United States with respect to epilepsy prevalence and socioeconomic disadvantage. Methods We created heat maps of the United States to visualize geographical locations of level IV epilepsy centers with respect to the 2015 state-wide epilepsy prevalence and 2017 county-wide Area Deprivation Index (ADI) scores, a composite measure of socioeconomic disadvantage. Univariate logistic regression was used to test for associations between the presence or absence of epilepsy centers and socioeconomic disadvantage. Results We found eight states within the United States without any level IV epilepsy centers. In states with level IV centers, centers were clustered in populated and metropolitan regions. Disadvantaged counties (with increased ADI scores) were less likely to have level IV centers compared to counties that were less disadvantaged (lower ADI scores) (p Conclusion Further work is required to remedy the decreased access to specialized epilepsy care due to geographical disparities, and to better understand its contribution to overall disparities affecting epilepsy surgery referrals.

Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model’s C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities (P-value = 0.02). Adding quantitative MRI data increases the performance of a model developed to predict post-operative seizure outcomes. The distribution of the regions of interest included in the final model supports the notion that focal epilepsies are network disorders and that subtle cortical volume loss outside the surgical site influences seizure outcome., Abbreviated summary Morita-Sherman et al. demonstrate how sophisticated research imaging can cross the divide to clinical practice. They show how quantitative MRI can enhance epilepsy surgery outcome prediction. Volume changes in the structures highlighted were identified as outcome predictors, supporting the hypothesis that subtle cortical atrophy outside the resection influences seizure outcome., Graphical Abstract Graphical Abstract