Racial disparities in the utilization of invasive neuromodulation devices for the treatment of drug-resistant focal epilepsy.

Racial disparities affect multiple dimensions of epilepsy care including epilepsy surgery. This study aims to further explore these disparities by determining the utilization of invasive neuromodulation devices according to race and ethnicity in a multicenter study of patients living with focal drug-resistant epilepsy (DRE). We performed a post hoc analysis of the Human Epilepsy Project 2 (HEP2) data. HEP2 is a prospective study of patients living with focal DRE involving 10 sites distributed across the United States. There were no statistical differences in the racial distribution of the study population compared to the US population using census data except for patients reporting more than one race. Of 154 patients enrolled in HEP2, 55 (36%) underwent invasive neuromodulation for DRE management at some point in the course of their epilepsy. Of those, 36 (71%) were patients who identified as White. Patients were significantly less likely to have a device if they identified solely as Black/African American than if they did not (odds ratio = .21, 95% confidence interval = .05-.96, p = .03). Invasive neuromodulation for management of DRE is underutilized in the Black/African American population, indicating a new facet of racial disparities in epilepsy care.

Optimizing Surgical Planning for Epilepsy Patients With Multimodal Neuroimaging and Neurophysiology Integration: A Case Study.

SUMMARY: Current preoperative evaluation of epilepsy can be challenging because of the lack of a comprehensive view of the network's dysfunctions. To demonstrate the utility of our multimodal neurophysiology and neuroimaging integration approach in the presurgical evaluation, we present a proof-of-concept for using this approach in a patient with nonlesional frontal lobe epilepsy who underwent two resective surgeries to achieve seizure control. We conducted a post-hoc investigation using four neuroimaging and neurophysiology modalities: diffusion tensor imaging, resting-state functional MRI, and stereoelectroencephalography at rest and during seizures. We computed region-of-interest-based connectivity for each modality and applied betweenness centrality to identify key network hubs across modalities. Our results revealed that despite seizure semiology and stereoelectroencephalography indicating dysfunction in the right orbitofrontal region, the maximum overlap on the hubs across modalities extended to right temporal areas. Notably, the right middle temporal lobe region served as an overlap hub across diffusion tensor imaging, resting-state functional MRI, and rest stereoelectroencephalography networks and was only included in the resected area in the second surgery, which led to long-term seizure control of this patient. Our findings demonstrated that transmodal hubs could help identify key areas related to epileptogenic network. Therefore, this case presents a promising perspective of using a multimodal approach to improve the presurgical evaluation of patients with epilepsy.

Later onset focal epilepsy with roots in childhood: Evidence from early learning difficulty and brain volumes in the Human Epilepsy Project.

OBJECTIVE: Visual assessment of magnetic resonance imaging (MRI) from the Human Epilepsy Project 1 (HEP1) found 18% of participants had atrophic brain changes relative to age without known etiology. Here, we identify the underlying factors related to brain volume differences in people with focal epilepsy enrolled in HEP1. METHODS: Enrollment data for participants with complete records and brain MRIs were analyzed, including 391 participants aged 12-60 years. HEP1 excluded developmental or cognitive delay with intelligence quotient <70, and participants reported any formal learning disability diagnoses, repeated grades, and remediation. Prediagnostic seizures were quantified by semiology, frequency, and duration. T1-weighted brain MRIs were analyzed using Sequence Adaptive Multimodal Segmentation (FreeSurfer v7.2), from which a brain tissue volume to intracranial volume ratio was derived and compared to clinically relevant participant characteristics. RESULTS: Brain tissue volume changes observable on visual analyses were quantified, and a brain tissue volume to intracranial volume ratio was derived to compare with clinically relevant variables. Learning difficulties were associated with decreased brain tissue volume to intracranial volume, with a ratio reduction of .005 for each learning difficulty reported (95% confidence interval [CI] = -.007 to -.002, p = .0003). Each 10-year increase in age at MRI was associated with a ratio reduction of .006 (95% CI = -.007 to -.005, p < .0001). For male participants, the ratio was .011 less than for female participants (95% CI = -.014 to -.007, p < .0001). There were no effects from seizures, employment, education, seizure semiology, or temporal lobe electroencephalographic abnormalities. SIGNIFICANCE: This study shows lower brain tissue volume to intracranial volume in people with newly treated focal epilepsy and learning difficulties, suggesting developmental factors are an important marker of brain pathology related to neuroanatomical changes in focal epilepsy. Like the general population, there were also independent associations between brain volume, age, and sex in the study population.

Mood and Anxiety Disorders and Suicidality in Patients With Newly Diagnosed Focal Epilepsy: An Analysis of a Complex Comorbidity.

BACKGROUND AND OBJECTIVES: Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS: The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. RESULTS: A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION: In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

Seizure forecasting using machine learning models trained by seizure diaries.

Objectives.People with refractory epilepsy are overwhelmed by the uncertainty of their next seizures. Accurate prediction of future seizures could greatly improve the quality of life for these patients. New evidence suggests that seizure occurrences can have cyclical patterns for some patients. Even though these cyclicalities are not intuitive, they can be identified by machine learning (ML), to identify patients with predictable vs unpredictable seizure patterns.Approach.Self-reported seizure logs of 153 patients from the Human Epilepsy Project with more than three reported seizures (totaling 8337 seizures) were used to obtain inter-seizure interval time-series for training and evaluation of the forecasting models. Two classes of prediction methods were studied: (1) statistical approaches using Bayesian fusion of population-wise and individual-wise seizure patterns; and (2) ML-based algorithms including least squares, least absolute shrinkage and selection operator, support vector machine (SVM) regression, and long short-term memory regression. Leave-one-person-out cross-validation was used for training and evaluation, by training on seizure diaries of all except one subject and testing on the left-out subject.Main results.The leading forecasting models were the SVM regression and a statistical model that combined the median of population-wise seizure time-intervals with a test subject's prior seizure intervals. SVM was able to forecast 50%, 70%, 81%, 84%, and 87% of seizures of unseen subjects within 0, 1, 2, 3 to 4 d of mean absolute forecasting error, respectively. The subject-wise performances show that patients with more frequent seizures were generally better predicted.Significance.ML models can leverage non-random patterns within self-reported seizure diaries to forecast future seizures. While diary-based seizure forecasting alone is only one of many aspects of clinical care of patients with epilepsy, studying the level of predictability across seizures and patients paves the path towards a better understanding of predictable vs unpredictable seizures on individualized and population-wise bases.

EEG Abnormalities and Their Radiographic Correlates in a COVID-19 Inpatient Cohort.

Background and Objectives: To identify the prevalence of EEG abnormalities in patients with coronavirus disease 2019 (COVID-19) with neurologic changes, their associated neuroimaging abnormalities, and rates of mortality. Methods: A retrospective case series of 192 adult COVID-19-positive inpatients with EEG performed between March and June 2020 at 4 hospitals: 161 undergoing continuous, 24 routine, and 7 reduced montage EEG. Study indication, epilepsy history, intubation status, administration of sedatives or antiseizure medications (ASMs), metabolic abnormalities, neuroimaging pathology associated with epileptiform abnormalities, and in-hospital mortality were analyzed. Results: EEG indications included encephalopathy (54.7%), seizure (18.2%), coma (17.2%), focal deficit (5.2%), and abnormal movements (4.6%). Epileptiform abnormalities occurred in 39.6% of patients: focal intermittent epileptiform discharges in 25.0%, lateralized periodic discharges in 6.3%, and generalized periodic discharges in 19.3%. Seizures were recorded in 8 patients, 3 with status epilepticus. ASM administration, epilepsy history, and older age were associated with epileptiform abnormalities. Only 26.3% of patients presented with any epileptiform abnormality, 37.5% with electrographic seizures, and 25.7% patients with clinical seizures had known epilepsy. Background findings included generalized slowing (88.5%), focal slowing (15.6%), burst suppression (3.6%), attenuation (3.1%), and normal EEG (3.1%). Neuroimaging pathology was identified in 67.1% of patients with epileptiform abnormalities, over two-thirds acute. In-hospital mortality was 39.5% for patients with epileptiform abnormalities and 36.2% for those without. Risk factors for mortality were coma and ventilator support at time of EEG. Discussion: This article highlights the range of EEG abnormalities frequently associated with acute neuroimaging abnormalities in COVID-19. Mortality rates were high, particularly for patients in coma requiring mechanical ventilation. These findings may guide the prognosis and management of patients with COVID-19 and neurologic changes.

Structural Neuroimaging in Adults and Adolescents With Newly Diagnosed Focal Epilepsy: The Human Epilepsy Project.

BACKGROUND AND OBJECTIVES: Identification of an epileptogenic lesion on structural neuroimaging in individuals with focal epilepsy is important for management and treatment planning. The objective of this study was to determine the frequency of MRI-identified potentially epileptogenic structural abnormalities in a large multicenter study of adolescent and adult patients with newly diagnosed focal epilepsy. METHODS: Patients with a new diagnosis of focal epilepsy enrolled in the Human Epilepsy Project observational cohort study underwent 3-Tesla (3T) brain MRI using a standardized protocol. Imaging findings were classified as normal, abnormal, or incidental. Abnormal findings were classified as focal or diffuse, and as likely epilepsy-related or of unknown relationship to epilepsy. Fisher exact tests were performed to determine whether abnormal imaging or abnormality type was associated with clinical characteristics. RESULTS: 418 participants were enrolled. 218 participants (59.3%) had no abnormalities detected, 149 (35.6%) had abnormal imaging, and 21 (5.0%) had incidental findings. 78 participants (18.7%) had abnormalities that were considered epilepsy-related and 71 (17.0%) had abnormalities of unknown relationship to epilepsy. Older participants were more likely to have imaging abnormalities, while participants with focal and epilepsy-related imaging abnormalities were younger than those without these abnormalities. 131 participants (31.3%) had a family history of epilepsy. Epilepsy-related abnormalities were not associated with participant sex, family history of epilepsy, or seizure type. DISCUSSION: We found that one in five patients with newly diagnosed focal epilepsy has an MRI finding that is likely causative and may alter treatment options. An additional one in five patients has abnormalities of unknown significance. This information is important for patient counseling, prognostication, and management.

Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study.

Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.

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

PURPOSE: 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.

Association Between Anatomical Location of Surgically Induced Lesions and Postoperative Seizure Outcome in Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: To determine the association between surgical lesions of distinct gray and white structures and connections with favorable postoperative seizure outcomes. METHODS: Patients with drug-resistant temporal lobe epilepsy (TLE) from 3 epilepsy centers were included. We employed a voxel-based and connectome-based mapping approach to determine the association between favorable outcomes and surgery-induced temporal lesions. Analyses were conducted controlling for multiple confounders, including total surgical resection/ablation volume, hippocampal volumes, side of surgery, and site where the patient was treated. RESULTS: The cohort included 113 patients with TLE (54 women; 86 right-handed; mean age at seizure onset 16.5 years [SD 11.9]; 54.9% left) who were 61.1% free of disabling seizures (Engel Class 1) at follow-up. Postoperative seizure freedom in TLE was associated with (1) surgical lesions that targeted the hippocampus as well as the amygdala-piriform cortex complex and entorhinal cortices; (2) disconnection of temporal, frontal, and limbic regions through loss of white matter tracts within the uncinate fasciculus, anterior commissure, and fornix; and (3) functional disconnection of the frontal (superior and middle frontal gyri, orbitofrontal region) and temporal (superior and middle pole) lobes. DISCUSSION: Better postoperative seizure freedom is associated with surgical lesions of specific structures and connections throughout the temporal lobes. These findings shed light on the key components of epileptogenic networks in TLE and constitute a promising source of new evidence for future improvements in surgical interventions. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with TLE, postoperative seizure freedom is associated with surgical lesions of specific temporal lobe structures and connections.

Expanding the phenotype: Four new cases and hope for treatment in Bachmann-Bupp syndrome.

Bachmann-Bupp syndrome (BABS) is a rare syndrome caused by gain-of-function variants in the C-terminus of ornithine decarboxylase (ODC coded by the ODC1 gene). BABS is characterized by developmental delay, macrocephaly, macrosomia, and an unusual pattern of non-congenital alopecia. Recent diagnosis of four more BABS patients provides further characterization of the phenotype of this syndrome including late-onset seizures in the oldest reported patient at 23 years of age, representing the first report for this phenotype in BABS. Neuroimaging abnormalities continue to be an inconsistent feature of the syndrome. This may be related to the yet unknown impact of ODC/polyamine dysregulation on the developing brain in this syndrome. Variants continue to cluster, providing support to a universal biochemical mechanism related to elevated ODC protein, enzyme activity, and abnormalities in polyamine levels. Recommendations for medical management can now be suggested as well as the potential for targeted molecular or metabolic testing when encountering this unique phenotype. The natural history of this syndrome will evolve with difluoromethylornithine (DFMO) therapy and raise new questions for further study and understanding.

Cortical disconnection in temporal lobe epilepsy.

A critical concept in neurology is cortical disconnection, in which seemingly normal gray matter can have reduced function due to loss of white matter (WM) connections. White matter damage has been extensively described in temporal lobe epilepsy (TLE), but the anatomical distribution of cortical disconnection in TLE is not fully characterized. Here, we studied 221 participants (64 left-TLE, 55 right-TLE, 102 controls) from three different epilepsy treatment centers. We employed a group connectometry diffusion imaging tractography approach to identify WM fibers with reduced integrity in TLE. We then assessed the anatomical distribution of the gray matter endpoint projections of abnormal fibers to map the anatomical pattern of disconnections. As expected, left- and right-TLE were associated with multiple WM pathways with reduced integrity, which were associated with extensive cortical disconnection involving predominantly limbic structures. Controlling for medial temporal gray matter atrophy, cortical disconnection of the left cingulum and the thalamus as well as disconnection of the bilateral putamen and the amygdala was associated with lower verbal memory immediate recall. In conclusion, our results support that cortical disconnection is an underappreciated but pervasive phenomenon in TLE, and cortical disconnection of limbic structures beyond the medial temporal regions is related to verbal memory performance.

Validation of an EEG seizure detection paradigm optimized for clinical use in a chronically implanted subcutaneous device.

BACKGROUND: Many electroencephalography (EEG) based seizure detection paradigms have been developed and validated over the last two decades. The majority of clinical approaches use scalp or intracranial EEG electrodes. Scalp EEG is limited by patient discomfort and short duration of useful EEG signals. Intracranial EEG involves an invasive surgical procedure associated with significant risk making it unsuitable for widespread use as a practical clinical biometric. A less invasive EEG monitoring approach, that is between invasive intracranial procedures and noninvasive methods, would fill the need of a safe, accurate, chronic (ultra-long term) and objective seizure detection method. We present validation of a continuous EEG seizure detection paradigm using human single-channel EEG recordings from subcutaneously placed electrodes that could be used to fulfill this need. METHODS: Ten-minute long sleep, awake and ictal EEG epochs obtained from 21 human subjects with subscalp electrodes and validated against simultaneous iEEG recordings were analyzed by three experienced clinical neurophysiologists. The 201subscalp EEG time series epochs where classified as diagnostic for awake, asleep, or seizure by the clinicians who were blinded to all other information. Seventy of the epochs were classified in this way as representing seizure activity. A subject specific seizure detection algorithm was trained and then evaluated offline for each patient in the data set using the expert consensus classification as the gold standard. RESULTS: The average seizure detection performance of the algorithm across 21 subjects exceeded 90 % accuracy: 97 % sensitivity, 91 % specificity, and 93 % accuracy. For 19 of 21 patient datasets the algorithm achieved 100 % sensitivity. For 15 of 21 patients, the algorithm achieved 100 % specificity. For 13 of 21 patients the algorithm achieved 100 % accuracy. COMPARISON: No comparable published methods are available for subgaleal EEG seizure detection. CONCLUSIONS: These findings suggest that a simple seizure detection algorithm using subcutaneous EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.

Acute Seizures Occurring in Association With SARS-CoV-2.

Seizures are an infrequent and serious neurological complication of SARS-CoV-2 infection, with limited data describing the etiology and the clinical context in which these occur or the associated electrographic and imaging findings. This series details four cases of seizures occurring in patients with COVID-19 with distinct time points, underlying pathology, and proposed physiological mechanisms. An enhanced understanding of seizure manifestations in COVID-19 and their clinical course may allow for earlier detection and improved patient management.

MRI-negative PET-negative epilepsy long-term surgical outcomes: A single-institution retrospective review.

PURPOSE: Surgical planning for people with drug resistant non-lesional focal epilepsy can be challenging. Prior studies focus on cases that are only MRI-negative or MRI-negative with PET-positive imaging, but little is known about outcomes in patients with non-lesional findings on both MRI and PET imaging. In this study, we investigate 5-year surgical outcomes in patients who underwent epilepsy surgery for drug resistant MRI/PET-negative focal epilepsy. METHODS: We collected clinical and testing data on 131 consecutive patients with drug resistant non-lesional epilepsy who were presented at a multidisciplinary epilepsy surgery conference at the New York University Comprehensive Epilepsy Center between 2010 and 2014, and identified those who underwent epilepsy surgery in order to review 5-year surgical outcomes. RESULTS: There were 103 with non-lesional MRI studies, and of these, 22 had corresponding non-lesional PET imaging. 14 MRI/PET-negative patients pursued a surgical treatment option and 9 underwent resections after intracranial EEG. At 5 years, 77.8 % of patients had favorable (ILAE class 1 and 2) outcomes. Most (77.8 %) had focal cortical dysplasia type Ia (FCDIa) on pathology. CONCLUSION: These findings suggest that with careful planning and patient selection, surgery for patients with drug resistant MRI/PET-negative focal epilepsy can be successful.

Cerebrovascular Complications of COVID-19.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) evolved quickly into a global pandemic with myriad systemic complications, including stroke. We report the largest case series to date of cerebrovascular complications of COVID-19 and compare with stroke patients without infection. METHODS: Retrospective case series of COVID-19 patients with imaging-confirmed stroke, treated at 11 hospitals in New York, between March 14 and April 26, 2020. Demographic, clinical, laboratory, imaging, and outcome data were collected, and cases were compared with date-matched controls without COVID-19 from 1 year prior. RESULTS: Eighty-six COVID-19-positive stroke cases were identified (mean age, 67.4 years; 44.2% women). Ischemic stroke (83.7%) and nonfocal neurological presentations (67.4%) predominated, commonly involving multivascular distributions (45.8%) with associated hemorrhage (20.8%). Compared with controls (n=499), COVID-19 was associated with in-hospital stroke onset (47.7% versus 5.0%; P<0.001), mortality (29.1% versus 9.0%; P<0.001), and Black/multiracial race (58.1% versus 36.9%; P=0.001). COVID-19 was the strongest independent risk factor for in-hospital stroke (odds ratio, 20.9 [95% CI, 10.4-42.2]; P<0.001), whereas COVID-19, older age, and intracranial hemorrhage independently predicted mortality. CONCLUSIONS: COVID-19 is an independent risk factor for stroke in hospitalized patients and mortality, and stroke presentations are frequently atypical.

Central nervous system complications associated with SARS-CoV-2 infection: integrative concepts of pathophysiology and case reports.

Coronavirus disease 2019 (COVID-19) is a highly infectious pandemic caused by a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It frequently presents with unremitting fever, hypoxemic respiratory failure, and systemic complications (e.g., gastrointestinal, renal, cardiac, and hepatic involvement), encephalopathy, and thrombotic events. The respiratory symptoms are similar to those accompanying other genetically related beta-coronaviruses (CoVs) such as severe acute respiratory syndrome CoV (SARS-CoV) and Middle East Respiratory Syndrome CoV (MERS-CoV). Hypoxemic respiratory symptoms can rapidly progress to Acute Respiratory Distress Syndrome (ARDS) and secondary hemophagocytic lymphohistiocytosis, leading to multi-organ system dysfunction syndrome. Severe cases are typically associated with aberrant and excessive inflammatory responses. These include significant systemic upregulation of cytokines, chemokines, and pro-inflammatory mediators, associated with increased acute-phase proteins (APPs) production such as hyperferritinemia and elevated C-reactive protein (CRP), as well as lymphocytopenia. The neurological complications of SARS-CoV-2 infection are high among those with severe and critical illnesses. This review highlights the central nervous system (CNS) complications associated with COVID-19 attributed to primary CNS involvement due to rare direct neuroinvasion and more commonly secondary CNS sequelae due to exuberant systemic innate-mediated hyper-inflammation. It also provides a theoretical integration of clinical and experimental data to elucidate the pathogenesis of these disorders. Specifically, how systemic hyper-inflammation provoked by maladaptive innate immunity may impair neurovascular endothelial function, disrupt BBB, activate CNS innate immune signaling pathways, and induce para-infectious autoimmunity, potentially contributing to the CNS complications associated with SARS-CoV-2 infection. Direct viral infection of the brain parenchyma causing encephalitis, possibly with concurrent neurovascular endotheliitis and CNS renin angiotensin system (RAS) dysregulation, is also reviewed.

Temporal Lobe Epilepsy Surgical Outcomes Can Be Inferred Based on Structural Connectome Hubs: A Machine Learning Study.

OBJECTIVE: Medial temporal lobe epilepsy (TLE) is the most common form of medication-resistant focal epilepsy in adults. Despite removal of medial temporal structures, more than one-third of patients continue to have disabling seizures postoperatively. Seizure refractoriness implies that extramedial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. METHODS: Presurgical magnetic resonance images from 121 patients with drug-resistant TLE across 3 independent epilepsy centers were used to train feed-forward neural network models based on tissue volume or graph-theory measures from whole-brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from 3 other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions toward surgical treatment results. RESULTS: The receiver operating characteristic area under the curve based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. INTERPRETATION: Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness. ANN NEUROL 2020;88:970-983.

Expert opinion: Proposed diagnostic and treatment algorithms for Lennox-Gastaut syndrome in adult patients.

Lennox-Gastaut syndrome (LGS) is a severe developmental epileptic encephalopathy diagnosed in childhood that persists through adolescence and into adulthood. While the characteristics of LGS in pediatric patients are well defined, including "drop attacks", interictal slow spike and wave electroencephalogram (EEG) activity, and intellectual disability, these features can evolve over time, and different EEG activities may be present in adult patients with LGS. This may result in missed diagnoses in these patients and subsequent challenges for the adequate treatment of their seizures. Based on discussions held during the LGS Transition of Care advisory board meeting and thereafter, we developed proposed diagnostic and treatment algorithms for LGS in adult patients. We highlight readily available assessments to facilitate diagnosis of LGS, based on past medical history and physical examination. The LGS diagnostic algorithm recommends that clinicians consider the occurrence of wider seizure types and abnormal EEG activities to be potentially indicative of LGS. Seizure types may include atypical absence seizures, myoclonic seizures, focal seizures, and tonic-clonic seizures, and EEG may demonstrate background slowing, focal or multifocal epileptiform discharges, and diffuse fast rhythms during sleep, among other activities. Extended EEG during sleep and video-EEG should be used in equivocal cases. Treatment of LGS in adult patients should incorporate both antiseizure drug (ASD) therapy and nonpharmacologic approaches. Frequent reassessment of patients is considered a central aspect. ASDs were categorized based on order of preference for use in the treatment of LGS; Tier 1 comprises recommended first-line ASDs, and includes valproate, clobazam, lamotrigine, rufinamide, topiramate, and cannabidiol. Other treatment options include diet, neurostimulation, and surgical approaches. Developments with the potential to improve diagnosis in the future include genetic screening, while novel ASDs and advances in neurostimulation techniques may provide valuable treatment options. These algorithms should be frequently revisited to incorporate improved techniques and therapies.