Faculty Bibliography

One-third of epilepsy patients have drug-resistant epilepsy (DRE), which is often complicated by polydrug toxicity and psychiatric and cognitive comorbidities. Advances in understanding the microbiome and gut-brain-axis are likely to shed light on epilepsy pathogenesis, anti-seizure medication (ASM) resistance, and potential therapeutic targets. Gut dysbiosis is associated with inflammation, blood-brain barrier disruption, and altered neuromodulators. High-throughput and metagenomic sequencing has advanced the characterization of microbial species and functional pathways. DRE patients show altered gut microbiome composition compared to drug-sensitive patients and healthy controls. The ketogenic and modified Atkins diets can reduce seizures in some patients with DRE. These low-carbohydrate dietary therapies alter the taxonomic and functional composition of the gut microbiome, and composition varies between diet responders and nonresponders. Murine models suggest that specific phyla are necessary to confer efficacy from the diet, and antibiotic treatment may eliminate efficacy. The impact of diet might involve alterations in microbiota, promotion of select microbial interactions, and variance in brain neurotransmitter levels that then influence seizures. Understanding the mechanics of how diet manipulates seizures may suggest novel therapies. Most ASMs act on neuronal transmission via effects on ion channels and neurotransmitters. However, ASMs may also assert their effects via the gut microbiota. In animal models, the microbiota composition (eg, abundance of certain phyla) can vary with ASM active drug metabolites. Given the developing understanding of the gut microbiome in DRE, probiotics are another potential therapy. Probiotics alter the microbiota composition, and small studies suggest that these supplements can reduce seizures in some patients. DRE has enormous consequences to patients and society, and the gut microbiome holds promise as a potential therapeutic target. However, the exact mechanism and recognition of which patients are likely to be responders remain elusive. Further studies are warranted.

OBJECTIVE:To test the hypothesis that people with focal epilepsy experience diagnostic delays that may be associated with preventable morbidity, particularly when seizures have only nonmotor symptoms, we compared time to diagnosis, injuries, and motor vehicle accidents (MVAs) in people with focal nonmotor versus focal seizures with motor involvement at epilepsy onset. METHODS:This retrospective study analyzed the enrollment data from the Human Epilepsy Project, which enrolled participants between 2012 and 2017 across 34 sites in the USA, Canada, Europe, and Australia, within 4 months of treatment for focal epilepsy. A total of 447 participants were grouped by initial seizure semiology (focal nonmotor or focal with motor involvement) to compare time to diagnosis and prediagnostic injuries including MVAs. RESULTS:Demographic characteristics were similar between groups. There were 246 participants (55%) with nonmotor seizures and 201 participants (45%) with motor seizures at epilepsy onset. Median time to diagnosis from first seizure was 10 times longer in patients with nonmotor seizures compared to motor seizures at onset (P < .001). The number and severity of injuries were similar between groups. However, 82.6% of MVAs occurred in patients with undiagnosed nonmotor seizures. SIGNIFICANCE/CONCLUSIONS:This study identifies reasons for delayed diagnosis and consequences of delay in patients with new onset focal epilepsy, highlighting a treatment gap that is particularly significant in patients who experience nonmotor seizures at epilepsy onset.

OBJECTIVE:There is evidence for central nervous system complications of coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Encephalopathy caused by or arising from seizures, especially nonconvulsive seizures (NCS), often requires electroencephalography (EEG) monitoring for diagnosis. The prevalence of seizures and other EEG abnormalities among COVID-19-infected patients is unknown. METHODS:Medical records and EEG studies of patients hospitalized with confirmed COVID-19 infections over a 2-month period at a single US academic health system (four hospitals) were reviewed to describe the distribution of EEG findings including epileptiform abnormalities (seizures, periodic discharges, or nonperiodic epileptiform discharges). Factors including demographics, remote and acute brain injury, prior history of epilepsy, preceding seizures, critical illness severity scores, and interleukin 6 (IL-6) levels were compared to EEG findings to identify predictors of epileptiform EEG abnormalities. RESULTS:Of 111 patients monitored, most were male (71%), middle-aged or older (median age 64 years), admitted to an intensive care unit (ICU; 77%), and comatose (70%). Excluding 11 patients monitored after cardiac arrest, the most frequent EEG finding was moderate generalized slowing (57%), but epileptiform findings were observed in 30% and seizures in 7% (4% with NCS). Three patients with EEG seizures did not have epilepsy or evidence of acute or remote brain injury, although all had clinical seizures prior to EEG. Only having epilepsy (odds ratio [OR] 5.4, 95% confidence interval [CI] 1.4-21) or seizure(s) prior to EEG (OR 4.8, 95% CI 1.7-13) was independently associated with epileptiform EEG findings. SIGNIFICANCE/CONCLUSIONS:Our study supports growing evidence that COVID-19 can affect the central nervous system, although seizures are unlikely a common cause of encephalopathy. Seizures and epileptiform activity on EEG occurred infrequently, and having a history of epilepsy or seizure(s) prior to EEG testing was predictive of epileptiform findings. This has important implications for triaging EEG testing in this population.

PURPOSE/OBJECTIVE:A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings. METHODS:We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated. RESULTS:The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97). CONCLUSION/CONCLUSIONS:This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.

Background/UNASSIGNED:It is unknown whether postanoxic cortical and subcortical myoclonus are distinct entities with different prognoses. Methods/UNASSIGNED:In this retrospective cohort study of 604 adult survivors of cardiac arrest over 8.5 years, we identified 111 (18%) patients with myoclonus. Basic demographics and clinical characteristics of myoclonus were collected. EEG reports, and, when available, raw video EEG, were reviewed, and all findings adjudicated by 3 authors blinded to outcomes. Myoclonus was classified as cortical if there was a preceding, time-locked electrographic correlate and otherwise as subcortical. Outcome at discharge was determined using Cerebral Performance Category. Results/UNASSIGNED:Patients with myoclonus had longer arrests with less favorable characteristics compared to patients without myoclonus. Cortical myoclonus occurred twice as often as subcortical myoclonus (59% vs 23%, respectively). Clinical characteristics during hospitalization did not distinguish the two. Rates of electrographic seizures were higher in patients with cortical myoclonus (43%, vs 8% with subcortical). Survival to discharge was worse for patients with myoclonus compared to those without (26% vs 39%, respectively), but did not differ between subcortical and cortical myoclonus (24% and 26%, respectively). Patients with cortical myoclonus were more likely to be discharged in a comatose state than those with subcortical myoclonus (82% vs 33%, respectively). Among survivors, good functional outcome at discharge was equally possible between those with cortical and subcortical myoclonus (12% and 16%, respectively). Conclusions/UNASSIGNED:Cortical and subcortical myoclonus are seen in every sixth patient with cardiac arrest and cannot be distinguished using clinical criteria. Either condition may have good functional outcomes.

Introduction: Post-anoxic myoclonus is seen in up to 20% of patients who remain comatose, and historically was felt to be a poor prognostic sign. Little distinction has been made in the literature between epileptic (cortical) versus subcortical myoclonus. Methods: 604 cardiac arrest patients in a nine year period that did not return to baseline were admitted to an ICU. We identified 18% (N = 111) with clinical myoclonus. Demographics and characteristics of the arrest were collected and EEG reports were analyzed. Raw EEG including video was reviewed by two epilepsy-trained neurologists, whenever available. Myoclonus was categorized into subcortical and cortical based on presence of a preceding EEG correlate. Patients with cortical myoclonus sufficiently frequent to meet criteria for myoclonic status epilepticus were included. Results: The average age was 63 +/- 17 years, and 29% (N = 32) survived to discharge. 23% of patients had subcortical, 59% cortical, 5% had both subcortical and cortical myoclonus, and in 13% it was impossible to clearly determine the subcategory of myoclonus. Among patients that underwent TTM (n = 99), onset of myoclonus was seen during initiation of cooling in 52%, while at target temperature in 35%, during rewarming in 9%, after fully rewarmed in 1%, and timing was unclear in 3%. Timing of myoclonus did not differ between those with subcortical and cortical myoclonus. No arrest characteristics were associated with each subtype. Survival to discharge did not differ between subcortical and cortical myoclonus (24% and 26%, respectively). Among survivors, 16% of those with subcortical and 18% with cortical myoclonus had a good functional outcome at discharge (Cerebral Performance Category 1-2). One of six patients with both subcortical and cortical myoclonus had a good outcome. Conclusion: Cortical myoclonus is twice as common as subcortical myoclonus, and both can be associated with good outcome

We report a case with therapeutic hypothermia after cardiac arrest where meaningful recovery far exceeded anticipated negative endpoints following cardiac arrest with loss of brainstem reflexes and subsequent status epilepticus. This man survived and recovered after an out-of-hospital cardiac arrest followed by a 6-week coma with absent motor responses and 5 weeks of burst suppression. Standard criteria suggested no chance of recovery. His recovery may relate to the effect of burst-suppression on EEG to rescue neurons near neuronal cell death. Further research to understand the mechanisms of therapeutic hypothermia and late restoration of neuronal functional capacity may improve prediction and aid end-of-life decisions after cardiac arrest.