Faculty Bibliography

OBJECTIVE:Prolonged postictal generalized electroencephalographic suppression (PGES) is a potential biomarker for sudden unexpected death in epilepsy (SUDEP), which may be associated with dysfunctional autonomic responses and serotonin signaling. To better understand molecular mechanisms, PGES duration was correlated to 5HT1A and 5HT2A receptor protein expression and RNAseq from resected hippocampus and temporal cortex of temporal lobe epilepsy patients with seizures recorded in preoperative evaluation. METHODS:Analyses included 36 cases (age = 14-64 years, age at epilepsy onset = 0-51 years, epilepsy duration = 2-53 years, PGES duration = 0-93 s), with 13 cases in all hippocampal analyses. 5HT1A and 5HT2A protein was evaluated by Western blot and histologically in hippocampus (n = 16) and temporal cortex (n = 9). We correlated PGES duration to our previous RNAseq dataset for serotonin receptor expression and signaling pathways, as well as weighted gene correlation network analysis (WGCNA) to identify correlated gene clusters. RESULTS: = .25). WGCNA identified four modules correlated with PGES duration, including positive correlation with synaptic transcripts (p = .040, Pearson correlation r = .52), particularly potassium channels (KCNA4, KCNC4, KCNH1, KCNIP4, KCNJ3, KCNJ6, KCNK1). No modules were associated with serotonin receptor signaling. SIGNIFICANCE/CONCLUSIONS:Higher hippocampal 5HT2A receptor protein and potassium channel transcripts may reflect underlying mechanisms contributing to or resulting from prolonged PGES. Future studies with larger cohorts should assess functional analyses and additional brain regions to elucidate mechanisms underlying PGES and SUDEP risk.

Clinical studies of rescue medications for seizure clusters are limited and designed to satisfy regulatory requirements, which may not fully consider the needs of the diverse patient population that experiences seizure clusters or utilize rescue medication. The purpose of this narrative review is to examine factors that contribute to, or may influence the quality of, seizure cluster research with a goal of improving clinical practice. We address five areas of unmet needs and provide advice of how they could enhance future trials of seizure cluster treatments. The topics addressed in this manuscript are: 1) unaddressed endpoints to pursue in future studies, 2) roles for devices to enhance rescue medication clinical development programs, 3) tools to study seizure cluster prediction and prevention, 4) the value of other designs for seizure cluster studies, and 5) unique challenges of future trial paradigms for seizure clusters. By focusing on novel endpoints and technologies with value to patients, caregivers, and clinicians, data obtained from future studies can benefit the diverse patient population that experiences seizure clusters, providing more effective, appropriate care as well as alleviating demands on healthcare resources.

Neural responses to visual stimuli exhibit complex temporal dynamics, including sub-additive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic (ECoG) recordings from patients presented with stimuli that varied in contrast, duration, and inter-stimulus interval (ISI). Aggregating data across patients from both sexes yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several non-linear features: peak response amplitude saturates with high contrast and longer stimulus durations; the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs; response latency decreases with increasing contrast. These features are accurately captured by a computational model comprised of a small set of canonical neuronal operations: linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptation-related response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex, and demonstrate that a simple model captures these dynamics at millisecond resolution.SIGNIFICANCE STATEMENTSensory inputs and neural responses change continuously over time. It is especially challenging to understand a system that has both dynamic inputs and outputs. Here we use a computational modeling approach that specifies computations to convert a time-varying input stimulus to a neural response time course, and use this to predict neural activity measured in the human visual cortex. We show that this computational model predicts a wide variety of complex neural response shapes that we induced experimentally by manipulating the duration, repetition and contrast of visual stimuli. By comparing data and model predictions, we uncover systematic properties of temporal dynamics of neural signals, allowing us to better understand how the brain processes dynamic sensory information.

OBJECTIVE:The force that an electrocorticography (ECoG) array exerts on the brain manifests when it bends to match the curvature of the skull and cerebral cortex. This force can negatively impact both short-term and long-term patient outcomes. Here we provide a mechanical characterization of a novel Liquid Crystal Polymer (LCP) ECoG array prototype to demonstrate that its thinner geometry reduces the force potentially applied to the cortex of the brain. APPROACH/METHODS:We built a low-force flexural testing machine to measure ECoG array bending forces, calculate their effective flexural moduli, and approximate the maximum force they could exerted on the human brain. MAIN RESULTS/RESULTS:The LCP ECoG prototype was found to have a maximal force less than 20% that of any commercially available ECoG arrays that was tested. However, as a material, LCP was measured to be as much as 24x more rigid than silicone, which is traditionally used in ECoG arrays. This suggests that the lower maximal force resulted from the prototype's thinner profile (2.9x-3.25x). SIGNIFICANCE/CONCLUSIONS:While decreasing material stiffness can lower the force an ECoG array exhibits, our LCP ECoG array prototype demonstrated that flexible circuit manufacturing techniques can also lower these forces by decreasing ECoG array thickness. Flexural tests of ECoG arrays are necessary to accurately assess these forces, as material properties for polymers and laminates are often scale dependent. As the polymers used are anisotropic, elastic modulus cannot be used to predict ECoG flexural behavior. Accounting for these factors, we used our four-point flexure testing procedure to quantify the forces exerted on the brain by ECoG array bending. With this experimental method, ECoG arrays can be designed to minimize force excerted on the brain, potentially improving both acute and chronic clinical utility.

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as "engaging in dialogue" and "using electronics". Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity's covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.

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

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

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