Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment.

For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.

Seizure Cycles under Pharmacotherapy.

OBJECTIVE: This study was undertaken to determine the effects of antiseizure medications (ASMs) on multidien (multiday) cycles of interictal epileptiform activity (IEA) and seizures and evaluate their potential clinical significance. METHODS: We retrospectively analyzed up to 10 years of data from 88 of the 256 total adults with pharmacoresistant focal epilepsy who participated in the clinical trials of the RNS System, an intracranial device that keeps records of IEA counts. Following adjunctive ASM trials, we evaluated changes over months in (1) rates of self-reported disabling seizures and (2) multidien IEA cycle strength (spectral power for periodicity between 4 and 40 days). We used a survival analysis and the receiver operating characteristics to assess changes in IEA as a predictor of seizure control. RESULTS: Among 56 (33.3%) of the 168 adjunctive ASM trials suitable for analysis, ASM introduction was followed by an average 50 to 70% decrease in multidien IEA cycle strength and a concomitant 50 to 70% decrease in relative seizure rate for up to 12 months. Individuals with a ≥50% decrease in IEA cycle strength in the first 3 months of an ASM trial had a higher probability of remaining seizure responders (≥50% seizure rate reduction, p < 10-7) or super-responders (≥90%, p < 10-8) over the next 12 months. INTERPRETATION: In this large cohort, a decrease in multidien IEA cycle strength following initiation of an adjunctive ASM correlated with seizure control for up to 12 months, suggesting that fluctuations in IEA mirror "disease activity" in pharmacoresistant focal epilepsy and may have clinical utility as a biomarker to predict treatment response. ANN NEUROL 2024;95:743-753.

Benzodiazepines for the Treatment of Seizure Clusters.

Patients with epilepsy may experience seizure clusters, which are described by the US Food and Drug Administration (FDA) as intermittent, stereotypic episodes of frequent seizure activity that are distinct from a patient's usual seizure pattern. Untreated seizure clusters may increase the risk for status epilepticus, as well as decrease quality of life and increase burden on patients and care partners. Benzodiazepine therapies are the mainstay for acute treatment of seizure clusters and are often administered by nonmedical care partners outside a healthcare facility. Three rescue therapies are currently FDA-approved for this indication, with diazepam rectal gel being the first in 1997, for patients aged ≥  2 years. Limitations of rectal administration (e.g., positioning and disrobing the patient, which may affect ease of use and social acceptability; interpatient variation in bioavailability) led to the investigation of the potential for nasal administration as an alternative. Midazolam nasal spray (MDS) was approved by the FDA in 2019 for patients aged ≥  12 years and diazepam nasal spray (DNS) in 2020 for patients aged ≥  6 years; these two intranasal therapies have differences in their formulations [e.g., organic solvents (MDS) vs. Intravail and vitamin E for absorption and solubility (DNS)], effectiveness (e.g., proportion of seizure clusters requiring only one dose), and safety profiles. In clinical studies, the proportion of seizure clusters for which only one dose of medication was used varied between the three approved rescue therapies with the highest single-dose rate for any time period for DNS; however, although studies for all three preparations enrolled patients with highly intractable epilepsy, inclusion and exclusion criteria varied, so the three cannot be directly compared. Treatments that have been used off-label for seizure clusters in the USA include midazolam for injection as an intranasal spray (indicated for sedation/anxiolysis/amnesia and anesthesia) and tablet forms of clonazepam (indicated for treatment for seizure disorders) and lorazepam (indicated for anxiety). In the European Union, buccal and intranasal midazolam are used for treating the indication of prolonged, acute convulsive seizures and rectal diazepam solution for the indication of epileptic and febrile convulsions; duration of effectiveness for these medications for the treatment of seizure clusters has not been established. This paper examines the literature context for understanding seizure clusters and their treatment and provides effectiveness, safety, and administration details for the three FDA-approved rescue therapies. Additionally, other medications that are used for rescue therapy in the USA and globally are discussed. Finally, the potential benefits of seizure action plans and candidates for their use are addressed. This paper is intended to provide details about the unique characteristics of rescue therapies for seizure clusters to help clarify appropriate treatment for individual patients.

Analyses of patients who self-administered diazepam nasal spray for acute treatment of seizure clusters.

For acute treatment of seizure clusters in patients with epilepsy, intranasal administration of acute seizure therapies has been shown to provide accessibility and ease of use to care partners as well as the potential for self-administration by patients. Diazepam nasal spray (Valtoco®) was approved by the US Food and Drug Administration for acute treatment of intermittent, stereotypic episodes of frequent seizure activity (ie, seizure clusters, acute repetitive seizures) in patients with epilepsy aged ≥6 years. Self-administration consistent with the prescribing information is feasible and was reported by a subgroup of patients (n = 27 of 163) in a long-term phase 3 safety study. Data regarding self-administration among these patients with seizure clusters are examined here to explore the safety profiles and measures of effectiveness, as well as the quality of life of those who self-treated. In addition, this focused look at patients who self-administered diazepam nasal spray may offer some insights into the characteristics of patients who may be appropriate for self-administration.

Unearthing the mechanisms of responsive neurostimulation for epilepsy.

Responsive neurostimulation (RNS) is an effective therapy for people with drug-resistant focal epilepsy. In clinical trials, RNS therapy results in a meaningful reduction in median seizure frequency, but the response is highly variable across individuals, with many receiving minimal or no benefit. Understanding why this variability occurs will help improve use of RNS therapy. Here we advocate for a reexamination of the assumptions made about how RNS reduces seizures. This is now possible due to large patient cohorts having used this device, some long-term. Two foundational assumptions have been that the device's intracranial leads should target the seizure focus/foci directly, and that stimulation should be triggered only in response to detected epileptiform activity. Recent studies have called into question both hypotheses. Here, we discuss these exciting new studies and suggest future approaches to patient selection, lead placement, and device programming that could improve clinical outcomes.

Four State Sleep Staging From a Multilayered Algorithm Using Electrocardiographic and Actigraphic Data.

PURPOSE: Sleep studies are important to evaluate sleep and sleep-related disorders. The standard test for evaluating sleep is polysomnography, during which several physiological signals are recorded separately and simultaneously with specialized equipment that requires a technologist. Simpler recordings that can model the results of a polysomnography would provide the benefit of expanding the possibilities of sleep recordings. METHODS: Using the publicly available sleep data set from the multiethnic study of atherosclerosis and 1769 nights of sleep, we extracted a distinct data subset with engineered features of the biomarkers collected by actigraphic, oxygenation, and electrocardiographic sensors. We then applied scalable models with recurrent neural network and Extreme Gradient Boosting (XGBoost) with a layered approach to produce an algorithm that we then validated with a separate data set of 177 nights. RESULTS: The algorithm achieved an overall performance of 0.833 accuracy and 0.736 kappa in classifying into four states: wake, light sleep, deep sleep, and rapid eye movement (REM). Using feature analysis, we demonstrated that heart rate variability is the most salient feature, which is similar to prior reports. CONCLUSIONS: Our results demonstrate the potential benefit of a multilayered algorithm and achieved higher accuracy and kappa than previously described approaches for staging sleep. The results further the possibility of simple, wearable devices for sleep staging. Code is available at https://github.com/NovelaNeuro/nEureka-SleepStaging.

Ictal EEG Source Imaging With Supplemental Electrodes.

INTRODUCTION: Noninvasive brain imaging tests play a major role in guiding decision-making and the usage of invasive, costly intracranial electroencephalogram (ICEEG) in the presurgical epilepsy evaluation. This study prospectively examined the concordance in localization between ictal EEG source imaging (ESI) and ICEEG as a reference standard. METHODS: Between August 2014 and April 2019, patients during video monitoring with scalp EEG were screened for those with intractable focal epilepsy believed to be amenable to surgical treatment. Additional 10-10 electrodes (total = 31-38 per patient, "31+") were placed over suspected regions of seizure onset in 104 patients. Of 42 patients requiring ICEEG, 30 (mean age 30, range 19-59) had sufficiently localized subsequent intracranial studies to allow comparison of localization between tests. ESI was performed using realistic forward boundary element models used in dipole and distributed source analyses. RESULTS: At least partial sublobar concordance between ESI and ICEEG solutions was obtained in 97% of cases, with 73% achieving complete agreement. Median Euclidean distances between ESI and ICEEG solutions ranged from 25 to 30 mm (dipole) and 23 to 38 mm (distributed source). The latter was significantly more accurate with 31+ compared with 21 electrodes (P < 0.01). A difference of ≤25 mm was present in two thirds of the cases. No significant difference was found between dipole and distributed source analyses. CONCLUSIONS: A practical method of ictal ESI (nonuniform placement of 31-38 electrodes) yields high accuracy for seizure localization in epilepsy surgery candidates. These results support routine clinical application of ESI in the presurgical evaluation.

Focal seizures unfold variably over time.

This scientific commentary refers to 'Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states' by Schroeder et al. (https://doi.org/10.1093/braincomms/fcad205).

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder.

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Intracranial electrical stimulation of corticolimbic sites modulates arousal in humans.

BACKGROUND: Humans routinely shift their sleepiness and wakefulness levels in response to emotional factors. The diversity of emotional factors that modulates sleep-wake levels suggests that the ascending arousal network may be intimately linked with networks that mediate mood. Indeed, while animal studies have identified select limbic structures that play a role in sleep-wake regulation, the breadth of corticolimbic structures that directly modulates arousal in humans remains unknown. OBJECTIVE: We investigated whether select regional activation of the corticolimbic network through direct electrical stimulation can modulate sleep-wake levels in humans, as measured by subjective experience and behavior. METHODS: We performed intensive inpatient stimulation mapping in two human participants with treatment resistant depression, who underwent intracranial implantation with multi-site, bilateral depth electrodes. Stimulation responses of sleep-wake levels were measured by subjective surveys (i.e. Stanford Sleepiness Scale and visual-analog scale of energy) and a behavioral arousal score. Biomarker analyses of sleep-wake levels were performed by assessing spectral power features of resting-state electrophysiology. RESULTS: Our findings demonstrated three regions whereby direct stimulation modulated arousal, including the orbitofrontal cortex (OFC), subgenual cingulate (SGC), and, most robustly, ventral capsule (VC). Modulation of sleep-wake levels was frequency-specific: 100Hz OFC, SGC, and VC stimulation promoted wakefulness, whereas 1Hz OFC stimulation increased sleepiness. Sleep-wake levels were correlated with gamma activity across broad brain regions. CONCLUSIONS: Our findings provide evidence for the overlapping circuitry between arousal and mood regulation in humans. Furthermore, our findings open the door to new treatment targets and the consideration of therapeutic neurostimulation for sleep-wake disorders.

A pilot study of closed-loop neuromodulation for treatment-resistant post-traumatic stress disorder.

The neurophysiological mechanisms in the human amygdala that underlie post-traumatic stress disorder (PTSD) remain poorly understood. In a first-of-its-kind pilot study, we recorded intracranial electroencephalographic data longitudinally (over one year) in two male individuals with amygdala electrodes implanted for the management of treatment-resistant PTSD (TR-PTSD) under clinical trial NCT04152993. To determine electrophysiological signatures related to emotionally aversive and clinically relevant states (trial primary endpoint), we characterized neural activity during unpleasant portions of three separate paradigms (negative emotional image viewing, listening to recordings of participant-specific trauma-related memories, and at-home-periods of symptom exacerbation). We found selective increases in amygdala theta (5-9 Hz) bandpower across all three negative experiences. Subsequent use of elevations in low-frequency amygdala bandpower as a trigger for closed-loop neuromodulation led to significant reductions in TR-PTSD symptoms (trial secondary endpoint) following one year of treatment as well as reductions in aversive-related amygdala theta activity. Altogether, our findings provide early evidence that elevated amygdala theta activity across a range of negative-related behavioral states may be a promising target for future closed-loop neuromodulation therapies in PTSD.

Written seizure action plans for adult patients with epilepsy: Distilling insights from emergency action plans for other chronic conditions.

Seizure emergencies and potential emergencies, ranging from seizure clusters to prolonged seizure and status epilepticus, may affect adults with epilepsy despite stable antiseizure therapy. Seizure action plans (SAPs) are designed for patients and their caregivers/care partners to provide guidance on the individualized treatment plan, including response to potential seizure emergencies and appropriate use of rescue therapy. The use of pediatric SAPs is common (typically required by schools), however, most adults with epilepsy do not have a plan. Patient-centered action plans are integral to care for other chronic conditions and may offer insights applicable to the care of adults with epilepsy. This review analyzes the potential benefits of action plans for medical conditions by exploring their utility in conditions such as asthma, diabetes, chronic obstructive pulmonary disease, heart disease, and opioid overdose. Evidence across these conditions substantiates the value of action plans for patients, and the benefits of adult SAPs in epilepsy are emerging. Because wide implementation of SAPs has faced barriers, other conditions may provide insights that are relevant to implementing SAPs in epilepsy. Based on these analyses, we propose concrete steps to improve the use of SAPs among adults. A recent consensus statement promoting the use of formal SAPs in epilepsy and advances in rescue therapy delivery methods provides support to engage patients around the value of SAPs. The precedent for use of SAPs for pediatric epilepsy patients serves as the foundation to support increased usage in adults. Seizure action plans in the context of improved clinical outcomes are expected to reduce healthcare utilization, improve patient quality of life, and optimize epilepsy management.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study.

OBJECTIVE: In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy. METHODS: A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria. RESULTS: A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n = 95, 87.2%; caregivers, n = 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n = 43, 78.2%; caregivers, n = 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity. SIGNIFICANCE: Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING.

A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker™ system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.

Intact speech perception after resection of dominant hemisphere primary auditory cortex for the treatment of medically refractory epilepsy: illustrative case.

BACKGROUND: In classic speech network models, the primary auditory cortex is the source of auditory input to Wernicke's area in the posterior superior temporal gyrus (pSTG). Because resection of the primary auditory cortex in the dominant hemisphere removes inputs to the pSTG, there is a risk of speech impairment. However, recent research has shown the existence of other, nonprimary auditory cortex inputs to the pSTG, potentially reducing the risk of primary auditory cortex resection in the dominant hemisphere. OBSERVATIONS: Here, the authors present a clinical case of a woman with severe medically refractory epilepsy with a lesional epileptic focus in the left (dominant) Heschl's gyrus. Analysis of neural responses to speech stimuli was consistent with primary auditory cortex localization to Heschl's gyrus. Although the primary auditory cortex was within the proposed resection margins, she underwent lesionectomy with total resection of Heschl's gyrus. Postoperatively, she had no speech deficits and her seizures were fully controlled. LESSONS: While resection of the dominant hemisphere Heschl's gyrus/primary auditory cortex warrants caution, this case illustrates the ability to resect the primary auditory cortex without speech impairment and supports recent models of multiple parallel inputs to the pSTG.

A Bayesian switching linear dynamical system for estimating seizure chronotypes.

Epilepsy is a disorder characterized by paroxysmal transitions between multistable states. Dynamical systems have been useful for modeling the paroxysmal nature of seizures. At the same time, intracranial electroencephalography (EEG) recordings have recently discovered that an electrographic measure of epileptogenicity, interictal epileptiform activity, exhibits cycling patterns ranging from ultradian to multidien rhythmicity, with seizures phase-locked to specific phases of these latent cycles. However, many mechanistic questions about seizure cycles remain unanswered. Here, we provide a principled approach to recast the modeling of seizure chronotypes within a statistical dynamical systems framework by developing a Bayesian switching linear dynamical system (SLDS) with variable selection to estimate latent seizure cycles. We propose a Markov chain Monte Carlo algorithm that employs particle Gibbs with ancestral sampling to estimate latent cycles in epilepsy and apply unsupervised learning on spectral features of latent cycles to uncover clusters in cycling tendency. We analyze the largest database of patient-reported seizures in the world to comprehensively characterize multidien cycling patterns among 1,012 people with epilepsy, spanning from infancy to older adulthood. Our work advances knowledge of cycling in epilepsy by investigating how multidien seizure cycles vary in people with epilepsy, while demonstrating an application of an SLDS to frame seizure cycling within a nonlinear dynamical systems framework. It also lays the groundwork for future studies to pursue data-driven hypothesis generation regarding the mechanistic drivers of seizure cycles.

Learning to generalize seizure forecasts.

OBJECTIVE: Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years-long electroencephalographic (EEG) recordings, we previously found that patient-reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal-ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. METHODS: We used retrospective long-term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24-h horizon. RESULTS: With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) = .70 and .69 and median Brier skill score (BSS) = .07 and .08. In direct comparison, individualized models had similar median performance (AUC = .67, BSS = .08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. SIGNIFICANCE: Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient-reported vs. electrographic) or IEA (icEEG vs. sqEEG).

Seizure count forecasting to aid diagnostic testing in epilepsy.

OBJECTIVE: Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts. METHODS: A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead. RESULTS: One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error = .99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. SIGNIFICANCE: This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.