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.

Expert and deep learning model identification of iEEG seizures and seizure onset times.

Hundreds of 90-s iEEG records are typically captured from each NeuroPace RNS System patient between clinic visits. While these records provide invaluable information about the patient's electrographic seizure and interictal activity patterns, manually classifying them into electrographic seizure/non-seizure activity, and manually identifying the seizure onset channels and times is an extremely time-consuming process. A convolutional neural network based Electrographic Seizure Classifier (ESC) model was developed in an earlier study. In this study, the classification model is tested against iEEG annotations provided by three expert reviewers board certified in epilepsy. The three experts individually annotated 3,874 iEEG channels from 36, 29, and 35 patients with leads in the mesiotemporal (MTL), neocortical (NEO), and MTL + NEO regions, respectively. The ESC model's seizure/non-seizure classification scores agreed with the three reviewers at 88.7%, 89.6%, and 84.3% which was similar to how reviewers agreed with each other (92.9%-86.4%). On iEEG channels with all 3 experts in agreement (83.2%), the ESC model had an agreement score of 93.2%. Additionally, the ESC model's certainty scores reflected combined reviewer certainty scores. When 0, 1, 2 and 3 (out of 3) reviewers annotated iEEG channels as electrographic seizures, the ESC model's seizure certainty scores were in the range: [0.12-0.19], [0.32-0.42], [0.61-0.70], and [0.92-0.95] respectively. The ESC model was used as a starting-point model for training a second Seizure Onset Detection (SOD) model. For this task, seizure onset times were manually annotated on a relatively small number of iEEG channels (4,859 from 50 patients). Experiments showed that fine-tuning the ESC models with augmented data (30,768 iEEG channels) resulted in a better validation performance (on 20% of the manually annotated data) compared to training with only the original data (3.1s vs 4.4s median absolute error). Similarly, using the ESC model weights as the starting point for fine-tuning instead of other model weight initialization methods provided significant advantage in SOD model validation performance (3.1s vs 4.7s and 3.5s median absolute error). Finally, on iEEG channels where three expert annotations of seizure onset times were within 1.5 s, the SOD model's seizure onset time prediction was within 1.7 s of expert annotation.

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).

Non-linear Embedding Methods for Identifying Similar Brain Activity in 1 Million iEEG Records Captured From 256 RNS System Patients.

Finding electrophysiological features that are similar across patients with epilepsy may facilitate identifying treatment options for one patient that worked in patients with similar brain activity patterns. Three non-linear iEEG (intracranial electroencephalogram) embedding methods of finding similar cross-patient iEEG records in a large iEEG dataset were developed and compared. About 1 million iEEG records from 256 patients with drug-resistant focal onset seizures who were treated in prospective trials of the RNS System were used for analyses. Data from 200, 25, and 31 patients were randomly selected to be in the train, validation, and test datasets. In method 1, ResNet50 convolutional neural network (CNN) model pre-trained on the ImageNet dataset was used for extracting feature maps from spectrogram images (ImageNet-ResNet) of iEEG records. In method 2, ResNet50 custom trained on an iEEG classification task using ~138,000 manually labeled iEEG records was used as the feature extractor (ESC-ResNet). Feature maps were passed through dimensionality reduction and k nearest neighbors were found in the reduced feature space. In method 3, a 256 dimensional iEEG embedding space was learned via contrastive learning by training a ResNet50 model with triplet training sets generated using within-patient iEEG clustering (CL-ResNet). All three methods had comparable performance when identifying iEEG records from the search dataset similar to test iEEG records of baseline (non-seizure) and interictal spiking activity. Epileptic interictal spikes are represented by vertical (broadband) edges in spectrogram images, and hence even generic features extracted using models trained on everyday images appear to be sufficient to represent iEEG records with similar levels of interictal spiking activity in close proximity. In the case of electrographic seizures, however, the ESC-ResNet model, identified cross-patient iEEG records with electrographic seizure morphology features that were most similar to the test iEEG records. For nuanced electrographic seizure iEEG representation learning, domain specific model training with manually generated labels had the advantage. Finally, representative iEEG records were selected from every patient using an unsupervised clustering method which effectively reduced the number of iEEG records in the search dataset from ~750,000 to 2,148, thus substantially reducing the time required for finding similar cross-patient iEEG records.

A High Accuracy Electrographic Seizure Classifier Trained Using Semi-Supervised Labeling Applied to a Large Spectrogram Dataset.

The objective of this study was to explore using ECoG spectrogram images for training reliable cross-patient electrographic seizure classifiers, and to characterize the classifiers' test accuracy as a function of amount of training data. ECoG channels in ∼138,000 time-series ECoG records from 113 patients were converted to RGB spectrogram images. Using an unsupervised spectrogram image clustering technique, manual labeling of 138,000 ECoG records (each with up to 4 ECoG channels) was completed in 320 h, which is an estimated 5 times faster than manual labeling without ECoG clustering. For training supervised classifier models, five random folds of data were created; with each fold containing 72, 18, and 23 patients' data for model training, validation and testing respectively. Five convolutional neural network (CNN) architectures, including two with residual connections, were trained. Cross-patient classification accuracies and F1 scores improved with model complexity, with the shallowest 6-layer model (with ∼1.5 million trainable parameters) producing a class-balanced seizure/non-seizure classification accuracy of 87.9% on ECoG channels and the deepest ResNet50-based model (with ∼23.5 million trainable parameters) producing a classification accuracy of 95.7%. The trained ResNet50-based model additionally had 93.5% agreement in scores with an independent expert labeller. Visual inspection of gradient-based saliency maps confirmed that the models' classifications were based on relevant portions of the spectrogram images. Further, by repeating training experiments with data from varying number of patients, it was found that ECoG spectrogram images from just 10 patients were sufficient to train ResNet50-based models with 88% cross-patient accuracy, while at least 30 patients' data was required to produce cross-patient classification accuracies of >90%.

Acute effects of brain-responsive neurostimulation in drug-resistant partial onset epilepsy.

OBJECTIVE: Understanding the acute effects of responsive stimulation (AERS) based on intracranial EEG (iEEG) recordings in ambulatory patients with drug-resistant partial epilepsy, and correlating these with changes in clinical seizure frequency, may help clinicians more efficiently optimize responsive stimulation settings. METHODS: In patients implanted with the NeuroPace® RNS® System, acute changes in iEEG spectral power following active and sham stimulation periods were quantified and compared within individual iEEG channels. Additionally, acute stimulation-induced acute iEEG changes were compared within iEEG channels before and after patients experienced substantial reductions in clinical seizure frequency. RESULTS: Responsive stimulation resulted in a 20.7% relative decrease in spectral power in the 2-4 second window following active stimulation, compared to sham stimulation. On several detection channels, the AERS features changed when clinical outcomes improved but were relatively stable otherwise. AERS change direction associated with clinical improvement was generally consistent within detection channels. CONCLUSIONS: In this retrospective analysis, patients with drug-resistant partial epilepsy treated with direct brain-responsive neurostimulation showed an acute stimulation related reduction in iEEG spectral power that was associated with reductions in clinical seizure frequency. SIGNIFICANCE: Identifying favorable stimulation related changes in iEEG activity could help physicians to more rapidly optimize stimulation settings for each patient.

Seizure Cycles in Focal Epilepsy.

Importance: Focal epilepsy is characterized by the cyclical recurrence of seizures, but, to our knowledge, the prevalence and patterns of seizure cycles are unknown. Objective: To establish the prevalence, strength, and temporal patterns of seizure cycles over timescales of hours to years. Design, Setting, and Participants: This retrospective cohort study analyzed data from continuous intracranial electroencephalography (cEEG) and seizure diaries collected between January 19, 2004, and May 18, 2018, with durations up to 10 years. A total of 222 adults with medically refractory focal epilepsy were selected from 256 total participants in a clinical trial of an implanted responsive neurostimulation device. Selection was based on availability of cEEG and/or self-reports of disabling seizures. Exposures: Antiseizure medications and responsive neurostimulation, based on clinical indications. Main Outcomes and Measures: Measures involved (1) self-reported daily seizure counts, (2) cEEG-based hourly counts of electrographic seizures, and (3) detections of interictal epileptiform activity (IEA), which fluctuates in daily (circadian) and multiday (multidien) cycles. Outcomes involved descriptive characteristics of cycles of IEA and seizures: (1) prevalence, defined as the percentage of patients with a given type of seizure cycle; (2) strength, defined as the degree of consistency with which seizures occur at certain phases of an underlying cycle, measured as the phase-locking value (PLV); and (3) seizure chronotypes, defined as patterns in seizure timing evident at the group level. Results: Of the 222 participants, 112 (50%) were male, and the median age was 35 years (range, 18-66 years). The prevalence of circannual (approximately 1 year) seizure cycles was 12% (24 of 194), the prevalence of multidien (approximately weekly to approximately monthly) seizure cycles was 60% (112 of 186), and the prevalence of circadian (approximately 24 hours) seizure cycles was 89% (76 of 85). Strengths of circadian (mean [SD] PLV, 0.34 [0.18]) and multidien (mean [SD] PLV, 0.34 [0.17]) seizure cycles were comparable, whereas circannual seizure cycles were weaker (mean [SD] PLV, 0.17 [0.10]). Across individuals, circadian seizure cycles showed 5 peaks: morning, mid-afternoon, evening, early night, and late night. Multidien cycles of IEA showed peak periodicities centered around 7, 15, 20, and 30 days. Independent of multidien period length, self-reported and electrographic seizures consistently occurred during the days-long rising phase of multidien cycles of IEA. Conclusions and Relevance: Findings in this large cohort establish the high prevalence of plural seizure cycles and help explain the natural variability in seizure timing. The results have the potential to inform the scheduling of diagnostic studies, the delivery of time-varying therapies, and the design of clinical trials in epilepsy.

Cues for seizure timing.

The cyclical organization of seizures in epilepsy has been described since antiquity. However, historical explanations for seizure cycles-based on celestial, hormonal, and environmental factors-have only recently become testable with the advent of chronic electroencephalography (cEEG) and modern statistical techniques. Here, factors purported over millennia to influence seizure timing are viewed through a contemporary lens. We discuss the emerging concept that seizures are organized over multiple timescales, each involving differential influences of external and endogenous rhythm generators. Leveraging large cEEG datasets and circular statistics appropriate for cyclical phenomena, we present new evidence for circadian (day-night), multidien (multi-day), and circannual (about-yearly) variation in seizure activity. Modulation of seizure timing by multiscale temporal variables has implications for diagnosis and therapy in clinical epilepsy. Uncovering the mechanistic basis for seizure cycles, particularly the factors that govern multidien periodicity, will be a major focus of future work.

Forecasting seizure risk in adults with focal epilepsy: a development and validation study.

BACKGROUND: People with epilepsy are burdened with the apparent unpredictability of seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) revealed that epileptic brain activity shows robust cycles, operating over hours (circadian) and days (multidien). We hypothesised that these cycles can be leveraged to estimate future seizure probability, and we tested the feasibility of forecasting seizures days in advance. METHODS: We did a feasibility study in distinct development and validation cohorts, involving retrospective analysis of cEEG data recorded with an implanted device in adults (age ≥18 years) with drug-resistant focal epilepsy followed at 35 centres across the USA between Jan 19, 2004, and May 18, 2018. Patients were required to have had 20 or more electrographic seizures (development cohort) or self-reported seizures (validation cohort). In all patients, the device recorded interictal epileptiform activity (IEA; ≥6 months of continuous hourly data), the fluctuations in which helped estimate varying seizure risk. Point process statistical models trained on initial portions of each patient's cEEG data (both cohorts) generated forecasts of seizure probability that were tested on subsequent unseen seizure data and evaluated against surrogate time-series. The primary outcome was the percentage of patients with forecasts showing improvement over chance (IoC). FINDINGS: We screened 72 and 256 patients, and included 18 and 157 patients in the development and validation cohorts, respectively. Models incorporating information about multidien IEA cycles alone generated daily seizure forecasts for the next calendar day with IoC in 15 (83%) patients in the development cohort and 103 (66%) patients in the validation cohort. The forecasting horizon could be extended up to 3 days while maintaining IoC in two (11%) of 18 patients and 61 (39%) of 157 patients. Forecasts with a shorter horizon of 1 h, possible only for electrographic seizures in the development cohort, showed IoC in all 18 (100%) patients. INTERPRETATION: This study shows that seizure probability can be forecasted days in advance by leveraging multidien IEA cycles recorded with an implanted device. This study will serve as a basis for prospective clinical trials to establish how people with epilepsy might benefit from seizure forecasting over long horizons. FUNDING: None. VIDEO ABSTRACT.

Brain-Responsive Neurostimulation for Loss of Control Eating: Early Feasibility Study.

BACKGROUND: Loss of control (LOC) is a pervasive feature of binge eating, which contributes significantly to the growing epidemic of obesity; approximately 80 million US adults are obese. Brain-responsive neurostimulation guided by the delta band was previously found to block binge-eating behavior in mice. Following novel preclinical work and a human case study demonstrating an association between the delta band and reward anticipation, the US Food and Drug Administration approved an Investigational Device Exemption for a first-in-human study. OBJECTIVE: To assess feasibility, safety, and nonfutility of brain-responsive neurostimulation for LOC eating in treatment-refractory obesity. METHODS: This is a single-site, early feasibility study with a randomized, single-blinded, staggered-onset design. Six subjects will undergo bilateral brain-responsive neurostimulation of the nucleus accumbens for LOC eating using the RNS® System (NeuroPace Inc). Eligible participants must have treatment-refractory obesity with body mass index ≥ 45 kg/m2. Electrophysiological signals of LOC will be characterized using real-time recording capabilities coupled with synchronized video monitoring. Effects on other eating disorder pathology, mood, neuropsychological profile, metabolic syndrome, and nutrition will also be assessed. EXPECTED OUTCOMES: Safety/feasibility of brain-responsive neurostimulation of the nucleus accumbens will be examined. The primary success criterion is a decrease of ≥1 LOC eating episode/week based on a 28-d average in ≥50% of subjects after 6 mo of responsive neurostimulation. DISCUSSION: This study is the first to use brain-responsive neurostimulation for obesity; this approach represents a paradigm shift for intractable mental health disorders.

Sleep disruption is not observed with brain-responsive neurostimulation for epilepsy.

OBJECTIVE: Neurostimulation devices that deliver electrical impulses to the nervous system are widely used to treat seizures in patients with medically refractory epilepsy, but the effects of these therapies on sleep are incompletely understood. Vagus nerve stimulation can contribute to obstructive sleep apnea, and thalamic deep brain stimulation can cause sleep disruption. A device for brain-responsive neurostimulation (RNS® System, NeuroPace, Inc) is well tolerated in clinical trials, but potential effects on sleep are unknown. METHODS: Six adults with medically refractory focal epilepsy treated for at least six months with the RNS System underwent a single night of polysomnography (PSG). RNS System lead locations included mesial temporal and neocortical targets. Sleep stages and arousals were scored according to standard guidelines. Stimulations delivered by the RNS System in response to detections of epileptiform activity were identified by artifacts on scalp electroencephalography. RESULTS: One subject was excluded for technical reasons related to unreliable identification of stimulation artifact on EEG during PSG. In the remaining five subjects, PSG showed fragmented sleep with frequent arousals. Arousal histograms aligned to stimulations revealed a significant peak in arousals just before stimulation. In one of these subjects, the arousal peak began before stimulation and extended ~1 seconds after stimulation. A peak in arousals occurring only after stimulation was not observed. SIGNIFICANCE: In this small cohort of patients, brain-responsive neurostimulation does not appear to disrupt sleep. If confirmed in larger studies, this could represent a potential clinical advantage of brain-responsive neurostimulation over other neurostimulation modalities.

Quantitative electrocorticographic biomarkers of clinical outcomes in mesial temporal lobe epileptic patients treated with the RNS® system.

OBJECTIVES: Find interictal electrocorticographic (ECoG) biomarkers of clinical outcomes in mesiotemporal lobe (MTL) epilepsy patients. METHODS: In the NeuroPace® RNS® System clinical trials with 256 patients, 20 MTL patients with the most reduction in clinical seizures at Year 7 compared to baseline (upper response quartile; -96.5% median change) and 20 with the least reduction in clinical seizures (lower response quartile; -17.4% median change) were evaluated. Clinical and interictal ECoG features from the two response quartiles were compared. RESULTS: Demographic and clinical features were similar in the upper and lower response quartiles. Interictal spike rate (ISR) was substantially lower (p < 0.0001) in the upper quartile patients, while normalized theta (4-8 Hz) and normalized gamma (>25 Hz) were also different (p < 0.05) between the two response quartiles. ISR was positively correlated (p < 0.05) with clinical seizure rates in 71% of the channels analyzed. ECoG records captured during months with no clinical seizures had the lowest ISR. CONCLUSIONS: ISR is a strong differentiator of clinical response in MTL patients. Normalized theta and gamma also differentiates clinical response. SIGNIFICANCE: In MTL patients, the interictal spike rate along with spectral power computed from chronic ambulatory baseline ECoGs may serve as biomarkers of clinical outcomes and maybe used as treatment endpoints.

Treatment of drug-resistant epilepsy in patients with periventricular nodular heterotopia using RNS® System: Efficacy and description of chronic electrophysiological recordings.

OBJECTIVES: Describe changes in clinical seizure frequency and electrophysiological data recorded in patients with medically-intractable seizures and periventricular nodular heterotopias (PVNH) treated with the RNS® System (NeuroPace, Inc., Mountain View, CA). METHODS: Clinical seizures from eight patients (mean follow-up of 10.1 years) were analyzed pre- and post-treatment. Chronic ambulatory electrocorticograms (ECoGs) recorded from PVNHs, hippocampus and neocortex were evaluated to identify the earliest electrographic seizure onset type, pattern of spread, and interictal characteristics. RESULTS: Mean reduction in disabling seizures was 85.7 % (n = 8); seven patients had >50% seizure reduction and two were seizure-free in the final year of analysis. Seizure rate showed a progressive reduction over the course of the study with the highest rate of improvement in the first two to three years after implantation. Four of seven patients with one PVNH lead and a second lead in the hippocampus or neocortex had some electrographic seizures first recorded at either lead location, suggesting two foci or seizure propagation patterns. Low voltage fast type activity was the prominent seizure onset pattern. Interictal ECoG power was lower in PVNH than hippocampus. CONCLUSIONS: RNS® System treatment substantially reduced clinical seizure frequency in patients with PVNH. Analysis of ictal ECoG records suggests PVNH may be involved in seizure generation. SIGNIFICANCE: Chronic ECoG recordings suggest PVNH tissue can actively participate in epileptogenic networks. Direct brain-responsive neurostimulation is a safe and effective treatment option in such patients, progressively reducing seizure rate over a period of years.

Hippocampal gamma predicts associative memory performance as measured by acute and chronic intracranial EEG.

Direct recordings from the human brain have historically involved epilepsy patients undergoing invasive electroencephalography (iEEG) for surgery. However, these measurements are temporally limited and affected by clinical variables. The RNS System (NeuroPace, Inc.) is a chronic, closed-loop electrographic seizure detection and stimulation system. When adapted by investigators for research, it facilitates cognitive testing in a controlled ambulatory setting, with measurements collected over months to years. We utilized an associative learning paradigm in 5 patients with traditional iEEG and 3 patients with chronic iEEG, and found increased hippocampal gamma (60-100 Hz) sustained at 1.3-1.5 seconds during encoding in successful versus failed trials in surgical patients, with similar results in our RNS System patients (1.4-1.6 seconds). Our findings replicate other studies demonstrating that sustained hippocampal gamma supports encoding. Importantly, we have validated the RNS System to make sensitive measurements of hippocampal dynamics during cognitive tasks in a chronic ambulatory research setting.

Cognitive tasks and human ambulatory electrocorticography using the RNS System.

BACKGROUND: Electrocorticography studies are typically conducted in patients undergoing video EEG monitoring, but these studies are subject to confounds such as the effects of pain, recent anesthesia, analgesics, drug changes, antibiotics, and implant effects. NEW METHOD: Techniques were developed to obtain electrocorticographic (ECoG) data from freely moving subjects performing navigational tasks using the RNS® System (NeuroPace, Inc., Mountain View, CA), a brain-responsive neurostimulation medical device used to treat focal onset epilepsy, and to align data from the RNS System with cognitive task events with high precision. These subjects had not had recent surgery, and were therefore not confounded by the perioperative variables that affect video EEG studies. RESULTS: Task synchronization using the synchronization marker technique provides a quantitative measure of clock uncertainty, and can align data to task events with less than 4 ms of uncertainty. Hippocampal ECoG activity was found to change immediately before an incorrect response to a math problem compared to hippocampal activity before a correct response. In addition, subjects were found to have variable but significant changes in theta band power in the hippocampus during navigation compared to when subjects were not navigating. We found that there is theta-gamma phase-amplitude coupling in the right hippocampus while subjects stand still during a navigation task. COMPARISON WITH EXISTING METHODS: An alignment technique described in this study improves the upper bound on task-ECoG alignment uncertainty from approximately 30 ms to under 4 ms. The RNS System is one of the first platforms capable of providing untethered ambulatory ECoG recording in humans, allowing for the study of real world instead of virtual navigation. Compared to intracranial video EEG studies, studies using the RNS System platform are not subject to confounds caused by the drugs and recent surgery inherent to the perioperative environment. Furthermore, these subjects provide the opportunity to record from the same electrodes over the course of many years. CONCLUSIONS: The RNS System enables us to study human navigation with unprecedented clarity. While RNS System patients have fewer electrodes implanted than video EEG patients, the lack of external artifact and confounds from recent surgery make this system a useful tool to further human electrophysiology research.

Clinical and electrocorticographic response to antiepileptic drugs in patients treated with responsive stimulation.

OBJECTIVE: The objective of this study was to explore whether chronic electrocorticographic (ECoG) data recorded by a responsive neurostimulation system could be used to assess clinical responses to antiepileptic drugs (AEDs). METHODS: Antiepileptic drugs initiated and maintained for ≥3 months by patients participating in clinical trials of the RNS® System were identified. Such "AED Starts" that produced an additional ≥50% reduction in patient-reported clinical seizure frequency were categorized as clinically beneficial, and the remaining as not beneficial. Electrocorticographic features recorded by the RNS® Neurostimulator were analyzed during three periods: 3 months before the AED Start, first month after the AED Start, and the first 3 months after the AED Start. RESULTS: The most commonly added medications were clobazam (n = 41), lacosamide (n = 96), levetiracetam (n = 31), and pregabalin (n = 25). Across all four medications, there were sufficient clinical data for 193 AED Starts to be included in the analyses, and 59 AED Starts were considered clinically beneficial. The proportion of AED Starts that qualified as clinically beneficial was higher for clobazam (53.7%) and levetiracetam (51.6%) than for lacosamide (18.8%) and pregabalin (12%). Across all AED Starts for which RNS ECoG detection settings were held constant, the clinically beneficial AED Starts were associated with a significantly greater reduction in the detection of epileptiform activity (p < 0.001) at 1 (n = 33) and 3 months (n = 30) compared with AED Starts that were not beneficial at 1 (n = 71) and 3 months (n = 60). Furthermore, there was a significant reduction in interictal spike rate and spectral power (1-125 Hz) associated with a clinically beneficial response to an AED Start at 1 (n = 32) and 3 months (n = 35) (p < 0.001). These reductions were not observed at either 1 (n = 59) or 3 months (n = 60) for AED Starts that were not clinically beneficial. CONCLUSIONS: Significant quantitative changes in ECoG data recorded by the RNS System were observed in patients who experienced an additional clinical response to a new AED. While there was variability across patients in the changes observed, the results suggest that quantitative ECoG data may provide useful information when assessing whether a patient may have a favorable clinical response to an AED.

Changes in the electrocorticogram after implantation of intracranial electrodes in humans: The implant effect.

OBJECTIVE: Subacute and long-term electrocorticographic (ECoG) changes in ambulatory patients with depth and cortical strip electrodes were evaluated in order to determine the length of the implant effect. METHODS: ECoG records were assessed in patients with medically intractable epilepsy who had depth and/or strip leads implanted in order to be treated with brain-responsive stimulation. Changes in total spectral power, band-limited spectral power, and spike rate were assessed. RESULTS: 121 patients participating in trials of the RNS® System had a total of 93994 ECoG records analyzed. Significant changes in total spectral power occurred from the first to second months after implantation, involving 55% of all ECoG channels (68% of strip and 47% of depth lead channels). Significant, but less pronounced, changes continued over the 2nd to 5th post-implant months, after which total power became more stable. Similar patterns of changes were observed within frequency bands and spike rate. CONCLUSIONS: ECoG spectral power and spike rates are not stable in the first 5 months after implantation, presumably due to neurophysiological and electrode-tissue interface changes. SIGNIFICANCE: ECoG data collected in the first 5 months after implantation of intracranial electrodes may not be fully representative of chronic cortical electrophysiology.

Theta Oscillations in the Human Medial Temporal Lobe during Real-World Ambulatory Movement.

The theta rhythm-a slow (6-12 Hz) oscillatory component of the local field potential-plays a critical role in spatial navigation and memory by coordinating the activity of neuronal ensembles within the medial temporal lobe (MTL). Although theta has been extensively studied in freely moving rodents, its presence in humans has been elusive and primarily investigated in stationary subjects. Here we used a unique clinical opportunity to examine theta within the human MTL during untethered, real-world ambulatory movement. We recorded intracranial electroencephalographic activity from participants chronically implanted with the wireless NeuroPace responsive neurostimulator (RNS) and tracked their motion with sub-millimeter precision. Our data revealed that movement-related theta oscillations indeed exist in humans, such that theta power is significantly higher during movement than immobility. Unlike in rodents, however, theta occurs in short bouts, with average durations of ∼400 ms, which are more prevalent during fast versus slow movements. In a rare opportunity to study a congenitally blind participant, we found that both the prevalence and duration of theta bouts were increased relative to the sighted participants. These results provide critical support for conserved neurobiological characteristics of theta oscillations during ambulatory spatial navigation, while highlighting some fundamental differences across species in these oscillations between humans and rodents.

Day-Night Patterns of Epileptiform Activity in 65 Patients With Long-Term Ambulatory Electrocorticography.

PURPOSE: To characterize cyclic day-night patterns of electrocorticographic epileptiform activity (EA) in patients with focal onset seizures. METHODS: Epileptiform events as defined by the physician (also termed more generally as "epileptiform activity" or EA) were recorded in 65 patients with partial onset (also referred to as "focal onset") seizures using the RNS System, which includes a cranially implanted neurostimulator connected to 1 or two 4-contact leads placed at the seizure focus. The neurostimulator is programmed to detect specific patterns of electrocorticographic activity and to provide responsive stimulation. The 24-hour periodicity of detections of EA was analyzed for individual patients and for subgroups of patients according to the type of EA, laterality, lobe of onset, and whether the onset was neocortical or hippocampal. The time of day when peaks in EA occurred was also analyzed. RESULTS: There were robust circadian patterns of detections of EA in most patients, with a primary peak in detections at night and a secondary peak in the late afternoon in some cases. Subset analyses were performed by lobe, region of the brain, and type of cortex (neocortical vs. hippocampal); significant circadian rhythmicity was present in all subsets. CONCLUSIONS: This is the first report of circadian cycles of EA as assessed through chronic ambulatory electrocorticographic recordings in adults with focal onset seizures. Epileptiform activity displayed circadian patterns in the majority of these patients. These findings suggest that epilepsy therapies might be optimized by adjusting the timing of therapy according to each patient's unique circadian pattern of EA.

Daily variation in an intracranial EEG feature in humans detected by a responsive neurostimulator system.

PURPOSE: Based on the observation that epileptic seizures can occur at specific times of the day, we looked for daily variation in an intracranial electrographic feature used by a responsive neurostimulator system to detect seizures. METHODS: A computationally efficient measure of intracranial EEG energy or complexity, the line length baseline, was calculated and reported by an external responsive neurostimulator during a clinical trial of device safety. Data were obtained from 24 consecutive patients with medically intractable epilepsy undergoing intracranial monitoring over 2 to 54 days to localize the seizure onset zone. Measurements from individual subjects made at different times of day over many days were displayed on a single 24-h cycle and fit with a cosine function to characterize the time of the maximum value. The timing of epileptic seizures was also noted. RESULTS: The time of the maximum line length baseline value had a bimodal distribution with relative peaks at 05:30 and 15:00 hours. The time of the maximum value did not associate with specific brain regions, except that a nocturnal peak was not measured from temporal neocortex. The temporal distribution of maximum values was similar to the timing of epileptic seizures. CONCLUSION: The line length baseline feature of the intracranial EEG shows daily variation with location specific characteristics within individual subjects.