Targeting thalamocortical circuits for closed-loop stimulation in Lennox-Gastaut syndrome.

This paper outlines the therapeutic rationale and neurosurgical targeting technique for bilateral, closed-loop, thalamocortical stimulation in Lennox-Gastaut syndrome, a severe form of childhood-onset epilepsy. Thalamic stimulation can be an effective treatment for Lennox-Gastaut syndrome, but complete seizure control is rarely achieved. Outcomes may be improved by stimulating areas beyond the thalamus, including cortex, but the optimal targets are unknown. We aimed to identify a cortical target by synthesizing prior neuroimaging studies, and to use this knowledge to advance a dual thalamic (centromedian) and cortical (frontal) approach for closed-loop stimulation. Multi-modal brain network maps from three group-level studies of Lennox-Gastaut syndrome were averaged to define the area of peak overlap: simultaneous EEG-functional MRI of generalized paroxysmal fast activity, [18F]fluorodeoxyglucose PET of cortical hypometabolism and diffusion MRI structural connectivity associated with clinical efficacy in a previous trial of thalamic deep brain stimulation. The resulting 'hotspot' was used as a seed in a normative functional MRI connectivity analysis to identify connected networks. Intracranial electrophysiology was reviewed in the first two trial patients undergoing bilateral implantations guided by this hotspot. Simultaneous recordings from cortex and thalamus were analysed for presence and synchrony of epileptiform activity. The peak overlap was in bilateral premotor cortex/caudal middle frontal gyrus. Functional connectivity of this hotspot revealed a distributed network of frontoparietal cortex resembling the diffuse abnormalities seen on EEG-functional MRI and PET. Intracranial electrophysiology showed characteristic epileptiform activity of Lennox-Gastaut syndrome in both the cortical hotspot and thalamus; most detected events occurred first in the cortex before appearing in the thalamus. Premotor frontal cortex shows peak involvement in Lennox-Gastaut syndrome and functional connectivity of this region resembles the wider epileptic brain network. Thus, it may be an optimal target for a range of neuromodulation therapies, including thalamocortical stimulation and emerging non-invasive treatments like focused ultrasound or transcranial magnetic stimulation. Compared to thalamus-only approaches, the addition of this cortical target may allow more rapid detections of seizures, more diverse stimulation paradigms and broader modulation of the epileptic network. A prospective, multi-centre trial of closed-loop thalamocortical stimulation for Lennox-Gastaut syndrome is currently underway.

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.

Vascular anomaly, lipoma, and polymicrogyria associated with schizencephaly: developmental and diagnostic insights. Illustrative case.

BACKGROUND: Schizencephaly is an uncommon central nervous system malformation. Intracranial lipomas are also rare, accounting for approximately 0.1% of brain "tumors." They are believed to be derived from a persistent meninx primitiva, a neural crest-derived mesenchyme that develops into the dura and leptomeninges. OBSERVATIONS: The authors present a case of heterotopic adipose tissue and a nonshunting arterial vascular malformation arising within a schizencephalic cleft in a 22-year-old male. Imaging showed right frontal gray matter abnormality and an associated suspected arteriovenous malformation with evidence of hemorrhage. Brain magnetic resonance imaging revealed right frontal polymicrogyria lining an open-lip schizencephaly, periventricular heterotopic gray matter, fat within the schizencephalic cleft, and gradient echo hypointensity concerning for prior hemorrhage. Histological assessment demonstrated mature adipose tissue with large-bore, thick-walled, irregular arteries. Mural calcifications and subendothelial cushions suggesting nonlaminar blood flow were observed. There were no arterialized veins or direct transitions from the arteries to veins. Hemosiderin deposition was scant, and hemorrhage was not present. The final diagnosis was consistent with ectopic mature adipose tissue and arteries with meningocerebral cicatrix. LESSONS: This example of a complex maldevelopment of derivatives of the meninx primitiva in association with cortical maldevelopment highlights the unique challenges from both a radiological and histological perspective during diagnostic workup.

Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activity.

The full neural circuits of conscious perception remain unknown. Using a visual perception task, we directly recorded a subcortical thalamic awareness potential (TAP). We also developed a unique paradigm to classify perceived versus not perceived stimuli using eye measurements to remove confounding signals related to reporting on conscious experiences. Using fMRI, we discovered three major brain networks driving conscious visual perception independent of report: first, increases in signal detection regions in visual, fusiform cortex, and frontal eye fields; and in arousal/salience networks involving midbrain, thalamus, nucleus accumbens, anterior cingulate, and anterior insula; second, increases in frontoparietal attention and executive control networks and in the cerebellum; finally, decreases in the default mode network. These results were largely maintained after excluding eye movement-based fMRI changes. Our findings provide evidence that the neurophysiology of consciousness is complex even without overt report, involving multiple cortical and subcortical networks overlapping in space and time.

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.

Neuromodulation in 2035: The Neurology Future Forecasting Series.

Neuromodulation devices are approved in the United States for the treatment of movement disorders, epilepsy, pain, and depression, and are used off-label for other neurologic indications. By 2035, advances in our understanding of neuroanatomical networks and in the mechanism of action of stimulation, coupled with developments in material science, miniaturization, energy storage, and delivery, will expand the use of neuromodulation devices. Neuromodulation approaches are flexible and modifiable. Stimulation can be targeted to a dysfunctional brain focus, region, or network, and can be delivered as a single treatment, continuously, according to a duty cycle, or in response to physiologic changes. Programming can be titrated and modified based on the clinical response or a physiologic biomarker. In addition to keeping pace with clinical and technological developments, neurologists in 2035 will need to navigate complex ethical and economic considerations to ensure access to neuromodulation technology for a rapidly expanding population of patients. This article provides an overview of systems in use today and those that are anticipated and highlights the opportunities and challenges for the future, some of which are technical, but most of which will be addressed by learning about brain networks, and from rapidly growing experience with neuromodulation devices.

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.

Mood and quality of life in patients treated with brain-responsive neurostimulation: The value of earlier intervention.

OBJECTIVE: To establish whether earlier treatment using direct brain-responsive neurostimulation for medically intractable focal-onset seizures is associated with better mood and Quality of Life (QoL) compared to later treatment intervention. METHODS: Data were retrospectively analyzed from prospective clinical trials of a direct brain-responsive neurostimulator (RNS® System) for treatment of adults with medically intractable focal-onset epilepsy. Participants completed the Quality of Life in Epilepsy Inventory (QOLIE-31) yearly through 9 years of follow-up and the Beck Depression Inventory-II (BDI-II) through 2 years of follow-up. Changes in each assessment after treatment with responsive neurostimulation were calculated for patients who began treatment within 10 years of seizure onset (early) and those who began treatment 20 years or more after seizure onset (late). RESULTS: The median duration of epilepsy was 18.3 years at enrollment. At 9 years, both the early (N = 51) and late (N = 109) treatment groups experienced similar and significant reductions in the frequency of disabling seizures (73.4% and 77.8%, respectively). The early treatment patients had significant improvements in QoL and mood. However, the late treatment patients not only failed to show these improvements but also declined in the emotional QoL subscale. CONCLUSIONS: Patients treated with brain-responsive neurostimulation earlier in the course of their epilepsy show significant improvements in multiple domains of QoL and mood that are not observed in patients treated later in the course of their epilepsy despite similar efficacy in seizure reduction. Even with similar and substantial reductions in seizure frequency, the comorbidities of uncontrolled epilepsy may be less responsive to treatment when too many years have passed. The results of this study suggest that, as with resective and ablative surgery, treatment with brain-responsive neurostimulation should be delivered as early as possible in the course of medically resistant epilepsy to maximize the opportunity for improvements in mood and QoL.

The RNS System: brain-responsive neurostimulation for the treatment of epilepsy.

Introduction: Epilepsy affects more than 1% of the US population, and over 30% of adults with epilepsy do not respond to antiseizure medications without life-impacting medication-related side effects. Resection of the seizure focus is not an option for many patients because it would cause unacceptable neurological or cognitive harm. For these patients, neuromodulation has emerged as a nondestructive, effective, and safe alternative. The NeuroPace® RNS® System, the only brain-responsive neurostimulation device, records neural activity from leads placed at one or two seizure foci. When the neurostimulator detects epileptiform activity, as defined for each patient by his or her physician, brief pulses of electrical stimulation are delivered to normalize the activity.Areas covered: This review describes the RNS System, the results of multi-year clinical trials, and the research discoveries enabled by the chronic ambulatory brain data collected by the RNS System.Expert commentary: Brain-responsive neurostimulation could potentially be used to treat any episodic neurological disorder that's accompanied by a neurophysiological biomarker of severity. Combining advanced machine learning approaches with the chronic ambulatory brain data collected by the RNS System could eventually enable automatic fine-tuning of detection and stimulation for each patient, creating a general-purpose neurotechnological platform for precision medicine.

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.

Electrocorticographic events from long-term ambulatory brain recordings can potentially supplement seizure diaries.

PURPOSE: To determine the feasibility of using epileptiform events detected by continuous electrocorticographic monitoring via a brain-responsive neurostimulation system to supplement patient-maintained seizure diaries. METHODS: Data were retrospectively analyzed from a randomized controlled trial of brain-responsive neurostimulation (RNS® System) for adjunctive treatment of medically intractable focal onset seizures in 191 subjects. The long-term (≥3 months) correspondence between daily counts of diary-reported seizures and device-recorded "long epileptiform events" (LEs), a proxy for electrographic seizures (ESs), was assessed using cross-correlation and logistic generalized estimating equation models. RESULTS: Diary-reported seizures and LEs significantly co-varied across days in 124 patients whose detection settings were held constant, with a significantly higher correlation in 54 patients (44 %) whose LEs were usually ESs (high concordance patients). There were more days in which LEs were detected than days in which patients reported a seizure (positive predictive value (PPV): 34 %). On days when there were no LEs, there were typically no diary-reported seizures (negative predictive value (NPV): 90 %). In patients with a high concordance between LEs and ESs, the PPV and NPV were both slightly higher, 43 % (35-52 %) and 93 % (95 % CI: 86-97 %) respectively. CONCLUSION: Although LEs can substantially outnumber diary reported seizures, the high across-day correlation and strong NPV between LEs and diary seizures suggests that LEs recorded by the RNS® System could potentially supplement seizure diaries by providing an objective biomarker for relative seizure burden.

Mesial temporal resection following long-term ambulatory intracranial EEG monitoring with a direct brain-responsive neurostimulation system.

OBJECTIVE: To describe seizure outcomes in patients with medically refractory epilepsy who had evidence of bilateral mesial temporal lobe (MTL) seizure onsets and underwent MTL resection based on chronic ambulatory intracranial EEG (ICEEG) data from a direct brain-responsive neurostimulator (RNS) system. METHODS: We retrospectively identified all patients at 17 epilepsy centers with MTL epilepsy who were treated with the RNS System using bilateral MTL leads, and in whom an MTL resection was subsequently performed. Presumed lateralization based on routine presurgical approaches was compared to lateralization determined by RNS System chronic ambulatory ICEEG recordings. The primary outcome was frequency of disabling seizures at last 3-month follow-up after MTL resection compared to seizure frequency 3 months before MTL resection. RESULTS: We identified 157 patients treated with the RNS System with bilateral MTL leads due to presumed bitemporal epilepsy. Twenty-five patients (16%) subsequently had an MTL resection informed by chronic ambulatory ICEEG (mean = 42 months ICEEG); follow-up was available for 24 patients. After MTL resection, the median reduction in disabling seizures at last follow-up was 100% (mean: 94%; range: 50%-100%). Nine patients (38%) had exclusively unilateral electrographic seizures recorded by chronic ambulatory ICEEG and all were seizure-free at last follow-up after MTL resection; eight of nine continued RNS System treatment. Fifteen patients (62%) had bilateral MTL electrographic seizures, had an MTL resection on the more active side, continued RNS System treatment, and achieved a median clinical seizure reduction of 100% (mean: 90%; range: 50%-100%) at last follow-up, with eight of fifteen seizure-free. For those with more than 1 year of follow-up (N = 21), 15 patients (71%) were seizure-free during the most recent year, including all eight patients with unilateral onsets and 7 of 13 patients (54%) with bilateral onsets. SIGNIFICANCE: Chronic ambulatory ICEEG data provide information about lateralization of MTL seizures and can identify additional patients who may benefit from MTL resection.

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.

Brain-responsive neurostimulation for epilepsy (RNS® System).

Controlled clinical trials in adults with medically intractable focal seizures treated with the RNS® System demonstrate that closed-loop responsive neurostimulation to the seizure focus reduces the frequency of disabling seizures, is well tolerated, and is acceptably safe. Seizure reductions begin with initiation of treatment and continue over time, reaching median reductions of 75% after 9 years of treatment. Treatment with responsive cortical stimulation is also associated with improvement in quality of life and cognitive function related to the functional area being treated. In addition, the RNS System's chronic ambulatory electrocorticographic monitoring provides unprecedented insight into each patient's disease management, and into the study of epilepsy itself, in ways that may enhance the treatment of epilepsy in the future.

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.

Infection and Erosion Rates in Trials of a Cranially Implanted Neurostimulator Do Not Increase with Subsequent Neurostimulator Placements.

BACKGROUND/AIMS: The RNS® System utilizes a cranially implanted neurostimulator attached to leads placed at the seizure focus to provide brain responsive stimulation for the treatment of medically intractable partial onset epilepsy. Infection and erosion rates related to the cranial implant site were assessed overall and by neurostimulator procedure to determine whether rates increased with additional procedures. METHODS: Infection and erosion rates were calculated as (1) chance per neurostimulator procedure, (2) incidence per patient implant year, and (3) rates for initial and each subsequent neurostimulator implant (generalized estimating equation). RESULTS: In 256 patients followed for an average of 7 years, the infection rate was 3.7% per neurostimulator procedure (n = 31/840), and the rate of erosions was 0.8% per neurostimulator procedure (n = 7/840). Rates did not increase with subsequent neurostimulator procedures (p = 0.66, infection; p = 0.70, erosion). A prior infection or erosion at the implant site did not significantly increase the risk at a later procedure (p ≥ 0.05 for all combinations). CONCLUSION: These data indicate that the risk for infection compares favorably to other neurostimulation devices and suggest that rates of infection and erosion do not increase with subsequent neurostimulator replacements.