sEEG‐guided responsive neurostimulation to treat neocortical epilepsy: A multicenter retrospective study of the efficacy and safety of depth electrode‐mediated neuromodulation

Sina Sadeghzadeh; David A Purger; Priya Bhanot; Noriah Johnson; Daniel A N Barbosa; Yuhao Huang; Jay J Park; Ethan Schonfeld; Matthew A Abikenari; Bhav Jain; Joshua P Aronson; Cornelia Drees; Tiffany L Fisher; Jason Gerrard; Tyler E Gray; Saadi Ghatan; Kevin Hines; Barbara C Jobst; Ioannis Karakis; Steven G Ojemann; Imran H Quraishi; Ahmed M Raslan; Michael D Sather; Christopher T Skidmore; Jon T Willie; Chengyuan Wu; Ji Yeoun Yoo; Kevin D Graber; Casey H Halpern; Vivek P Buch; Jonathon J Parker

doi:10.1002/epi4.70180

. 2025 Nov 17;11(1):146–161. doi: 10.1002/epi4.70180

sEEG‐guided responsive neurostimulation to treat neocortical epilepsy: A multicenter retrospective study of the efficacy and safety of depth electrode‐mediated neuromodulation

Sina Sadeghzadeh ^1,^✉, David A Purger ², Priya Bhanot ³, Noriah Johnson ⁴, Daniel A N Barbosa ⁵, Yuhao Huang ², Jay J Park ², Ethan Schonfeld ¹, Matthew A Abikenari ¹, Bhav Jain ¹, Joshua P Aronson ⁶, Cornelia Drees ⁷, Tiffany L Fisher ⁸, Jason Gerrard ⁹, Tyler E Gray ¹⁰, Saadi Ghatan ³, Kevin Hines ¹¹, Barbara C Jobst ¹², Ioannis Karakis ^13,¹⁴, Steven G Ojemann ¹⁵, Imran H Quraishi ¹⁰, Ahmed M Raslan ¹⁶, Michael D Sather ¹⁷, Christopher T Skidmore ¹⁸, Jon T Willie ¹⁹, Chengyuan Wu ¹¹, Ji Yeoun Yoo ²⁰, Kevin D Graber ²¹, Casey H Halpern ²², Vivek P Buch ², Jonathon J Parker ²³

^¹Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA

^²Department of Neurosurgery, Stanford University, Stanford, California, USA

^³Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA

^⁴Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA

^⁵Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA

^⁶Department of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA

^⁷Department of Neurology, Mayo Clinic Arizona, Phoenix, Arizona, USA

^⁸Department of Neurology, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA

^⁹Department of Neurosurgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA

^¹⁰Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA

^¹¹Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA

^¹²Department of Neurology, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA

^¹³Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA

^¹⁴Department of Neurology, University of Crete School of Medicine, Crete, Greece

^¹⁵Department of Neurosurgery, University of Colorado School of Medicine, Aurora, Colorado, USA

^¹⁶Department of Neurological Surgery, Oregon Health & Science University, Portland, Oregon, USA

^¹⁷Department of Neurosurgery, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA

^¹⁸Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA

^¹⁹Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, USA

^²⁰Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA

^²¹Department of Neurology, Stanford University, Stanford, California, USA

^²²Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA

^²³Department of Neurosurgery, Mayo Clinic Arizona, Phoenix, Arizona, USA

Correspondence , Sina Sadeghzadeh, Department of Neurosurgery, Stanford University School of Medicine, Stanford, 213 Quarry Rd, Palo Alto, CA 94304, USA. Email: sinas@stanford.edu

^✉

Corresponding author.

PMCID: PMC12903807 PMID: 41251192

Abstract

Objectives

Pivotal trials have established the effectiveness of the Responsive Neurostimulation System (RNS® System) in treating focal epilepsy. In clinical trials, depth leads were primarily used to treat mesial temporal seizure onsets while cortical strip leads were used to treat neocortical seizure onsets. Here, we systematically analyze the safety and efficacy of stereoelectroencephalography (sEEG)‐guided depth leads to provide responsive stimulation to neocortical gray matter.

Methods

Patients were stratified as strong responders (>median cohort seizure reduction %), weak responders (>0% and ≤median cohort seizure reduction %), and anti‐responders (≤0%) based on percent seizure reduction at 1 year post‐implant (1‐Y). Pre‐operative T1‐weighted magnetic resonance imaging and post‐operative computed tomography images were merged, and the Euclidean distance between the sEEG epileptic focus (sEEG‐EF) and the nearest RNS System depth lead contacts was calculated.

Results

A total of 87 depth leads were implanted in 55 patients across neocortical brain regions. The median reduction in clinical seizures improved from 66.7% at 1‐Y to 77.5% at long‐term follow‐up (LTFU: 2.35 ± 0.95 years), with 10 patients (18.2%) achieving complete seizure freedom. Seven patients (12.7%) experienced six serious adverse events. At 1‐Y, shorter Euclidean distance between the sEEG‐EF and RNS System depth leads predicted improved seizure outcome in strong responders (β = −0.84, p = 0.008) but not in weak responders (β = 0.21, p = 0.9) or anti‐responders (β = −20.34, p = 0.11). At LTFU, there was no significant relationship between Euclidean distance and seizure reduction in strong responders (β = 0.77, p = 0.18), weak responders (β = 2.05, p = 0.54), or anti‐responders (β = 0.24, p = 0.99). Exploratory analyses at 1‐Y showed nominal associations between older age (ρ = 0.32), longer epilepsy duration (ρ = 0.27), and non‐mesial temporal sEEG‐EFs and greater seizure reduction; however, none survived Bonferroni correction (adjusted α = 0.0027; all post‐correction p > 0.0027), and no associations were observed at LTFU.

Significance

In this series, neocortical depth leads for RNS therapy had favorable safety and efficacy and proximity to the sEEG‐EF drove initial outcomes for strong responders to RNS therapy.

Plain Language Summary

In this multi‐center study, patients with difficult‐to‐treat seizures received brain‐responsive stimulation using a device called responsive neurostimulation (RNS), which delivers small electrical pulses to reduce seizures. We focused on patients treated with electrodes placed in the brain's outer regions (the neocortex) and guided by a mapping procedure called sEEG. On average, patients had their seizures cut by two‐thirds after one year and by more than three‐quarters with longer follow‐up, with about one in five becoming seizure‐free. The treatment was safe, and closer electrode placement to the seizure source helped explain early—but not long‐term—improvements.

Keywords: depth electrode, neocortex, network remodeling, responsive neurostimulation (RNS), seizure, stereoelectroencephalography (sEEG)

Key points.

RNS with neocortical depth leads achieved 66.7% seizure reduction at 1 year and 77.5% at long term follow up.
Consistent with prior RNS studies, 18% of patients achieved seizure freedom, while maintaining a low serious adverse event rate of 13%.
Closer RNS lead proximity to sEEG‐identified seizure focus predicted greater 1‐year seizure reduction in strong responders to treatment.
At long‐term follow‐up, RNS lead proximity to the sEEG‐identified focus no longer predicted clinical response.

1. INTRODUCTION

Epilepsy is an unpredictable neurological disorder, affecting nearly 1.0% of the population, and significantly impacts quality of life and, often, neurocognitive function. ¹ , ² , ³ Despite significant advancements in medical management, up to a third of patients with epilepsy experience drug‐resistant epilepsy (DRE)—a condition marked by persistent seizures despite trial of two adequately selected anti‐seizure medications. ⁴ , ⁵ , ⁶ , ⁷ , ⁸

The Responsive Neurostimulation System (RNS® System, Neuropace Inc., Mountain View, CA), which harnesses personalized electrocorticographic data to detect activity of concern and provide responsive stimulation, has been successfully utilized to treat patients with DRE. Three clinical trials have established efficacy in the treatment of medication‐refractory focal epilepsy. ⁹ , ¹⁰ , ¹¹ In a seminal multicenter trial, 191 adults with medically intractable partial epilepsy experienced a 41.5% seizure reduction in the treated group by the end of a 12‐week blinded period, with no significant adverse event differences between the treatment and sham groups. ⁹ Subsequent long‐term studies of the same cohort demonstrated sustained seizure reductions of up to 53% at 2 years and 75% at 9 years post‐implantation. ¹⁰ , ¹² Cumulatively, these findings present Class I and Class IV evidence that responsive cortical stimulation is safe and effective for the treatment of medically refractory partial‐onset seizures.

The RNS System is frequently used to treat drug‐resistant neocortical seizures with seizure reductions ranging from 58% to 75%. ¹³ , ¹⁴ Typical implantation in clinical trials utilized cortical strip leads to treat seizures with neocortical onsets. However, a shift towards the use of stereoelectroencephalography (sEEG)‐guided depth electrode‐mediated intracranial monitoring has emerged as an alternative to traditional cortical strip electrodes in mapping complex neocortical seizure networks. ¹⁵ Depth leads, as an alternative to traditional cortical strip electrodes, present several advantages including providing more spatially precise recording and access to deeper brain structures. ¹⁶ , ¹⁷ Additionally, the utility of depth leads is evident in complex clinical scenarios where surface electrodes are contraindicated, such as dense cortical scarring from prior surgery. ¹³ , ¹⁴ , ¹⁸ However, despite an increased use of depth electrode‐mapped seizure onset zones to direct RNS System depth lead placement in the neocortex, the outcomes and safety of this strategy have not been systematically evaluated.

Here, we report a retrospective analysis of the efficacy, safety, and clinical outcomes of patients implanted with the RNS System with depth leads in the neocortex across nine centers in the United States.

2. MATERIALS AND METHODS

2.1. RNS system overview

The RNS System is a neurostimulation platform designed to reduce seizure frequency by identifying abnormal epileptiform activity and responding with a train of stimulation bursts. ⁹ This system integrates a cranially implanted programmable neurostimulator, connected to one or two depth leads or subdural cortical strip leads placed at identified seizure origins. Each lead houses four electrodes, and the neurostimulator can be connected to two leads simultaneously. Clinicians program the device to recognize specific intracranial electroencephalography (iEEG) abnormalities and subsequently administer short trains of stimulation pulses. Adjustments to the parameters of detection and stimulation are carried out in an iterative fashion by clinicians to maximize seizure control based on patient‐reported seizure frequency and device‐recorded events.

2.2. Inclusion and exclusion criteria

We conducted a retrospective assessment of patients implanted with the RNS System who met the following criteria:

Participant inclusion criteria

Implanted according to the RNS System indication for use,
Underwent pre‐implantation intracranial evaluation using sEEG electrodes,
At least one neocortical depth lead implanted, connected to the RNS System, and stimulated, and
An RNS System implanted for at least one year.

Participant exclusion criteria

Transferred care to another institution after the initial implantation, and
Lacked complete seizure frequency documentation at baseline and/or after 1 year of follow‐up.

2.3. Data collection

For each participant, clinical data including demographic information, epilepsy history, intracranial evaluation, RNS System metrics and programming, disabling seizure count (including focal‐onset aware seizures with motor or aphasia symptoms, focal‐onset impaired awareness seizures, and focal to bilateral tonic–clonic seizures), employment status, driver's license eligibility, serious adverse events (SAE), and a global impression scale was collected using a centralized electronic data capture system configured by NeuroPace specifically for this study using a secure cloud application (Amazon Web Services). Seizure frequency was determined from patient‐maintained diaries reviewed at each follow‐up, with treating epileptologists verifying counts through structured questioning during visits. These diary‐based counts were then used to calculate postoperative percent change. Charge density per contact, per phase was calculated as I _mA × PW_μs/(N × A × 10³), where A (0.08 cm²) is the per‐contact surface area and N is the number of simultaneously active cathodes determined by the stimulation pathway and depth count. Imaging data were collected including preoperative T1‐weighted Magnetic Resonance Imaging scan (preop‐MRI), post‐stereoelectroencephalography Computerized Tomography scan (post‐sEEG CT), and post‐RNS System Computerized Tomography scan (post‐RNS CT).

2.4. Ethical approval and compliance

Stanford University functioned as the primary organizing institution for this research (Institutional Review Board Protocol #54765). Participant data was contributed by Dartmouth Hitchcock Medical Center, Emory University, Mount Sinai Hospital, Oregon Health and Science University, Penn State Hershey Medical Center, Thomas Jefferson University, University of Colorado, and Yale University. The study protocol was reviewed and approved by each individual site's Institutional Review Board. A waiver of informed consent and a waiver of HIPAA authorization was obtained from all sites.

2.5. Patient‐level pre‐and‐post‐operative analysis

For each participant, the post‐sEEG CT and post‐RNS CT scans were co‐registered with pre‐op MRIs, to determine the exact coordinates of each electrode contact. Image co‐registration was performed using a rigid transformation via custom Python scripts (version 3.6, Python Software Foundation) with the assistance of the Stanford University Sherlock high‐performance computing cluster (Stanford Research Computing Center), as described previously. ¹⁹ , ²⁰ The co‐registered images were visually inspected using ITK‐SNAP Toolbox (version 3.8.0) by an expert reviewer (DANB) and manually adjusted, if necessary. ²¹ Next, the coordinates of each participant's RNS System depth lead and sEEG epileptic focus (sEEG‐EF) contacts were identified by two expert reviewers (S.S. and D.P.) using ITK‐SNAP Toolbox. ²¹ Distances were defined as straight‐line (Euclidean) separations in 3‐D between the sEEG‐identified onset contact(s) and the nearest RNS contact after rigid CT‐to‐MRI co‐registration. All relevant electrodes were intraparenchymal depth contacts; thus, geodesic surface distances were not used. When the sEEG‐EF encompassed multiple contacts, we computed all pairwise Euclidean distances between EF contacts and all RNS depth contacts and used the minimum as the per‐patient distance. The Euclidean distances were calculated using a custom MATLAB script (version R2021b, The MathWorks Inc.) (Figure 1).

sEEG and RNS system depth lead co‐localization pipeline. Figure 1 displays the sEEG and RNS System depth lead co‐localization pipeline. Post‐stereoelectroencephalography (post‐sEEG) and post‐responsive neurostimulation system (post‐RNS) CT scans were co‐registered with preoperative T1 weighted magnetic resonance imaging (preop‐MRI). The flowchart depicts the imaging process, resulting in the combined visualization of electrode placements. The central graph represents Euclidean distances between sEEG and RNS System depth lead contacts, highlighting the shortest distance being selected for analysis. An example co‐registered image is provided with identified RNS System depth leads and sEEG electrodes.

2.6. Statistical analysis

Baseline participant demographic and clinical characteristics were reported using descriptive statistics, with frequencies and proportions for categorical variables and medians with interquartile ranges for continuous variables. Distributions were tested for normality using the Shapiro–Wilk test and equality of variance using Levene's test. Non‐parametric tests were used for comparing two independent groups (Mann–Whitney U test/Wilcoxon rank‐sum test), two paired groups (Wilcoxon signed‐rank test), multiple independent groups (Kruskal–Wallis test), and paired proportions of categorical data (McNemar test). Spearman's rank correlation was used to assess correlations between non‐parametric continuous variables.

Association between post‐operative percent seizure decline at 1‐year (1‐Y) and long‐term follow‐up (LTFU, 2–3 years post‐operatively) and the Euclidean distance between the sEEG‐EF and the nearest RNS System depth lead contacts was assessed. Spearman correlations and simple linear regression models were employed to identify significant predictors of seizure decline, using percent seizure decline at 1‐Y or LTFU as the dependent variable and Euclidean distance as the independent variable. Associations were analyzed for the whole cohort and within sub‐groups based on patients' seizure reduction percent at 1‐Y categorized relative to the cohort median seizure reduction: strong responders (>median cohort seizure reduction %), weak responders (>0% and ≤median cohort seizure reduction %), and anti‐responders (≤0%). All 55 patients, including one with a thalamic lead, were included in the primary analyses. Sensitivity analyses removing the thalamic patient (n = 54) did not alter the conclusions regarding seizure frequency outcomes; therefore, results including the entire dataset were presented. The influence of individual centers on the headline outcomes was quantified using a leave‐one‐institution‐out (LOIO) jackknife, recalculating median and IQR seizure reduction at 1‐Y and LTFU after removing each site in turn.

All statistical analyses were performed using R statistical software (version 3.6.2, R Foundation for Statistical Computing). A two‐tailed p‐value of less than 0.05 was considered statistically significant. Bonferroni's correction was applied for multiple comparisons. The results were reported with 95% confidence intervals where applicable.

3. RESULTS

3.1. Demographic, surgical, and clinical characteristics

Fifty‐five participants were implanted with the RNS System across nine institutions in the United States. The cohort included 26 (47.3%) female and 29 (52.7%) male patients. The median age was 33 years [IQR 27–39; range 18–74]. A majority of patients (n = 26, 47.3%) had unknown etiologies, followed by cortical malformations (n = 12, 21.8%). Thirty‐nine patients (70.9%) had an abnormal MRI. A previous surgical procedure (outside of their sEEG implant) to treat the patient's epilepsy was performed in 30 (54.5%) patients. Furthermore, 18 (32.7%) patients had been previously implanted with a Vagus Nerve Stimulator (VNS). The implants encompassed 87 neocortical depth leads, with a subset of patients having additional leads in non‐neocortical brain locations. The distribution of lead placements varied, with the most common being frontal (n = 33, 30%) and mesial temporal (n = 22, 20%). All patients received sEEG depth electrode intracranial monitoring, with 8 (14.5%) also receiving subdural strips or grids (Table 1).

TABLE 1.

Patient demographic, surgical, and clinical characteristics.

Sex (n, %)	Female: 26 (47.3%)
Sex (n, %)	Male: 29 (52.7%)
Age at implant (years)	Mean: 34.6
	Median: 33 [IQR 26.5–39]
	Range: 18–74
Etiology (n, %)	Unknown: 26 (47.3%)
	Cortical Malformation: 12 (21.8%)
	Perinatal Injury: 1 (1.8%)
	Genetic: 3 (5.5%)
	Infection: 4 (7.3%)
	Stroke: 1 (1.8%)
	Tumor: 2 (3.6%)
	Traumatic Brain Injury (TBI): 4 (7.3%)
	Aneurysm: 1 (1.8%)
	Autoimmune: 1 (1.8%)
MRI (n, %)	Normal: 16 (29.1%) Cortical Dysplasia: 9 (16.4%)
	Other: 9 (16.4%)
	Post Surgical: 8 (14.5%)
	Sclerosis: 5 (9.1%)
	Encephalomalacia: 3 (5.5%)
	Vascular Abnormality: 3 (5.5%)
	Stroke: 1 (1.8%)
	Tumor: 1 (1.8%)
Intracranial EEG (n, %)	sEEG only: 47 (85.5%)
Intracranial EEG (n, %)	sEEG with subdural strips/grids: 8 (14.5%)
Prior surgery (n, %)	None: 25 (45.5%)
	Resection: 14 (25.5%)
	Laser Ablation: 3 (5.5%)
	Radiofrequency Ablation: 1 (1.8%)
	VNS, on (currently in treatment): 12 (21.8%)
	VNS, off/explanted: 6 (10.9%)
RNS system lead location (n, %)	Frontal: 33 (30%)
	Mesial Temporal: 22 (20%)
	Temporal (non‐mesial): 18 (16.4%)
	Parietal: 18 (16.4%)
	Insula: 10 (9.1%)
	Occipital: 8 (7.3%)
	Thalamus: 1 (0.9%)
Employment status (n, %)	Baseline: 21 (44.7%)
	1‐Y: 22 (50%)
	LTFU: 18 (46.2%)
Driver's license status (n, %)	Baseline: 6 (13.3%)
	1‐Y: 6 (14.3%)
	LTFU: 8 (20%)
SAEs (n, %)	Infection: 2 (3.6%)
	Chemical Meningitis: 1 (1.8%)
	Post‐Surgical Status Epilepticus: 1 (1.8%)
	New‐onset Non‐epileptic Seizures: 1 (1.8%)
	Epidural Hematoma: 1 (1.8%)
	Stimulation‐Triggered Nausea: 1 (1.8%)

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Abbreviations: 1‐Y, one‐year post‐operative; LTFU, long‐term follow up; MRI, magnetic resonance imaging; SAEs, serious adverse events; TBI, traumatic brain injury; VNS, vagus nerve stimulation.

3.2. RNS system parameters and stimulation settings

Stimulation was delivered at current amplitudes ranging from 0.9 to 7.0 mA, with the most common amplitude being between 2.0 and 3.0 mA. The stimulation frequency varied from 2 Hz to 333 Hz, with 200 Hz as the most common setting. Pulse width ranged from 80 to 200 μs, with 160 μs being the most common setting. Burst duration varied from 1 to 4000 ms, with 100 ms being the most common setting. The most common stimulation pathway was bipolar. Charge density varied between 0.625 and 8 μC/cm², with 1.5 μC/cm² being the most common. There was no relationship between charge density and Euclidean distance between the sEEG‐EF and the RNS System depth lead (p = 0.433).

3.3. Seizure reduction and secondary outcomes

Median reduction in clinical seizures increased from 66.7% [IQR: 0%–96.7%] at 1‐Y to 77.5% [50%–96.7%] at LTFU (2.35 ± 0.95 years post‐operatively), with 10 patients (18.2%) achieving complete seizure freedom, defined as continuous seizure‐free status through LTFU, while an additional 23 patients (41.8%) experienced extended periods of seizure freedom ranging from <3 months to >24 months (Figure 2A). The rate of employment among patients remained stable, from 44.7% (n = 21/47) at baseline to 50% (n = 22/44) at 1‐Y (p = 0.617), and to 46.2% (n = 18/39) at LTFU (p = 0.479). Similarly, the percentage of patients holding a driver's license remained stable, from 13.3% (n = 6/45) at baseline to 14.3% (n = 6/42) at 1‐Y (p = 1) and to 20% (n = 8/40) at LTFU (p = 0.248) (Table 1). In sensitivity analysis excluding patients with mesial temporal leads (n = 33), the median seizure reduction remained comparable, with 69.61% [IQR: 47.5–97.16%] at 1‐Y and 85.71% [IQR: 56.52%–99.16%] at LTFU, consistent with outcomes observed in the overall cohort. In LOIO analyses, the 1‐Y median ranged 60.0%–70.0% (largest Δ when excluding Stanford −6.7), and the LTFU median ranged 72.5%–81.7% (largest Δ when excluding Emory −5.0), supporting that no single site dominated the results (Table S1).

Distribution of sEEG‐EF and RNS System Electrode Proximity and Seizure Outcomes. (A) The distribution of seizure freedom time among patients with extended periods of seizure freedom (n = 23), ranging from less than 3 months to over 24 months until long‐term follow‐up (LTFU). (B) The distribution of Euclidean distances from sEEG‐EF to implanted RNS System depth leads. (C) The distribution of percent seizure reduction one‐year postoperatively at 1‐Y: strong responders (>median cohort seizure reduction %), weak responders (>0% and ≤median cohort seizure reduction %), and anti‐responders (≤0%). (D) The distribution of Euclidean distances among strong responders (n = 19), weak responders (n = 11), and anti‐responders (n = 11).

3.4. Safety

In terms of RNS System implant safety, six serious adverse events (SAEs) were reported in seven patients (12.7%), with five being surgery‐related and one being stimulation‐related. These include two cases of scalp infections (3.6%) and one case each of chemical meningitis (1.8%), epidural hematoma (1.8%), post‐implantation status epilepticus (1.8%) (resolved with stimulation), new‐onset non‐epileptic seizures (1.8%), and stimulation‐triggered nausea (1.8%) (resolved with programming) (Table 1).

3.5. Seizure reduction and seizure freedom by demographic, surgical, and clinical characteristics

Exploratory analysis was conducted to assess seizure outcomes at 1‐Y and LTFU by demographic, surgical, and clinical characteristics. At 1‐Y, we observed nominal associations between older patient age (β = 2.29, pre‐correction p = 0.02; ρ = 0.32, pre‐correction p = 0.02), longer epilepsy duration (β = 1.75, pre‐correction p = 0.06; ρ = 0.27, pre‐correction p = 0.05), and having non–mesial temporal sEEG‐EFs (75% vs. 31.67%, pre‐correction p = 0.026) with greater seizure reduction. However, none of these findings remained significant after Bonferroni correction (adjusted α = 0.0027), and all effects diminished by LTFU (pre‐correction p = 0.28, p = 0.26, p = 0.15, respectively) (Figure 3A–C). Presence of unilateral mesial temporal RNS System depth leads did not significantly impact seizure reduction (pre‐correction p = 0.32 at 1‐Y; pre‐correction p = 0.21 at LTFU) (Figure 3D). Although frontal lobe lead placement showed a nominal association with extended (36.9% vs. 25%, pre‐correction p = 0.048) and complete (50% vs. 25%, pre‐correction p = 0.046) seizure freedom, these also did not remain significant after Bonferroni correction. Charge density showed no association with seizure reduction at 1‐Y (pre‐correction p = 0.33) or LTFU (ρ = −0.31; pre‐correction p = 0.09). Patients with complete seizure freedom had lower charge density (pre‐correction p = 0.027); however, these exploratory findings did not survive multiplicity correction and likely reflect treatment escalation in more refractory cases. No other differences in seizure outcomes were noted based on sex, etiology, MRI findings, prior epilepsy surgery, prior VNS treatment, sEEG‐EF location, or stimulation with standard parameters (pulse width 160 μs, frequency 200 Hz, burst duration 100 ms) (Table S2).

Seizure reduction by age, epilepsy duration, mesial temporal sEEG‐EFs, and mesial temporal RNS leads. (A) Seizure outcomes based on age at 1‐Y and LTFU. (B) Seizure outcomes based on epilepsy duration at 1‐Y and LTFU. (C) Seizure outcomes based on presence of mesial temporal sEEG‐EF at 1‐Y and LTFU. (D) Seizure outcomes based on presence of mesial temporal RNS System depth leads at 1‐Y and LTFU. All p‐values are reported pre‐Bonferroni correction.

3.6. Seizure reduction relative to RNS system depth Lead distance from the sEEG‐EF

We hypothesized that improved seizure reduction would be associated with reduced distance between the sEEG‐EF and the RNS System depth lead. Thus, we investigated the association between the proximity of the sEEG‐EF and the RNS System depth lead and seizure reduction for patients with complete imaging data (n = 41/55). A majority of Euclidean distances were relatively small (under 10 mm) across the entire cohort, highlighting a trend of narrow variance in RNS System depth lead placement in comparison of mapped seizure onset (Figure 2B). When examining all patients as a single group, there was no significant association between Euclidean distances and seizure reduction at either 1‐Y (β = 2.04, p = 0.49; ρ = 0.14, p = 0.38) or LTFU (β = 3.86, p = 0.13; ρ = 0.31, p = 0.06). Consequently, we further analyzed this relationship by stratifying patients into 3 groups based on their seizure reduction percentage at 1‐Y relative to the cohort median: strong responders with seizure reduction at or above the cohort median (>66.7%), weak responders with seizure below the cohort median and above zero (>0% and ≤66.7%), and anti‐responders with seizure reduction at or below zero (≤0%). A total of 19 patients were classified as strong responders, 11 patients were classified as weak responders, and 11 patients were classified as anti‐responders (Figure 2C). The distribution of Euclidean distances did not vary across the three groups (p = 0.208) (Figure 2D and Table 2).

TABLE 2.

Regression analysis of euclidean distance in predicting 1‐Y and LTFU seizure decline.

Response to RNS system treatment	Follow up time	Average Euclidean distance (±SD)	Euclidean distance coefficient (95% CI)	Adjusted R ²	p‐value
Strong responders (n = 19)	1‐Y	7.35 ± 5.96	−0.84 (−1.43, −0.24)	0.304	0.008
Strong responders (n = 19)	LTFU	7.35 ± 5.96	+0.77 (−0.40, 1.94)	0.052	0.182
Weak responders (n = 11)	1‐Y	3.99 ± 2.90	+0.21 (−3.53, 3.96)	0.900	0.901
Weak responders (n = 11)	LTFU	3.99 ± 2.90	+2.05 (−5.52, 9.63)	−0.072	0.549
Anti‐responders (n = 11)	1‐Y	4.13 ± 2.43	−20.34 (−46.49, 5.83)	0.172	0.113
Anti‐responders (n = 11)	LTFU	4.13 ± 2.43	+0.24 (−71.35, 71.84)	−0.166	0.994

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Note: All p‐values are reported pre‐Bonferroni correction.

Abbreviations: 1‐Y, one‐year post‐operative; 95% CI, 95% confidence interval; LTFU, long‐term follow up; RNS, responsive neurostimulation; SD, standard deviation.

The Euclidean distance between the sEEG‐EF and the RNS System depth lead contacts appeared to influence seizure reduction differently among various response groups at 1‐Y and LTFU. To account for six comparisons, a Bonferroni correction was applied (adjusted α = 0.008). At 1‐Y, shorter distance was associated with greater seizure reduction in strong responders (β = −0.84, pre‐correction p = 0.008; ρ = −0.36, pre‐correction p = 0.092) but not in weak (β = 0.21, pre‐correction p = 0.90; ρ = 0.002, pre‐correction p = 0.99) or anti‐responders (β = −20.34, pre‐correction p = 0.11; ρ = −0.44, pre‐correction p = 0.16) (Figure 4). At LTFU, strong responders maintained a high median seizure reduction (1‐Y: 97.67% [IQR: 83.92%–100%]; LTFU: 95% [85.71%–100%]; pre‐correction p = 0.887), yet Euclidean distance no longer showed an association with further improvement (β = 0.77, pre‐correction p = 0.18; ρ = 0.24, pre‐correction p = 0.33) (Figure 4A and Table 2). There was similarly no significant association between distance and long‐term seizure reduction in weak responders (β = 2.05, pre‐correction p = 0.54; ρ = 0.27, pre‐correction p = 0.45) or anti‐responders (β = 0.24, pre‐correction p = 0.99; ρ = −0.09, pre‐correction p = 0.82) (Figure 4B,C and Table 2). Although weak (1‐Y: 50% [IQR: 40%–58.69%]; LTFU: 63.04% [41.67%–66.67%]; pre‐correction p = 0.208) and anti‐responders (1‐Y: −50% [−175%–0%]; LTFU: −14.29% [−118.7%–25%]; pre‐correction p = 0.302) showed some improvement at LTFU, these changes were not significant (Figure 4B,C). Using the literature standard >50% reduction to define responders, subgroup slopes relating Euclidean distance to seizure reduction were not significant at 1‐Y or LTFU for any response group (Table S3).

Seizure outcomes relative to electrode contact proximity at 1‐Y and LTFU. Figure 4 displays the relationship between the Euclidean distance between the sEEG‐identified epileptic foci and RNS System depth lead contacts with seizure reduction percentage at 1‐year follow‐up (1‐Y) and long‐term follow‐up (LTFU). Outcomes were stratified according to response at 1‐Y into (A) strong responders, (B) weak responders, and (C) anti‐responders. Points represent individual patients on each scatter plot, derived from a subset of patients with available imaging data, with best‐fit trend lines provided by linear regression. Boxplots include all patients, comparing seizure reduction percentages at 1‐Y and LTFU. All p‐values are reported pre‐Bonferroni correction.

3.7. Seizure reduction relative to the presence of HFOs and electrographic seizure onset patterns

The presence of high frequency oscillations (HFOs) in sEEG has been suggested to have strong concordance with the clinical gold standard seizure onset zone determination via visual assessment of iEEG patterns. ²² , ²³ , ²⁴ , ²⁵ , ²⁶ Thus, we assessed whether presence of HFOs (as noted in the epileptologist report) during sEEG in the sEEG‐EF or the seizure onset pattern was associated with eventual RNS System outcome. ²⁷ We found no association between seizure reduction at 1‐Y or LTFU and the presence of HFOs (1‐Y: p = 0.58; LTFU: p = 0.66) during sEEG, sEEG electrographic seizure onset pattern (1‐Y: p = 0.69; LTFU: p = 0.36), or RNS electrographic seizure onset pattern (1‐Y: p = 0.24; LTFU: p = 0.28). There were no differences in the previously mentioned characteristics between patients with and without extended or complete seizure freedom (Table S1). However, following the RNS System implantation, there was a notable shift in patients' seizure electrographic onset pattern from low‐voltage fast activity to spiking/polyspiking (p = 0.009). Prior to RNS System implantation, sEEG revealed that 62.8% of patients exhibited low‐voltage fast activity, 20% displayed rhythmic patterns, and 17.1% showed spiking or polyspiking activity. Post‐RNS System implantation, the spiking/polyspiking pattern became the most prevalent with 42.8% of patients, followed by low‐voltage fast activity (31.4%) and rhythmic patterns (25.7%). There was no association between Euclidean distances and the dissociation between the primary (p = 0.247) or secondary (p = 0.114) sEEG and RNS onset patterns (Figure 5).

Comparison of seizure electrographic onset patterns pre‐ versus post‐RNS system implantation. Figure 5 illustrates the changes in seizure electrographic onset patterns before and after RNS System implantation: low‐voltage fast activity, spiking/polyspiking, and rhythmic patterns.

4. DISCUSSION

In this multi‐center retrospective real‐world study, we examined the outcomes of 55 patients across nine institutions who were treated with the RNS System utilizing sEEG‐guided neocortical depth lead implantation. Patients treated with the RNS System using this strategy as a group experienced a median seizure reduction of 66.7% at 1 year post‐operatively, increasing to 77.5% at the last follow‐up ranging from 2 to 3 years post‐operatively. There was an 18% seizure freedom rate, consistent with literature on bitemporal and neocortical RNS therapy. ¹² , ¹³ , ¹⁴ , ²⁸ Additionally, extended periods of seizure freedom occurred in 42% of patients with long‐term therapy. In the patient cohort, there were six serious adverse events (13%), including surgery and stimulation‐related events, similar to those reported in previous studies. ¹⁰ , ²⁸

Our findings suggest that the efficacy and safety of the RNS System using sEEG‐guided neocortical depth leads are comparable to those reported in pivotal clinical trials. The seminal trial by Morrell et al. reported a 37.9% seizure reduction in a randomized controlled trial of 191 adults with medically refractory epilepsy, with comparable adverse events to the sham group. ⁹ Follow‐up of the same cohort by Heck et al. showed a 53% median seizure reduction at 2 years, with no change in adverse events. ¹⁰ Long‐term studies by Bergey et al., Nair et al., and Razavi et al. report seizure reductions ranging from 48% to 82% at 3–9 years post‐operatively, with adverse events including implant site infections and neurostimulator explantation but no serious complications related to stimulation. ¹¹ , ¹² , ²⁸ Rates of sudden unexplained death in epilepsy were lower than would be expected in this population. ²⁹ Additionally, the outcomes we report are equivalent to previous studies of the RNS System for neocortical epilepsy. In two multicenter studies of the RNS System for neocortical epilepsy using either strips or depth leads for intracranial monitoring by Ma et al. and Jobst et al., the authors reported median seizure reductions of 75.5% at 21 months follow‐up and 58% at 6 years follow‐up, respectively, which are comparable to an outcome we report here of 77.5% seizure reduction at 29 months follow‐up. ¹³ , ¹⁴

The working hypothesis is that information gleaned from the sEEG‐EF determination can direct precise neocortically targeted depth lead placement for the RNS System. Thus, we explored whether the absolute distance (Euclidean) between the sEEG seizure onset and the RNS System depth lead contacts was associated with seizure reduction. Although a closer proximity to the epileptic focus predicted better outcomes for strong responders at 1‐year post‐implant, we observed only a non‐significant association between greater distance and seizure reduction at longer follow‐up in both strong and weak responders. A parsimonious interpretation is that closer placement enhances early direct stimulation effects, whereas longer term benefit reflects network remodeling, diminishing the role of strict proximity. Consistent with this, we found no proximity–outcome association at LTFU. One potential mechanism is that the network's capacity for modulation may vary with the distance to the sEEG‐EF—hyperexcitability at the sEEG‐EF may induce downstream synaptic changes that limit effective stimulation if the leads are too close. ³⁰ , ³¹ Conversely, the heterogeneous nature of seizure networks—varying in size, location, and connectivity—may also contribute to these differential responses, underscoring the challenge of precisely defining the ictal onset zone.

Additionally, we observed that older patients and those with longer epilepsy duration had greater seizure reduction at 1 year, although these findings did not persist in the long term. It is possible that older age and prolonged disease duration facilitate compensatory network remodeling, yielding better early outcomes with RNS due to broader network connectivity. ³² , ³³ , ³⁴ , ³⁵ We also noted that patients with mesial temporal sEEG‐EFs showed lower seizure reduction at 1 year compared to those with neocortical sEEG‐EFs, but these differences diminished over time. Thus, patients with unilateral mesial temporal foci took longer to reach seizure control levels similar to those with strictly neocortical onsets. This might be attributable to differential functional connectivity of the mesial temporal lobe epilepsy compared to other neocortical regions, ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ , ⁴² requiring an extended period for neuromodulatory effects to accumulate. ⁴³ Further investigation is necessary to validate these observations and clarify the underlying mechanisms driving these differential responses.

Moreover, regarding patients' electrographic onset patterns, we observed a notable shift from low‐voltage fast activity (LVFA) pre‐RNS System implantation to a spiking/polyspiking pattern post‐RNS System implantation. While this data may be influenced by the lower sampling rate of the RNS System (250 Hz) compared to sEEG (typically 512–2000 Hz), it is unlikely that sampling limitations alone explain the shift, as LVFA does not always manifest as frequencies faster than 125 Hz. ⁴⁴ Other technical factors may also contribute, including differences in recording context, since RNS electrocorticography is captured around detections and may represent a slightly later onset segment than the earliest sEEG‐annotated epoch, and differences in montage and electrode geometry, which can influence the apparent morphology of ictal fast activity. Alternatively, the sEEG leads and RNS leads may have been positioned in slightly different locations, though we did not find a significant relationship between Euclidean distance and disassociation between sEEG and RNS electrographic onset patterns. Interestingly, this post‐implant shift in ictal morphology is consistent with prior single‐center observations that closed‐loop stimulation can alter electrographic features over time, supporting a network‐modulatory effect rather than a recording artifact alone. ⁴⁵ Further investigation is necessary to determine whether these changes should be attributed to device recording limitations or biological factors.

Epilepsy is increasingly understood as a network disease. ⁴⁶ The increasing efficacy of neurostimulation for epilepsy over time has been previously investigated. ⁹ , ¹⁰ , ¹¹ , ¹² , ¹³ , ²⁸ , ⁴³ This phenomenon is likely attributable to network remodeling over time, a critical factor in the therapeutic effect even when stimulation is not in immediate proximity to the seizure onset zone. ⁴⁷ Spencer et al. described the epileptic brain network as a functionally and anatomically interconnected set of cortical and subcortical structures, where activity in one region affects the entire network. ⁴⁶ , ⁴⁸ This understanding challenges the traditional seizure focus theory, suggesting instead that seizures arise from the dynamics of a distributed, large‐scale aberrant network. ⁴⁶ , ⁴⁹ Recent literature has described the brain as a complex network with non‐trivial topological features, with vertices representing cells or brain regions, and edges representing connections or interactions between these vertices. ⁴⁶ , ⁵⁰ , ⁵¹ This network is not static but changes over time in both the number of vertices and edges as well as their properties. ⁴⁶ , ⁵² , ⁵³ , ⁵⁴ , ⁵⁵ , ⁵⁶ , ⁵⁷ In epilepsy, the transition from interictal to ictal states is thought to be influenced by the complex interplay of factors within this network, which governs both normal and aberrant brain dynamics. ⁴⁶ , ⁵⁸ , ⁵⁹ , ⁶⁰ , ⁶¹ , ⁶² , ⁶³ , ⁶⁴ Crucially, in the context of RNS therapy, the evolving epileptic brain network undergoes changes in its global and local characteristics, profoundly influenced by biological rhythms. ⁴⁶ , ⁶⁵ , ⁶⁶ As variation in seizure reduction was not completely explained by variations in distance between sEEG onset and RNS System depth lead placement, these data support further directed investigation of whether a proximity threshold exists for maximal efficacy to RNS therapy. Unfortunately, in this retrospective data review there were an insufficient number of patients in each anatomic seizure onset region to evaluate region‐specific dependency of seizure suppression with RNS System depth lead proximity to the sEEG‐EF.

We acknowledge several limitations of this study. First, detailed longitudinal tracking of anti‐seizure medication changes was not performed, which may confound seizure reduction outcomes in individual patients. Second, seizure counts were self‐reported by patients, which may introduce potential biases in under‐ or over‐reporting. ⁶⁷ Third, we did not perform a calendar‐based correlation of RNS detection with daily seizure logs. Fourth, our cohort was drawn from nine participating centers, and selection bias in surgical approaches, patient populations, and practice patterns may limit generalizability. While we strived to incorporate as many sites as possible via discussion of the project in two open forums, there remain high‐volume RNS centers not included, which may limit the wider application of our findings. Furthermore, the long‐term follow‐up period was limited to a maximum of 3 years, which does not capture the long‐term efficacy and safety profile of the treatment reported in other studies. ¹² Additionally, volume of tissue activated (VTA) modeling and gray–white boundary proximity was not available across centers in this retrospective data set. Future prospective work should incorporate harmonized segmentations and patient‐specific conductivity models to estimate VTA and test whether tissue‐level features add predictive value beyond geometry alone. Lastly, a future opportunity would be objective centralized analysis of sEEG onset and the RNS System iEEG data which could provide deeper insights into the mechanisms of action and the factors influencing treatment efficacy, such as functional connectivity at the region of the RNS System depth leads. Despite these limitations the data we report provide significant insight into understanding the use of the RNS System with neocortical depth leads, paving the way for future work that could refine treatment methods and improve outcomes in patients with drug‐resistant epilepsy.

5. CONCLUSION

In this multi‐center analysis, neocortical depth leads for RNS therapy are shown to have favorable safety and efficacy outcomes with a similar seizure reduction compared to the pivotal and real‐world RNS System clinical studies. This observation suggests that depth leads are a reasonable strategy to target neocortical seizure onset zones if a target is identified by sEEG evaluation and/or if cortical strip placement poses increased surgical risk. We found the location of the RNS System depth lead with respect to the ictal onset as determined by sEEG may influence short‐term treatment response. Furthermore, strong responders to RNS therapy in the first year of treatment were more likely to have RNS System depth lead in closer proximity to the sEEG identified ictal onset. However, patients with RNS System depth leads that were further from the sEEG defined ictal onset were still able to achieve long‐term seizure reductions. These results suggest that shorter distance between the sEEG‐EF and RNS leads is not the driving factor to explain seizure reduction over the long term. Further investigation into the ideal distance of neuromodulation from an epileptic focus in the setting of individual seizure networks is warranted.

AUTHOR CONTRIBUTIONS

Conceptualization: J.P., C.H. Data curation: S.S., D.P. Formal analysis: S.S., P.B., J.P. Investigation: I.K., B.J., J.A., C.W., C.S., C.D., A.R., J.P., M.S., T.F., S.G., K.H., J.Y., KG, CH, I.Q., J.G., T.G., S.O., J.W. Methodology: S.S., V.B., J.P. Project administration: N.J. Software: D.B. Supervision: V.B., J.P. Writing – original draft: S.S., P.B., E.S., M.A., B.J. Writing – review & editing: S.S., V.B., J.P., Y.H., C.D., I.Q.

FUNDING INFORMATION

This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors. NeuroPace supported this research by configuring a secure cloud‐based electronic data capture system specifically for this study.

CONFLICT OF INTEREST STATEMENT

Sina Sadeghzadeh, David A. Purger, Priya Bhanot, Noriah Johnson, Daniel A. N. Barbosa, Yuhao Huang, Jay J. Park, Ethan Schonfeld, Matthew A. Abikenari, Bhav Jain, Cornelia Drees, Tiffany L. Fisher, Tyler E. Gray, Kevin Hines, Ahmed M. Raslan: None, Michael D. Sather, Jon T. Willie, Kevin D. Graber, Casey H. Halpern, and Jonathon J. Parker: None. Joshua P. Aronson: Consulting fees from Medtronic Inc., Abbott Laboratories, and Iota Biosciences. Research support from Abbott Laboratories. Jason Gerrard: Consulting fees from Medtronic Inc. and Boston Scientific. Travel support from Insightec, Inc. Saadi Ghatan: Unpaid consultant to NeuroPace Inc. Barbara C. Jobst: Research funding from CDC (HOBSCOTCH, MEW), DoD, and NIH (START), Ioannis Karakis: Travel support from NeuroPace Inc., Steven G. Ojemann: Consulting fees from Boston Scientific and Fellowship support from Medtronic, Boston Scientific, and Abbott, Imran H. Quraishi: Research funding from NeuroPace Inc. Christopher T. Skidmore: Principal investigator for the NeuroPace Nautilus and Jazz Pharmaceuticals Clinical Trials. Chengyuan Wu and Vivek P. Buch: Consulting fees from NeuroPace Inc, Ji Yeoun Yoo: Consulting fees from Zimmer Biomet, Inc., and salary support from 1UH3NS109557‐01A1. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

ETHICS APPROVAL STATEMENT

Stanford University functioned as the primary organizing institution for this research (Institutional Review Board Protocol #54765). The study protocol was reviewed and approved by each individual site's Institutional Review Board.

PATIENT CONSENT STATEMENT

A waiver of informed consent and a waiver of HIPAA authorization were obtained from all sites.

Supporting information

Table S1.

EPI4-11-146-s002.docx^{(18.3KB, docx)}

Table S2.

EPI4-11-146-s001.docx^{(20.2KB, docx)}

Table S3.

EPI4-11-146-s003.docx^{(18.1KB, docx)}

ACKNOWLEDGMENTS

We would like to thank Shirley McCartney, Ph.D., for editorial assistance and data acquisition, Martha Morrell, MD, for feedback on the initial draft of this manuscript, and Lise Johnson, Ph.D., Felicia Elefant, BS, and Emily Mirro, MS, for administrative support.

Sadeghzadeh S, Purger DA, Bhanot P, Johnson N, Barbosa DAN, Huang Y, et al. sEEG‐guided responsive neurostimulation to treat neocortical epilepsy: A multicenter retrospective study of the efficacy and safety of depth electrode‐mediated neuromodulation. Epilepsia Open. 2026;11:146–161. 10.1002/epi4.70180

DATA AVAILABILITY STATEMENT

Data utilized in this study are available upon reasonable request.

REFERENCES

1. Fiest KM, Sauro KM, Wiebe S, Patten SB, Kwon CS, Dykeman J, et al. Prevalence and incidence of epilepsy: a systematic review and meta‐analysis of international studies. Neurology. 2017;88(3):296–303. [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Kwan P, Schachter SC, Brodie MJ. Drug‐resistant epilepsy. N Engl J Med. 2011;365(10):919–926. [DOI] [PubMed] [Google Scholar]
3. Nadler JV, Spencer DD. What is a seizure focus? Adv Exp Med Biol. 2014;813:55–62. [DOI] [PubMed] [Google Scholar]
4. Dalic L, Cook MJ. Managing drug‐resistant epilepsy: challenges and solutions. Neuropsychiatr Dis Treat. 2016;12:2605–2616. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Sultana B, Panzini MA, Veilleux Carpentier A, Comtois J, Rioux B, Gore G, et al. Incidence and prevalence of drug‐resistant epilepsy: a systematic review and meta‐analysis. Neurology. 2021;96(17):805–817. [DOI] [PubMed] [Google Scholar]
6. Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Allen Hauser W, Mathern G, et al. Definition of drug resistant epilepsy: consensus proposal by the ad hoc task force of the ILAE commission on therapeutic strategies. Epilepsia. 2010;51(6):1069–1077. [DOI] [PubMed] [Google Scholar]
7. Picot MC, Baldy‐Moulinier M, Daurès JP, Dujols P, Crespel A. The prevalence of epilepsy and pharmacoresistant epilepsy in adults: a population‐based study in a Western European country. Epilepsia. 2008;49(7):1230–1238. [DOI] [PubMed] [Google Scholar]
8. Kalilani L, Sun X, Pelgrims B, Noack‐Rink M, Villanueva V. The epidemiology of drug‐resistant epilepsy: a systematic review and meta‐analysis. Epilepsia. 2018;59(12):2179–2193. [DOI] [PubMed] [Google Scholar]
9. Morrell MJ, Group RSiES . Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology. 2011;77(13):1295–1304. [DOI] [PubMed] [Google Scholar]
10. Heck CN, King‐Stephens D, Massey AD, Nair DR, Jobst BC, Barkley GL, et al. Two‐year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS system pivotal trial. Epilepsia. 2014;55(3):432–441. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Bergey GK, Morrell MJ, Mizrahi EM, Goldman A, King‐Stephens D, Nair D, et al. Long‐term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology. 2015;84(8):810–817. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Nair DR, Laxer KD, Weber PB, Murro AM, Park YD, Barkley GL, et al. Nine‐year prospective efficacy and safety of brain‐responsive neurostimulation for focal epilepsy. Neurology. 2020;95(9):e1244–e1256. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Jobst BC, Kapur R, Barkley GL, Bazil CW, Berg MJ, Bergey GK, et al. Brain‐responsive neurostimulation in patients with medically intractable seizures arising from eloquent and other neocortical areas. Epilepsia. 2017;58(6):1005–1014. [DOI] [PubMed] [Google Scholar]
14. Ma BB, Fields MC, Knowlton RC, Chang EF, Szaflarski JP, Marcuse LV, et al. Responsive neurostimulation for regional neocortical epilepsy. Epilepsia. 2020;61(1):96–106. [DOI] [PubMed] [Google Scholar]
15. Kim LH, Parker JJ, Ho AL, Feng AY, Kumar KK, Chen KS, et al. Contemporaneous evaluation of patient experience, surgical strategy, and seizure outcomes in patients undergoing stereoelectroencephalography or subdural electrode monitoring. Epilepsia. 2021;62(1):74–84. [DOI] [PubMed] [Google Scholar]
16. Voorhies JM, Cohen‐Gadol A. Techniques for placement of grid and strip electrodes for intracranial epilepsy surgery monitoring: pearls and pitfalls. Surg Neurol Int. 2013;4:98. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Anderson DN, Charlebois CM, Smith EH, Arain AM, Davis TS, Rolston JD. Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy. Sci Rep. 2021;11(1):24155. [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Miller KJ, Burns TC, Grant GA, Halpern CH. Responsive stimulation of motor cortex for medically and surgically refractive epilepsy. Seizure. 2015;33:38–40. [DOI] [PubMed] [Google Scholar]
19. Barbosa DAN, Gattas S, Salgado JS, Kuijper FM, Wang AR, Huang Y, et al. An orexigenic subnetwork within the human hippocampus. Nature. 2023;621(7978):381–388. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Barbosa DAN, Kuijper FM, Duda J, Wang AR, Cartmell SCD, Saluja S, et al. Aberrant impulse control circuitry in obesity. Mol Psychiatry. 2022;27(8):3374–3384. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User‐guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–1128. [DOI] [PubMed] [Google Scholar]
22. Liu S, Sha Z, Sencer A, Aydoseli A, Bebek N, Abosch A, et al. Exploring the time‐frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy. J Neural Eng. 2016;13(2):026026. [DOI] [PubMed] [Google Scholar]
23. Malinowska U, Bergey GK, Harezlak J, Jouny CC. Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations. Clin Neurophysiol. 2015;126(8):1505–1513. [DOI] [PubMed] [Google Scholar]
24. Jacobs J, Vogt C, LeVan P, Zelmann R, Gotman J, Kobayashi K. The identification of distinct high‐frequency oscillations during spikes delineates the seizure onset zone better than high‐frequency spectral power changes. Clin Neurophysiol. 2016;127(1):129–142. [DOI] [PubMed] [Google Scholar]
25. Dumpelmann M, Jacobs J, Schulze‐Bonhage A. Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy. Epilepsia. 2015;56(2):197–206. [DOI] [PubMed] [Google Scholar]
26. Kuhnke N, Schwind J, Dumpelmann M, Mader M, Schulze‐Bonhage A, Jacobs J. High frequency oscillations in the ripple band (80‐250 Hz) in scalp EEG: higher density of electrodes allows for better localization of the seizure onset zone. Brain Topogr. 2018;31(6):1059–1072. [DOI] [PubMed] [Google Scholar]
27. Perucca P, Dubeau F, Gotman J. Intracranial electroencephalographic seizure‐onset patterns: effect of underlying pathology. Brain. 2014;137(Pt 1):183–196. [DOI] [PubMed] [Google Scholar]
28. Razavi B, Rao VR, Lin C, Bujarski KA, Patra SE, Burdette DE, et al. Real‐world experience with direct brain‐responsive neurostimulation for focal onset seizures. Epilepsia. 2020;61(8):1749–1757. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Devinsky O, Friedman D, Duckrow RB, Fountain NB, Gwinn RP, Leiphart JW, et al. Sudden unexpected death in epilepsy in patients treated with brain‐responsive neurostimulation. Epilepsia. 2018;59(3):555–561. [DOI] [PubMed] [Google Scholar]
30. Nishimura M, Owens J, Swann JW. Effects of chronic network hyperexcitability on the growth of hippocampal dendrites. Neurobiol Dis. 2008;29(2):267–277. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Barker‐Haliski M, White HS. Glutamatergic mechanisms associated with seizures and epilepsy. Cold Spring Harb Perspect Med. 2015;5(8):a022863. [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Liang X, Pang X, Zhao J, Yu L, Wu P, Li X, et al. Altered static and dynamic functional network connectivity in temporal lobe epilepsy with different disease duration and their relationships with attention. J Neurosci Res. 2021;99(10):2688–2705. [DOI] [PubMed] [Google Scholar]
33. Chiang S, Levin HS, Wilde E, Haneef Z. White matter structural connectivity changes correlate with epilepsy duration in temporal lobe epilepsy. Epilepsy Res. 2016;120:37–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Wang B, Meng L. Functional brain network alterations in epilepsy: a magnetoencephalography study. Epilepsy Res. 2016;126:62–69. [DOI] [PubMed] [Google Scholar]
35. Englot DJ, Konrad PE, Morgan VL. Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings. Epilepsia. 2016;57(10):1546–1557. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Bettus G, Guedj E, Joyeux F, Confort‐Gouny S, Soulier E, Laguitton V, et al. Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Hum Brain Mapp. 2009;30(5):1580–1591. [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Burianova H, Faizo NL, Gray M, Hocking J, Galloway G, Reutens D. Altered functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res. 2017;137:45–52. [DOI] [PubMed] [Google Scholar]
38. Bartolomei F, Bettus G, Stam CJ, Guye M. Interictal network properties in mesial temporal lobe epilepsy: a graph theoretical study from intracerebral recordings. Clin Neurophysiol. 2013;124(12):2345–2353. [DOI] [PubMed] [Google Scholar]
39. Pittau F, Grova C, Moeller F, Dubeau F, Gotman J. Patterns of altered functional connectivity in mesial temporal lobe epilepsy. Epilepsia. 2012;53(6):1013–1023. [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, et al. Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp. 2011;32(6):883–895. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Narasimhan S, Gonzalez HFJ, Johnson GW, Wills KE, Paulo DL, Morgan VL, et al. Functional connectivity between mesial temporal and default mode structures may help lateralize surgical temporal lobe epilepsy. J Neurosurg. 2022;137(6):1571–1581. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Fu C, Aisikaer A, Chen Z, Yu Q, Yin J, Yang W. Different functional network connectivity patterns in epilepsy: a rest‐state fMRI study on mesial temporal lobe epilepsy and benign epilepsy with centrotemporal spike. Front Neurol. 2021;12:668856. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Rao VR, Rolston JD. Unearthing the mechanisms of responsive neurostimulation for epilepsy. Commun Med (Lond). 2023;3(1):166. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Abdallah C, Mansilla D, Minato E, Grova C, Beniczky S, Frauscher B. Systematic review of seizure‐onset patterns in stereo‐electroencephalography: current state and future directions. Clin Neurophysiol. 2024;163:112–123. [DOI] [PubMed] [Google Scholar]
45. Kokkinos V, Sisterson ND, Wozny TA, Richardson RM. Association of closed‐loop brain stimulation neurophysiological features with seizure control among patients with focal epilepsy. JAMA Neurol. 2019;76(7):800–808. [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Lehnertz K, Brohl T, Wrede RV. Epileptic‐network‐based prediction and control of seizures in humans. Neurobiol Dis. 2023;181:106098. [DOI] [PubMed] [Google Scholar]
47. Anderson DN, Charlebois CM, Smith EH, Davis TS, Peters AY, Newman BJ, et al. Closed‐loop stimulation in periods with less epileptiform activity drives improved epilepsy outcomes. Brain. 2024;147(2):521–531. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Spencer SS. Neural networks in human epilepsy: evidence of and implications for treatment. Epilepsia. 2002;43(3):219–227. [DOI] [PubMed] [Google Scholar]
49. Rosenow F, Luders H. Presurgical evaluation of epilepsy. Brain. 2001;124(Pt 9):1683–1700. [DOI] [PubMed] [Google Scholar]
50. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186–198. [DOI] [PubMed] [Google Scholar]
51. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13(5):336–349. [DOI] [PubMed] [Google Scholar]
52. Bassett DS, Muldoon SF. Network and multilayer network approaches to understanding human brain dynamics. Philos Sci. 2016;83(5):710–720. [Google Scholar]
53. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2(3):203–271. [Google Scholar]
54. Lehnertz K, Ansmann G, Bialonski S, Dickten H, Geier C, Porz S. Evolving networks in the human epileptic brain. Phys D Nonlinear Phenom. 2014;267:7–15. [Google Scholar]
55. Holme P. Modern temporal network theory: a colloquium. Eur Phys J B. 2015;88(9):234. [Google Scholar]
56. Damoiseaux JS. Effects of aging on functional and structural brain connectivity. Neuroimage. 2017;160:32–40. [DOI] [PubMed] [Google Scholar]
57. Edde M, Leroux G, Altena E, Chanraud S. Functional brain connectivity changes across the human life span: from fetal development to old age. J Neurosci Res. 2021;99(1):236–262. [DOI] [PubMed] [Google Scholar]
58. Lehnertz K, Dickten H. Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients. Philos Trans A Math Phys Eng Sci. 2015;373(2034):20140094. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Dickten H, Porz S, Elger CE, Lehnertz K. Weighted and directed interactions in evolving large‐scale epileptic brain networks. Sci Rep. 2016;6:34824. [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, et al. Source‐sink connectivity: a novel interictal EEG marker for seizure localization. Brain. 2022;145(11):3901–3915. [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, et al. The interictal suppression hypothesis in focal epilepsy: network‐level supporting evidence. Brain. 2023;146(7):2828–2845. [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Geier C, Bialonski S, Elger CE, Lehnertz K. How important is the seizure onset zone for seizure dynamics? Seizure. 2015;25:160–166. [DOI] [PubMed] [Google Scholar]
63. Khambhati AN, Davis KA, Lucas TH, Litt B, Bassett DS. Virtual cortical resection reveals push‐pull network control preceding seizure evolution. Neuron. 2016;91(5):1170–1182. [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Jiang H, Cai Z, Worrell GA, He B. Multiple oscillatory push‐pull antagonisms constrain seizure propagation. Ann Neurol. 2019;86(5):683–694. [DOI] [PMC free article] [PubMed] [Google Scholar]
65. von Wrede R, Brohl T, Rings T, Pukropski J, Helmstaedter C, Lehnertz K. Modifications of functional human brain networks by transcutaneous auricular vagus nerve stimulation: impact of time of day. Brain Sci. 2022;12(5):546. [DOI] [PMC free article] [PubMed] [Google Scholar]
66. Lehnertz K, Rings T, Brohl T. Time in brain: how biological rhythms impact on EEG signals and on EEG‐derived brain networks. Front Netw Physiol. 2021;1:755016. [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Goldstein J, Kwon CS, Harmon M, Buchhalter J, Kukla A, McCallum S, et al. Seizure documentation in people living with epilepsy. Epilepsy Behav. 2021;125:108383. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

EPI4-11-146-s002.docx^{(18.3KB, docx)}

Table S2.

EPI4-11-146-s001.docx^{(20.2KB, docx)}

Table S3.

EPI4-11-146-s003.docx^{(18.1KB, docx)}

Data Availability Statement

Data utilized in this study are available upon reasonable request.

[epi470180-bib-0001] 1. Fiest KM, Sauro KM, Wiebe S, Patten SB, Kwon CS, Dykeman J, et al. Prevalence and incidence of epilepsy: a systematic review and meta‐analysis of international studies. Neurology. 2017;88(3):296–303. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0002] 2. Kwan P, Schachter SC, Brodie MJ. Drug‐resistant epilepsy. N Engl J Med. 2011;365(10):919–926. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0003] 3. Nadler JV, Spencer DD. What is a seizure focus? Adv Exp Med Biol. 2014;813:55–62. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0004] 4. Dalic L, Cook MJ. Managing drug‐resistant epilepsy: challenges and solutions. Neuropsychiatr Dis Treat. 2016;12:2605–2616. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0005] 5. Sultana B, Panzini MA, Veilleux Carpentier A, Comtois J, Rioux B, Gore G, et al. Incidence and prevalence of drug‐resistant epilepsy: a systematic review and meta‐analysis. Neurology. 2021;96(17):805–817. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0006] 6. Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Allen Hauser W, Mathern G, et al. Definition of drug resistant epilepsy: consensus proposal by the ad hoc task force of the ILAE commission on therapeutic strategies. Epilepsia. 2010;51(6):1069–1077. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0007] 7. Picot MC, Baldy‐Moulinier M, Daurès JP, Dujols P, Crespel A. The prevalence of epilepsy and pharmacoresistant epilepsy in adults: a population‐based study in a Western European country. Epilepsia. 2008;49(7):1230–1238. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0008] 8. Kalilani L, Sun X, Pelgrims B, Noack‐Rink M, Villanueva V. The epidemiology of drug‐resistant epilepsy: a systematic review and meta‐analysis. Epilepsia. 2018;59(12):2179–2193. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0009] 9. Morrell MJ, Group RSiES . Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology. 2011;77(13):1295–1304. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0010] 10. Heck CN, King‐Stephens D, Massey AD, Nair DR, Jobst BC, Barkley GL, et al. Two‐year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS system pivotal trial. Epilepsia. 2014;55(3):432–441. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0011] 11. Bergey GK, Morrell MJ, Mizrahi EM, Goldman A, King‐Stephens D, Nair D, et al. Long‐term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology. 2015;84(8):810–817. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0012] 12. Nair DR, Laxer KD, Weber PB, Murro AM, Park YD, Barkley GL, et al. Nine‐year prospective efficacy and safety of brain‐responsive neurostimulation for focal epilepsy. Neurology. 2020;95(9):e1244–e1256. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0013] 13. Jobst BC, Kapur R, Barkley GL, Bazil CW, Berg MJ, Bergey GK, et al. Brain‐responsive neurostimulation in patients with medically intractable seizures arising from eloquent and other neocortical areas. Epilepsia. 2017;58(6):1005–1014. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0014] 14. Ma BB, Fields MC, Knowlton RC, Chang EF, Szaflarski JP, Marcuse LV, et al. Responsive neurostimulation for regional neocortical epilepsy. Epilepsia. 2020;61(1):96–106. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0015] 15. Kim LH, Parker JJ, Ho AL, Feng AY, Kumar KK, Chen KS, et al. Contemporaneous evaluation of patient experience, surgical strategy, and seizure outcomes in patients undergoing stereoelectroencephalography or subdural electrode monitoring. Epilepsia. 2021;62(1):74–84. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0016] 16. Voorhies JM, Cohen‐Gadol A. Techniques for placement of grid and strip electrodes for intracranial epilepsy surgery monitoring: pearls and pitfalls. Surg Neurol Int. 2013;4:98. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0017] 17. Anderson DN, Charlebois CM, Smith EH, Arain AM, Davis TS, Rolston JD. Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy. Sci Rep. 2021;11(1):24155. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0018] 18. Miller KJ, Burns TC, Grant GA, Halpern CH. Responsive stimulation of motor cortex for medically and surgically refractive epilepsy. Seizure. 2015;33:38–40. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0019] 19. Barbosa DAN, Gattas S, Salgado JS, Kuijper FM, Wang AR, Huang Y, et al. An orexigenic subnetwork within the human hippocampus. Nature. 2023;621(7978):381–388. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0020] 20. Barbosa DAN, Kuijper FM, Duda J, Wang AR, Cartmell SCD, Saluja S, et al. Aberrant impulse control circuitry in obesity. Mol Psychiatry. 2022;27(8):3374–3384. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0021] 21. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User‐guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–1128. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0022] 22. Liu S, Sha Z, Sencer A, Aydoseli A, Bebek N, Abosch A, et al. Exploring the time‐frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy. J Neural Eng. 2016;13(2):026026. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0023] 23. Malinowska U, Bergey GK, Harezlak J, Jouny CC. Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations. Clin Neurophysiol. 2015;126(8):1505–1513. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0024] 24. Jacobs J, Vogt C, LeVan P, Zelmann R, Gotman J, Kobayashi K. The identification of distinct high‐frequency oscillations during spikes delineates the seizure onset zone better than high‐frequency spectral power changes. Clin Neurophysiol. 2016;127(1):129–142. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0025] 25. Dumpelmann M, Jacobs J, Schulze‐Bonhage A. Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy. Epilepsia. 2015;56(2):197–206. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0026] 26. Kuhnke N, Schwind J, Dumpelmann M, Mader M, Schulze‐Bonhage A, Jacobs J. High frequency oscillations in the ripple band (80‐250 Hz) in scalp EEG: higher density of electrodes allows for better localization of the seizure onset zone. Brain Topogr. 2018;31(6):1059–1072. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0027] 27. Perucca P, Dubeau F, Gotman J. Intracranial electroencephalographic seizure‐onset patterns: effect of underlying pathology. Brain. 2014;137(Pt 1):183–196. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0028] 28. Razavi B, Rao VR, Lin C, Bujarski KA, Patra SE, Burdette DE, et al. Real‐world experience with direct brain‐responsive neurostimulation for focal onset seizures. Epilepsia. 2020;61(8):1749–1757. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0029] 29. Devinsky O, Friedman D, Duckrow RB, Fountain NB, Gwinn RP, Leiphart JW, et al. Sudden unexpected death in epilepsy in patients treated with brain‐responsive neurostimulation. Epilepsia. 2018;59(3):555–561. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0030] 30. Nishimura M, Owens J, Swann JW. Effects of chronic network hyperexcitability on the growth of hippocampal dendrites. Neurobiol Dis. 2008;29(2):267–277. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0031] 31. Barker‐Haliski M, White HS. Glutamatergic mechanisms associated with seizures and epilepsy. Cold Spring Harb Perspect Med. 2015;5(8):a022863. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0032] 32. Liang X, Pang X, Zhao J, Yu L, Wu P, Li X, et al. Altered static and dynamic functional network connectivity in temporal lobe epilepsy with different disease duration and their relationships with attention. J Neurosci Res. 2021;99(10):2688–2705. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0033] 33. Chiang S, Levin HS, Wilde E, Haneef Z. White matter structural connectivity changes correlate with epilepsy duration in temporal lobe epilepsy. Epilepsy Res. 2016;120:37–46. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0034] 34. Wang B, Meng L. Functional brain network alterations in epilepsy: a magnetoencephalography study. Epilepsy Res. 2016;126:62–69. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0035] 35. Englot DJ, Konrad PE, Morgan VL. Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings. Epilepsia. 2016;57(10):1546–1557. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0036] 36. Bettus G, Guedj E, Joyeux F, Confort‐Gouny S, Soulier E, Laguitton V, et al. Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Hum Brain Mapp. 2009;30(5):1580–1591. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0037] 37. Burianova H, Faizo NL, Gray M, Hocking J, Galloway G, Reutens D. Altered functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res. 2017;137:45–52. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0038] 38. Bartolomei F, Bettus G, Stam CJ, Guye M. Interictal network properties in mesial temporal lobe epilepsy: a graph theoretical study from intracerebral recordings. Clin Neurophysiol. 2013;124(12):2345–2353. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0039] 39. Pittau F, Grova C, Moeller F, Dubeau F, Gotman J. Patterns of altered functional connectivity in mesial temporal lobe epilepsy. Epilepsia. 2012;53(6):1013–1023. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0040] 40. Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, et al. Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp. 2011;32(6):883–895. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0041] 41. Narasimhan S, Gonzalez HFJ, Johnson GW, Wills KE, Paulo DL, Morgan VL, et al. Functional connectivity between mesial temporal and default mode structures may help lateralize surgical temporal lobe epilepsy. J Neurosurg. 2022;137(6):1571–1581. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0042] 42. Fu C, Aisikaer A, Chen Z, Yu Q, Yin J, Yang W. Different functional network connectivity patterns in epilepsy: a rest‐state fMRI study on mesial temporal lobe epilepsy and benign epilepsy with centrotemporal spike. Front Neurol. 2021;12:668856. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0043] 43. Rao VR, Rolston JD. Unearthing the mechanisms of responsive neurostimulation for epilepsy. Commun Med (Lond). 2023;3(1):166. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0044] 44. Abdallah C, Mansilla D, Minato E, Grova C, Beniczky S, Frauscher B. Systematic review of seizure‐onset patterns in stereo‐electroencephalography: current state and future directions. Clin Neurophysiol. 2024;163:112–123. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0045] 45. Kokkinos V, Sisterson ND, Wozny TA, Richardson RM. Association of closed‐loop brain stimulation neurophysiological features with seizure control among patients with focal epilepsy. JAMA Neurol. 2019;76(7):800–808. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0046] 46. Lehnertz K, Brohl T, Wrede RV. Epileptic‐network‐based prediction and control of seizures in humans. Neurobiol Dis. 2023;181:106098. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0047] 47. Anderson DN, Charlebois CM, Smith EH, Davis TS, Peters AY, Newman BJ, et al. Closed‐loop stimulation in periods with less epileptiform activity drives improved epilepsy outcomes. Brain. 2024;147(2):521–531. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0048] 48. Spencer SS. Neural networks in human epilepsy: evidence of and implications for treatment. Epilepsia. 2002;43(3):219–227. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0049] 49. Rosenow F, Luders H. Presurgical evaluation of epilepsy. Brain. 2001;124(Pt 9):1683–1700. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0050] 50. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186–198. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0051] 51. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13(5):336–349. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0052] 52. Bassett DS, Muldoon SF. Network and multilayer network approaches to understanding human brain dynamics. Philos Sci. 2016;83(5):710–720. [Google Scholar]

[epi470180-bib-0053] 53. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2(3):203–271. [Google Scholar]

[epi470180-bib-0054] 54. Lehnertz K, Ansmann G, Bialonski S, Dickten H, Geier C, Porz S. Evolving networks in the human epileptic brain. Phys D Nonlinear Phenom. 2014;267:7–15. [Google Scholar]

[epi470180-bib-0055] 55. Holme P. Modern temporal network theory: a colloquium. Eur Phys J B. 2015;88(9):234. [Google Scholar]

[epi470180-bib-0056] 56. Damoiseaux JS. Effects of aging on functional and structural brain connectivity. Neuroimage. 2017;160:32–40. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0057] 57. Edde M, Leroux G, Altena E, Chanraud S. Functional brain connectivity changes across the human life span: from fetal development to old age. J Neurosci Res. 2021;99(1):236–262. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0058] 58. Lehnertz K, Dickten H. Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients. Philos Trans A Math Phys Eng Sci. 2015;373(2034):20140094. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0059] 59. Dickten H, Porz S, Elger CE, Lehnertz K. Weighted and directed interactions in evolving large‐scale epileptic brain networks. Sci Rep. 2016;6:34824. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0060] 60. Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, et al. Source‐sink connectivity: a novel interictal EEG marker for seizure localization. Brain. 2022;145(11):3901–3915. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0061] 61. Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, et al. The interictal suppression hypothesis in focal epilepsy: network‐level supporting evidence. Brain. 2023;146(7):2828–2845. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0062] 62. Geier C, Bialonski S, Elger CE, Lehnertz K. How important is the seizure onset zone for seizure dynamics? Seizure. 2015;25:160–166. [DOI] [PubMed] [Google Scholar]

[epi470180-bib-0063] 63. Khambhati AN, Davis KA, Lucas TH, Litt B, Bassett DS. Virtual cortical resection reveals push‐pull network control preceding seizure evolution. Neuron. 2016;91(5):1170–1182. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0064] 64. Jiang H, Cai Z, Worrell GA, He B. Multiple oscillatory push‐pull antagonisms constrain seizure propagation. Ann Neurol. 2019;86(5):683–694. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0065] 65. von Wrede R, Brohl T, Rings T, Pukropski J, Helmstaedter C, Lehnertz K. Modifications of functional human brain networks by transcutaneous auricular vagus nerve stimulation: impact of time of day. Brain Sci. 2022;12(5):546. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0066] 66. Lehnertz K, Rings T, Brohl T. Time in brain: how biological rhythms impact on EEG signals and on EEG‐derived brain networks. Front Netw Physiol. 2021;1:755016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi470180-bib-0067] 67. Goldstein J, Kwon CS, Harmon M, Buchhalter J, Kukla A, McCallum S, et al. Seizure documentation in people living with epilepsy. Epilepsy Behav. 2021;125:108383. [DOI] [PubMed] [Google Scholar]

PERMALINK

sEEG‐guided responsive neurostimulation to treat neocortical epilepsy: A multicenter retrospective study of the efficacy and safety of depth electrode‐mediated neuromodulation

Sina Sadeghzadeh

David A Purger

Priya Bhanot

Noriah Johnson

Daniel A N Barbosa

Yuhao Huang

Jay J Park

Ethan Schonfeld

Matthew A Abikenari

Bhav Jain

Joshua P Aronson

Cornelia Drees

Tiffany L Fisher

Jason Gerrard

Tyler E Gray

Saadi Ghatan

Kevin Hines

Barbara C Jobst

Ioannis Karakis

Steven G Ojemann

Imran H Quraishi

Ahmed M Raslan

Michael D Sather

Christopher T Skidmore

Jon T Willie

Chengyuan Wu

Ji Yeoun Yoo

Kevin D Graber

Casey H Halpern

Vivek P Buch

Jonathon J Parker

Abstract

Objectives

Methods

Results

Significance

Plain Language Summary

Key points.

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. RNS system overview

2.2. Inclusion and exclusion criteria

2.3. Data collection

2.4. Ethical approval and compliance

2.5. Patient‐level pre‐and‐post‐operative analysis

FIGURE 1.

2.6. Statistical analysis

3. RESULTS

3.1. Demographic, surgical, and clinical characteristics

TABLE 1.

3.2. RNS system parameters and stimulation settings

3.3. Seizure reduction and secondary outcomes

FIGURE 2.

3.4. Safety

3.5. Seizure reduction and seizure freedom by demographic, surgical, and clinical characteristics

FIGURE 3.

3.6. Seizure reduction relative to RNS system depth Lead distance from the sEEG‐EF

TABLE 2.

FIGURE 4.

3.7. Seizure reduction relative to the presence of HFOs and electrographic seizure onset patterns

FIGURE 5.

4. DISCUSSION

5. CONCLUSION

AUTHOR CONTRIBUTIONS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

ETHICS APPROVAL STATEMENT

PATIENT CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES