Evidence map›Paper›PMID 42396343›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Virtual Responsive Neurostimulation Implantation: From Intracranial Connectivity to Optimized Lead Placement.

Odile Feys, Katherine G Walsh, Kerry C Nix, Mariam Josyula, Nishant Sinha, Sarah B Lavelle, Joost Wagenaar, Andrew Michalak, Martha J Morrell, Jay Jeschke and 7 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Odile FeysDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania.ORCID 0000-0001-5014-4094
Katherine G WalshCenter for Neuroengineering and Therapeutics, University of Pennsylvania.
Kerry C NixCenter for Neuroengineering and Therapeutics, University of Pennsylvania.
Mariam JosyulaDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania.
Nishant SinhaCenter for Neuroengineering and Therapeutics, University of Pennsylvania.
Sarah B LavelleCenter for Neuroengineering and Therapeutics, University of Pennsylvania.
Joost WagenaarCenter for Neuroengineering and Therapeutics, University of Pennsylvania.
Andrew MichalakComprehensive Epilepsy Center, NYU Grossman School of Medicine.
Martha J MorrellNeuropace, Inc.
Jay JeschkeComprehensive Epilepsy Center, NYU Grossman School of Medicine.
Ankit N KhambhatiDepartment of Neurological Surgery and Weill Institute for Neurosciences.
Erin C ConradDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania.
Jonathan K KleenDepartment of Neurology and Weill Institute for Neurosciences.
Brian LittDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania.
Vikram R RaoDepartment of Neurology and Weill Institute for Neurosciences.
Daniel FriedmanComprehensive Epilepsy Center, NYU Grossman School of Medicine.
Kathryn A DavisDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania.ORCID 0000-0002-7020-6480

Funding

Biomarkers to Predict Outcome from Responsive Brain Stimulation for EpilepsyR61NS125568 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2023 to 2025
$3.6M
Quantitative interictal networks to localize seizure generatorsK23NS121401 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Erin Conrad · 2022 to 2026
$1.2M
Biomarkers to Predict Outcome from Responsive Brain Stimulation for EpilepsyR33NS125568 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Kathryn Adamiak Davis · 2026 to 2026
$1.1M
Quantitative scalp and intracranial EEG in surgical planningR01NS148413 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Erin Conrad · 2026 to 2026
$646k
Scalable methods to quantify epileptic network and guide epilepsy surgeryR00NS138680 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Nishant Sinha · 2026 to 2026
$249k
Mechanisms and Prognostic Value of Ictal Onset Patterns for Epilepsy NeurostimulationK23NS146693 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Andrew Michalak · 2026 to 2026
$248k
NINDS NIH HHS K23 NS121401NINDS NIH HHS K23 NS146693NINDS NIH HHS R00 NS138680NINDS NIH HHS R01 NS148413NINDS NIH HHS R33 NS125568NINDS NIH HHS R61 NS125568
6 · The paper itself

Abstract

Responsive neurostimulation (RNS) is an implanted device that delivers direct brain stimulation for drug-resistant focal epilepsy. Individual responses are highly variable, and no validated framework exists to predict outcome or guide lead placement before implantation. We hypothesized that this variability is partly explained by lead placement in relation to patterns of functional connectivity in brain networks. Fourty-nine patients with drug-resistant focal epilepsy who underwent pre-implantation intracranial EEG (iEEG) and RNS implantation across three independent epilepsy centers were retrospectively studied. We developed a composite functional connectivity score, based on simple Spearman correlation, combining the standard deviation and kurtosis of interictal iEEG connectivity distributions to predict the response outcome in a training cohort (HUP, n=18) and validated in two independent cohorts (NYU, n=17; UCSF, n=14). We accounted for a spatial mismatch between iEEG and RNS electrodes with a distance-based correction. The score was extended to generate patient-specific 3D maps of predicted RNS efficacy across 200 simulated, or "virtual RNS", lead configurations. Accuracy of the score in predicting clinical outcome was 72% at the group level, 61% at the individual patient level, and, after distance-based optimization, 100% in patients with RNS electrodes placed close to location of iEEG electrodes. Applied to the validation cohort, the same score reached 68% accuracy (71% balanced accuracy, 55% sensitivity, 88% specificity). The spatial combination of the scores at different SEEG contacts localization gives a spatial score for each patient. Responders showed significantly higher spatial scores than non-responders, supporting that actual RNS lead placement in responders was located in map-identified favorable regions. Interictal iEEG functional connectivity predicts individual RNS response across independent epilepsy centers, and patient-specific 3D maps derived from this biomarker could prospectively guide lead implantation toward favorable network regions, opening a promising avenue toward network-informed RNS surgical planning.

Identifiers

PMID42396343
PMCPMC13320936

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.