Evidence map›Paper›PMID 41946715›Full record

ArticleNature communications2026

A deep representation learning model to predict response to vagus nerve stimulation.

Hrishikesh Suresh, Karim Mithani, Vicki Li, Timur H Latypov, Nebras M Warsi, Simeon M Wong, Lauren Erdman, Jaeyoung Kang, Jurgen Germann, Flavia Venetucci Gouveia and 37 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

47 authors.

Hrishikesh SureshInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-0412-5655
Karim MithaniInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Vicki LiInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0009-0005-2091-4605
Timur H LatypovProgram in Neuroscience and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.
Nebras M WarsiInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-4885-3080
Simeon M WongInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0001-9070-0665
Lauren ErdmanDepartment of Pediatrics, Cincinnati Children's Hospital, Cincinnati, OH, USA.ORCID http://orcid.org/0000-0002-7106-2669
Jaeyoung KangProgram in Neuroscience and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.ORCID http://orcid.org/0009-0002-9924-7937
Jurgen GermannInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-0995-8226
Flavia Venetucci GouveiaProgram in Neuroscience and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.
Sebastian C ColemanProgram in Neuroscience and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.
Alexandre BergerProgram in Neuroscience and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.
Vann ChauDivision of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.
Shelly WeissDivision of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.
Carolina GorodetskyDivision of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.ORCID http://orcid.org/0000-0003-0007-9591
Elizabeth DonnerDivision of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.
Alexander G WeilDivision of Neurosurgery, CHU Sainte-Justine and Centre hospitalier de l'Université de Montréal, Montréal, QC, Canada.
Jignesh TailorDeparment of Neurosurgery, Riley Hospital for Children, Indianapolis, IN, USA.ORCID http://orcid.org/0000-0001-8137-7202
Taylor J AbelDepartment of Neurosurgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0002-5089-460X
Madison RemickDepartment of Neurosurgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
Emefa AkwayenaDepartment of Neurosurgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
Dewi SchraderDivision of Neurology, BC Children's Hospital, Vancouver, BC, Canada.
Robert J BolloDepartment of Neurosurgery, University of Utah Health, Salt Lake City, UT, USA.
Matthew D SmythJohns Hopkins University, Department of Neurosurgery, Johns Hopkins All Children's Hospital, St. Petersburg, FL, USA.
Diana AumDepartment of Neurosurgery, St. Louis Children's Hospital, St. Louis, MO, USA.
Sean M LewDepartment of Neurosurgery, Medical College Wisconsin, Milwaukee, WI, USA.
Shelly WangDivision of Neurosurgery, Nicklaus Children's Hospital, Miami, FL, USA.
Toba N NiaziDivision of Neurosurgery, Nicklaus Children's Hospital, Miami, FL, USA.
Aria FallahDepartment of Neurosurgery, UCLA Mattel Children's Hospital, Los Angeles, CA, USA.
Jeffrey S RaskinDepartment of Neurosurgery, Children's Hospital of Chicago, Chicago, IL, USA.
Howard L WeinerDepartment of Neurosurgery, Texas Children's Hospital, Houston, TX, USA.
Nisha GadgilDepartment of Neurosurgery, Texas Children's Hospital, Houston, TX, USA.
Gregory W AlbertDepartment of Neurosurgery, Arkansas Children's Hospital, Little Rock, AR, USA.
Aristides HadjinicolaouDivision of Neurology, CHU Sainte-Justine, Montréal, QC, Canada.ORCID http://orcid.org/0000-0002-9926-2080
Philippe MajorDivision of Neurology, CHU Sainte-Justine, Montréal, QC, Canada.ORCID http://orcid.org/0000-0002-0507-2543
Farbod NiaziCentre de recherche du CHUM, Montréal, QC, Canada.
Guillaume TheaudCentre de recherche du CHUM, Montréal, QC, Canada.
Sami ObaidDepartment of Surgery, Université de Montréal, Montréal, QC, Canada.
Elysa WidjajaDepartment of Medical Imaging, Children's Hospital of Chicago, Chicago, IL, USA.
Birgit Ertl-WagnerDepartment of Diagnostic & Interventional Radiology, The Hospital for Sick Children, Toronto, ON, Canada.
Logi VidarssonDepartment of Diagnostic & Interventional Radiology, The Hospital for Sick Children, Toronto, ON, Canada.
Margot J TaylorDepartment of Diagnostic & Interventional Radiology, The Hospital for Sick Children, Toronto, ON, Canada.
Alexandre BoutetJoint Department of Medical Imaging, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0000-0001-6942-5195
James T RutkaDivision of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Melissa A LoPrestiDepartment of Neurosurgery, University of Rochester Medical Center, Rochester, NY, USA.
Puneet JainDivision of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-6009-0859
George M IbrahimInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada. george.ibrahim@sickkids.ca.ORCID http://orcid.org/0000-0001-9068-8184

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Implantable neurotechnologies are increasingly used to reduce seizure burden in pediatric epilepsy. Vagus nerve stimulation (VNS), the most common option, is effective for only half of patients, with no means to predict outcome prior to surgery. As a result, many children undergo invasive and costly procedures without benefit. Although T1-weighted magnetic resonance imaging (T1w) is routinely acquired presurgically and may capture structural brain differences relevant to treatment outcome, its high dimensionality relative to sample sizes has limited its utility in predictive modelling. To address this challenge, we present VQ-VNS, a deep representation learning model to predict VNS outcome based on preoperative T1w (n = 263). First, we present data from the largest paediatric VNS cohort (n = 1046), wherein presurgical clinical data could not predict response (AUC 0.54,p > 0.99). Next, VQ-VNS was pretrained on 7433 T1w images to learn compact anatomical representations enabling its classifier to predict VNS response (AUC = 0.73,p = 0.007). Model predictions localized to serotonin-rich brain regions and inferred large-scale disruptions in network connectivity among non-responders. This biologically interpretable predictor based on routine structural imaging improves upon current clinical decision-making.

Indexed as

Deep LearningEpilepsyVagus Nerve StimulationAdolescentBrainChildChild, PreschoolFemaleHumansMagnetic Resonance ImagingMalePredictive Learning ModelsRepresentation Machine LearningTreatment Outcome

Identifiers

PMID41946715
PMCPMC13234157

What OpenQuestion holds

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Registered trials

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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.