OSCR

Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.

Overview

Authors: Hmayag Partamian1,2, Saeed Jahromi1,2, M. Scott Perry1, Daniel Hansen1, Madhan Bosemani1, Jason Wang3, Eleonora Tamilia4,5, Joseph R. Madsen6, Jeffrey Bolton5, Scellig S. D. Stone6, Phillip L. Pearl5, Christos Papadelis1,2,7
ORCID iDs: Hmayag Partamian, Madhan Bosemani, Christos Papadelis
  1. Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System,1500 Cooper St, Fort Worth, TX 76104 USA
  2. Department of Bioengineering, The University of Texas at Arlington,Arlington, TX USA
  3. Cook Children’s Pathology and Laboratory Medicine, Cook Children’s Health Care System,Fort Worth, TX USA
  4. Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School,Boston, MA USA
  5. Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School,Boston, MA USA
  6. Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School,Boston, MA USA
  7. Burnett School of Medicine, Texas Christian University,Fort Worth, TX USA
Institutions: Cook Children's Health Care System (United States); The University of Texas at Arlington (United States); Boston Children's Hospital (United States); Texas Christian University (United States)
Journal: Scientific reports, volume 16, issue 1, article 15166
Dates: received 23 January 2026; accepted 17 March 2026; published online 26 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-45177-x · PMID 41888587 · PMCID PMC13172485 · OpenAlex W7140664197
Open access: gold, a free copy (OpenAlex)
Status: code on request (how the code is found and verified)
Categories: EEG (modality), human (organism), epilepsy (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Interictal epilepsy biomarkers, Epilepsy surgery, Combined interictal features, Supervised machine learning, Biomarkers, Computational biology and bioinformatics, Neurology, Neuroscience
MeSH: Drug Resistant Epilepsy*, Machine Learning*, Adolescent, Biomarkers, Child, Child, Preschool, Electroencephalography, Female, Humans, Male, Predictive Learning Models, Random Forest, Retrospective Studies, ROC Curve, Treatment Outcome (* major topic)
Topic: Machine Learning in Healthcare (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (R01NS104116, R01NS134944)
Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41598-026-45177-x.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41598-026-45177-x.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 8 keywords, 15 MeSH terms, 1 funder, 94 references.

Cite

This paper

Partamian, H., Jahromi, S., Perry, M. S., Hansen, D., Bosemani, M., Wang, J., Tamilia, E., Madsen, J. R., Bolton, J., Stone, S. S. D., Pearl, P. L., & Papadelis, C. (2026). Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework. Scientific reports, 16(1), 15166. https://doi.org/10.1038/s41598-026-45177-x

BibTeX

@article{partamian2026predicting,
author = {Partamian, Hmayag and Jahromi, Saeed and Perry, M. Scott and Hansen, Daniel and Bosemani, Madhan and Wang, Jason and Tamilia, Eleonora and Madsen, Joseph R. and Bolton, Jeffrey and Stone, Scellig S. D. and Pearl, Phillip L. and Papadelis, Christos},
title = {{Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {15166},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-45177-x},
url = {https://doi.org/10.1038/s41598-026-45177-x},
pmid = {41888587},
pmcid = {PMC13172485}
}

RIS

TY - JOUR
AU - Partamian, Hmayag
AU - Jahromi, Saeed
AU - Perry, M. Scott
AU - Hansen, Daniel
AU - Bosemani, Madhan
AU - Wang, Jason
AU - Tamilia, Eleonora
AU - Madsen, Joseph R.
AU - Bolton, Jeffrey
AU - Stone, Scellig S. D.
AU - Pearl, Phillip L.
AU - Papadelis, Christos
TI - Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/26
VL - 16
IS - 1
SP - 15166
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-45177-x
UR - https://doi.org/10.1038/s41598-026-45177-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-45177-x",
"type": "article-journal",
"title": "Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework",
"container-title": "Scientific reports",
"author": [
{
"family": "Partamian",
"given": "Hmayag"
},
{
"family": "Jahromi",
"given": "Saeed"
},
{
"family": "Perry",
"given": "M. Scott"
},
{
"family": "Hansen",
"given": "Daniel"
},
{
"family": "Bosemani",
"given": "Madhan"
},
{
"family": "Wang",
"given": "Jason"
},
{
"family": "Tamilia",
"given": "Eleonora"
},
{
"family": "Madsen",
"given": "Joseph R."
},
{
"family": "Bolton",
"given": "Jeffrey"
},
{
"family": "Stone",
"given": "Scellig S. D."
},
{
"family": "Pearl",
"given": "Phillip L."
},
{
"family": "Papadelis",
"given": "Christos"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "15166",
"DOI": "10.1038/s41598-026-45177-x",
"PMID": "41888587",
"PMCID": "PMC13172485",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-45177-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
26
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41746-025-01531-3 [code]
Machine learning on interictal intracranial EEG predicts surgical outcome in drug resistant epilepsy.
Journal: NPJ digital medicine
In common: epilepsy, clinical / translational, 30 references, author Hmayag Partamian
[2] doi:10.1093/braincomms/fcaf056 [code]
Spikes on ripples are better interictal biomarkers of epilepsy than spikes or ripples.
Journal: Brain communications
In common: epilepsy, clinical / translational, 26 references, author Christos Papadelis
[3] doi:10.1093/braincomms/fcaf170 [code]
Noninvasive classification of physiological and pathological high frequency oscillations in children
Journal: n/a
In common: epilepsy, EEG, clinical / translational, 18 references, author Christos Papadelis
[4] doi:10.1093/brain/awad118 [code]
Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy.
Journal: Brain : a journal of neurology
In common: epilepsy, EEG, clinical / translational, 15 references, author Christos Papadelis
[5] doi:10.1093/braincomms/fcaf403 [code]
Normative high-frequency oscillation phase-amplitude coupling and effective connectivity under sevoflurane.
Journal: Brain communications
In common: epilepsy, 17 references
[6] doi:10.1093/braincomms/fcae367 [code]
Simulated resections and responsive neurostimulator placement can optimize postoperative seizure outcomes when guided by fast ripple networks.
Journal: Brain communications
In common: epilepsy, clinical / translational, 13 references
[7] doi:10.1038/s43856-024-00654-0 [code]
Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone.
Journal: Communications medicine
In common: epilepsy, clinical / translational, 11 references
[8] doi:10.1038/s41598-023-48764-4 [code]
High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom.
Journal: Scientific reports
In common: epilepsy, EEG, clinical / translational, 10 references
[9] doi:10.1093/braincomms/fcac151 [code]
Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom.
Journal: Brain communications
In common: epilepsy, clinical / translational, 7 references, author Christos Papadelis
[10] doi:10.1093/brain/awae361 [code]
Multicentre analysis of seizure outcome predicted by removal of high-frequency oscillations.
Journal: Brain : a journal of neurology
In common: epilepsy, EEG, clinical / translational, 6 references, author Christos Papadelis

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.