Leveraging interictal multimodal features and graph neural networks for automated planning of epilepsy surgery

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F25%3A00643250" target="_blank" >RIV/68081731:_____/25:00643250 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/25:00141554

Result on the web

<a href="https://academic.oup.com/braincomms/article/7/3/fcaf140/8114735" target="_blank" >https://academic.oup.com/braincomms/article/7/3/fcaf140/8114735</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/braincomms/fcaf140" target="_blank" >10.1093/braincomms/fcaf140</a>

Result language

angličtina

Original language name

Leveraging interictal multimodal features and graph neural networks for automated planning of epilepsy surgery

Original language description

Precise localization of the epileptogenic zone is pivotal for planning minimally invasive surgeries in drug-resistant epilepsy. Here, we present a graph neural network (GNN) framework that integrates interictal intracranial EEG features, electrode topology, and MRI features to automate epilepsy surgery planning. We retrospectively evaluated the model using leave-one-patient-out cross-validation on a dataset of 80 drug-resistant epilepsy patients treated at St. Anne's University Hospital (Brno, Czech Republic), comprising 31 patients with good postsurgical outcomes (Engel I) and 49 with poor outcomes (Engel II-IV). The GNN predictions demonstrated a significantly better (P < 0.05, Mann-Whitney-U test) area under the precision-recall curve in patients with good outcomes (area under the precision-recall curve: 0.69) compared with those with poor outcomes (area under the precision-recall curve: 0.33), indicating that the model captures clinically relevant targets in successful cases. In patients with poor outcomes, the graph neural network proposed alternative intervention sites that diverged from the original clinical plans, highlighting its potential to identify alternative therapeutic targets. We show that topology-aware GNNs significantly outperformed (P < 0.05, Wilcoxon signed-rank test) traditional neural networks while using the same intracranial EEG features, emphasizing the importance of incorporating implantation topology into predictive models. These findings uncover the potential of GNNs to automatically suggest targets for epilepsy surgery, which can assist the clinical team during the planning process.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30210 - Clinical neurology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Brain communications

ISSN

2632-1297

e-ISSN

2632-1297

Volume of the periodical

7

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

fcaf140

UT code for WoS article

001485240700001

EID of the result in the Scopus database

2-s2.0-105004987666