2026-04-17T17:03:46Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/715852025-10-23T15:41:21Zcom_2072_6col_2072_452952
RECERCAT
author
Ivankovic, Karla
author
Principe, Alessandro
author
Zucca, Riccardo
author
Dierssen, Mara
author
Rocamora Zúñiga, Rodrigo Alberto
2025-10-23T15:41:21Z
2025-10-23T15:41:21Z
2025-10-20T17:02:54Z2025-10-20T17:02:54Z20242025-10-20T17:02:54Z
http://hdl.handle.net/10230/71585
Background: The pre-surgical evaluation for drug-resistant epilepsy achieves seizure freedom in only 50-60% of patients. Efforts to identify quantitative intracranial EEG (qEEG) biomarkers of epileptogenicity are needed. This review summarizes and evaluates the design of qEEG studies, discusses barriers to biomarker adoption, and proposes refinements of qEEG study protocols. Methods: We included exploratory and prediction prognostic studies from MEDLINE and Scopus published between 2017 and 2023 that investigated qEEG markers for identifying the epileptogenic network as the surgical target. Cohort parameters, ground truth references, and analytical approaches were extracted. Results: Out of 1789 search results, 128 studies were included. The study designs were highly heterogeneous. Half of the studies included a non-consecutive cohort, with sample sizes ranging from 2 to 166 patients (median of 16). The most common minimum follow-up was one year, and the seizure onset zone was the most common ground truth. Prediction studies were heterogeneous in their analytical approaches, and only 25 studies validated the marker through post-surgical outcome prediction. Outcome prediction performance decreased in larger cohorts. Conversely, longer follow-up periods correlated with higher prediction accuracy, and connectivity-based approaches yielded better predictions. The data and code were available in only 9% of studies. Conclusions: To enhance the validation qEEG markers, we propose standardizing study designs to resemble clinical trials. This includes using a consecutive cohort with long-term follow-up, validating against surgical resection as ground truth, and evaluating markers through post-surgical outcome prediction. These considerations would improve the reliability and clinical adoption of qEEG markers.K.I. is supported by Ajuts Destinats a Universitats, centres de recerca i fundacions hospitalàries per contractar personal investigador novell per a l'any 2022 (FI-DGR Grant Number: 14802). A.P. is supported by Fundación Tatiana-Proyectos en Neurociencia (Grant 2022-1514). R.Z. is supported by Neurotech-EU (Grant agreement ID: 101035817). M.D. is funded by the Agencia Estatal de Investigación (PID2022-141900OB-I00 INTO-DS), H2020 European Union's Horizon 2020 research and innovation programme under grant agreement No. 848077 and 899986, Jerôme Lejeune Foundation #2002. The APC was funded by Fundación Tatiana¿Proyectos en Neurociencia (Grant 20.22-15.14).
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
Biomarkers
Epilepsy surgery
Invasive EEG
Outcome prediction
Pharmacoresistant epilepsy
Quantitative EEG
Methods for identifying epilepsy surgery targets using invasive EEG: a systematic review
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion