A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction

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• dc.contributor.author Ivankovic, Karla
• dc.contributor.author Principe, Alessandro
• dc.contributor.author Montoya Gálvez, Justo
• dc.contributor.author Manubens-Gil, Linus, 1989-
• dc.contributor.author Zucca, Riccardo
• dc.contributor.author Villoslada, Pablo
• dc.contributor.author Dierssen, Mara
• dc.contributor.author Rocamora Zúñiga, Rodrigo Alberto
• dc.date.accessioned 2025-02-25T07:34:00Z
• dc.date.available 2025-02-25T07:34:00Z
• dc.date.issued 2025
• dc.description.abstract The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptogenic network (EN). However, EN dynamics is highly variable across patients, hindering the development of diagnostic biomarkers. Without relying on specific connectivity variables, we focused on a general hypothesis that the EN undergoes the greatest magnitude of connectivity change during seizure generation, compared to other brain networks. To test this hypothesis, we developed a novel method for quantifying connectivity change between network states and applied it to identify surgical resection areas. A network state was represented by random snapshots of connectivity within a defined time interval of an intracranial EEG recording. A binary classifier was applied to classify two network states. The classifier generalization performance estimated by cross-validation was employed as a continuous measure of connectivity change. The algorithm generated a network by iteratively adding nodes until the connectivity change magnitude decreased. The resulting network was compared to the surgical resection, and the overlap score was used to predict post-surgical outcomes. The framework was evaluated in a consecutive cohort of 21 patients with a post-surgical follow-up of minimum 3 years. The best overlap between connectivity change networks and resections was obtained at the transition from pre-seizure to seizure (surgical outcome prediction ROC-AUC=90.3 %). However, all patients except one were correctly classified when considering the most informative time intervals. Time intervals proportional to seizure length were more informative than the almost universally used fixed intervals. This study demonstrates that connectivity can be successfully classified with a machine learning analysis and provide information for distinguishing a separate epileptogenic functional network. In summary, the connectivity change analysis could accurately identify epileptogenic networks validated by surgery outcome classification. Connectivity change magnitude at seizure transition could potentially serve as an EN biomarker. The tool provided by this study may aid surgical decision-making.
• dc.description.sponsorship FLAG–ERA JTC 2017 Human Brain Project, CAUSALTOMICS (PCI2018–092860) (AP, KI). Fundación Tatiana - Proyectos en Neurociencia - Grant 2022–1514 (AP). 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) (KI). Emergent Cluster of the Human Brain (CECH) and the European Regional Development Fund (FEDER) 001-P-001682 (RZ). The lab of MD was supported by the Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (Grups consolidats 2017 SGR 926, 2017 SGR 138). Agencia Estatal de Investigación (PID2019–110755RB-I00/AEI/10.13039/501100011033) (MD). The European Union's Horizon 2020 Research and Innovation Program under grant agreement No 848077 (MD). NIH (Grant Number: 1R01EB 028159–01) (MD). The CRG acknowledges the support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Program/Generalitat de Catalunya. The CIBER of Rare Diseases (CIBERER) is an initiative of the ISCIII.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ivankovic K, Principe A, Montoya-Gálvez J, Manubens-Gil L, Zucca R, Villoslada P, et al. A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction. Neuroimage. 2025 Feb 1;306:120990. DOI: 10.1016/j.neuroimage.2024.120990
• dc.identifier.doi http://dx.doi.org/10.1016/j.neuroimage.2024.120990
• dc.identifier.issn 1053-8119
• dc.identifier.uri http://hdl.handle.net/10230/69731
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Neuroimage. 2025 Feb 1;306:120990
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/848077
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PCI2018–092860
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019–110755RB-I00
• dc.rights © 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Epilepsy surgery
• dc.subject.keyword Epileptogenic network
• dc.subject.keyword Functional connectivity
• dc.subject.keyword Machine learning
• dc.subject.keyword Outcome prediction
• dc.subject.keyword Seizure generation
• dc.title A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion