ArticleIEEE transactions on bio-medical engineering2023
DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity.
Article in IEEE transactions on bio-medical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Resting-State vs. Task-Based Functional Magnetic Resonance Imaging in Neurosurgical Planning: A Narrative Review of Clinical Applications.Biomedicines · 2026Review
- A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.Scientific data · 2026Article
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- A DEEP LEARNING FRAMEWORK TO CHARACTERIZE NOISY LABELS IN EPILEPTOGENIC ZONE LOCALIZATION USING FUNCTIONAL CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- A DEEP LEARNING FRAMEWORK TO LOCALIZE THE EPILEPTOGENIC ZONE FROM DYNAMIC FUNCTIONAL CONNECTIVITY USING A COMBINED GRAPH CONVOLUTIONAL AND TRANSFORMER NETWORK.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI.Frontiers in neurology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveEpileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data.
methodsOur network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds.
resultsWe validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort.
conclusionOur results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. SIGNIFICANCE: While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.
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