ArticleFrontiers in neuroimaging2022
Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy.
Article in Frontiers in neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Resting-state fMRI seizure onset localization meta-analysis: comparing rs-fMRI to other modalities including surgical outcomes.Frontiers in neuroimaging · 2024Pooled it
- Focused Ultrasound Ablation for Neurological Disorders.Biological psychiatry · 2026Review
- Advanced neuroimaging in pediatric epilepsy surgery: state of the art and future perspectives.Neuroradiology · 2026Review
- Automated, anatomy-based, heuristic post-processing reduces false positives and improves interpretability of deep learning intracranial aneurysm detection models.Scientific reports · 2025Article
- Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.Biomedical engineering online · 2025Article
- Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.Epilepsia · 2025Article
- Treatable brain network biomarkers in children in coma using task and resting-state functional MRI: a case series.Frontiers in neurology · 2023Article
- The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI.Frontiers in neurology · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Objective: Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE. Methods: EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset ( Results: EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening. Significance: Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.
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