ArticleFrontiers in neuroscience2017
Improved Localization of Seizure Onset Zones Using Spatiotemporal Constraints and Time-Varying Source Connectivity.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 26 citations in OpenAlex.
- Low-density scalp electrical source imaging of the ictal onset zone network using source coherence maps.Frontiers in neurology · 2024Article
- Electroencephalography derived connectivity informing epilepsy surgical planning: Towards clinical applications and future perspectives.NeuroImage. Clinical · 2024Review
- Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram.Cognitive neurodynamics · 2023Article
- Repeated hippocampal seizures lead to brain-wide reorganization of circuits and seizure propagation pathways.Neuron · 2022Article
- Source localization of epileptic spikes using Multiple Sparse Priors.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2021Article
- Auto-Weighted Multi-View Discriminative Metric Learning Method With Fisher Discriminative and Global Structure Constraints for Epilepsy EEG Signal Classification.Frontiers in neuroscience · 2020Article
- Influence of Patient-Specific Head Modeling on EEG Source Imaging.Computational and mathematical methods in medicine · 2020Article
- An Intelligence EEG Signal Recognition Method via Noise Insensitive TSK Fuzzy System Based on Interclass Competitive Learning.Frontiers in neuroscience · 2020Article
- Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity.Frontiers in neurology · 2019Review
- Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed.Frontiers in neuroscience · 2018Article
- Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study.Frontiers in integrative neuroscience · 2018Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Presurgical evaluation of brain neural activity is commonly carried out in refractory epilepsy patients to delineate as accurately as possible the seizure onset zone (SOZ) before epilepsy surgery. In practice, any subjective interpretation of electroencephalographic (EEG) recordings is hindered mainly because of the highly stochastic behavior of the epileptic activity. We propose a new method for dynamic source connectivity analysis that aims to accurately localize the seizure onset zones by explicitly including temporal, spectral, and spatial information of the brain neural activity extracted from EEG recordings. In particular, we encode the source nonstationarities in three critical stages of processing: Inverse problem solution, estimation of the time courses extracted from the regions of interest, and connectivity assessment. With the aim to correctly encode all temporal dynamics of the seizure-related neural network, a directed functional connectivity measure is employed to quantify the information flow variations over the time window of interest. Obtained results on simulated and real EEG data confirm that the proposed approach improves the accuracy of SOZ localization.
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