ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- A Systematic Review of Aperiodic Neural Activity in Clinical Investigations.The European journal of neuroscience · 2025Pooled it
- Bridging computational and clinical strategies for presurgical identification of epileptogenic networks.Epilepsia open · 2026Article
- Glymphatic dysfunction couples with cortical excitation-inhibition imbalance in epilepsy: Evidence from Rasmussen encephalitis.Epilepsia · 2026Article
- Thalamocortical network dynamics in focal epilepsy: SEEG investigation.bioRxiv : the preprint server for biology · 2026Article
- Utility of Interictal Data in Guiding Pediatric Epilepsy Surgery.Pediatric neurosurgery · 2026Review
- Locating the epileptogenic zone for drug-resistant epilepsy through neuroelectrophysiological brain network topology.Frontiers in neuroscience · 2026Article
- Aperiodic Activity Reflects Pathologic Waveform Shapes in Focal Epilepsy.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Seizure onset zone (SoZ) identification from interictal intracranial electroencephalogram.Scientific reports · 2025Article
- Differentiation of tumor versus peritumoral cortex in gliomas by intraoperative electrocorticography.Neuro-oncology · 2025Article
- Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.Epilepsia · 2025Article
- γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.Military Medical Research · 2025Review
- Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning.Scientific reports · 2025Article
- Non-Invasive Localization of Epileptogenic Zone in Drug-Resistant Epilepsy Based on Time-Frequency Analysis and VGG Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2025Article
- Advancements in Surgical Therapies for Drug-Resistant Epilepsy: A Paradigm Shift towards Precision Care.Neurology and therapy · 2025Review
- Robust interpolation of EEG/MEG sensor time-series via electromagnetic source imaging.Journal of neural engineering · 2025Article
- Preictal connectivity dynamics: Exploring inflow and outflow in iEEG networks.Frontiers in network physiology · 2025Article
- Source Causal Connectivity Noninvasively Predicting Surgical Outcomes of Drug-Refractory Epilepsy.CNS neuroscience & therapeutics · 2025Article
- Interictal neural fragility predicts seizure onset zone and surgical outcomes in drug-resistant epilepsy.PeerJ · 2025Article
- Directionality of neural activity in and out of the seizure onset zone in focal epilepsy.Network neuroscience (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.
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