ArticleEpilepsia2020
Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy.
Article in Epilepsia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- Pooled it
- Network reorganization of anterograde thalamic connectivity during non-periodic patterned deep brain stimulation.iScience · 2026Article
- Directed graph neural networks with partial directed coherence for seizure prediction and epileptogenic network characterization.iScience · 2026Article
- Bridging computational and clinical strategies for presurgical identification of epileptogenic networks.Epilepsia open · 2026Article
- Article
- Utility of Interictal Data in Guiding Pediatric Epilepsy Surgery.Pediatric neurosurgery · 2026Review
- Static and dynamic intracerebral signal analysis reveals protective networks against seizures in drug-resistant focal epilepsy.Brain communications · 2026Article
- Collapse of interictal suppressive networks permits seizure spread.Brain : a journal of neurology · 2025Article
- Dynamic excitation/inhibition balance preceding seizure onset and its link to functional and structural brain architecture.BMC medicine · 2025Article
- Spatiotemporal Dynamics of Language Information Representation within the Left Inferior Parietal Lobule Network: An Intracranial SEEG Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Multimodal quantitative analysis guides precise preoperative localization of epilepsy.Journal of neurology · 2025Review
- Very High-Frequency Oscillations in Mesial Temporal Lobe Epilepsy: Identifying the Epileptogenic Zone.Annals of clinical and translational neurology · 2025Article
- Frequency-band specific directed connectivity networks reveal functional disruptions and pathogenic patterns in temporal lobe epilepsy: a MEG study.Scientific reports · 2025Article
- Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study.Sensors (Basel, Switzerland) · 2025Article
- Dynamic reconfiguration of aperiodic brain activity supports cognitive functioning in epilepsy: A neural fingerprint identification.iScience · 2025Article
- Preictal connectivity dynamics: Exploring inflow and outflow in iEEG networks.Frontiers in network physiology · 2025Article
- Directionality of neural activity in and out of the seizure onset zone in focal epilepsy.Network neuroscience (Cambridge, Mass.) · 2025Article
- Spatiotemporal Neural Network for Sublexical Information Processing: An Intracranial SEEG Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy.Scientific reports · 2024Article
- Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network.Brain topography · 2024Article
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveIn patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity.
methodsIn 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity.
resultsIctogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE: Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
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