ArticleCNS neuroscience & therapeutics2025
Source Causal Connectivity Noninvasively Predicting Surgical Outcomes of Drug-Refractory Epilepsy.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Non-invasive preoperative prediction of surgical outcomes in drug-resistant epilepsy using seizure-related EEG network dynamics.Frontiers in neuroscience · 2026Article
- Locating the epileptogenic zone for drug-resistant epilepsy through neuroelectrophysiological brain network topology.Frontiers in neuroscience · 2026Article
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Authors and funding
6 authors.
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Abstract
aimsDrug-refractory epilepsy (DRE) refers to the failure of controlling seizures with adequate trials of two tolerated and appropriately chosen anti-seizure medications (ASMs). For patients with DRE, surgical intervention becomes the most effective and viable treatment, but its success rate is unsatisfactory at only approximately 50%. Predicting surgical outcomes in advance can provide additional guidance to clinicians. Despite the high accuracy of invasive methods, they inevitably carry the risk of post-operative infection and complications. Herein, to noninvasively predict surgical outcomes of DRE, we propose the "source causal connectivity" framework.
methodsIn this framework, sLORETA, an EEG source imaging technique, was first used to inversely reconstruct intracranial neuronal electrical activity. Then, full convergent cross mapping (FCCM), a robust causal measure was introduced to calculate the causal connectivity between remodeled neuronal signals within epileptogenic zones (EZs). After that, statistical tests were performed to find out if there was a significant difference between the successful and failed surgical groups. Finally, a model for surgical outcome prediction was developed by combining causal network features with machine learning.
resultsA total of 39 seizures with 205 ictal EEG segments were included in this prospective study. Experimental results exhibit that source causal connectivity in α-frequency band (8~13 Hz) gains the most significant differences between the surgical success and failure groups, with a p-value of 5.00e-05 and Cohen's d effect size of 0.68. All machine learning models can achieve an average accuracy of higher than 85%. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest accuracy of 90.73%, with a PPV of 87.91%, an NPV of 92.98%, a sensitivity of 90.91%, a specificity of 90.60%, and an F1-score of 89.39%.
conclusionOur results demonstrate that the source causal network of EZ is a reliable biomarker for predicting DRE surgical outcomes. The findings promote noninvasive precision medicine for DRE.
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