Clinical validation of magnetoencephalography network analysis for presurgical epilepsy evaluation
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Read the article on pubmed.ncbi.nlm.nih.gov ↗Article · Aug 2022 · 1 min read
In brief
In brief
This validation study demonstrates that MEG network analysis using eigenvector centrality to identify primary hubs localizes seizure onset zones more accurately than traditional equivalent current dipole methods when compared to stereotactic-EEG findings. The research shows spatially restricted connectivity patterns correlate with better surgical outcomes, supporting MEG network analysis as a valuable non-invasive tool for presurgical epilepsy evaluation.
Written by the GCMD Library team from the article.
Objective: To clinically validate the connectivity-based magnetoencephalography (MEG) analyses to identify seizure onset zone (SOZ) with comparing to equivalent current dipole (ECD). Methods: The ECD cluster was quantitatively analyzed by calculating the centroid of the cluster and maximum distance (the largest distance between all dipoles). The "primary hub" was determined by the highest eigencentrality. The distribution of nodes in the top 5% of eigenvector centrality values was quantified by generating the convex hull between each node. Results: Thirty-one patients who underwent MEG, stereotactic-EEG, and focal surgery were included. The primary hub was significantly closer to the sEEG-defined SOZ compared to ECD (p = 0.009). The seizure freedom positive and negative predictive values of complete ECD cluster and primary hub resections did not significantly differ, although complete resection of the primary hub showed slightly better negative predictive value (ECD: 50.0% NPV, hub: 64.7% NPV). Both quantitative ECD and functional connectivity analyses suggested that spatially restricted dipole distributions and higher connectivity in a smaller region correlate with better seizure outcomes. Conclusions: Our findings suggest that MEG network analysis could be a valuable complement to the ECD methods. Significance: The results of this study are an important step towards using non-invasive neurophysiologic recordings to accurately define the epileptic network.
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