Predictive value of magnetoencephalography in guiding the intracranial implant strategy for intractable epilepsy

Adrish AnandDepartments of Neurosurgery and

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John F. MagnottiDepartment of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania

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David N. SmithDepartments of Neurosurgery and

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Ron GadotDepartments of Neurosurgery and

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Ricardo A. NajeraDepartments of Neurosurgery and

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Mohamed I. R. HegazyNeurology, Baylor College of Medicine, Houston, Texas; and

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Jay R. GavvalaNeurology, Baylor College of Medicine, Houston, Texas; and

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Ben ShoftyDepartments of Neurosurgery and

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Sameer A. ShethDepartments of Neurosurgery and

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OBJECTIVE

Magnetoencephalography (MEG) is a useful component of the presurgical evaluation of patients with epilepsy. Due to its high spatiotemporal resolution, MEG often provides additional information to the clinician when forming hypotheses about the epileptogenic zone (EZ). Because of the increasing utilization of stereo-electroencephalography (sEEG), MEG clusters are used to guide sEEG electrode targeting with increasing frequency. However, there are no predefined features of an MEG cluster that predict ictal activity. This study aims to determine which MEG cluster characteristics are predictive of the EZ.

METHODS

The authors retrospectively analyzed all patients who had an MEG study (2017–2021) and underwent subsequent sEEG evaluation. MEG dipoles and sEEG electrodes were reconstructed in the same coordinate space to calculate overlap among individual contacts on electrodes and MEG clusters. MEG cluster features—including number of dipoles, proximity, angle, density, magnitude, confidence parameters, and brain region—were used to predict ictal activity in sEEG. Logistic regression was used to identify important cluster features and to train a binary classifier to predict ictal activity.

RESULTS

Across 40 included patients, 196 electrodes (42.2%) sampled MEG clusters. Electrodes that sampled MEG clusters had higher rates of ictal and interictal activity than those that did not sample MEG clusters (ictal 68.4% vs 39.8%, p < 0.001; interictal 71.9% vs 44.6%, p < 0.001). Logistic regression revealed that the number of dipoles (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.04–1.14, t = 3.43) and confidence volume (OR 0.02, 95% CI 0.00–0.86, t = −2.032) were predictive of ictal activity. This model was predictive of ictal activity with 77.3% accuracy (sensitivity = 80%, specificity = 74%, C-statistic = 0.81). Using only the number of dipoles had a predictive accuracy of 75%, whereas a threshold between 14 and 17 dipoles in a cluster detected ictal activity with 75.9%–85.2% sensitivity.

CONCLUSIONS

MEG clusters with approximately 14 or more dipoles are strong predictors of ictal activity and may be useful in the preoperative planning of sEEG implantation.

ABBREVIATIONS

CI = confidence interval; DBS = deep brain stimulation; EEG = electroencephalography; EZ = epileptogenic zone; MEG = magnetoencephalography; OR = odds ratio; PPV = positive predictive value; RNS = responsive neurostimulation; ROC = receiver operating characteristic; SECD = single moving equivalent current dipole model; sEEG = stereo-EEG; SOZ = seizure onset zone; SVM = support vector machine; vEEG = video-EEG.
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Illustration from Xu et al. (pp 1418–1430). With permission from Juan Carlos Fernandez-Miranda and The Neurosurgical Atlas by Aaron Cohen-Gadol.

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