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Stereoelectroencephalography Versus Subdural Electrodes for Localization of the Epileptogenic Zone: What Is the Evidence?

ABSTRACT: Accurate and safe localization of epileptic foci is the crux of surgical therapy for focal epilepsy. As an initial evaluation, patients with drug-resistant epilepsy often undergo evaluation by noninvasive methods to identify the epileptic focus (i.e., the epileptogenic zone (EZ)). When there is incongruence of noninvasive neuroimaging, electroencephalographic, and clinical data, direct intracranial recordings of the brain are often necessary to delineate the EZ and determine the best course of treatment. Stereoelectroencephalography (SEEG) and subdural electrodes (SDEs) are the 2 most common methods for recording directly from the cortex to delineate the EZ. For the past several decades, SEEG and SDEs have been used almost exclusively in specific geographic regions (i.e., France and Italy for stereo-EEG and elsewhere for SDEs) for virtually the same indications. In the last decade, however, stereo-EEG has started to spread from select centers in Europe to many locations worldwide. Nevertheless, it is still not the preferred method for invasive localization of the EZ at many centers that continue to employ SDEs exclusively. Despite the increased dissemination of the SEEG method throughout the globe, important questions remain unanswered. Which method (SEEG or SDEs) is superior for identification of the EZ and does it depend on the etiology of epilepsy? Which technique is safer and does this hold for all patient populations? Should these 2 methods have equivalent indications or be used selectively for different focal epilepsies? In this review, we seek to address these questions using current invasive monitoring literature. Available meta-analyses of observational data suggest that SEEG is safer than SDEs, but it is less clear from available data which method is more accurate at delineating the EZ.

SUBMITTER: Katz JS

PROVIDER: S-EPMC6361059 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Stereoelectroencephalography Versus Subdural Electrodes for Localization of the Epileptogenic Zone: What Is the Evidence?

Katz Joel S JS Abel Taylor J TJ

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20190101 1

Accurate and safe localization of epileptic foci is the crux of surgical therapy for focal epilepsy. As an initial evaluation, patients with drug-resistant epilepsy often undergo evaluation by noninvasive methods to identify the epileptic focus (i.e., the epileptogenic zone (EZ)). When there is incongruence of noninvasive neuroimaging, electroencephalographic, and clinical data, direct intracranial recordings of the brain are often necessary to delineate the EZ and determine the best course of t ...[more]

PMID: 30652253

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Project description:Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)-a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ.

Project description:Background and purposePatients with MR imaging-negative epilepsy could have subtle FCD. Our aim was to determine if structural changes could be identified by using DTI in children with intractable epilepsy, from MR imaging-visible FCD and MR imaging-negative localization-related epilepsy, that were concordant with the epileptogenic zone as defined by using the MEG dipole cluster.Materials and methodsEight children with MR imaging-visible FCD and 16 with MR imaging-negative epilepsy underwent DTI and MEG. Twenty-six age-matched healthy children underwent DTI. Analysis was performed on controls across individual patients. Agreement between the location of DTI abnormalities and FCD and MEG dipole clusters was assessed.ResultsIn patients with MR imaging-visible FCD, abnormal FA, MD, λ(1), λ(2), and λ(3) were lobar concordant with the MEG dipole cluster in 4/8 (50.0%), 5/8 (62.5%), 3/8 (37.5%), 6/8 (75.0%), and 5/8 (62.5%), respectively. In patients with MR imaging-visible FCD, abnormal FA, MD, λ(1), λ(2), and λ(3) overlapped the x-, y-, and z-axes of the MEG dipole cluster in 1/8 (12.5%), 4/8 (50%), 4/8 (50%), 6/8 (75%), and 4/8 (50%), respectively, and with FCD in 1/8 (12.5%), 3/8 (37.5%), 0/8 (0%), 3/8 (37.5%), and 1/8 (12.5%), respectively. In patients with MR imaging-negative epilepsy, abnormal FA, MD, λ(1), λ(2), and λ(3) were lobar-concordant with the MEG dipole cluster in 11/16 (68.8%), 11/16 (68.8%), 8/16 (50.0%), 10/16 (62.5%), and 10/16 (62.5%), respectively, and overlapped the x-, y-, and z-axes of the MEG dipole cluster in 9/16 (56.3%), 10/16 (62.5%), 8/16 (50%), 8/16 (50%), and 8/16 (50%), respectively. There was no significant difference between abnormal DTI lobar concordance with the MEG dipole cluster in patients with MR imaging-visible FCD and MR imaging-negative epilepsy.ConclusionsWhite matter changes can be detected with DTI in children with MR imaging-visible FCD and MR imaging-negative epilepsy, which were concordant with the epileptogenic zone in more than half of the patients.

Project description:Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and thus the need of modeling the underlying brain dynamics for regularizations. Advances in generative models provide opportunities for more accurate and realistic source modeling that could offer an alternative approach to ESI for modeling the underlying brain dynamics beyond equivalent physical source models. However, it is not straightforward to explicitly formulate the knowledge arising from these generative models within the conventional ESI framework. Here we investigate a novel source imaging framework based on mesoscale neuronal modeling and deep learning (DL) that can learn the sensor-source mapping relationship directly from MEG data for ESI. Two DL-based ESI models were trained based on data generated by neural mass models and either generic or personalized head models. The robustness of the two DL models was evaluated by systematic computer simulations and clinical validation in a cohort of 29 drug-resistant focal epilepsy patients who underwent intracranial EEG (iEEG) evaluation or surgical resection. Results estimated from pre-operative MEG interictal spikes were quantified using the overlap with resection regions and the distance to the seizure-onset zone (SOZ) defined by iEEG recordings. The DL-based ESI provided robust results when no personalized head geometry is considered, reaching a spatial dispersion of 21.90 ± 19.03 mm, sublobar concordance of 83 %, and sublobar sensitivity and specificity of 66 and 97 % respectively. When using personalized head geometry derived from individual patients' MRI in the training data, personalized DL-based ESI model can further improve the performance and reached a spatial dispersion of 8.19 ± 8.14 mm, sublobar concordance of 93 %, and sublobar sensitivity and specificity of 77 and 99 % respectively. When compared to the SOZ, the localization error of the personalized approach is 15.78 ± 5.54 mm, outperforming the conventional benchmarks. This work demonstrates that combining generative models and deep learning enables an accurate and robust imaging of epileptogenic zone from MEG recordings with strong sublobar precision, suggesting its added value to enhancing MEG source localization and imaging, and to epilepsy source localization and other clinical applications.

Project description:Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom. Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67-60.22%) for SVM and 60.34% (59.98-60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08-45.45%) for SVM and 49.03% (47.25-50.82%) for random forest]. Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.

Dataset Information

Stereoelectroencephalography Versus Subdural Electrodes for Localization of the Epileptogenic Zone: What Is the Evidence?

Publications

Stereoelectroencephalography Versus Subdural Electrodes for Localization of the Epileptogenic Zone: What Is the Evidence?

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