The sensitivity and significance of lateralized interictal slow activity on magnetoencephalography in focal epilepsy

Englot, Dario J.; Nagarajan, Srikantan S.; Wang, Doris D.; Rolston, John D.; Mizuiri, Danielle; Honma, Susanne; Mantle, Mary; Tarapore, Phiroz E.; Knowlton, Robert C.; Chang, Edward F.; Kirsch, Heidi E.

doi:10.1016/j.eplepsyres.2016.01.009

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…Previous studies have shown that dipole modeling of high density EEG and MEG delta activity is related to lesion location on MRI in the majority of patients with lesional epilepsy ( Baayen et al, 2003 ). Asymmetric slowing in MEG was found to reliably lateralize the resected area, although localizing value was limited ( Englot et al, 2016a ; Ishibashi et al, 2002 ). Our observations from MEG-VE and SEEG are in line with these results, and in addition provide localizing information.…”

Section: Discussionmentioning

confidence: 99%

Virtual localization of the seizure onset zone: Using non-invasive MEG virtual electrodes at stereo-EEG electrode locations in refractory epilepsy patients

Juárez-Martinez¹,

Nissen²,

Idema

et al. 2018

NeuroImage: Clinical

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In some patients with medically refractory epilepsy, EEG with intracerebrally placed electrodes (stereo-electroencephalography, SEEG) is needed to locate the seizure onset zone (SOZ) for successful epilepsy surgery. SEEG has limitations and entails risk of complications because of its invasive character. Non-invasive magnetoencephalography virtual electrodes (MEG-VEs) may overcome SEEG limitations and optimize electrode placement making SOZ localization safer. Our purpose was to assess whether interictal activity measured by MEG-VEs and SEEG at identical anatomical locations were comparable, and whether MEG-VEs activity properties could determine the location of a later resected brain area (RA) as an approximation of the SOZ. We analyzed data from nine patients who underwent MEG and SEEG evaluation, and surgery for medically refractory epilepsy. MEG activity was retrospectively reconstructed using beamforming to obtain VEs at the anatomical locations corresponding to those of SEEG electrodes. Spectral, functional connectivity and functional network properties were obtained for both, MEG-VEs and SEEG time series, and their correlation and reliability were established. Based on these properties, the approximation of the SOZ was characterized by the differences between RA and non-RA (NRA). We found significant positive correlation and reliability between MEG-VEs and SEEG spectral measures (particularly in delta [0.5–4 Hz], alpha2 [10–13 Hz], and beta [13–30 Hz] bands) and broadband functional connectivity. Both modalities showed significantly slower activity and a tendency towards increased broadband functional connectivity in the RA compared to the NRA. Our findings show that spectral and functional connectivity properties of non-invasively obtained MEG-VEs match those of invasive SEEG recordings, and can characterize the SOZ. This suggests that MEG-VEs might be used for optimal SEEG planning and fewer depth electrode implantations, making the localization of the SOZ safer and more successful.

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Section: Discussionmentioning

confidence: 99%

Virtual localization of the seizure onset zone: Using non-invasive MEG virtual electrodes at stereo-EEG electrode locations in refractory epilepsy patients

Juárez-Martinez¹,

Nissen²,

Idema

et al. 2018

NeuroImage: Clinical

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“…MEG data more closely reflect neural signals, but source localization is challenging with deep structures . Although scalp EEG is also widely available and noninvasive, connectivity analyses are limited by low spatial resolution and signal degradation by the scalp and skull …”

Section: Connectivity Analysis Modalities and Methodsmentioning

confidence: 99%

“…16 Although scalp EEG is also widely available and noninvasive, connectivity analyses are limited by low spatial resolution and signal degradation by the scalp and skull. 17 Analysis techniques in functional connectivity studies are often categorized as either model based or data driven. Table 2 summarizes some of the most common modelbased and data-driven methods utilized.…”

Section: Key Pointsmentioning

confidence: 99%

Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings

2016

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Epilepsy is among the most common brain network disorders, and it is associated with substantial morbidity and increased mortality. While focal epilepsy was traditionally thought of as a regional brain disorder, growing evidence has demonstrated widespread network alterations in this disorder which extend beyond the epileptogenic zone from which seizures originate. The goal of this review is to summarize recent investigations examining functional and structural connectivity alterations in focal epilepsy, including neuroimaging and electrophysiology studies utilizing model-based or data-driven analysis methods. A significant subset of studies in both mesial temporal lobe epilepsy and focal neocortical epilepsy have demonstrated patterns of increased connectivity related to the epileptogenic zone, coupled with decreased connectivity in widespread distal networks. Connectivity patterns appear to be related to the duration and severity of disease, suggesting progressive connectivity reorganization in the setting of recurrent seizures over time. Global resting-state connectivity disturbances in focal epilepsy have been linked to neurocognitive problems, including memory and language disturbances. While it is possible that increased connectivity in a particular brain region may enhance the propensity for seizure generation, it is not clear if global reductions in connectivity represent the damaging consequences of recurrent seizures, or an adaptive mechanism to prevent seizure propagation away from the epileptogenic zone. Overall, studying the connectome in focal epilepsy is a critical endeavor which may lead to improved strategies for epileptogenic zone localization, surgical outcome prediction, and a better understanding of the neuropsychological implications of recurrent seizures.

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“…Thus, method that can analyze the effects of different frequency bands, especially the frequency band in which epileptiform discharges, was generated. Studies have shown that asymmetric and slow activity of the delta band (1-4 Hz) can reliably lateralize to the epileptogenic hemisphere [14,[60][61][62]. Indeed, significant differences in delta band activity were found between TLE patients and controls in network analyses [31,63,64].…”

Section: Complexitymentioning

confidence: 99%

“…During the last decade, MEG has become increasingly available as a noninvasive, reliable, fast, and patient-friendly technique for recording brain activity [11][12][13][14]. MEG has been widely applied for studying epilepsy disorders, particularly the localization of pathological brain activity or lesions in candidates for epilepsy surgery [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Chen

et al. 2018

Complexity

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Correct lateralization of temporal lobe epilepsy (TLE) is critical for improving surgical outcomes. As a relatively new noninvasive clinical recording system, magnetoencephalography (MEG) has rarely been applied for determining lateralization of unilateral TLE. Here we propose a framework for using resting-state brain-network features and support vector machine (SVM) for TLE lateralization based on MEG. We recruited 15 patients with left TLE, 15 patients with right TLE, and 15 age-and sex-matched healthy controls. The lateralization problem was then transferred into a series of binary classification problems, including left TLE versus healthy control, right TLE versus healthy control, and left TLE versus right TLE. Brain-network features were extracted for each participant using three network metrics (nodal degree, betweenness centrality, and nodal efficiency). A radial basis function kernel SVM (RBF-SVM) was employed as the classifier. The leave-one-subject-out cross-validation strategy was used to test the ability of this approach to overcome individual differences. The results revealed that the nodal degree performed best for left TLE versus healthy control and right TLE versus healthy control, with accuracy of 80.76% and 75.00%, respectively. Betweenness centrality performed best for left TLE versus right TLE with an accuracy of 88.10%. The proposed approach demonstrated that MEG is a good candidate for solving the lateralization problem in unilateral TLE using various brain-network features.

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The sensitivity and significance of lateralized interictal slow activity on magnetoencephalography in focal epilepsy

Cited by 12 publications

References 33 publications

Virtual localization of the seizure onset zone: Using non-invasive MEG virtual electrodes at stereo-EEG electrode locations in refractory epilepsy patients

Virtual localization of the seizure onset zone: Using non-invasive MEG virtual electrodes at stereo-EEG electrode locations in refractory epilepsy patients

Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

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