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

User-defined virtual sensors: A new solution to the problem of temporal plus epilepsy sources

OBJECTIVE

The most common medically resistant epilepsy (MRE) involves the temporal lobe (TLE), and children designated as temporal plus epilepsy (TLE+) have a five-times increased risk of postoperative surgical failure. This retrospective, blinded, cross-sectional study aimed to correlate visual and computational analyses of magnetoencephalography (MEG) virtual sensor waveforms with surgical outcome and epilepsy classification (TLE and TLE+).

METHODS

Patients with MRE who underwent MEG and iEEG monitoring and had at least 1 year of postsurgical follow-up were included in this retrospective analysis. User-defined virtual sensor (UDvs) beamforming was completed with virtual sensors placed manually and symmetrically in the bilateral amygdalohippocampi, inferior/middle/superior temporal gyri, insula, suprasylvian operculum, orbitofrontal cortex, and temporoparieto-occipital junction. Additionally, MEG effective connectivity was computed and quantified using eigenvector centrality (EC) to identify hub regions. More conventional MEG methods (equivalent current dipole [ECD], standardized low-resolution brain electromagnetic tomography, synthetic aperture magnetometry beamformer), UDvs beamformer, and EC hubs were compared to iEEG.

RESULTS

Eighty patients (38 female, 42 male) with MRE (mean age = 11.3 ± 6.2 years, range = 1.0-31.5) were identified and included. Twenty-five patients (31.3%) were classified as TLE, whereas 55 (68.8%) were TLE+. When modeling the association between MEG method, iEEG, and postoperative surgical outcome (odds of a worse [International League Against Epilepsy (ILAE) class > 2] outcome), a significant result was seen only for UDvs beamformer (odds ratio [OR] = 1.22, 95% confidence interval [CI] = 1.01-1.48). Likewise, when the relationship between MEG method, iEEG, and classification (TLE and TLE+) was modeled, only UDvs beamformer had a significant association (OR = 1.47, 95% CI = 1.13-1.92). When modeling the association between EC hub location and resection/ablation to postoperative surgical outcome (odds of a good [ILAE 1-2] outcome), a significant association was seen (OR = 1.22, 95% CI = 1.05-1.43).

SIGNIFICANCE

This study demonstrates a concordance between UDvs beamforming and iEEG that is related to both postsurgical seizure outcome and presurgical classification of epilepsy (TLE and TLE+). UDvs beamforming could be a complementary approach to the well-established ECD, improving invasive electrode and surgical resection planning for patients undergoing epilepsy surgery evaluations and treatments.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy

PURPOSE

To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.

METHODS

MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.

RESULTS

Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.

CONCLUSION

HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1-70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient's MRI without requiring long-term iEEG inspection.

Non-invasive mapping of epileptogenic networks predicts surgical outcome

Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.

Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG

Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.

Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings

The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.

Virtual implantation using conventional scalp EEG delineates seizure onset and predicts surgical outcome in children with epilepsy

OBJECTIVE

Delineation of the seizure onset zone (SOZ) is required in children with drug resistant epilepsy (DRE) undergoing neurosurgery. Intracranial EEG (icEEG) serves as gold standard but has limitations. Here, we examine the utility of virtual implantation with electrical source imaging (ESI) on ictal scalp EEG for mapping the SOZ and predict surgical outcome.

METHODS

We retrospectively analyzed EEG data from 35 children with DRE who underwent surgery and dichotomized into seizure-free (SF) and non-seizure-free (NSF). We estimated virtual sensors (VSs) at brain locations that matched icEEG implantation and compared ictal patterns at VSs vs icEEG. We calculated the agreement between VSs SOZ and clinically defined SOZ and built receiver operating characteristic (ROC) curves to test whether it predicted outcome.

RESULTS

Twenty-one patients were SF after surgery. Moderate agreement between virtual and icEEG patterns was observed (kappa = 0.45, p < 0.001). Virtual SOZ agreement with clinically defined SOZ was higher in SF vs NSF patients (66.6% vs 41.6%, p = 0.01). Anatomical concordance of virtual SOZ with clinically defined SOZ predicted outcome (AUC = 0.73; 95% CI: 0.57-0.89; sensitivity = 66.7%; specificity = 78.6%; accuracy = 71.4%).

CONCLUSIONS

Virtual implantation on ictal scalp EEG can approximate the SOZ and predict outcome.

SIGNIFICANCE

SOZ mapping with VSs may contribute to tailoring icEEG implantation and predict outcome.

Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach

There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.

Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements

Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients' interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.

Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation

Children with medically refractory epilepsy (MRE) require resective neurosurgery to achieve seizure freedom, whose success depends on accurate delineation of the epileptogenic zone (EZ). Functional connectivity (FC) can assess the extent of epileptic brain networks since intracranial EEG (icEEG) studies have shown its link to the EZ and predictive value for surgical outcome in these patients. Here, we propose a new noninvasive method based on magnetoencephalography (MEG) and high-density (HD-EEG) data that estimates FC metrics at the source level through an "implantation" of virtual sensors (VSs). We analyzed MEG, HD-EEG, and icEEG data from eight children with MRE who underwent surgery having good outcome and performed source localization (beamformer) on noninvasive data to build VSs at the icEEG electrode locations. We analyzed data with and without Interictal Epileptiform Discharges (IEDs) in different frequency bands, and computed the following FC matrices: Amplitude Envelope Correlation (AEC), Correlation (CORR), and Phase Locking Value (PLV). Each matrix was used to generate a graph using Minimum Spanning Tree (MST), and for each node (i.e., each sensor) we computed four centrality measures: betweenness, closeness, degree, and eigenvector. We tested the reliability of VSs measures with respect to icEEG (regarded as benchmark) via linear correlation, and compared FC values inside vs. outside resection. We observed higher FC inside than outside resection (p<0.05) for AEC [alpha (8-12 Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data with IEDs and AEC theta (4-8 Hz) on data without IEDs for icEEG, AEC broadband (1-50 Hz) on data without IEDs for MEG-VSs, as well as for all centrality measures of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG and VSs metrics presented high correlation (0.6-0.9, p<0.05). Our data support the notion that the proposed method can potentially replicate the icEEG ability to map the epileptogenic network in children with MRE.Clinical Relevance - The estimation of FC with noninvasive techniques, such as MEG and HD-EEG, via VSs is a promising tool that would help the presurgical evaluation by delineating the EZ without waiting for a seizure to occur, and potentially improve the surgical outcome of patients with MRE undergoing surgery.

Localizing the Epileptogenic Zone with Novel Biomarkers

Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.

Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network

BACKGROUND

Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions.

METHODS

In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network.

RESULTS

Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly.

CONCLUSION

In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.

Changes in the Functional Brain Network of Children Undergoing Repeated Epilepsy Surgery: An EEG Source Connectivity Study

About 30% of children with drug-resistant epilepsy (DRE) continue to have seizures after epilepsy surgery. Since epilepsy is increasingly conceptualized as a network disorder, understanding how brain regions interact may be critical for planning re-operation in these patients. We aimed to estimate functional brain connectivity using scalp EEG and its evolution over time in patients who had repeated surgery (RS-group, n = 9) and patients who had one successful surgery (seizure-free, SF-group, n = 12). We analyzed EEGs without epileptiform activity at varying time points (before and after each surgery). We estimated functional connectivity between cortical regions and their relative centrality within the network. We compared the pre- and post-surgical centrality of all the non-resected (untouched) regions (far or adjacent to resection) for each group (using the Wilcoxon signed rank test). In alpha, theta, and beta frequency bands, the post-surgical centrality of the untouched cortical regions increased in the SF group (p < 0.001) whereas they decreased (p < 0.05) or did not change (p > 0.05) in the RS group after failed surgeries; when re-operation was successful, the post-surgical centrality of far regions increased (p < 0.05). Our data suggest that removal of the epileptogenic focus in children with DRE leads to a gain in the network centrality of the untouched areas. In contrast, unaltered or decreased connectivity is seen when seizures persist after surgery.

Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data

Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra‐hemispheric networks of healthy controls (HCs) and patients with left‐ or right‐hemispheric temporal lobe or temporal plus extra‐temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra‐hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve‐Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network‐based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra‐hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra‐hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.

Localizing non-epileptiform abnormal brain function in children using high density EEG: Electric Source Imaging of focal slowing

BACKGROUND

Electric Source Imaging (ESI) of interictal epileptiform discharges (IED) is increasingly validated for localizing epileptic activity. In children, IED can be absent or multifocal even in cases of a focal epileptogenic zone and additional electrophysiological markers are needed. Here, we investigated ESI of pathological focal slowing (FS) recorded on EEG as a new localizing marker in children with drug-resistant epilepsy.

METHODS

We selected 15 children (median: 12; range: 4-18yrs), with high-density EEG (hdEEG), presurgical evaluation and surgical resection. One patient had a non-lesional MRI. ESI of patient-specific focal slow activity was performed (distributed linear inverse solution and individual head model). The maximal average power in the band of interest was considered as the source of focal slowing (ESI-FS). The Euclidian distance between ESI-FS and the resection (5 mm margin) was compared to the localization of maximal ESI of interictal epileptiform discharges (ESI-IED), interictal FDG-PET and ictal SPECT/SISCOM.

RESULTS

In 9/15 patients (60%), ESI of focal slowing (ESI-FS) was inside or ≤5 mm from resection margins. The remaining 6/15 cases had distances ≤15 mm. In 9/15 patients with interictal spikes, the ESI-IED was concordant with the resection. 6/15 patients with concordant ESI-FS showed also interictal concordant ESI of IED; in 3/15 patients, ESI-FS but not ESI-IED was concordant with the resection. In 10/15 patients, ESI-FS was concordant with MRI lesion and for ESI-IED this concordance was on 8/15 patients. Maximal hypometabolism and SISCOM were concordant with the resection for 7/15 and 7/12, respectively.

CONCLUSION

These findings suggest that "non-epileptiform" EEG activity, such as focal slowing, could be a complementary useful marker to localize the epileptogenic zone. ESI-FS may notably be applied in young patients without focal interictal spikes or multifocal spikes. This potential new marker of brain dysfunction has potential applications to other neurological disorders associated with slow EEG activity.

Presurgical epilepsy evaluation and epilepsy surgery

With a prevalence of 0.8 to 1.2%, epilepsy represents one of the most frequent chronic neurological disorders; 30 to 40% of patients suffer from drug-resistant epilepsy (that is, seizures cannot be controlled adequately with antiepileptic drugs). Epilepsy surgery represents a valuable treatment option for 10 to 50% of these patients. Epilepsy surgery aims to control seizures by resection of the epileptogenic tissue while avoiding neuropsychological and other neurological deficits by sparing essential brain areas. The most common histopathological findings in epilepsy surgery specimens are hippocampal sclerosis in adults and focal cortical dysplasia in children. Whereas presurgical evaluations and surgeries in patients with mesial temporal sclerosis and benign tumors recently decreased in most centers, non-lesional patients, patients requiring intracranial recordings, and neocortical resections increased. Recent developments in neurophysiological techniques (high-density electroencephalography [EEG], magnetoencephalography, electrical and magnetic source imaging, EEG-functional magnetic resonance imaging [EEG-fMRI], and recording of pathological high-frequency oscillations), structural magnetic resonance imaging (MRI) (ultra-high-field imaging at 7 Tesla, novel imaging acquisition protocols, and advanced image analysis [post-processing] techniques), functional imaging (positron emission tomography and single-photon emission computed tomography co-registered to MRI), and fMRI significantly improved non-invasive presurgical evaluation and have opened the option of epilepsy surgery to patients previously not considered surgical candidates. Technical improvements of resective surgery techniques facilitate successful and safe operations in highly delicate brain areas like the perisylvian area in operculoinsular epilepsy. Novel less-invasive surgical techniques include stereotactic radiosurgery, MR-guided laser interstitial thermal therapy, and stereotactic intracerebral EEG-guided radiofrequency thermocoagulation.

Utility of stereo-electroencephalography recording guided by magnetoencephalography in the surgical treatment of epilepsy patients with negative magnetic resonance imaging results

Objective: It is challenging for neurosurgeons to perform surgeries on patients without detectable structural lesions. Therefore, this retrospective study aimed to explore the outcome of stereo-electroencephalography (SEEG) in suspicious areas guided by magnetoencephalography (MEG)-magnetic resonance imaging (MRI) reconstruction in MRI-negative epilepsy patients. Methods: This study included 47 patients with negative-MRI epilepsy. Seizure outcome at 24 months was assessed using a modified Engel's classification. Accordingly, class I and II were considered favorable outcomes, whereas classes III and IV were unfavorable. Furthermore, patients were classified into a consistent group if the results of MEG and SEEG indicated the same area of the brain. The relationship between surgical outcome and the concordance of MEG and SEEG was analyzed. Results: A complete seizure-free condition was achieved in 22 (47%) patients. Sex, handedness, age and duration of illness were not significantly associated with seizure-free outcome (p = .187 [Pearson chi-squared test]). The number of patients with favorable outcome (Engle I and II) was as high as 68% at the time of follow-up. Furthermore, more seizure-free patients were found in the SEEG and MEG consistent group. Conclusions: SEEG is a valuable tool in the pre-evaluation for resective epilepsy surgery, particularly in negative-MRI epilepsy patients; MEG greatly facilitates localization for SEEG electrode implantation. However, none of these tools are absolutely sensitive and reliable; therefore, collecting as much information as possible is necessary to achieve satisfactory results in epilepsy surgery.

Epileptic foci localization based on mapping the synchronization of dynamic brain network

BACKGROUND

Characterizing the synchronous changes of epileptic seizures in different stages between different regions is profound to understand the transmission pathways of epileptic brain network and epileptogenic foci. There is currently no adequate quantitative calculation method for describing the propagation pathways of electroencephalogram (EEG) signals in the brain network from the short and long term. The goal of this study is to explore the innovative method to locate epileptic foci, mapping synchronization in the brain networks based on EEG.

METHODS

Mutual information was used to analyze the short-term synchronization in the full electrodes; while nonlinear dynamics quantifies the statistical independencies in the long -term among all electrodes. Then graph theory based on the complex network was employed to construct a dynamic brain network for epilepsy patients when they were awake, asleep and in seizure, analyzing the changing topology indexes.

RESULTS

Epileptic network achieved a high degree of nonlinear synchronization compared to awake time. and the main path of epileptiform activity was revealed by searching core nodes. The core nodes of the brain network were in connection with the onset zone. Seizures always happened with a high degree of distribution.

CONCLUSIONS

This study indicated the path of EEG synchronous propagation in seizures, and core nodes could locate the epileptic foci accurately in some epileptic patients.