Localization Of Seizure Onset Zone Research Articles

Seizure Onset Zone Detection Based on Convolutional Neural Networks and EEG Signals

Background: The localization of seizure onset zones (SOZs) is a critical step before the surgical treatment of epilepsy. Methods and Results: In this paper, we propose an SOZ detection method based on convolutional neural networks and EEG signals. This method aims to locate SOZs through the seizure status of each channel in multi-channel EEG signals. First, we preprocess the data with filtering, segmentation, resampling, and standardization to ensure their quality and consistency. Then, the single-channel UCI epilepsy seizure recognition dataset is used to train and test the convolutional neural network (CNN) model, achieving an accuracy of 98.70%, a sensitivity of 97.53%, and a specificity of 98.98%. Next, the multi-channel clinical EEG dataset collected by a hospital is divided into 21 single-channel site datasets and input into the model for detection, and then the seizure results of 21 sites per second are obtained. Finally, the seizure sites are visualized through the international 10–20 system electrode distribution map, diagrams of the change process of the seizure sites during seizures are drawn, and patients’ SOZs are located. Conclusions: Our proposed method well classifies seizure and non-seizure data and successfully locates SOZs by detecting the seizure results of 21 sites through a single-channel model. This study can effectively assist doctors in locating the SOZs of patients and provide help for the diagnosis and treatment of epilepsy.

Characterizing Depth Electrode Coverage in Stereoelectroencephalography on Seizure Onset Zone Localization and Seizure Outcomes

Introduction: The number of intracranial depth electrodes implanted in stereoelectroencephalography (SEEG) investigations is primarily driven by the preimplantation hypothesis about SOZ location. Targeting is not standardized and highly variable between centers. Whether some of these electrodes may prove redundant, or target low-yield areas too frequently, is uncertain. Methods: We identified a retrospective multi-institutional cohort implanted with depth electrodes for iEEG monitoring between 2003 and 2022. We collected preoperative clinical features and iEEG investigation parameters, including the number of depth electrodes and contacts implanted. We built a propensity-matched cohort with respect to these covariates and evaluated outcomes, which included (1) the likelihood of SOZ localization, (2) complications, and (3) seizure-free outcomes as a function of electrode contact coverage. In addition, we aimed to identify brain regions commonly explored in conjunction with each other and identify the likelihood of a region being implicated in initial electroclinical seizure onset. Results: One hundred and sixty-seven patients were followed for a median of 3.8 (range 2, 18) years after SEEG. Propensity-matched cohorts demonstrated that a higher number of implanted contacts were associated with a greater likelihood of proceeding to treatment, but were not associated with SOZ localization, seizure freedom (Engel I), favorable seizure outcomes (Engel I/II), or complications, per Bayes factor analysis. Lateral orbitofrontal, supramarginal, posterior cingulate, inferior parietal, and inferior temporal areas were least likely to be implicated in initial electrographic onset, whereas hippocampus, caudal middle frontal, pericalcarine, and parahippocampal areas were most likely when controlling for electrode coverage. Conclusions: SEEG effectively localizes the SOZ in both lesional and non-lesional etiologies, and clinicians are generally optimizing the electrode coverage for hypothetical SOZ localization, leading to further therapeutic surgeries that may confer seizure freedom. Nevertheless, several areas are possibly being explored despite low likelihood (<2.5%) of participation within the SOZ.

Epilepsy lesion localization method based on brain function network.

In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Determinants of successful ictal SPECT injection in phase 1 epilepsy presurgical evaluation: Findings from the pediatric epilepsy research consortium surgery database project.

The main goal of presurgical evaluation in drug-resistant focal epilepsy is to identify a seizure onset zone (SOZ). Of the noninvasive, yet resource-intensive tests available, ictal single-photon emission computed tomography (SPECT) aids SOZ localization by measuring focal increases in blood flow within the SOZ via intravenous peri-ictal radionuclide administration. Recent studies indicate that geographic and center-specific factors impact utilization of these diagnostic procedures. Our study analyzed successful ictal SPECT acquisition (defined as peri-ictal injection during inpatient admission) using surgery-related data from the Pediatric Epilepsy Research Consortium (PERC) surgery database. We hypothesized that a high seizure burden, longer duration of video EEG monitoring (VEEG), and more center-specific hours of SPECT availability would increase the likelihood of successful ictal SPECT. We identified study participants (≤18 years of age) who underwent SPECT as part of their phase 1 VEEG from January 2018 to June 2022. We assessed association between ictal SPECT outcomes (success vs. failure) and variables including patient demographics, epilepsy history, and center-specific SPECT practices. Phase 1 VEEG monitoring with ictal SPECT injection was planned in 297 participants and successful in 255 participants (85.86%). On multivariable analysis, the likelihood of a successful SPECT injection was higher in patients of non-Hispanic ethnicity (p = 0.040), shorter duration VEEG (p = 0.004), and higher hours of available SPECT services (p < 0.001). Higher seizure frequency (p = 0.033) was significant only in bivariate analysis. Patients treated at centers with more operational hours were more likely to experience pre-admission protocols prior to VEEG (p = 0.002). There is inter-center variability in protocols and SPECT acquisition capabilities. Shorter duration of EEG monitoring, non-Hispanic ethnicity (when on private insurance), extended operational hours of nuclear medicine as noted on multivariate analysis and higher seizure frequency in bivariate analysis are strongly associated with successful ictal SPECT injection. In pediatric patients with drug-resistant epilepsy, single-photon emission computed tomography (SPECT) scans can be helpful in localizing seizure onset zone. However, due to many logistical challenges described below, which include not only the half-life of the technetium isotope used to inject intravenously during a seizure (called the ictal SPECT scan) but also available nuclear scanner time in addition to the unpredictability of seizures, obtaining an ictal SPECT during a planned elective inpatient hospital stay is not guaranteed. Thus, as healthcare costs increase, planning a prolonged hospital stay during which an ictal SPECT scan is not feasible is not optimal. We leveraged our prospective surgery database to look at center-specific factors and patient-specific factors associated with an ictal SPECT injection in the first, pediatric-focussed, large-scale, multicenter, prospective, SPECT feasibility study. We found that longer availability of the scanner is the most important center-specific factor in assuring ictal SPECT injection. Although seizure frequency is an important patient-specific factor on bivariate analysis, this factor lost statistical significance when other factors like patient insurance status and video EEG duration were also considered in our multivariable logistical model.

Predictors of SOZ localization, subsequent surgical intervention, and seizure outcomes in iEEG.

A third of the patients who undergo intracranial EEG (iEEG) for seizure-onset zone (SOZ) localization do not proceed to resective surgery for epilepsy, and over half of those who do continue to have seizures following treatment. To better identify candidates who are more likely to see benefits from undergoing iEEG, we investigated preoperative and iEEG peri-operative features associated with the localization of a putative SOZ, undergoing subsequent surgical treatment, and seizure outcomes. We conducted a retrospective cohort study of consecutive patients who underwent iEEG from 2001 to 2022 at two institutions. Outcomes included SOZ identification, proceeding to surgical treatment (resection vs. neuromodulation), and subsequent seizure freedom. We identified 329 unique patients who were followed for a median of 3.9 (IQR:7) years, with a minimum of 2-year follow-up for seizure outcomes analyses. Multivariate analysis identified lateralized and lobar localization on scalp EEG (OR 3.8, p = 0.001) to be associated with SOZ localization. Patients with unilateral localization on scalp EEG (OR 3.0, p = 0.003), unilateral preimplantation hypothesis (OR 3.1, p = 0.001), and lesional preoperative MRI (OR 2.1, p = 0.033) were more likely to undergo resection than neuromodulation. Similarly, a unilateral pre-implantation hypothesis (OR 2.6, p < 0.001) favored seizure freedom, whereas prior neuromodulation (OR 0.3, p = 0.013) decreased the odds. Larger number of preoperative anti-seizure medications (ASMs) did not influence seizure freedom rates but did decrease favorable (Engel I, II) seizure outcomes (OR 0.7, p = 0.026). Non-invasive localization data prior to iEEG are associated with subsequent resection and seizure freedom, independent of iEEG localization. Factors predictive of SOZ localization are not necessarily predictive of post-operative seizure freedom.

Enhancing the Diagnostic Utility of ASL Imaging in Temporal Lobe Epilepsy through FlowGAN: An ASL to PET Image Translation Framework.

Positron Emission Tomography (PET) using fluorodeoxyglucose (FDG-PET) is a standard imaging modality for detecting areas of hypometabolism associated with the seizure onset zone (SOZ) in temporal lobe epilepsy (TLE). However, FDG-PET is costly and involves the use of a radioactive tracer. Arterial Spin Labeling (ASL) offers an MRI-based quantification of cerebral blood flow (CBF) that could also help localize the SOZ, but its performance in doing so, relative to FDG-PET, is limited. In this study, we seek to improve ASL's diagnostic performance by developing a deep learning framework for synthesizing FDG-PET-like images from ASL and structural MRI inputs. We included 68 epilepsy patients, out of which 36 had well lateralized TLE. We compared the coupling between FDG-PET and ASL CBF values in different brain regions, as well as the asymmetry of these values across the brain. We additionally assessed each modality's ability to lateralize the SOZ across brain regions. Using our paired PET-ASL data, we developed FlowGAN, a generative adversarial neural network (GAN) that synthesizes PET-like images from ASL and T1-weighted MRI inputs. We tested our synthetic PET images against the actual PET images of subjects to assess their ability to reproduce clinically meaningful hypometabolism and asymmetries in TLE. We found variable coupling between PET and ASL CBF values across brain regions. PET and ASL had high coupling in neocortical temporal and frontal brain regions (Spearman's r > 0.30, p < 0.05) but low coupling in mesial temporal structures (Spearman's r < 0.30, p > 0.05). Both whole brain PET and ASL CBF asymmetry values provided good separability between left and right TLE subjects, but PET (AUC = 0.96, 95% CI: [0.88, 1.00]) outperformed ASL (AUC = 0.81; 95% CI: [0.65, 0.96]). FlowGAN-generated images demonstrated high structural similarity to actual PET images (SSIM = 0.85). Globally, asymmetry values were better correlated between synthetic PET and original PET than between ASL CBF and original PET, with a mean correlation increase of 0.15 (95% CI: [0.07, 0.24], p<0.001, Cohen's d = 0.91). Furthermore, regions that had poor ASL-PET correlation (e.g. mesial temporal structures) showed the greatest improvement with synthetic PET images. FlowGAN improves ASL's diagnostic performance, generating synthetic PET images that closely mimic actual FDG-PET in depicting hypometabolism associated with TLE. This approach could improve non-invasive SOZ localization, offering a promising tool for epilepsy presurgical assessment. It potentially broadens the applicability of ASL in clinical practice and could reduce reliance on FDG-PET for epilepsy and other neurological disorders.

Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization

ObjectiveHigh frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm’s parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. MethodsWe detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. ResultsSOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. ConclusionsSOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SignificanceVisual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.

Interictal discharge traveling waves recorded from stereoelectroencephalography electrodes.

Interictal epileptiform discharges (IEDs) are intermittent high-amplitude electrical signals that occur between seizures. They have been shown to propagate through the brain as traveling waves when recorded with epicortical grid-type electrodes and small penetrating microelectrode arrays. However, little work has been done to translate experimental IED analyses to more clinically relevant platforms such as stereoelectroencephalography (SEEG). In this pilot study, the authors aimed to define a computational method to identify and characterize IEDs recorded from clinical SEEG electrodes and leverage the directionality of IED traveling waves to localize the seizure onset zone (SOZ). Continuous SEEG recordings from 15 patients with medically refractory epilepsy were collected, and IEDs were detected by identifying overlapping peaks of a minimum prominence. IED pathways of propagation were defined and compared to the SOZ location determined by a clinical neurologist based on the ictal recordings. For further analysis of the IED pathways of propagation, IED detections were divided into triplets, defined as a set of 3 consecutive contacts within the same IED detection. Univariate and multivariate linear regression models were employed to associate IED characteristics with colocalization to the SOZ. A median (range) of 22.6 (4.4-183.9) IEDs were detected per hour from 15 patients over a mean of 23.2 hours of recording. Depending on the definition of the SOZ, a median (range) of 20.8% (0.0%-54.5%) to 62.1% (19.2%-99.4%) of IEDs per patient traversed the SOZ. IEDs passing through the SOZ followed discrete pathways that had little overlap with those of the IEDs passing outside the SOZ. Contact triplets that occurred more than once were significantly more likely to be detected in an IED passing through the SOZ (p < 0.001). Per our multivariate model, patients with a greater proportion of IED traveling waves had a significantly greater proportion of IEDs that localized to the SOZ (β = 0.64, 95% CI 0.01-1.27, p = 0.045). By using computational methods, IEDs can be meaningfully detected from clinical-grade SEEG recordings of patients with epilepsy. In some patients, a high proportion of IEDs are traveling waves according to multiple metrics that colocalize to the SOZ, offering hope that IED detection, with further refinement, could serve as an alternative method for SOZ localization.

Comparative effectiveness of stereotactic, subdural, or hybrid intracranial EEG monitoring in epilepsy surgery.

Surgical intervention can be curative or palliative for drug-resistant focal epilepsy. However, if the seizure onset zone (SOZ) cannot be adequately localized via noninvasive tests, intracranial EEG (iEEG) recordings are often carried out to develop surgical plans in appropriate candidates. Stereotactic EEG (SEEG), subdural EEG (SDE), and SDE with depth electrodes (hybrid) are major tools used for investigation, but there is no class 1 or 2 evidence comparing the effectiveness of these modalities. The authors identified an institutional cohort of patients who underwent iEEG monitoring between 2001 and 2022. Demographic data, preoperative clinical features, iEEG intervention, and follow-up data were identified. Primary study endpoints included the following: 1) likelihood of SOZ localization; 2) likelihood of surgical treatment after iEEG; 3) seizure outcomes; and 4) complications. A total of 329 patients were identified (176 in the SEEG, 60 in the SDE, and 93 in the hybrid cohort) who were followed for a median of 5.4 (IQR 6.8) years. Baseline characteristics, including demographics, mean age at epilepsy diagnosis, mean age at iEEG investigation, number of preoperative antiseizure medications, and preoperative seizure frequency, were not statistically different across the 3 cohorts. Patients in the SEEG cohort were more likely to have their SOZ localized than were the patients in the SDE group (OR 2.3) and were less likely to undergo subsequent resection (OR 0.3) or to have complications (OR 0.4), although there was no statistical difference with respect to likelihood of undergoing any subsequent neurosurgical treatment, or with respect to favorable seizure outcomes. Patients in the hybrid cohort were more likely to have SOZ localized than were patients in the SDE group (OR 3.1), but were more likely to undergo resection (OR 4.9) or any neurosurgical treatment (OR 2.5) compared to patients in the SEEG group. Patients in the hybrid cohort had better seizure outcomes compared to the SDE (OR 2.3) but not to the SEEG group. Patients in the SEEG group were more likely to have their SOZ localized and patients in the SDE group were more likely to undergo resection, but they did not differ with respect to seizure outcomes.

Comparison of Ictal SPECT with 99mTc-HMPAO versus MRI for the Epileptic Seizure Onset Zone Detection in Saudi Patients

Background: In the presurgical evaluation of patients with drug-resistant epilepsy, magnetic resonance imaging (MRI) and ictal brain perfusion SPECT with the chemical microspheres 99mTc-HMPAO are widely used for diagnosing the seizure onset zone (SOZ). For both modalities, there is theoretical controversy over favoring one over the other. This study aimed to compare the performance of 99mTc-HMPAO SPECT with MRI for SOZ identification in EEG-proved epileptic Saudi patients. Methods and Results: For this observational retrospective study, the database of the nuclear medicine departments at the Prince Sultan Military Medical City (PSMMC) and King Abdullah bin Abdulaziz University Hospital (KAAUH) were searched for male and female patients with suspected unifocal epilepsy in whom ictal brain perfusion SPECT and MRI had been performed for presurgical evaluation. A total of 14 adult epileptic patients above 18 years were included who have undergone SPECT scans using 99mTc-HMPAO and MRI between Jan 2014 and Dec 2021. 99mTc-HMPAO SPECT and MRI scans were performed simultaneously for each patient, and there was almost general agreement that 99mTc-HMPAO SPECT accurately localized and detected the SOZ in 12/14 patients (sensitivity 85.71%). It was superior to MRI, which detected and localized the SOZ in only 7/14 patients (sensitivity 50.0%). Unfortunately, the specificity of ictal brain perfusion SPECT, by using 99mTc-HMPAO in seizure localization in epileptic patients, was not detected due to the shortage of data that led to all the EEG findings being 100% positive. Conclusion: This study reported that ictal SPECT using 99mTc-HMPAO provides more valuable information about SOZ localization than MRI. However, future studies with a larger sample size are needed to assess the specificity of 99mTc-HMPAO SPECT in detecting the SOZ.

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI.

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI was collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8 ± 4.5% and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7 ± 2.6%. Conversely, a DL only model yielded an accuracy of <50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter, and end in vascular regions are seen as the most discriminative expert-identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and preoperative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy

ObjectiveWe aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes. MethodsFifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area. ResultsThe area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036). ConclusionsThis new method can be used to predict the SOZ localization in various focal epilepsy phenotypes. SignificanceThis study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

Connectivity of high-frequency bursts as SOZ localization biomarker.

For patients with refractory epilepsy, the seizure onset zone (SOZ) plays an essential role in determining the specific regions of the brain that will be surgically resected. High-frequency oscillations (HFOs) and connectivity-based approaches have been identified among the potential biomarkers to localize the SOZ. However, there is no consensus on how connectivity between HFO events should be estimated, nor on its subject-specific short-term reliability. Therefore, we propose the channel-level connectivity dispersion (CLCD) as a metric to quantify the variability in synchronization between individual electrodes and to identify clusters of electrodes with abnormal synchronization, which we hypothesize to be associated with the SOZ. In addition, we developed a specialized filtering method that reduces oscillatory components caused by filtering broadband artifacts, such as sharp transients, spikes, or direct current shifts. Our connectivity estimates are therefore robust to the presence of these waveforms. To calculate our metric, we start by creating binary signals indicating the presence of high-frequency bursts in each channel, from which we calculate the pairwise connectivity between channels. Then, the CLCD is calculated by combining the connectivity matrices and measuring the variability in each electrode's combined connectivity values. We test our method using two independent open-access datasets comprising intracranial electroencephalography signals from 89 to 15 patients with refractory epilepsy, respectively. Recordings in these datasets were sampled at approximately 1000Hz, and our proposed CLCDs were estimated in the ripple band (80-200Hz). Across all patients in the first dataset, the average ROC-AUC was 0.73, and the average Cohen's d was 1.05, while in the second dataset, the average ROC-AUC was 0.78 and Cohen's d was 1.07. On average, SOZ channels had lower CLCD values than non-SOZ channels. Furthermore, based on the second dataset, which includes surgical outcomes (Engel I-IV), our analysis suggested that higher CLCD interquartile (as a measure of CLCD distribution spread) is associated with favorable outcomes (Engel I). This suggests that CLCD could significantly assist in identifying SOZ clusters and, therefore, provide an additional tool in surgical planning for epilepsy patients.

Visually or auditorily induced seizures involve the activation of nonhippocampal brain areas and hippocampal removal does not alleviate seizures in a mouse model of temporal lobe epilepsy.

Several studies have attributed epileptic activities in temporal lobe epilepsy (TLE) to the hippocampus; however, the participation of nonhippocampal neuronal networks in the development of TLE is often neglected. Here, we sought to understand how these nonhippocampal networks are involved in the pathology that is associated with TLE disease. A kainic acid (KA) model of temporal lobe epilepsy was induced by injecting KA into dorsal hippocampus of C57BL/6J mice. Network activation after spontaneous seizure was assessed using c-Fos expression. Protocols to induce seizure using visual or auditory stimulation were developed, and seizure onset zone (SOZ) and frequency of epileptic spikes were evaluated using electrophysiology. The hippocampus was removed to assess seizure recurrence in the absence of hippocampus. Our results showed that cortical and hippocampal epileptic networks are activated during spontaneous seizures. Perturbation of these networks using visual or auditory stimulation readily precipitates seizures in TLE mice; the frequency of the light-induced or noise-induced seizures depends on the induction modality adopted during the induction period. Localization of SOZ revealed the existence of cortical and hippocampal SOZ in light-induced and noise-induced seizures, and the development of local and remote epileptic spikes in TLE occurs during the early stage of the disease. Importantly, we further discovered that removal of the hippocampi does not stop seizure activities in TLE mice, revealing that seizures in TLE mice can occur independent of the hippocampus. This study has shown that the network pathology that evolves in TLE is not localized to the hippocampus; rather, remote brain areas are also recruited. The occurrence of light-induced or noise-induced seizures and epileptic discharges in epileptic mice is a consequence of the activation of nonhippocampal brain areas. This work therefore demonstrates the fundamental role of nonhippocampal epileptic networks in generating epileptic activities with or without the hippocampus in TLE disease.

On the clinical utility of on-scalp MEG: A modeling study of epileptic activity source estimation

ObjectiveEpilepsy surgery requires localization of the seizure onset zone (SOZ). Today this can only be achieved by intracranial electroencephalography (iEEG). The iEEG electrode placement is guided by findings from non-invasive modalities that cannot themselves detect SOZ-generated initial seizure activity. On scalp magnetoencephalography (osMEG), with sensors placed on the scalp, demonstrates higher sensitivity than conventional MEG (convMEG) and could potentially detect early seizure activity. Here, we modeled EEG, convMEG and osMEG to compare the modalities’ ability to localize SOZ activity and to detect epileptic spikes. MethodsWe modeled seizure propagation within ten epileptic networks located in the mesial and lateral temporal lobe; basal, dorsal, central and frontopolar frontal lobe; parietal and occipital lobe as well as insula and cingulum. The networks included brain regions often involved in focal epilepsy. 128-channel osMEG, convMEG, EEG and combined osMEG + EEG and convMEG + EEG were modeled, and the SOZ source estimation accuracy was quantified and compared using Student’s t-test. ResultsOsMEG was significantly (p-value <0.01) better than both convMEG and EEG at detecting the earliest SOZ-generated seizure activity and epileptic spikes, and better at localizing seizure activity from all epileptic networks (p < 0.01). ConclusionsOur modeling results clearly show that osMEG has an unsurpassed potential to detect both epileptic spikes and seizure activity from all simulated anatomical sites. SignificanceNo clinically available non-invasive technique can detect SOZ activity from all brain regions. Our study indicates that osMEG has the potential to become an important clinical tool, improving both non-invasive SOZ localization and iEEG electrode placement accuracy.

Cross-bispectrum connectivity of intracranial EEG: A novel approach to seizure onset zone localization.

Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and β (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.

The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex

Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs’ high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.

Multisite thalamic recordings to characterize seizure propagation in the human brain.

Neuromodulation of the anterior nuclei of the thalamus (ANT) has shown to be efficacious in a subset of patients with refractory focal epilepsy. One important uncertainty is to what extent thalamic subregions other than the ANT could be recruited more prominently in the propagation of focal onset seizures. We designed the current study to simultaneously monitor the engagement of the ANT, mediodorsal (MD) and pulvinar (PUL) nuclei during seizures in patients who could be candidates for thalamic neuromodulation. We studied 11 patients with clinical manifestations of presumed temporal lobe epilepsy (TLE) undergoing invasive stereo-encephalography (sEEG) monitoring to confirm the source of their seizures. We extended cortical electrodes to reach the ANT, MD and PUL nuclei of the thalamus. More than one thalamic subdivision was simultaneously interrogated in nine patients. We recorded seizures with implanted electrodes across various regions of the brain and documented seizure onset zones (SOZ) in each recorded seizure. We visually identified the first thalamic subregion to be involved in seizure propagation. Additionally, in eight patients, we applied repeated single pulse electrical stimulation in each SOZ and recorded the time and prominence of evoked responses across the implanted thalamic regions. Our approach for multisite thalamic sampling was safe and caused no adverse events. Intracranial EEG recordings confirmed SOZ in medial temporal lobe, insula, orbitofrontal and temporal neocortical sites, highlighting the importance of invasive monitoring for accurate localization of SOZs. In all patients, seizures with the same propagation network and originating from the same SOZ involved the same thalamic subregion, with a stereotyped thalamic EEG signature. Qualitative visual reviews of ictal EEGs were largely consistent with the quantitative analysis of the corticothalamic evoked potentials, and both documented that thalamic nuclei other than ANT could have the earliest participation in seizure propagation. Specifically, pulvinar nuclei were involved earlier and more prominently than ANT in more than half of the patients. However, which specific thalamic subregion first demonstrated ictal activity could not be reliably predicted based on clinical semiology or lobar localization of SOZs. Our findings document the feasibility and safety of bilateral multisite sampling from the human thalamus. This may allow more personalized thalamic targets to be identified for neuromodulation. Future studies are needed to determine if a personalized thalamic neuromodulation leads to greater improvements in clinical outcome.

External Validation of Novel Method of Seizure Onset Zone Localization Based on Neural Resonance (P2-1.006)

<h3>Objective:</h3> We aimed to validate a recently published technique that used neural resonance to localize seizure onset zones (SOZ) with a retrospective dataset collected at an external institution. Further, we critically examined the need for a larger, tailored prospective validation study. <h3>Background:</h3> Accurate localization of the SOZ could improve surgical outcomes in medically refractory epilepsy patients. A recent study successfully used a novel metric of neural resonance to retrospectively identify SOZ regions and predict surgical success. The technique leverages distinct electrophysiological features of evoked responses during single pulse electrode stimulation (SPES) and incorporates them into a dynamical model. We aimed to validate these findings with a retrospective cohort of six patients to assess whether neural resonance predicted surgical outcomes. <h3>Design/Methods:</h3> We collected intracranial electroencephalographic (iEEG) data from six patients that underwent intracranial monitoring, single-pulse electrical stimulation protocol, and resection surgery between June 2020 and May 2021. Four out of six patients were stimulated in both SOZ regions and non-SOZ regions and thus were tested using the logistic regression model trained on the original dataset at the primary study institution. <h3>Results:</h3> Three of the four patients had successful surgical outcomes. Of the four patients tested with the logistic regression model, only one out of four was predicted correctly; the model predicted success for one true success and the failure patient. <h3>Conclusions:</h3> We hypothesize that the non-reproducibility of the original findings are resultant of differences in a research-driven SPES protocol and a clinically driven stimulation protocol. The external validation dataset stimulated a fewer number of sites, used a different clinical EEG and stimulation system, and varied stimulation parameters in a different subspace. However, this work highlights the need for a large, prospective validation dataset that is multi-center and encompasses a wide range of parameters; the gathering of this dataset is currently underway. <b>Disclosure:</b> Mr. Barnagian has nothing to disclose. Dr. Smith has nothing to disclose. Dr. Kang has nothing to disclose. Mark Hays has nothing to disclose. Dr. Gonzalez has nothing to disclose. Dr. Sarma has stock in Neurologic Solutions, Inc.. Dr. Barot has nothing to disclose.

MEG Node Degree for Focus Localization: Comparison with Invasive EEG.

Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients. Resting state data were subjected to whole brain, all-to-all connectivity analysis using the imaginary part of coherence. Graphs were described using parcellated ND. SOZ localization was investigated on a lobar and sublobar level. On a lobar level, all frequency bands except alpha showed significantly higher maximal ND (mND) values inside the SOZ compared to outside (ratios 1.11-1.20, alpha 1.02). Area-under-the-curve (AUC) was 0.67-0.78 for all expected alpha (0.44, ns). On a sublobar level, mND inside the SOZ was higher for all frequency bands (1.13-1.38, AUC 0.58-0.78) except gamma (1.02). MEG ND is significantly related to SOZ in delta, theta and beta bands. ND may provide new localization tools for presurgical evaluation of epilepsy surgery.