Localization Of Epileptogenic Zone Research Articles

18F]DPA-714 PET Imaging in the Presurgical Evaluation of Patients With Drug-Resistant Focal Epilepsy.

Translocator protein 18 kDa (TSPO) PET imaging is used to monitor glial activation. Recent studies have proposed TSPO PET as a marker of the epileptogenic zone (EZ) in drug-resistant focal epilepsy (DRFE). This study aims to assess the contributions of TSPO imaging using [18F]DPA-714 PET and [18F]FDG PET for localizing the EZ during presurgical assessment of DRFE, when phase 1 presurgical assessment does not provide enough information. We compared [18F]FDG and [18F]DPA-714 PET images of 23 patients who had undergone a phase 1 presurgical assessment, using qualitative visual analysis and quantitative analysis, at both the voxel and the regional levels. PET abnormalities (increase in binding for [18F]DPA-714 vs decrease in binding for [18F]FDG) were compared with clinical hypotheses concerning the localization of the EZ based on phase 1 presurgical assessment. The additional value of [18F]DPA-714 PET imaging to [18F]FDG for refining the localization of the EZ was assessed. To strengthen the visual analysis, [18F]DPA-714 PET imaging was also reviewed by 2 experienced clinicians blind to the EZ location. The study included 23 patients. Visual analysis of [18F]DPA-714 PET was significantly more accurate than [18F]FDG PET to both, show anomalies (95.7% vs 56.5%, p = 0.022), and provide additional information to refine the EZ localization (65.2% vs 17.4%, p = 0.019). All 10 patients with normal [18F]FDG PET had anomalies when using [18F]DPA-714 PET. The additional value of [18F]DPA-714 PET seemed to be greater in patients with normal brain MRI or with neocortical EZ (especially if insula is involved). Regional analysis of [18F]DPA-714 and [18F]FDG PET provided similar results. However, using voxel-wise analysis, [18F]DPA-714 was more effective than [18F]FDG for unveiling clusters whose localization was more often consistent with the EZ hypothesis (87.0% vs 39.1%, p = 0.019). Nonrelevant bindings were seen in 14 of 23 patients in visual analysis and 9 patients of 23 patients in voxel-wise analysis. [18F]DPA-714 PET imaging provides valuable information for presurgical assessments of patients with DRFE. TSPO PET could become an additional tool to help to the localization of the EZ, especially in patients with negative [18F]FDG PET. Eudract 2017-003381-27. Inclusion of the first patient: September 24, 2018. This study provides Class IV evidence on the utility of [18F]DPA-714 PET compared with [18F]FDG PET in identifying the epileptic zone in patients undergoing phase 1 presurgical evaluation for intractable epilepsy.

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes

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.

Improving the accuracy of epileptogenic zone localization in stereo EEG with machine learning algorithms

The precise identification of the epileptogenic zone (EZ) is paramount in the presurgical evaluation of epilepsy patients to ensure successful surgical outcomes. The analysis of Stereo EEG, an instrumental tool for EZ localization, poses considerable challenges even for experienced epileptologists. Consequently, the development of machine learning (ML)-based computational tools for enhanced EZ localization is imperative. In this investigation, we developed ML models utilizing Stereo EEG from 15 patients, who remained seizure-free (Engel 1 a-d) following EZ resection, over an average follow-up period of 44.4 months. Utilizing Delphos and MNI detectors, spikes and High Frequency Oscillations (HFOs) were identified from Stereo EEG in Resected Zone (RZ) and non-Resected Zone (non-RZ). Linear and non-linear features were estimated from each modality using MATLAB. A total of 27,744 spikes, 7,790 ripples, and 7,632 fast ripples, along with their combinations, were employed to train the ML models. The Gradient Boosting classifier demonstrated the highest prediction accuracy of 98.5% for EZ localization in Mesial Temporal Lobe Epilepsy (MTLE) when trained with features derived from the spike-ripple combination. In the case of Neocortical Epilepsy (NE), the Extra Trees classifier achieved an accuracy of 87.6% when utilizing features from fast ripples. The Random Forest, Extra Trees, and Gradient Boosting algorithms were the most effective for predicting the RZ. Linear features outperformed non-linear features in predicting epileptogenic zones within the epileptic brain. Our study establishes the capability of ML methodologies in localizing epileptogenic zones with high accuracy. Future studies that focus on increasing the training sample size and incorporating more advanced machine learning (ML) algorithms have the potential to significantly improve the accuracy of these models in pinpointing epileptogenic networks. Additionally, implementing this ML approach across multiple research centers would contribute to the broader validation and generalizability of this technique.

The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review.

Epilepsy is a neurological disorder characterized by recurrent seizures affecting millions worldwide. Medically intractable seizures in epilepsy patients are not only detrimental to the quality of life but also pose a significant threat to their safety. Outcomes of epilepsy therapy can be improved by early detection and intervention during the interictal window period. Electroencephalography is the primary diagnostic tool for epilepsy, but accurate interpretation of seizure activity is challenging and highly time-consuming. Machine learning (ML) and deep learning (DL) algorithms enable us to analyze complex EEG data, which can not only help us diagnose but also locate epileptogenic zones and predict medical and surgical treatment outcomes. DL models such as convolutional neural networks (CNNs), inspired by visual processing, can be used to classify EEG activity. By applying preprocessing techniques, signal quality can be enhanced by denoising and artifact removal. DL can also be incorporated into the analysis of magnetic resonance imaging (MRI) data, which can help in the localization of epileptogenic zones in the brain. Proper detection of these zones can help in good neurosurgical outcomes. Recent advancements in DL have facilitated the implementation of these systems in neural implants and wearable devices, allowing for real-time seizure detection. This has the potential to transform the management of drug-refractory epilepsy. This review explores the application of ML and DL techniques to Electroencephalograms (EEGs), MRI, and wearable devices for epileptic seizure detection. This review briefly explains the fundamentals of both artificial intelligence (AI) and DL, highlighting these systems' potential advantages and undeniable limitations.

Spontaneous fast-ultradian dynamics of polymorphic interictal events in drug-resistant focal epilepsy.

We studied the rate dynamics of interictal events occurring over fast-ultradian time scales, as commonly examined in clinics to guide surgical planning in epilepsy. Stereo-electroencephalography (SEEG) traces of 35 patients with good surgical outcome (Engel I) were analyzed. For this we developed a general data mining method aimed at clustering the plethora of transient waveform shapes including interictal epileptiform discharges (IEDs) and assessed the temporal fluctuations in the capability of mapping the epileptogenic zone (EZ) of each type of event. We found that the fast-ultradian dynamics of the IED rate may effectively impair the precision of EZ identification, and appear to occur spontaneously, that is, not triggered by or exclusively associated with a particular cognitive task, wakefulness, sleep, seizure occurrence, post-ictal state, or antiepileptic drug withdrawal. Propagation of IEDs from the EZ to the propagation zone (PZ) could explain the observed fast-ultradian fluctuations in a reduced fraction of the analyzed patients, suggesting that other factors like the excitability of the epileptogenic tissue could play a more relevant role. A novel link was found between the fast-ultradian dynamics of the overall rate of polymorphic events and the rate of specific IEDs subtypes. We exploited this feature to estimate in each patient the 5 min interictal epoch for near-optimal EZ and resected-zone (RZ) localization. This approach produces at the population level a better EZ/RZ classification when compared to both (1) the whole time series available in each patient (p = .084 for EZ, p < .001 for RZ, Wilcoxon signed-rank test) and (2) 5 min epochs sampled randomly from the interictal recordings of each patient (p < .05 for EZ, p < .001 for RZ, 105 random samplings). Our results highlight the relevance of the fast-ultradian IED dynamics in mapping the EZ, and show how this dynamics can be estimated prospectively to inform surgical planning in epilepsy.

Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power.

Successful epilepsy surgery depends on localizing and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesizing a greater overlap in seizure-free patients. Thirty-four individuals with refractory focal epilepsy underwent pre-surgical resting-state interictal magnetoencephalography (MEG) recording. Fourteen individuals were totally seizure-free (ILAE 1) after surgery and 20 continued to have some seizures post-operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k-means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC = 0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (a) a data-driven framework to validate current hypotheses of the epileptogenic zone localization or (b) to guide further investigation.

Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study.

A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input. Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients. Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification. These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention. This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.

N°90 – Respective contribution of ictal and interictal electrical source imaging to epileptogenic zone localization

N°90 – Respective contribution of ictal and interictal electrical source imaging to epileptogenic zone localization

Advances in Image Processing for Epileptogenic Zone Detection with MRI.

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

PET/MRI Applications in Pediatric Epilepsy.

Epilepsy neuroimaging assessment requires exceptional anatomic detail, physiologic and metabolic information. Magnetic resonance (MR) protocols are often time-consuming necessitating sedation and positron emission tomography (PET)/computed tomography (CT) comes with a significant radiation dose. Hybrid PET/MRI protocols allow for exquisite assessment of brain anatomy and structural abnormalities, in addition to metabolic information in a single, convenient imaging session, which limits radiation dose, sedation time, and sedation events. Brain PET/MRI has proven especially useful for accurate localization of epileptogenic zones in pediatric seizure cases, providing critical additional information and guiding surgical decision making in medically refractory cases. Accurate localization of seizure focus is necessary to limit the extent of the surgical resection, preserve healthy brain tissue, and achieve seizure control. This review provides a systematic overview with illustrative examples demonstrating the applications and diagnostic utility of PET/MRI in pediatric epilepsy.

A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.

Investigating the Inverse Problem of Epileptogenic Zone Localization: Insights From Simultaneous EEG and SEEG.

Investigating the Inverse Problem of Epileptogenic Zone Localization: Insights From Simultaneous EEG and SEEG.

Automatic focal EEG identification based on deep reinforcement learning

Electroencephalogram (EEG) signals convey information about the electrical activity of neurons and are commonly used in clinical practice to evaluate the epileptic activity of patients. There is a part of the human brain that is closely linked to epileptic activity, namely the epileptogenic zone. Successful resection of the epileptogenic zone requires high precision classification of focal (F) and non-focal (NF) EEG signals. In this paper, we improve the Deep Q-Network (DQN) by adding Additional Functional Modules (AFM) to propose a novel focal EEG recognition method which is named AFM-DQN. Compared to the traditional Reinforcement Learning (RL), this model incorporates a deep convolutional neural network (CNN), and unifies the decision making capability of Q-learning and the perceptual capability of CNN, which greatly improves the learning of this network. The AFM, including pre-training, the high performance classifier (HPC), reward control mechanism and three RL related techniques, imparts this model a stronger generalization ability. To reduce computational burden of the network while preserving the correlation and interdependence of EEG signals, we introduce different kinds of EEG features and a semi-supervised feature selection algorithm based on joint mutual information (Semi-JMI algorithm). Experiments on the two publicly available EEG databases have achieved classification accuracy of 95.87% and 97.5%, respectively, which demonstrates that the proposed method has the potential for clinical application of accurate localization of epileptogenic zone. Compared with other published methods, this method has better performance and generalization ability.

Usefulness of Intraoperative Electrocorticography for the Localization of Epileptogenic Zones.

Intraoperative electrocorticography (iECoG) is widely performed to identify irritative zones in the cortex during brain surgery; however, several limitations (e.g., short recording times and the effects of general anesthesia) reduce its effectiveness. The present study aimed to evaluate the utility of iECoG for localizing epileptogenic zones. We compared the results of iECoG and chronic electrocorticography (cECoG) in 25 patients with refractory epilepsy. Subdural electrodes were implanted with iECoG under general anesthesia (2% sevoflurane). cECoG recordings were performed for 3-14 days. The distribution of iECoG spikes was compared with cECoG spike, seizure onset zone, and resection areas. The concordance patterns of each distribution were classified into four patterns: Group 1: No spike in iECoG, Group 2: concordant (2a: iECoG smaller, 2b: iECoG larger, Group 3: discordant >50%). The concordance rate of interictal spikes, seizure onset zones, and resection areas were 88.0% (Group 2a: 72.0%, Group 2b: 16.0%), 70.0% (Group 2a: 25.0%, Group 2b: 45.0%), and 81.0% (Group 2a: 42.9%, Group 2b: 38.1%), respectively. The resection of iECoG spike areas significantly correlated with good surgical outcomes. The indication and limitations of iECoG need to be realized, and the complementary use of iECoG and cECoG may enhance clinical utility.

Semiology characteristics and location of epileptogenic zone in motor seizures of axial and shoulder girdle muscles assessed by video stereoelectroencephalography study in 65 patients.

To study the semiology characteristics of motor seizures of axial and shoulder girdle muscles (ASMs) by stereoelectroencephalography (SEEG) and its value in determining location of epileptogenic zone. A total of 598 patients underwent SEEG assessment in Sanbo Brain Hospital were reviewed; 65 patients with ASMs selected. Thirteen semiology feature items were extracted according to the location and symmetry of involved axial muscles, direction of movement, etc. Seizures were grouped with items, and the k-means was used to analyze association between ASMs semiology characteristics and seizure-onset zone (SOZ). The SOZs of ASMs involved 23 combinations of seven different brain regions: 31 patients (47.7%) had one brain region, 19 (29.2%) had two, 14 (21.5%) had three, and one patient (1.5%) had four. One hundred and fifteen brain regions were analyzed. Seven brain regions accounted for a significant difference in chi-square test, χ2 = 62.79, p < 0.0001, with the highest proportion of insular and perisylvian. The k-means method identified two clusters: cluster 1 had a high degree of agreement with temporal lobe epilepsy (12/15), characterized by less shrug-like movement, later involvement of axial shoulder girdle muscles, longer duration, and lower seizure frequency; cluster 2 had a high degree of agreement with posterior cortex epilepsy (14/18), characterized by earlier involvement of axial shoulder girdle muscles, shorter duration, and higher seizure frequency. In frontal lobe, insular and perisylvian, anterior and middle cingulate gyrus, are the two categories accounted for similar proportion. Seizure-onset lateralized at the contralateral of unilateral cervical tonic, with rate of seizure-free was 73.7%. The incidence of ASMs is high in insular and perisylvian. Unilateral cervical tonic seizures have good lateralizing value. Based on semiology characteristics, ASMs can be roughly clustered into two categories, which can only effectively distinguish the origins of temporal lobe and posterior cortex, with low discrimination for the seizure-onset of other lobes.

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity.

Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data. Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds. We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort. Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.

Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection

Drug-refractory epilepsy (DRE) may be treated by surgical intervention. Intracranial EEG has been widely used to localize the epileptogenic zone (EZ). Most studies of epileptic network focus on the features of EZ nodes, such as centrality and degrees. It is difficult to apply those features to the treatment of individual patients. In this study, we proposed a spatial neighbor expansion approach for EZ localization based on a neural computational model and epileptic network reconstruction. The virtual resection method was also used to validate the effectiveness of our approach. The electrocorticography (ECoG) data from 11 patients with DRE were analyzed in this study. Both interictal data and surgical resection regions were used. The results showed that the rate of consistency between the localized regions and the surgical resections in patients with good outcomes was higher than that in patients with poor outcomes. The average deviation distance of the localized region for patients with good outcomes and poor outcomes were 15 mm and 36 mm, respectively. Outcome prediction showed that the patients with poor outcomes could be improved when the brain regions localized by the proposed approach were treated. This study provides a quantitative analysis tool for patient-specific measures for potential surgical treatment of epilepsy.

Surgical outcomes in patients with drug-resistant bilateral temporal lobe epilepsy confirmed via magnetic resonance imaging

Introduction. In patients with drug-resistant temporal lobe epilepsy (TLE), surgical treatment is aimed to resect an epileptogenic zone (EZ) followed by seizure control. Despite complicated EZ location, surgical resection should be considered as a treatment of choice in bilateral TLE.&#x0D; Objective: to evaluate surgical outcomes and factors contributing to outcomes in patients with drug-resistant bilateral TLE confirmed via magnetic resonance imaging.&#x0D; Materials and methods. The study included patients with unilateral (n = 50) and bilateral (n = 50) temporal lobe involvement. The results of surgical treatment were evaluated according to the classification of J. Engel (1993).&#x0D; Results. Favorable outcomes of surgical treatment (Engel I and Engel II) in the group with unilateral temporal lobe involvement were found in 98% of patients after 12 months, in 88% after 24 months, and in 100% after 48 and 60 months after surgery. In the group with bilateral temporal lobe involvement outcomes of surgical treatment were favorable in 41% of patients after 12 months, in 50% after 24 months, in 39% after 48 months, and in 50% of patients after 60 months post-surgery.&#x0D; Conclusion. Early onset, burdened perinatal history, and MRI-confirmed left temporal lobe involvement contribute to the poor outcome (Engel III and Engel IV) in the bilateral TLE group. Engel I outcomes were more common in the patients with unilateral TLE while Engel IIIV outcomes were more common in the patients with bilateral TLE.

Source-sink connectivity: a novel interictal EEG marker for seizure localization.

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Localizing epileptogenic network from SEEG using non-linear correlation, mutual information and graph theory analysis.

The key challenge in epilepsy surgery is precise localization and removal of the epileptogenic zone (EZ) from the brain. Localization of the epileptogenic network by visual analysis of intracranial EEG is extremely difficult. In this retrospective study, we used interictal connectivity and graph theory analysis on intracranial EEG to better delineate the epileptogenic zone. Patients who underwent surgery for drug-refractory mesial temporal and neocortical epilepsy were included. Computational measures, such as h2 nonlinear correlation and mutual information, were used to estimate the interdependency of intracranial EEGs. We observed that the Out-Degree, Out-Strength, and Betweenness centrality (graph properties) were the best predictors of EZ. From the results, we also found that graph properties with a normalized value above 0.75 were found to be a useful measure to localize the EZ with a sensitivity of 87.88 and a specificity of 87.13. Our results also validate that frequently occurring types of interictal fast discharges (IFD) with connectivity measures and graph properties can better localize the EZ. We foresee graph theory analysis of interictal intracranial EEG data can help precise localization of EZ for cortical resection as well as in minimally invasive radiofrequency ablation of epileptogenic hubs. Further, prospective validation is required for clinical use.