[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.

A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model / 生物医学工程学杂志

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.

[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.

Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection / 生物医学工程学杂志

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.

Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy.

Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resampling procedures have been developed to face this problem and a lot of work has been done in comparing their effectiveness in different scenarios. Notably, the robustness of such techniques has been tested among a wide variety of different datasets, without considering the performance of each specific dataset. In this study, we compare the performances of different resampling procedures for the imbalanced domain in stereo-electroencephalography (SEEG) recordings of the patients with focal epilepsies who underwent surgery. Methods: We considered data obtained by network analysis of interictal SEEG recorded from 10 patients with drug-resistant focal epilepsies, for a supervised classification problem aimed at distinguishing between the epileptogenic and non-epileptogenic brain regions in interictal conditions. We investigated the effectiveness of five oversampling and five undersampling procedures, using 10 different machine learning classifiers. Moreover, six specific ensemble methods for the imbalanced domain were also tested. To compare the performances, Area under the ROC curve (AUC), F-measure, Geometric Mean, and Balanced Accuracy were considered. Results: Both the resampling procedures showed improved performances with respect to the original dataset. The oversampling procedure was found to be more sensitive to the type of classification method employed, with Adaptive Synthetic Sampling (ADASYN) exhibiting the best performances. All the undersampling approaches were more robust than the oversampling among the different classifiers, with Random Undersampling (RUS) exhibiting the best performance despite being the simplest and most basic classification method. Conclusions: The application of machine learning techniques that take into consideration the balance of features by resampling is beneficial and leads to more accurate localization of the epileptogenic zone from interictal periods. In addition, our results highlight the importance of the type of classification method that must be used together with the resampling to maximize the benefit to the outcome.

Validation of virtual resection on intraoperative interictal data acquired during epilepsy surgery.

Objective. A 'Virtual resection' consists of computationally simulating the effect of an actual resection on the brain. We validated two functional connectivity based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings.Approach. A non-linear association index was applied to pre-resection recordings from 11 extra-temporal focal epilepsy patients. We computed two virtual resection strategies: first, a 'naive' one obtained by simply removing from the connectivity matrix the electrodes that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. We validated the virtual resections with two analysis: (1) we tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; (2) we tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test.Main results. The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings. Our findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection.Significance. We pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making.

Distinguishing Dependent-Stage Secondary Epileptogenesis in a Complex Case of Giant Hypothalamic Hamartoma With Assistance of a Computational Method.

Besides gelastic seizures, hypothalamic hamartoma (HH) is also noted for its susceptibility to remote secondary epileptogenesis. Although clinical observations have demonstrated its existence, and a three-stage theory has been proposed, how to determine whether a remote symptom is spontaneous or dependent on epileptic activities of HH is difficult in some cases. Herein, we report a case of new non-gelastic seizures in a 9-year-old female associated with a postoperatively remaining HH. Electrophysiological examinations and stereo-electroencephalography (SEEG) demonstrated seizure onsets with slow-wave and fast activities on the outside of the HH. By using computational methodologies to calculate the network dynamic effective connectivities, the importance of HH in the epileptic network was revealed. After SEEG-guided thermal coagulation of the remaining HH, the patient finally was seizure-free at the 2-year follow-up. This case showed the ability of computational methods to reveal information underlying complex SEEG signals, and further demonstrated the dependent-stage secondary epileptogenesis, which has been rarely reported.

Multi-feature localization of epileptic foci from interictal, intracranial EEG.

OBJECTIVE: When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data. METHODS: We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording. RESULTS: The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95. CONCLUSION: SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects. SIGNIFICANCE: In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.

Localization of Epileptogenic Zone With the Correction of Pathological Networks.

Patients with focal drug-resistant epilepsy are potential candidates for surgery. Stereo-electroencephalograph (SEEG) is often considered as the "gold standard" to identify the epileptogenic zone (EZ) that accounts for the onset and propagation of epileptiform discharges. However, visual analysis of SEEG still prevails in clinical practice. In addition, epilepsy is increasingly understood to be the result of network disorder, but the specific organization of the epileptic network is still unclear. Therefore, it is necessary to quantitatively localize the EZ and investigate the nature of epileptogenic networks. In this study, intracranial recordings from 10 patients were analyzed through adaptive directed transfer function, and the out-degree of effective network was selected as the principal indicator to localize the epileptogenic area. Furthermore, a coupled neuronal population model was used to qualitatively simulate electrical activity in the brain. By removing individual populations, virtual surgery adjusting the network organization could be performed. Results suggested that the accuracy and detection rate of the EZ localization were 82.86 and 85.29%, respectively. In addition, the same stage shared a relatively stable connectivity pattern, while the patterns changed with transition to different processes. Meanwhile, eight cases of simulations indicated that networks in the ictal stage were more likely to generate rhythmic spikes. This indicated the existence of epileptogenic networks, which could enhance local excitability and facilitate synchronization. The removal of the EZ could correct these pathological networks and reduce the amount of spikes by at least 75%. This might be one reason why accurate resection could reduce or even suppress seizures. This study provides novel insights into epilepsy and surgical treatments from the network perspective.

Epileptogenic zone localization and seizure control in coupled neural mass models.

Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals. In this study, EZ localization is changed into a driver identification problem, and a nonlinear interdependence measure, the weighted rank interdependence, is proposed and used as a driver indicator because it can detect coupling information, especially directionality, from EEG signals. A proportional integral derivative (PID) controller is then explored, using simulations, to establish its suitability for seizure control. The seizure control we propose rests on identifying the EZ using nonlinear interdependence measures of directed functional connectivity. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters can adjust the sensitivity and completeness of the weighted rank interdependence for different applications, and their effect is discussed in the context of neural mass models. Simulation results demonstrate that use of the weighted rank interdependence for EZ identification can be applied to different EZ types, and the approach achieves an overall identification rate of 98.84 % for several EZ types. Simulations also indicate that PID control can effectively regulate synchronization between neural masses.