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Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables., (© 2024. The Author(s).)

Objective: Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy., Methods: Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording. Scalp SSWs and intracranial spikes and ripples (80-250 Hz) were detected; ripples were classified as type I (co-occurring with spikes) or type II (occurring alone). The Hilbert transform was used to analyze the distributions of spikes and ripples in the phases of SSWs., Results: Thirty patients with discrete seizure-onset zone (SOZ) and discernable sleep architecture were included. Intracranial spikes and ripples accumulated around the negative peaks of SSWs and increased with SSW amplitude. Phase analysis revealed that spikes and both ripple subtypes in SOZ were similarly facilitated by SSWs exclusively during down state. In exclusively irritative zones outside SOZ (EIZ), SSWs facilitated spikes and type I ripples across a wider range of phases and to a greater extent than those in SOZ. The type II and type I ripples in EIZ were modulated by SSWs in different patterns. Ripples in normal zones decreased specifically during the up-to-down transition and then increased after the negative peak of SSW, with a characteristically high post-/pre-negative peak ratio., Significance: SSWs modulate interictal events in an amplitude-dependent and region-specific pattern. Pathological ripples and spikes were facilitated predominantly during the cortical down state. Coupling analysis of SSWs could improve the discrimination of pathological and physiological ripples and facilitate seizure localization., (© 2023 International League Against Epilepsy.)

Objective: The authors investigated alterations in functional connectivity (FC) and EEG power during ictal onset patterns of low-voltage fast activity (LVFA) in drug-resistant focal epilepsy. They hypothesized that such changes would be useful to classify epilepsy surgical outcomes., Methods: In a cohort of 79 patients with drug-resistant focal epilepsy who underwent stereoelectroencephalography (SEEG) evaluation as well as resective surgery, FC changes during the peri-LVFA period were measured using nonlinear regression (h2) and power spectral properties within/between three regions: the seizure onset zone (SOZ), early propagation zone (PZ), and noninvolved zone (NIZ). Desynchronization and power desynchronization h2 indices were calculated to assess the degree of EEG desynchronization during LVFA. Multivariate logistic regression was employed to control for confounding factors. Finally, receiver operating characteristic curves were generated to evaluate the performance of desynchronization indices in predicting surgical outcome., Results: Fifty-three patients showed ictal LVFA and distinct zones of the SOZ, PZ, and NIZ. Among them, 39 patients (73.6%) achieved seizure freedom by the final follow-up. EEG desynchronization, measured by h2 analysis, was found in the seizure-free group during LVFA: FC decreased within the SOZ and between regions compared with the pre-LVFA and post-LVFA periods. In contrast, the non-seizure-free group showed no prominent EEG desynchronization. The h2 desynchronization index, but not the power desynchronization index, enabled classification of seizure-free versus non-seizure-free patients after resective surgery., Conclusions: EEG desynchronization during the peri-LVFA period, measured by within-zone and between-zone h2 analysis, may be helpful for identifying patients with favorable postsurgical outcomes and also may potentially improve epileptogenic zone identification in the future.

Background: Low-grade epilepsy-associated neuroepithelial tumor (LEAT) is highly responsive to surgery in general. The appropriate surgical strategy remains controversial in temporal LEAT. The aim of this study is to analyze the surgical seizure outcome of temporal LEAT, focusing on the aspects of surgical strategy., Methods: Sixty-one patients from a single epilepsy center with temporal LEAT underwent surgery. The surgical strategy was according to the multidisciplinary presurgical evaluation. Electrocorticogram (ECoG)-assisted resection was utilized. Surgical extent including lesionectomy and extended resection was described in detail. Seizure outcome was classified as satisfactory (Engel class I) and unsatisfactory (Engel classes II-IV)., Results: After a median follow-up of 36.0 (30.0) months, 83.6% of patients achieved satisfactory outcome, including 72.1% with Engel class Ia. There was 39.3% (24/61) of patients with antiepileptic drug (AED) withdrawal. Use of ECoG (χ2 = 0.000, P > 0.1), preresection spike (χ2 = 0.000, P = 0.763), or spike residue (P = 0.545) was not correlated with the seizure outcome. For lateral temporal LEAT, outcome from lesionectomy was comparable to extended resection (χ2 = 0.499, P > 0.1). For mesial temporal LEAT, 94.7% (18/19) of patients who underwent additional hippocampectomy were satisfactory, whereas only 25% (1/4) of patients who underwent lesionectomy were satisfactory (P = 0.009)., Conclusion: Surgical treatment was highly effective for temporal LEAT. ECoG may not influence the seizure outcome. For lateral temporal LEAT, lesionectomy with or without cortectomy was sufficient in most patients. For mesial temporal LEAT, extended resection was recommended., (© 2021. The Author(s).)

Background: PET imaging has been widely used in diagnosis of neurological disorders; however, its application to pediatric population is limited due to lacking pediatric age-specific PET template. This study aims to develop a pediatric age-specific PET template (PAPT) and conduct a pilot study of epileptogenic focus localization in pediatric epilepsy., Methods: We recruited 130 pediatric patients with epilepsy and 102 age-matched controls who underwent 18 F-FDG PET examination. High-resolution PAPT was developed by an iterative nonlinear registration-averaging optimization approach for two age ranges: 6-10 years (n = 17) and 11-18 years (n = 50), respectively. Spatial normalization to the PAPT was evaluated by registration similarities of 35 validation controls, followed by estimation of potential registration biases. In a pilot study, epileptogenic focus was localized by PAPT-based voxel-wise statistical analysis, compared with multi-disciplinary team (MDT) diagnosis, and validated by follow-up of patients who underwent epilepsy surgery. Furthermore, epileptogenic focus localization results were compared among three templates (PAPT, conventional adult template, and a previously reported pediatric linear template)., Results: Spatial normalization to the PAPT significantly improved registration similarities (P < 0.001), and nearly eliminated regions of potential biases (< 2% of whole brain volume). The PAPT-based epileptogenic focus localization achieved a substantial agreement with MDT diagnosis (Kappa = 0.757), significantly outperforming localization based on the adult template (Kappa = 0.496) and linear template (Kappa = 0.569) (P < 0.05). The PAPT-based localization achieved the highest detection rate (89.2%) and accuracy (80.0%). In postsurgical seizure-free patients (n = 40), the PAPT-based localization also achieved a substantial agreement with resection areas (Kappa = 0.743), and the highest detection rate (95%) and accuracy (80.0%)., Conclusion: The PAPT can significantly improve spatial normalization and epileptogenic focus localization in pediatric epilepsy. Future pediatric neuroimaging studies can also benefit from the unbiased spatial normalization by PAPT., Trial Registration: NCT04725162: https://clinicaltrials.gov/ct2/show/NCT04725162., (© 2021. The Author(s).)

Purpose: Positron emission tomography (PET) with 18 F-fluorodeoxyglucose ([ 18 F]-FDG) has been increasingly applied in precise localization of epileptogenic focus in epilepsy patients, including pediatric patients. The aim of this international consensus is to provide the guideline and specific considerations for [ 18 F]-FDG PET in pediatric patients affected by epilepsy., Methods: An international, multidisciplinary task group is formed, and the guideline for brain [ 18 F]-FDG PET/CT in pediatric epilepsy patients has been discussed and approved, which include but not limited to the clinical indications, patient preparation, radiopharmaceuticals and administered activities, image acquisition, image processing, image interpretation, documentation and reporting, etc. CONCLUSION: This is the first international consensus and practice guideline for brain [ 18 F]-FDG PET/CT in pediatric epilepsy patients. It will be an international standard for this purpose in clinical practice., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Seizure types and characteristics may vary with time in a patient with distinct mechanisms underlying the propagation of ictal activity. Similarly, we found that both focal and generalized seizures coexist in some pilocarpine-induced chronic temporal lobe epilepsy (TLE) rats. In different seizure patterns, mapping complex networks and analyzing epileptic characteristics involved in seizure propagation are likely to reflect seizure propagation mechanisms, and indicate the establishment of stimulation strategy for epilepsy treatment, especially on the selection of stimulation targets. In our study, we used Granger causality method to track the time-variant epileptic brain functional connectivity in focal and generalized seizures from multi-site local field potentials (LFPs). Results showed that these two major types of seizures had different propagation patterns during ictal period. When comparing them, generalized seizures involved in a network with more complex relationships and spread to more extensive brain regions than in local seizures at mid-ictal stage. Moreover, we observed that focal seizures had a focused causal hub with strong interactions, while generalized seizures had relative distributed causal hubs to drive the development of seizure during seizure-onset stage. These findings suggest that stimulation strategy might need to be adapted to different seizure types thus allowing for retuning abnormal epileptic brain network and obtaining better treatment effect on seizure suppression.

Objective: Brain network connectivity analysis plays an important role in computer-aided automatic localization of seizure onset zone (SOZ) from Intracranial Electroencephalography (iEEG). However, how to accurately compute brain network dynamics is still not well addressed. This work aims to develop an effective measure to find out the dynamics for SOZ localization., Methods: Given multiple-channel iEEG signals, the ictal process involves continuous changes of information propagation. In each time slot, the connectivity relationship between channels can be represented as a matrix. Since the matrices from different time slots do not lie on vector spaces, the similarity between them cannot be computed directly. In this paper, we regard the matrices as points on a Riemannian manifold, so that the similarity can be measured by the geodesic distance on the manifold. It addresses the information-losing problem in existing methods using a vector to approximate a matrix. With the Riemannian method, the brain network dynamics are figured out by clustering methods. A temporal segmentation process is applied to refine the segments for SOZ localization., Results: Our method is evaluated on six epilepsy patients, and the SOZ localization performance is evaluated by the area under the curve (AUC) score. Overall, our method obtains an average AUC score of 0.875, which outperforms the existing approaches., Conclusion: Our method preserves more information in measuring the relationship between brain connectivity descriptors, thus is more robust for SOZ localization., Significance: Our method has great potentials for clinical epilepsy treatments.

Objective: This study first aimed to establish the prevalence and predictors of subclinical seizures in patients with epilepsy undergoing video electroencephalographic monitoring, then to evaluate the relationship of sleep/wake and circadian pattern with subclinical seizures., Methods: We retrospectively reviewed the charts of 742 consecutive patients admitted to our epilepsy center between July 2012 and October 2014. Demographic, electro-clinical data and neuroimage were collected., Results: A total of 148 subclinical seizures were detected in 39 patients (5.3%) during video electroencephalographic monitoring. The mean duration of subclinical seizures was 47.18 s (range, 5-311). Pharmacoresistant epilepsy, abnormal MRI and the presence of interictal epileptiform discharges were independently associated with subclinical seizures in multivariate logistic regression analysis. Subclinical seizures helped localizing the presumed epileptogenic zone in 24 (61.5%) patients, and suggested multifocal epilepsy in five (12.8%). In addition, subclinical seizures occurred more frequently in sleep and night than wakefulness and daytime, respectively, and they were more likely seen between 21:00-03:00 h, and less likely seen between 09:00-12:00 h. Thirty patients (76.9%) had their first subclinical seizures within the first 24 h of monitoring while only 7.7% of patients had their first subclinical seizures detected within 20 min., Conclusion: Subclinical seizures are not uncommon in patients with epilepsy, particularly in those with pharmacoresistant epilepsy, abnormal MRI or interictal epileptiform discharges. Subclinical seizures occur in specific circadian patterns and in specific sleep/wake distributions. A 20-min VEEG monitoring might not be long enough to allow for their detection.

Brain inflammation is a major factor in epilepsy, and the high mobility group box-1 (HMGB1) protein is known to contribute significantly to the generation of seizures. Here, we investigated the therapeutic potential of an anti-HMGB1 monoclonal antibody (mAb) in epilepsy. anti-HMGB1 mAb attenuated both acute seizure models (maximal electroshock seizure, pentylenetetrazole-induced and kindling-induced), and chronic epilepsy model (kainic acid-induced) in a dose-dependent manner. Meanwhile, the anti-HMGB1 mAb also attenuated seizure activities of human brain slices obtained from surgical resection from drug-resistant epilepsy patients. The mAb showed an anti-seizure effect with a long-term manner and appeared to be minimal side effects at even very high dose (no disrupted physical EEG rhythm and no impaired basic physical functions, such as body growth rate and thermoregulation). This anti-seizure effect of mAb results from its inhibition of translocated HMGB1 from nuclei following seizures, and the anti-seizure effect was absent in toll-like receptor 4 knockout (TLR4 -/- ) mice. Interestingly, the anti-HMGB1 mAb also showed a disease-modifying anti-epileptogenetic effect on epileptogenesis after status epileptics, which is indicated by reducing seizure frequency and improving the impaired cognitive function. These results indicate that the anti-HMGB1 mAb should be viewed as a very promising approach for the development of novel therapies to treat refractory epilepsy., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Video-electroencephalographic monitoring (VEEG) is useful in the diagnosis of seizure disorders; however, its diagnostic yield in developing countries is not well known. The current study retrospectively reviewed the charts of 484 consecutive patients who were admitted to our center between July 2012 and September 2013. Of these patients, 298 (61.6%) were admitted for diagnostic clarification and underwent VEEG for a mean duration of 1.3days (range=1-9days). The patients were divided into two groups: those whose diagnosis was changed and those whose diagnosis was not changed as a result of VEEG. A patient with a preadmission diagnosis of epilepsy who was discharged with a diagnosis of nonepileptic events (NEEs) or who was further classified as focal/generalized epilepsy on discharge was included in the "change in diagnosis" group. A patient admitted with an uncertain diagnosis and discharged with a diagnosis of NEEs or epilepsy (including focal epilepsy and generalized epilepsy) was also included in the "change in diagnosis" group. Video-electroencephalographic monitoring recorded typical ictal events (epileptic events or nonepileptic events) in 147 (49.3%) of the patients admitted for diagnostic clarification. In total, 181 (60.7%) patients had a change in diagnosis after VEEG. Among them, 103 (56.9%) patients had a preadmission diagnosis of epilepsy, which was further classified as focal epilepsy (88 patients) or generalized epilepsy (15 patients); the diagnosis of NEEs and epilepsy was clarified in 78 (43.1%) patients. The number of patients diagnosed with NEEs increased from 31 (10.4%) on admission to 88 (29.5%) on discharge. Among all the patients admitted for diagnostic clarification, therapeutic plans were changed for 104 (57.5%) patients. In 117 (39.3%) patients with no diagnostic change, VEEG evaluation provided confirmative diagnostic information in 47 (15.8%) patients and no additional diagnostic information in 70 (23.5%) patients. The study indicates that VEEG is useful in terms of clarifying seizure diagnoses and evaluating seizure frequency. In our cohort study, VEEG of a relatively short mean duration produced a comparable diagnostic yield as that reported in other studies., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Automatic seizure detection from the electroen-cephalogram (EEG) plays an important role in an on-demand closed-loop therapeutic system. A new feature, called IMF-VoE, is proposed to predict the occurrence of seizures. The IMF-VoE feature combines three intrinsic mode functions (IMFs) from the empirical mode decomposition of a EEG signal and the variance of the range between the upper and lower envelopes (VoE) of the signal. These multiple cues encode the intrinsic characteristics of seizure states, thus are able to distinguish them from the background. The feature is tested on 80.4 hours of EEG data with 10 seizures of 4 patients. The sensitivity of 100% is obtained with a low false detection rate of 0.16 per hour. Average time delays are 19.4s, 13.2s, and 10.7s at the false detection rates of 0.16 per hour, 0.27 per hour, and 0.41 per hour respectively, when different thresholds are used. The result is competitive among recent studies. In addition, since the IMF-VoE is compact, the detection system is of high computational efficiency and able to run in real time.

In this paper, a spike detection method is introduced. Traditional morphological filter is improved for extracting spikes from epileptic EEG signals and two key problems are addressed: morphological operation design and structure elements optimization. An average weighted combination of open-closing and close-opening operation, which can eliminate statistical deflection of amplitude, is utilized to separate background EEG and spikes. Then, according to the characteristic of spike component, the structure elements are constructed with two parabolas and a new criterion is put forward to optimize the structure elements. The proposed method is evaluated using normal and epileptic EEG data recorded from 12 test subjects. A comparison between the improved morphological filter, traditional morphological filter and wavelet analysis with Mexican hat function is presented, which indicates that the improved morphological filter is superior in restraining background activities. We demonstrate that the average detection rate of the improved morphological filter is much higher than that of the other two methods, and there is no false detection for normal EEG signals with the proposed method.

• Introducing three-dimension convolutional neural network can improve the performance of automated detection of focal cortical dysplasia. • Introducing positron emission tomography data aids in detecting lesions of focal cortical dysplasia. • Automated detection of focal cortical dysplasia with high sensitivity and few false-positive findings is feasible based on multi-modal data. Purpose: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN). Methods: MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN. Results: On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65. Significance: Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection. [ABSTRACT FROM AUTHOR]

Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance. [ABSTRACT FROM AUTHOR]

Background: PET imaging has been widely used in diagnosis of neurological disorders; however, its application to pediatric population is limited due to lacking pediatric age–specific PET template. This study aims to develop a pediatric age–specific PET template (PAPT) and conduct a pilot study of epileptogenic focus localization in pediatric epilepsy. Methods: We recruited 130 pediatric patients with epilepsy and 102 age-matched controls who underwent 18F-FDG PET examination. High-resolution PAPT was developed by an iterative nonlinear registration-averaging optimization approach for two age ranges: 6–10 years (n = 17) and 11–18 years (n = 50), respectively. Spatial normalization to the PAPT was evaluated by registration similarities of 35 validation controls, followed by estimation of potential registration biases. In a pilot study, epileptogenic focus was localized by PAPT-based voxel-wise statistical analysis, compared with multi-disciplinary team (MDT) diagnosis, and validated by follow-up of patients who underwent epilepsy surgery. Furthermore, epileptogenic focus localization results were compared among three templates (PAPT, conventional adult template, and a previously reported pediatric linear template). Results: Spatial normalization to the PAPT significantly improved registration similarities (P < 0.001), and nearly eliminated regions of potential biases (< 2% of whole brain volume). The PAPT-based epileptogenic focus localization achieved a substantial agreement with MDT diagnosis (Kappa = 0.757), significantly outperforming localization based on the adult template (Kappa = 0.496) and linear template (Kappa = 0.569) (P < 0.05). The PAPT-based localization achieved the highest detection rate (89.2%) and accuracy (80.0%). In postsurgical seizure-free patients (n = 40), the PAPT-based localization also achieved a substantial agreement with resection areas (Kappa = 0.743), and the highest detection rate (95%) and accuracy (80.0%). Conclusion: The PAPT can significantly improve spatial normalization and epileptogenic focus localization in pediatric epilepsy. Future pediatric neuroimaging studies can also benefit from the unbiased spatial normalization by PAPT. Trial registration. NCT04725162: https://clinicaltrials.gov/ct2/show/NCT04725162 [ABSTRACT FROM AUTHOR]

Purpose: Positron emission tomography (PET) with 18F-fluorodeoxyglucose ([18F]-FDG) has been increasingly applied in precise localization of epileptogenic focus in epilepsy patients, including pediatric patients. The aim of this international consensus is to provide the guideline and specific considerations for [18F]-FDG PET in pediatric patients affected by epilepsy. Methods: An international, multidisciplinary task group is formed, and the guideline for brain [18F]-FDG PET/CT in pediatric epilepsy patients has been discussed and approved, which include but not limited to the clinical indications, patient preparation, radiopharmaceuticals and administered activities, image acquisition, image processing, image interpretation, documentation and reporting, etc. Conclusion: This is the first international consensus and practice guideline for brain [18F]-FDG PET/CT in pediatric epilepsy patients. It will be an international standard for this purpose in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Malformation of cortical development (MCD) is one of the most common causes of pharmacoresistant epilepsy. Improving the knowledge of antiseizure medications (ASMs) treatment response in epileptic patients with MCD is crucial for optimal treatment options, either pharmacological therapy or non-pharmacological intervention. Aim: To investigate the patterns of medical treatment outcome and the predictors for seizure freedom (SF) with ASM regimens in epilepsy caused by MCD. Methods: The epileptic patients with MCD were consecutively enrolled from March 2013 to June 2019. SF was defined as no seizures for at least 12 months or three times the longest pretreatment inter-seizure interval, whichever was longer. Outcomes were classified into three patterns: pattern A: patients achieved SF at one point and remained so throughout follow-up; pattern B: patients' seizures fluctuated between periods of SF and relapse; pattern C: SF never attained. The terminal SF was defined if the patients remained SF at the last follow-up visit. Results: A total of 164 epileptic patients with MCD were included. Pattern A was observed in 22, pattern B in 42, and pattern C in 100 patients. SF was ever achieved in 64 (pattern A and B) patients. Twenty-nine patients had terminal SF after a median follow-up time of 4.3 years. With continuing ASM treatment, seizure relapse risk was very low after a 5-year seizure-free period. The pretreatment seizure frequency was the only independent predictor for pattern A and seizure relapse. Sodium channel blockers monotherapy (33.8%) was more effective than levetiracetam (4.5%) in rendering SF in the initial ASM regimen. Conclusion: Medical treatment can be successful in a minority of epileptic patients with MCD, and pretreatment seizure frequency helps to predict the treatment outcome. An unequal efficacy of ASMs in epilepsy caused by MCD suggests etiological evaluation is vital in the management of focal epilepsy. [ABSTRACT FROM AUTHOR]

Objectives: Low‐grade tumors are the most common neoplasms inducing focal epilepsy; however, the short‐ and medium‐term efficacy of surgery in epilepsy patients with low‐grade tumors remains underappreciated. This study aims to summarize the clinical characteristics of epilepsy patients with low‐grade tumors and to identify factors associated with postsurgical seizure‐free outcomes. Methods: We retrospectively reviewed consecutive patients with low‐grade tumors who underwent subsequent epilepsy surgery in our epilepsy center, between 2012 and 2018 with a minimum follow‐up of 1 year. Using Engel's classification and Kaplan–Meier survival analysis, we assessed postoperative seizure freedom over time. Demographical, electroclinical, and other presurgical evaluations were then evaluated for association with postoperative seizure outcome. Results: The cohort included a total of 132 patients: 79 males and 53 females. Among them, 110 (83.33%) were seizure‐free through their last follow‐up. The Engel class I outcomes were 90.15%, 87.76%, 85.53%, 82.46%, and 73.17% at the end of the 1st, 2nd, 3rd, 4th, and 5th postoperative years, respectively. Multivariate logistic analysis revealed that longer epilepsy duration (p < 0.001, OR 1.091, 95% CI 1.040–1.144) and incomplete resection (p = 0.009, OR 3.673, 95% CI 1.393–9.684) were independently associated with seizure recurrence through the last follow‐up. Conclusions: Surgical treatment for seizure control in patients with low‐grade tumors provides excellent short‐ and median‐term outcomes. [ABSTRACT FROM AUTHOR]

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy. Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study. Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature "age at seizure onset" with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD. Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes. [ABSTRACT FROM AUTHOR]

Responsive neurostimulation (RNS) is becoming a promising therapy in refractory epilepsy control. In a RNS system, a critical challenge is how to detect seizure onsets accurately with low computational costs. In this study, an energy efficient AdaBoost cascade method for robust long-term seizure detection from local field potential (LFP) signals was proposed and evaluated in a portable neurostimulator. The AdaBoost cascade method included two stages: a seizure candidate detection stage (stage1) and a false alarm rejection stage (stage2). Since seizure activities occurred occasionally in most cases, most normal signal segments can be efficiently classified in stage1. A small percent of suspicious segments were fed into stage2 for more strict examination, where more sophisticated features were extracted to precisely identify seizure activities and reduce false alarms. To further optimize energy efficiency for hardware implementation, we proposed a soft-cascade algorithm for stage2 to reduce the computational costs. Our method was implemented in a generalized neurostimulator and evaluated online with four rats with chronic temporal lobe epilepsy (TLE). A total of 2280.1 hours of LFP signals were recorded and analyzed. Our approach achieves a detection rate of 91.6%, 3.85/h false alarm rate, and 2.31 second detection delay. With the two-stage cascade approach, 98.13% of computational costs could be reduced, with respect to the time of calculation of all features. Our method can detect seizure onsets precisely with high energy efficiency, which is suitable for hardware implementation in portable neuro-stimulators. Therefore, this proposed approach is promising to provide effective and robust performances in long-term seizure detection in neurostimulators for responsive seizure control. [ABSTRACT FROM AUTHOR]

The closed-loop electrical stimulation is emerging as a promising neural modulation therapy for refractory epilepsy. However, the efficacy of electrical stimulation is less than optimal and the mechanism of seizure control is still unclear. In this paper, we evaluated the acute seizure control efficacy of the multi-site closed-loop stimulation (MSCLS) in a rodent model with a custom designed closed-loop neurostimulator. A total of 18 rats were injected with kainic-acid in CA3 of the left hippocampus to induce acute temporal lobe seizures. Instead of single target stimulation, four target sites in left hemisphere including CA1 and CA3 of the hippocampus, sub-thalamic nucleus, and M1 region of the motor cortex were selected for both recording and stimulation. A low-cost efficient multi-site seizure detection algorithm was implemented in the neurostimulator for MSCLS. With MSCLS treatment, the rats without status-epilepsy (SE) significantly reduced the seizure duration and the number of generalized seizures in each site. When considering the rats developed SE, the MSCLS could also alleviate the seizure severity, but had little effect on the seizure duration and seizure number. In conclusion, although the efficacy of MSCLS was still limited by the stimulation sites, stimulation parameters, and seizure model chosen in this paper, the MSCLS itself would be a promising direction for the refractory seizure treatment. [ABSTRACT FROM AUTHOR]

Objective: To investigate the neuroimaging and clinical features associated with sleep-related epilepsy (SRE) in patients with focal cortical dysplasia (FCD) type II. Methods: Patients with histopathologically proven FCD type II were included from three epilepsy centers. SRE was defined according to the video EEG findings and seizure history. Cortical surface reconstruction and volume calculation were performed using FreeSurfer. The lesions were manually delineated on T1 volumetric MRI using the ITK-SNAP software. The lesion volumes were normalized by the intracranial volume of each patient. The lesions were classified as small or large by placing a threshold based on quantitative (whether the lesion was detected on MRI report) and qualitative (volume) criteria. results: A total of 77 consecutive patients were included. Of them, 36 had SRE and 41 had non-SRE. An earlier age of epilepsy onset, high seizure frequency, regional interictal EEG findings, and favorable surgical outcome were characteristic in both groups. Small lesions were defined as those having a volume <3,217 mm3. In total, 60.9% of the patients with SRE (25/41) had small FCD lesion, which was significantly higher than the non-SRE group (9/34, 26.5%, p = 0.005). Small lesion size was the only predictor significantly associated with SRE in the overall type II group by multivariate analyses (p = 0.016). Although the proportion of patients who had frontal FCD and SRE was higher than non-frontal FCD (54.5 vs. 27.3%, p = 0.043), the relationship between SRE and lesion location was not confirmed by multivariate analysis. Thalamic volume and seizure semiology were not statistically different between the SRE and non-SRE group. The significant association between lesion size and SRE was reproducible in type IIb and IIa subgroups. significance: SRE is common in patients with FCD type II. Small FCD type II lesions are significantly associated with SRE. Although our findings cannot be applied to the entire spectrum of SRE, potential existence of small FCD lesions should be considered when evaluating patients with SRE, and utilization of all other supportive electroclinical information for lesion detection is highly desirable. [ABSTRACT FROM AUTHOR]

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures. [ABSTRACT FROM AUTHOR]