Ictal EEG desynchronization during low-voltage fast activity for prediction of surgical outcomes in focal 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.

Clinical Characteristics and Prognostic Significance of Subclinical Seizures in Focal Epilepsy: A Retrospective Study.

INTRODUCTION: The aim was to evaluate the clinical characteristics and prognostic significance of subclinical seizures (SCSs) on scalp video-electroencephalogram (VEEG) monitoring with or without intracranial electroencephalogram (IEEG) monitoring in patients who had epilepsy surgery. METHODS: We reviewed 286 epileptic patients who underwent subsequent epilepsy surgery during scalp-VEEG evaluation with or without IEEG monitoring between 2013 and 2020, with a minimum follow-up of 1 year. The prevalence and clinical characteristics of SCSs, as well as their prognostic significance, were analyzed. RESULTS: A total of 286 patients were enrolled for analysis, and 80 patients had IEEG implanted. SCSs were recorded in 9.79% of the patients based on VEEG and 50% based on IEEG. In the VEEG group (n = 286), younger seizure onset (P = 0.004) was associated with the presence of s-SCSs (SCSs detected on scalp VEEG). In the IEEG group (n = 80), temporal lobe epilepsy (P = 0.015) was associated with the presence of i-SCSs (SCSs detected on IEEG). Of 286 patients, 208 (72.73%) were seizure-free in the VEEG group, and 56 0f 80 patients (70%) were seizure-free in the IEEG group through the last follow-up. In the VEEG group, the presence of s-SCSs did not affect seizure outcome; predictors of seizure recurrence were longer epilepsy duration (P = 0.003, OR 1.003, 95% CI 1.001-1.005), history of focal to bilateral tonic-clonic seizure (P = 0.027, OR 1.665, 95% CI 1.060-2.613), nonspecific pathology (P = 0.018, OR 2.184, 95% CI 1.145-4.163), and incomplete resection (P = 0.004, OR 2.705, 95% CI 1.372-5.332). In the IEEG group, i-SCSs were significantly associated with seizure outcome (P = 0.028, OR 0.371, 95% CI 0.153-0.898). CONCLUSION: The rate of SCSs captured on IEEG monitoring was higher than that on VEEG monitoring during presurgical evaluation. SCSs detected on VEEG monitoring were associated with younger seizure onset. SCSs detected on IEEG monitoring were associated with temporal lobe epilepsy and also predicted surgical outcomes in focal epilepsy.

Focal cortical dysplasia links to sleep-related epilepsy in symptomatic focal epilepsy.

OBJECTIVE: In sleep-related epilepsy (SRE), epileptic seizures predominantly occur during sleep, but the clinical characteristics of SRE remain elusive. We aimed to identify the clinical features associated with the occurrence of SRE in a large cohort of symptomatic focal epilepsy. METHODS: We retrospectively included patients with four etiologies, including focal cortical dysplasia (FCD), low-grade tumors (LGT), temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), and encephalomalacia. SRE was defined as more than 70% of seizures occurring during sleep according to the seizure diary. The correlation between SRE and other clinical variables, such as etiology of epilepsy, pharmacoresistance, seizure frequency, history of bilateral tonic-clonic seizures, and seizure localization was analyzed. RESULTS: A total of 376 patients were included. Among them 95 (25.3%) were classified as SRE and the other 281(74.7%) as non-SRE. The incidence of SRE was 53.5% in the FCD group, which was significantly higher than the other three groups (LGT: 19.0%; TLE-HS: 9.9%; encephalomalacia: 16.7%; P < 0.001). The etiology of FCD (p < 0.001) was significantly associated with SRE (OR: 9.71, 95% CI: 3.35-28.14) as an independent risk factor. In addition, small lesion size (p = 0.009) of FCD further increased the risk of SRE (OR: 3.18, 95% CI: 1.33-7.62) in the FCD group. SIGNIFICANCE: Our data highlight that FCD markedly increased the risk of sleep-related epilepsy independently of seizure localization. A small lesion of FCD further increased the risk of sleep-related epilepsy by 2.18 times in the FCD group.

Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning.

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.

An insulinoma with clinical and electroencephalographic features resembling complex partial seizures.

We described a female patient with insulinoma who experienced recurrent episodes of automatism, confusion and convulsion. Furthermore, her electroencephalography (EEG) findings resembled the pattern in complex partial seizures with secondary generalization. The interictal EEG showed spikes and sharp waves, as well as focal slowing over the left temporal lobe, and the ictal EEG revealed generalized spikes and sharp waves associated with diffused slowing. She was initially misdiagnosed as pharmacoresistant epilepsy. After the insulinoma was found and surgically removed, her EEG turned normal and she was seizure-free during the 4-year follow-up. This report highlights the need for careful reassessment of all seizures refractory to medication, even for the patients associated with epileptiform discharges on EEG.