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Objectives: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. Methods: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. Results: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. Conclusions: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. Significance: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level. [ABSTRACT FROM AUTHOR]

Background: Low-grade epilepsy-associated neuroepithelial tumors (LEATs) are a frequent etiology in pediatric patients with epilepsy undergoing surgery. Objective: To identify differences in clinical and post-surgical follow-up between patients with focal meningeal involvement (MI) and those without MI within our cohort of pediatric patients with LEATs. Methods: We retrospectively reviewed all pediatric patients (<18 y) who underwent epilepsy surgery between 2011 and 2017 at our hospital. Cohort inclusion required histological diagnosis of LEATs and post-surgical follow-up of ≥2 y. We subsequently stratified patients according to presence of neuroradiological MI. Results: We identified 37 patients: five with MI and 32 without. Half of patients (19) were drug sensitive at surgery; similar between groups. The group with MI differed mainly for age of epilepsy-onset (0.6 vs. 7.0 y) but not for epilepsy duration (0.9 vs. 1.5 y). Post-surgery radiological follow-up (median 4.0 y; IQR 2.8–5.0 y) did not indicate disease progression. Seizure outcome was excellent in both groups, with 34 patients overall being both drug- and seizure-free. Conclusions: Our study identified a new subgroup of LEATs with focal MI and excellent post-surgical outcome. Moreover, this highlights the effectiveness of early surgery in pediatric LEATs. [ABSTRACT FROM AUTHOR]

• Ictal phase-amplitude coupling (PAC) between high-frequency and low-frequency activity can be used as a preoperative biomarker of focal cortical dysplasia (FCD) subtypes. • Ictal PAC was significantly higher in patients with FCD type II compared to type I, only on the electrodes of the seizure-onset zone (SOZ). • Ictal PAC applied to SOZ-electrodes during stereo-EEG offers a high predictive value of FCD histopathology. To evaluate whether ictal phase-amplitude coupling (PAC) between high-frequency activity and low-frequency activity could be used as a preoperative biomarker of Focal Cortical Dysplasia (FCD) subtypes. We hypothesize that FCD seizures present unique PAC characteristics that may be linked to their specific histopathological features. We retrospectively examined 12 children with FCD and refractory epilepsy who underwent successful epilepsy surgery. We identified ictal onsets recorded with stereo-EEG. We estimated the strength of PAC between low-frequencies and high-frequencies for each seizure by means of modulation index. Generalized mixed effect models and receiver operating characteristic (ROC) curve analysis were used to test the association between ictal PAC and FCD subtypes. Ictal PAC was significantly higher in patients with FCD type II compared to type I, only on SOZ-electrodes (p < 0.005). No differences in ictal PAC were found on non-SOZ electrodes. Pre-ictal PAC registered on SOZ electrodes predicted FCD histopathology with a classification accuracy > 0.9 (p < 0.05). The correlations between histopathology and neurophysiology provide evidence for the contribution of ictal PAC as a preoperative biomarker of FCD subtypes. Developed into a proper clinical application, such a technique may help improve clinical management and facilitate the prediction of surgical outcome in patients with FCD undergoing stereo-EEG monitoring. [ABSTRACT FROM AUTHOR]