Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.

Objective: To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.

Methods: We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes).

MRI findings reveal three different types of tubers in patients with tuberous sclerosis complex.

Cortical tubers are very common in tuberous sclerosis complex (TSC) and widely vary in size, appearance and location. The relationship between tuber features and clinical phenotype is unclear. The aim of the study is to propose a classification of tuber types along a spectrum of severity, using magnetic resonance imaging (MRI) characteristics in 35 patients with TSC and history of epilepsy, and to investigate the relationship between tuber types and genetics, as well as clinical manifestations.

Developmental neural networks in children performing a Categorical N-Back Task.

The prefrontal and temporal networks subserving object working memory tasks in adults have been reported as immature in young children; yet children are adequately capable of performing such tasks. We investigated the basis of this apparent contradiction using a complex object working memory task, a Categorical n-back (CN-BT). We examined whether the neural networks engaged by the CN-BT in children consist of the same brain regions as those in adults, but with a different magnitude of activation, or whether the networks are qualitatively different.