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). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. RESULTS: FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. SIGNIFICANCE: We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

De novo variant in KIF26B is associated with pontocerebellar hypoplasia with infantile spinal muscular atrophy.

KIF26B is a member of the kinesin superfamily with evolutionarily conserved functions in controlling aspects of embryogenesis, including the development of the nervous system, though its function is incompletely understood. We describe an infant with progressive microcephaly, pontocerebellar hypoplasia, and arthrogryposis secondary to the involvement of anterior horn cells and ventral (motor) nerves. We performed whole exome sequencing on the trio and identified a de novo KIF26B missense variant, p.Gly546Ser, in the proband. This variant alters a highly conserved amino acid residue that is part of the phosphate-binding loop motif and motor-like domain and is deemed pathogenic by several in silico methods. Functional analysis of the variant protein in cultured cells revealed a reduction in the KIF26B protein's ability to promote cell adhesion, a defect that potentially contributes to its pathogenicity. Overall, KIF26B may play a critical role in the brain development and, when mutated, cause pontocerebellar hypoplasia with arthrogryposis.

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. Three types of tubers were identified based on the MRI signal intensity of their subcortical white matter component. (1) Tubers Type A are isointense on volumetric T1 images and subtly hyperintense on T2 weighted and fluid-attenuated inversion recovery (FLAIR); (2) Type B are hypointense on volumetric T1 images and homogeneously hyperintense on T2 weighted and FLAIR; (3) Type C are hypointense on volumetric T1 images, hyperintense on T2 weighted, and heterogeneous on FLAIR characterized by a hypointense central region surrounded by a hyperintense rim. Based on the dominant tuber type present, three distinct patient groups were also identified: Patients with Type A tuber dominance have a milder phenotype. Patients with Type C tuber dominance have more MRI abnormalities such as subependymal giant cell tumors, and were more likely to have an autism spectrum disorder, a history of infantile spasms, and a higher frequency of epileptic seizures, compared to patients who have a dominance in Type B tubers, and especially to those with a Type A dominance.

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. Event-related fMRI was used to study differences in brain activation between healthy children ages 6 and 10 years, and young adults (20-28 years). Performance accuracy and RTs in 10-year-olds and adults were comparable, but the performance in 6-year-olds was lower. In adults, the CN-BT was highly effective in engaging the bilateral (L>R) ventral prefrontal cortex, the bilateral fusiform gyrus, posterior cingulate and precuneus, thus suggesting an involvement of the ventral visual stream, with related feature extraction and semantic labeling strategies. In children, the brain networks were distinctly different. They involved the premotor and parietal cortex, anterior insula, caudate/putamen, and the cerebellum, thus suggesting a predominant involvement of the visual dorsal and sensory-motor pathways, with related visual-spatial and action cognitive strategies. The findings indicate engagement of developmental networks in children reflecting task-effective brain activation. The age-related pattern of fMRI activation suggests a working hypothesis of a developmental shift from reliance on the dorsal visual stream and premotor/striatal/cerebellar networks in young children to reliance on the ventral prefrontal and inferior temporal networks in adults.