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The aetiology of nodding syndrome remains unclear, and comprehensive genotyping and phenotyping data from patients remain sparse. Our objectives were to characterize the phenotype of patients with nodding syndrome, investigate potential contributors to disease aetiology, and evaluate response to immunotherapy. This cohort study investigated members of a single-family unit from Lamwo District, Uganda. The participants for this study were selected by the Ugandan Ministry of Health as representative for nodding syndrome and with a conducive family structure for genomic analyses. Of the eight family members who participated in the study at the National Institutes of Health (NIH) Clinical Center, three had nodding syndrome. The three affected patients were extensively evaluated with metagenomic sequencing for infectious pathogens, exome sequencing, spinal fluid immune analyses, neurometabolic and toxicology testing, continuous electroencephalography and neuroimaging. Five unaffected family members underwent a subset of testing for comparison. A distinctive interictal pattern of sleep-activated bursts of generalized and multifocal epileptiform discharges and slowing was observed in two patients. Brain imaging showed two patients had mild generalized cerebral atrophy, and both patients and unaffected family members had excessive metal deposition in the basal ganglia. Trace metal biochemical evaluation was normal. CSF was non-inflammatory and one patient had CSF-restricted oligoclonal bands. Onchocerca volvulus-specific antibodies were present in all patients and skin snips were negative for active onchocerciasis. Metagenomic sequencing of serum and CSF revealed hepatitis B virus in the serum of one patient. Vitamin B6 metabolites were borderline low in all family members and CSF pyridoxine metabolites were normal. Mitochondrial DNA testing was normal. Exome sequencing did not identify potentially causal candidate gene variants. Nodding syndrome is characterized by a distinctive pattern of sleep-activated epileptiform activity. The associated growth stunting may be due to hypothalamic dysfunction. Extensive testing years after disease onset did not clarify a causal aetiology. A trial of immunomodulation (plasmapheresis in two patients and intravenous immunoglobulin in one patient) was given without short-term effect, but longer-term follow-up was not possible to fully assess any benefit of this intervention.

In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs. However, traditional GNNs are limited in their ability to characterize FC edge attributes because they typically emphasize the importance of ROI node-based brain activation data. Line GNNs convert the edges of the original graph to nodes in the transformed graph, thereby emphasizing the FC between brain regions. We hypothesize that line GNNs will outperform traditional GNNs in FC applications. We investigated the performance of two common GNN architectures (GraphSAGE and GCN) trained on line and traditional graphs predicting task-associated FC changes across two datasets. The first dataset was from the Human Connectome Project (HCP) with 205 participants, the second was a dataset with 12 participants. The HCP dataset detailed FC changes in participants during a story-listening task, while the second dataset included the FC changes in a different auditory language task. Our findings from the HCP dataset indicated that line GNNs achieved lower mean squared error compared to traditional GNNs, with the line GraphSAGE model outperforming the traditional GraphSAGE by 18% (p < 0.0001). When applying the same models to the second dataset, both line GNNs also showed statistically significant improvements over their traditional counterparts with little to no overfitting. We believe this shows that line GNN models demonstrate promising utility in FC studies. [ABSTRACT FROM AUTHOR]

Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG. [ABSTRACT FROM AUTHOR]

The blood–brain barrier (BBB) is a barrier protecting the brain and a milieu of continuous exchanges between blood and brain. There is emerging evidence that the BBB plays a major role in epileptogenesis and drug‐resistant epilepsy, through several mechanisms, such as water homeostasis dysregulation, overexpression of drug transporters, and inflammation. Studies have shown abnormal water homeostasis in epileptic tissue and altered aquaporin‐4 water channel expression in animal epilepsy models. This review focuses on abnormal water exchange in epilepsy and describes recent non‐invasive MRI methods of quantifying water exchange. Plain Language Summary: Abnormal exchange between blood and brain contribute to seizures and epilepsy. The authors describe why correct water balance is necessary for healthy brain functioning and how it is impacted in epilepsy. This review also presents recent MRI methods to measure water exchange in human brain. These measures would improve our understanding of factors leading to seizures. [ABSTRACT FROM AUTHOR]

Seizures occur in up to 59% of boys with creatine transporter deficiency (CTD). While seizure phenotypes have been previously described, electroencephalogram (EEG) findings have only been reported in several case reports. In this prospective observational study, we report seizure characteristics and EEG findings in combination with neurobehavioral and SLC6A8 pathogenic variants in twenty males with CTD. Eighteen study participants (SP) underwent video‐EEG, and seven had follow‐up EEG recordings. Seizures typically occurred by age of 2 years. Thirteen (65%) had non‐febrile seizures, requiring anti‐seizure medications in nine. Four had febrile seizures. Seizures were bilateral tonic–clonic in 7 SP and focal impaired awareness in 5 SP; often responding to 1 to 2 antiseizure medications. EEG showed slowing in 5 SP, beta activity in 6 SP, and focal/multifocal, and/or generalized epileptiform activity in 9 SP. Follow‐up EEGs in 7 SP showed emergence of epileptiform activity in 1 SP, and increased activity in 2 SP. In conclusion, seizures were frequent in our cohort but tended to respond to antiseizure medications. Longitudinal follow up provided further insight into emergence of seizures and EEG abnormalities soliciting future studies with long term follow up. Biomarkers of epileptogenicity in CTD are needed to predict seizures in this population. [ABSTRACT FROM AUTHOR]