Background: Juvenile myoclonus epilepsy (JME) is an idiopathic generalized epilepsy syndrome. Functional connectivity studies based on graph theory have demonstrated changes in functional connectivity among different brain regions in patients with JME and healthy controls. However, previous studies have not been able to clarify why visual stimulation or increased cognitive load induces epilepsy symptoms in only some patients with JME.Methods: This study constructed a small-world network for the visualization of functional connectivity of brain regions in patients with JME, based on system mapping. We used the node reduction method repeatedly to identify the core nodes of the resting brain network of patients with JME. Thereafter, a functional connectivity network of the core brain regions in patients with JME was established, and it was analyzed manually with white matter tracks restriction to explain the differences in symptom distribution in patients with JME.Results: Patients with JME had 21 different functional connections in their resting state, and no significant differences in their distribution were noted. The thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal lobe were the core brain regions that comprised the functional connectivity network in patients with JME during their resting state. The betweenness centrality of the prefrontal lobe and the visual cortex in the core functional connectivity network of patients with JME was lower than that of the other brain regions.Conclusion: The functional connectivity and node importance of brain regions of patients with JME changed dynamically in the resting state. Abnormal discharges originating from the thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal cortex are most likely to lead to seizures in patients with JME. Further, the low average value of betweenness centrality of the prefrontal and visual cortices explains why visual stimulation or increased cognitive load can induce epileptic symptoms in only some patients with JME.

The mainstream method for treating drug-resistant epilepsy (DRE) is surgical resection of the epileptogenic zone. Non-invasive automatic localization of epileptogenic zone can be used to guide electrode implantation and improve the effectiveness and safety of neurosurgical treatments. Previous researchers have proposed a range of methods for this purpose, but these suffer from limits such as unclear post-operative outcomes, invasiveness, limited data volume, and single DRE type. This study constructed a non-invasive epilepsy localization method, integrating sLORETA source imaging, time–frequency analysis, and Visual Geometry Group (VGG-16) deep learning. Firstly, 16-channel scalp electroencephalogram (EEG) from 25 successfully operated DRE patients were included. Secondly, time–frequency features by short-time Fourier transform (STFT), continuous wavelet transform (CWT), and superlets algorithm were extracted. Finally, the VGG-16 network was applied to automatically locate the epileptogenic zone. All three feature extraction methods achieved significant accuracy on the dataset. Using STFT for processing and combining it with VGG-16 for image classification achieved an average classification accuracy of 80.2% and a channel identification rate of 80.7% for epileptogenic zones. After processing with CWT, the accuracy increased to 81.7% and the epileptogenic zone channel recognition rate increased to 81.4%. After processing with the superlets method, the classification accuracy was further improved to 83.1%, and the epileptogenic zone channel recognition rate was increased to 83.3%. This marks the pioneering proposal of a systematic framework for non-invasive localization to the epileptogenic zone.

Background: Studies on the association between oxidative stress and epilepsy have yielded varied results. In this study, we aimed to investigate the causal relationship between oxidative stress markers and epilepsy. Methods: A bidirectional two-sample Mendelian randomization (MR) study was performed based on publicly available statistics from genome-wide association studies. To explore the causal effects, single nucleotide polymorphisms were selected as instrumental variables. Inverse-variance weighted method was performed for primary analysis, supplemented by weighted median, MR-Egger, simple mode, and weighted mode. Furthermore, sensitivity analyses were performed to detect heterogeneity and pleiotropy. Results: Our results showed that part of the oxidative stress biomarkers are associated with epilepsy and its subtypes. Zinc is associated with increased risk of epilepsy and generalized epilepsy (odds ratio [OR] = 1.064 and 1.125, respectively). Glutathione transferase is associated with increased risk of generalized epilepsy (OR = 1.055), while albumin is associated with decreased risk of generalized epilepsy (OR = 0.723). Inverse MR analysis revealed that epilepsy is associated with increased levels of uric acid and total bilirubin (beta = 1.266 and 0.081, respectively), as well as decreased zinc level (beta = − 0.278). Furthermore, generalized epilepsy is associated with decreased ascorbate and retinol levels (beta = − 0.029 and − 0.038, respectively). Conclusions: Our study presented novel evidence of potential causal relationships between oxidative stress and epilepsy, suggesting potential therapeutic targets for epilepsy.

Objective. Epilepsy may cause chronic cognitive impairment by disturbing sleep plasticity. Sleep spindles play a crucial role in sleep maintenance and brain plasticity. This study explored the relationship between cognition and spindle characteristics in adult epilepsy. Methods. Participants underwent one-night sleep electroencephalogram recording and neuropsychological tests on the same day. Spindle characteristics during N2 sleep were extracted using a learning-based system for sleep staging and an automated spindle detection algorithm. We investigated the difference between cognitive subgroups in spindle characteristics. Multiple linear regressions were applied to analyze associations between cognition and spindle characteristics. Results. Compared with no/mild cognitive impairment, epilepsy patients who developed severe cognitive impairment had lower sleep spindle density, the differences mainly distributed in central, occipital, parietal, middle temporal, and posterior temporal ( ), and had relatively long spindle duration in occipital and posterior temporal . Mini-Mental State Examination (MMSE) was associated with spindle density (pars triangularis of the inferior frontal gyrus (IFGtri): , , and ) and spindle duration (IFGtri: , , and ). Montreal Cognitive Assessment (MoCA) was associated with spindle duration (IFGtri: , , and