Juvenile myoclonic epilepsy (JME) is associated with brain dysconnectivity in the default mode network (DMN). Most previous studies of patients with JME have assessed static functional connectivity in terms of the temporal correlation of signal intensity among different brain regions. However, more recent studies have shown that the directionality of brain information flow has a more significant regional impact on patients\' brains than previously assumed in the present study. Here, we introduced an empirical approach incorporating independent component analysis (ICA) and spectral dynamic causal modeling (spDCM) analysis to study the variation in effective connectivity in DMN in JME patients. We began by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data from 37 patients and 37 matched controls. Then, we selected 8 key nodes within the DMN using ICA; finally, the key nodes were analyzed for effective connectivity using spDCM to explore the information flow and detect patient abnormalities. This study found that compared with normal subjects, patients with JME showed significant changes in the effective connectivity among the precuneus, hippocampus, and lingual gyrus (p < 0.05 with false discovery rate (FDR) correction) with most of the effective connections being strengthened. In addition, previous studies have found that the self-connection of normal subjects\' nodes showed strong inhibition, but the self-connection inhibition of the anterior cingulate cortex and lingual gyrus of the patient was decreased in this experiment (p < 0.05 with FDR correction); as the activity in these areas decreased, the nodes connected to them all appeared abnormal. We believe that the changes in the effective connectivity of nodes within the DMN are accompanied by changes in information transmission that lead to changes in brain function and impaired cognitive and executive function in patients with JME. Overall, our findings extended the dysconnectivity hypothesis in JME from static to dynamic causal and demonstrated that aberrant effective connectivity may underlie abnormal brain function in JME patients at early phase of illness, contributing to the understanding of the pathogenesis of JME.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11571-023-09994-4.

Sleep disturbances significantly impact the lives of individuals with Juvenile Myoclonic Epilepsy (JME). This study aimed to investigate sleep studies, disturbances, and the impact of anti-seizure drugs on sleep in JME patients. Relevant studies were retrieved from the National Library of Medicine (Pubmed) database and the Cochrane Library utilizing the search terms \"Juvenile Myoclonic Epilepsy\" and \"sleep\". A total of 160 papers\' review, data extraction, and resolution of discrepancies were performed independently by two reviewers according to the PRISMA protocol and were registered in PROSPERO (CRD42023472439). A systematic review of 31 studies was conducted, encompassing various methodologies, including sleep questionnaires (Pittsburgh Sleep Quality Index (n = 13), Epworth Sleepiness Scale (n = 10)), polysomnography (n = 8), EEG (n = 9), actigraphy (n = 1), and transcranial magnetic stimulation (n = 1). Most studies were hospital-based (n = 31), cross-sectional (n = 11), and prospective (n = 25). Patients with JME exhibit a higher prevalence of sleep disturbances, worse quality of sleep (n = 4), daytime sleepiness (n = 2), sleep efficiency (n = 7), and increased sleep latency (n = 1) compared to controls. These disruptions are characterized by increased wakefulness (n = 3), frequent arousals (n = 3), decreased REM sleep (n = 2), and conflicting NREM sleep findings (n = 3). Additional sleep-related issues observed in JME patients include insomnia (n = 1) and increased prevalence of parasomnias such as nightmares and sleep talking. Periodic limb movement and obstructive sleep apnea are similar or less frequent (3/28). REM behavioral disorders and sleepwalking were not seen. Valproate showed conflicting effects on sleep (n = 7), while levetiracetam did not impact sleep (n = 1). These findings underlined the need for more sufficient evidence of sleep studies in JME. Future research should prioritize understanding the nature of sleep in JME and its impact on management.

BACKGROUND: To explore the time-frequency structure and cross-scale coupling of electroencephalography (EEG) signals during seizure in juvenile myoclonic epilepsy (JME), correlations between different leads, as well as dynamic evolution in epileptic discharge, progression and end of seizure were examined.
METHODS: EEG data were obtained for 10 subjects with JME and 10 normal controls and were decomposed using gauss continuous wavelet transform (CWT). The phase amplitude coupling (PAC) relationship between the 11th (4.57 Hz) and 17th (0.4 Hz) scale was investigated. Correlations were examined between the 11th and 17th scale EEG signals in different leads during seizure, using multi-scale cross correlation analysis.
RESULTS: The time-frequency structure of JME subjects showed strong rhythmic activity in the 11th and 17th scales and a close PAC was identified. Correlation analysis revealed that the ictal JME correlation first increased in the anterior head early in seizure and gradually expanded to the posterior head.
CONCLUSIONS: PAC was exhibited between the 11th and 17th scales during JME seizure. The results revealed that the correlation in the anterior leads was higher than the posterior leads. In the perictal period, the 17th scale EEG signal preceded the 11th scale signal and remained for some time after a seizure. This suggests that the 17th scale signal may play an important role in JME seizure.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

BACKGROUND: Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins.
METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients\' and controls\' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels.
RESULTS: Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN.
CONCLUSIONS: Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients\' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.

BACKGROUND: There are rising evidences that subcortical structures, including the basal ganglia, are affected in patients with epilepsy. These structures are thought to influence the modulation and phenotypic expression of epileptic seizures. Our study aimed to evaluate the presence of structural abnormalities in subcortical structures in patients with juvenile myoclonic epilepsy (JME).
METHODS: This cross-sectional study included 51 patients who were diagnosed with JME and who were monitored on an outpatient basis at the Clinic for Neurology and Psychiatry for Children and Youth in Belgrade from January 1985 to October 2017. All patients underwent transcranial parenchymal sonography (TCS) from October 2015 to October 2017. Relation of clinical parameters (seizure control andcognitive functioning,) with TCS results was assessed.
RESULTS: Hyperechogenicity of the substantia nigra (SN) was detected in 37.2% of JME subjects and it was significantly more common in patients with JME than in the control group. The marked echogenicity of the red nucleus (RN) was detected in 17.6% of cases, while 11.8% of subjects had hyperechogenic RN. The presence of hyperechogenic RN (both right and left) was significantly more frequent in the group of patients with JME compared to the control group. The third ventricle diameter was larger in patients with JME than in controls.
CONCLUSIONS: Structural changes of certain subcortical structures, primarily SN and RN, detected in JME patients indicate additional non-lesional abnormalities of the basal ganglia and midbrain structures in these patients.

UNASSIGNED: A third of patients with epilepsy continue to have seizures despite receiving adequate antiseizure medication. Transcranial direct current stimulation (tDCS) might be a viable adjunct treatment option, having been shown to reduce epileptic seizures in patients with focal epilepsy. Evidence for the use of tDCS in genetic generalized epilepsy (GGE) is scarce. We aimed to establish the feasibility of applying tDCS during fMRI in patients with GGE to study the acute neuromodulatory effects of tDCS, particularly on sensorimotor network activity.
UNASSIGNED: Seven healthy controls and three patients with GGE received tDCS with simultaneous fMRI acquisition while watching a movie. Three tDCS conditions were applied: anodal, cathodal and sham. Periods of 60 s without stimulation were applied between each stimulation condition. Changes in sensorimotor cortex connectivity were evaluated by calculating the mean degree centrality across eight nodes of the sensorimotor cortex defined by the Automated Anatomical Labeling atlas (primary motor cortex (precentral left and right), supplementary motor area (left and right), mid-cingulum (left and right), postcentral gyrus (left and right)), across each of the conditions, for each participant.
UNASSIGNED: Simultaneous tDCS-fMRI was well tolerated in both healthy controls and patients without adverse effects. Anodal and cathodal stimulation reduced mean degree centrality of the sensorimotor network (Friedman\'s ANOVA with Dunn\'s multiple comparisons test; adjusted p = 0.02 and p = 0.03 respectively). Mean degree connectivity of the sensorimotor network during the sham condition was not different to the rest condition (adjusted p = 0.94).
UNASSIGNED: Applying tDCS during fMRI was shown to be feasible and safe in a small group of patients with GGE. Anodal and cathodal stimulation caused a significant reduction in network connectivity of the sensorimotor cortex across participants. This initial research supports the feasibility of using fMRI to guide and understand network modulation by tDCS that might facilitate its clinical application in GGE in the future.

OBJECTIVE: Myotonia is a clinical sign typical of a group of skeletal muscle channelopathies, the non-dystrophic myotonias. These disorders are electrophysiologically characterized by altered membrane excitability, due to specific genetic variants in known causative genes (CLCN1 and SCN4A). Juvenile Myoclonic Epilepsy (JME) is an epileptic syndrome identified as idiopathic generalized epilepsy, its genetics is complex and still unclarified. The co-occurrence of these two phenotypes is rare and the causes likely have a genetic background. In this study, we have genetically investigated an Italian family in which co-segregates myotonia, JME, or abnormal EEG without seizures was observed.
METHODS: All six individuals of the family, 4 affected and 2 unaffected, were clinically evaluated; EMG and EEG examinations were performed. For genetic testing, Exome Sequencing was performed for the six family members and Sanger sequencing was used to confirm the candidate variant.
RESULTS: Four family members, the mother and three siblings, were affected by myotonia. Moreover, EEG recordings revealed interictal generalized sharp-wave discharges in all affected individuals, and two siblings were affected by JME. All four affected members share the same identified variant, c.644 T > C, p.Ile215Thr, in SCN4A gene. Variants that could account for the epileptic phenotype alone, separately from the myotonic one, were not identified.
CONCLUSIONS: These results provide supporting evidence that both myotonic and epileptic phenotypes could share a common genetic background, due to variants in SCN4A gene. SCN4A pathogenic variants, already known to be causative of myotonia, likely increase the susceptibility to epilepsy in our family.
CONCLUSIONS: This study analyzed all members of an Italian family, in which the mother and three siblings had myotonia and epilepsy. Genetic analysis allowed to identify a variant in the SCN4A gene, which appears to be the cause of both clinical signs in this family.

A key aspect of management of genetic generalized epilepsy involves assessing seizure control and deciding suitability for driving motor vehicles. We surveyed child neurologists and pediatric epileptologists on key questions that practitioners should ask prior to providing clearance for driving. The results showed a wide variability of practice among responders. We propose a likely appropriate process necessary to determine seizure control.