Analysis of Clinical Characteristics, Background, and Paroxysmal Activity in EEG of Patients with Juvenile Myoclonic Epilepsy

Juvenile myoclonic epilepsy (JME) appears in adolescence with myoclonic, absence, and generalized tonic clonic (GTC) seizures with paroxysmal activity of polyspike and slow wave (PSW), or spike and wave (SW) complexes in EEG. Our aim was to analyze the clinical characteristics, background EEG activity, and paroxysmal events in 41 patients with JME. Background EEG activity was analyzed with visual, quantitative (QEEG), and neurometric parameters. Our JME patients started with absence seizures at 11.4 ± 1.5 years old, myoclonic seizures at 13.6 ± 2.5 years, and GTC seizures at 15.1 ± 0.8 years. The seizures presented in awakening at 7:39 h with sleep deprivation, alcoholic beverage intake, and stress as the most frequent precipitant factors. Paroxysmal activity was of PSW and fast SW complexes with 40.5 ± 62.6 events/hour and a duration of 1.7 s. Right asymmetric paroxysmal activity was present in 68.3% of patients. Background EEG activity was abnormal in 31.7% of patients with visual analysis. With QEEG beta AP (absolute power) increase and AP delta decrease were the most frequent abnormalities found. Spectral analysis showed that 48.7% of patients had normal results, and 26.83% and 24.4% had higher and lower frequencies than 10.156 Hz, respectively. We concluded that, with visual analysis, background EEG activity was abnormal in a few patients and the abnormalities increased when QEEG was used.

Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy (JME) has been repeatedly revealed to be associated with brain dysconnectivity in the default mode network (DMN). However, the implicit assumption of stationary and nondirectional functional connectivity (FC) in most previous resting-state fMRI studies raises an open question of JME-related aberrations in dynamic causal properties of FC. Here, we introduces an empirical method incorporating sliding-window approach and a multivariate Granger causality analysis to investigate, for the first time, the reorganization of dynamic effective connectivity (DEC) in DMN for patients with JME. DEC was obtained from resting-state fMRI of 34 patients with newly diagnosed and drug-naïve JME and 34 matched controls. Through clustering analysis, we found two distinct states that characterize the DEC patterns (i.e., a less frequent, strongly connected state (State 1) and a more frequent, weakly connected state (State 2)). Patients showed altered ECs within DMN subnetworks in the State 2, whereas abnormal ECs between DMN subnetworks were found in the State 1. Furthermore, we observed that the causal influence flows of the medial prefrontal cortex and angular gyrus were altered in a manner of state specificity, and associated with disease severity of patients. Overall, our findings extend the dysconnectivity hypothesis in JME from static to dynamic causal FC and demonstrate that aberrant DEC may underlie abnormal brain function in JME at early phase of illness.

Genetics of Epileptic Networks: from Focal to Generalized Genetic Epilepsies

PURPOSE OF REVIEW

Seizures can arise in neocortical, thalamocortical, limbic or brainstem networks. Here, we review recent genetic mechanisms implicated in focal and genetic generalized epilepsies (GGEs).

RECENT FINDINGS

Pathogenic variation in GAP activity toward RAGs 1 (GATOR1) complex genes (i.e., DEPDC5, NPRL2 and NPRL3) mainly result in focal epilepsies. They are associated with high rates of sudden unexpected death in epilepsy and malformations of cortical development (MCD), where "two-hits" in GATOR1-related pathways are also found in MCDs. Large-scale sequencing studies continue to reveal new genetic risk (germline or somatic) variants, and new genes relevant to epileptic encephalopathies (EEs). Genes previously associated with EEs, including GABA_A receptor genes, are now known to play a role in both common focal and GGEs in individuals without intellectual disabilities. These findings suggest that there may be a common pathophysiological mechanism in GGEs and focal epilepsies. Finally, polygenic risk scores, based on common genetic variation, offer promise in helping to differentiate between GGEs and common forms of focal epilepsies. Genetic abnormalities are a significant cause of common sporadic epilepsies, epilepsies associated with inflammatory markers, and focal epilepsies with or without MCD. Future studies using genome sequencing may provide more answers to the remaining unresolved epilepsy cases.

Developmental MRI markers cosegregate juvenile patients with myoclonic epilepsy and their healthy siblings

OBJECTIVE

MRI studies of genetic generalized epilepsies have mainly described group-level changes between patients and healthy controls. To determine the endophenotypic potential of structural MRI in juvenile myoclonic epilepsy (JME), we examined MRI-based cortical morphologic markers in patients and their healthy siblings.

METHODS

In this prospective, cross-sectional study, we obtained 3T MRI in patients with JME, siblings, and controls. We mapped sulco-gyral complexity and surface area, morphologic markers of brain development, and cortical thickness. Furthermore, we calculated mean geodesic distance, a surrogate marker of cortico-cortical connectivity.

RESULTS

Compared to controls, patients and siblings showed increased folding complexity and surface area in prefrontal and cingulate cortices. In these regions, they also displayed abnormally increased geodesic distance, suggesting network isolation and decreased efficiency, with strongest effects for limbic, fronto-parietal, and dorsal-attention networks. In areas of findings overlap, we observed strong patient-sibling correlations. Conversely, neocortical thinning was present in patients only and related to disease duration. Patients showed subtle impairment in mental flexibility, a frontal lobe function test, as well as deficits in naming and design learning. Siblings' performance fell between patients and controls.

CONCLUSION

MRI markers of brain development and connectivity are likely heritable and may thus serve as endophenotypes. The topography of morphologic anomalies and their abnormal structural network integration likely explains cognitive impairments in patients with JME and their siblings. By contrast, cortical atrophy likely represents a marker of disease.

BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.

Anxiety and depression symptoms disrupt resting state connectivity in patients with genetic generalized epilepsies

OBJECTIVE

To analyze the lifetime trajectories in genetic generalized epilepsies (GGEs) and investigate the impact of symptoms of anxiety and depression on resting state functional connectivity (FC).

METHODS

Seventy-four GGE patients were classified according to the pharmacological response as seizure-free (12 patients), pharmacoresistant (PhR; 14 patients), and fluctuating (FL; 48 patients). Fifty-four subjects completed both the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), and 38 also underwent 3-T resting state functional magnetic resonance imaging. These 38 patients were subdivided into a positive group (13 patients with concurrent symptoms of depression and anxiety) and a negative group (21 asymptomatic patients and four with mild anxiety or depression symptoms). For FC analysis of resting state networks, we matched 38 healthy asymptomatic volunteers and used the UF2C toolbox running on MATLAB2017/SPM12.

RESULTS

The PhR group presented shorter duration of epilepsy (P = 0.016) and follow-up (P < 0.001) compared to the FL group. The PhR group showed higher levels (median = 20) on the BAI and BDI. Myoclonic seizures were the most difficult to control, as 50% of subjects persisted with them at last appointment, compared to generalized tonic-clonic seizures and absence seizures (<40%). Patients with concurrent anxiety and depression symptoms were 7.7 times more likely to exhibit pharmacoresistant seizures, although an increase of 1 year of epilepsy duration was associated with a decrease in the odds of presenting pharmacoresistance by a factor of 0.9. Overall, FC was altered between default mode network (DMN) and visuospatial/dorsal attention. However, only the positive group displayed abnormal FC between DMN and left executive control network, and between salience and visuospatial/dorsal attention.

SIGNIFICANCE

Our findings may help clinicians to have a better understanding of GGE clinical course and increase attention to the potential relationship of psychopathologies and brain connectivity.

Epilepsy in adults

Epilepsy is one of the most common serious brain conditions, affecting over 70 million people worldwide. Its incidence has a bimodal distribution with the highest risk in infants and older age groups. Progress in genomic technology is exposing the complex genetic architecture of the common types of epilepsy, and is driving a paradigm shift. Epilepsy is a symptom complex with multiple risk factors and a strong genetic predisposition rather than a condition with a single expression and cause. These advances have resulted in the new classification of epileptic seizures and epilepsies. A detailed clinical history and a reliable eyewitness account of a seizure are the cornerstones of the diagnosis. Ancillary investigations can help to determine cause and prognosis. Advances in brain imaging are helping to identify the structural and functional causes and consequences of the epilepsies. Comorbidities are increasingly recognised as important aetiological and prognostic markers. Antiseizure medication might suppress seizures in up to two-thirds of all individuals but do not alter long-term prognosis. Epilepsy surgery is the most effective way to achieve long-term seizure freedom in selected individuals with drug-resistant focal epilepsy, but it is probably not used enough. With improved understanding of the gradual development of epilepsy, epigenetic determinants, and pharmacogenomics comes the hope for better, disease-modifying, or even curative, pharmacological and non-pharmacological treatment strategies. Other developments are clinical implementation of seizure detection devices and new neuromodulation techniques, including responsive neural stimulation.

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.

Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.

Progressive dissociation of cortical and subcortical network development in children with new-onset juvenile myoclonic epilepsy

OBJECTIVE

Structural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children.

METHODS

We examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22). We indexed developmental brain changes using graph theory by computing network metrics based on the correlation of the cortical and subcortical structural covariance near the time of epilepsy and 2 years later.

RESULTS

Over 2 years, normally developing children showed modular cortical development and network integration between cortical and subcortical regions. In contrast, children with JME developed a highly correlated and less modular cortical network, which was atypically dissociated from subcortical structures. Furthermore, the JME group also presented higher clustering and lower modularity indices than controls, indicating weaker modules or communities. The local efficiency in JME was higher than controls across the majority of cortical nodes. Regarding network hubs, controls presented a higher number than youth with JME that were spread across the brain with ample representation from the different modules. In contrast, children with JME showed a lower number of hubs that were mainly from one module and comprised mostly subcortical structures.

SIGNIFICANCE

Youth with JME prospectively developed a network of highly correlated cortical regions dissociated from subcortical structures during the first 2 years after epilepsy onset. The cortical-subcortical network dissociation provides converging insights into the disparate literature of cortical and subcortical abnormalities found in previous studies.

Neuroscience Information Toolbox: An Open Source Toolbox for EEG-fMRI Multimodal Fusion Analysis

Recently, scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) multimodal fusion has been pursued in an effort to study human brain function and dysfunction to obtain more comprehensive information on brain activity in which the spatial and temporal resolutions are both satisfactory. However, a more flexible and easy-to-use toolbox for EEG–fMRI multimodal fusion is still lacking. In this study, we therefore developed a freely available and open-source MATLAB graphical user interface toolbox, known as the Neuroscience Information Toolbox (NIT), for EEG–fMRI multimodal fusion analysis. The NIT consists of three modules: (1) the fMRI module, which has batch fMRI preprocessing, nuisance signal removal, bandpass filtering, and calculation of resting-state measures; (2) the EEG module, which includes artifact removal, extracting EEG features (event onset, power, and amplitude), and marking interesting events; and (3) the fusion module, in which fMRI-informed EEG analysis and EEG-informed fMRI analysis are included. The NIT was designed to provide a convenient and easy-to-use toolbox for researchers, especially for novice users. The NIT can be downloaded for free at http://www.neuro.uestc.edu.cn/NIT.html, and detailed information, including the introduction of NIT, user’s manual and example data sets, can also be observed on this website. We hope that the NIT is a promising toolbox for exploring brain information in various EEG and fMRI studies.

Juvenile Myoclonic Epilepsy Presenting with Neurocognitive Impairment: A Case Report

Juvenile myoclonic epilepsy (JME) is a genetically and clinically diverse disorder which is characterized by myoclonic jerks, usually after awakening from sleep. It affects both genders equally and manifests during the second decade of life. The various precipitating factors include stress, light, sleep deprivation, and alcohol. A history of morning clumsiness supported by typical electroencephalography (EEG) findings, together with a normal clinical examination all point towards a diagnosis of JME.

We present the case of a nine-year-old girl who presented with cognitive dysfunction in addition to myoclonic jerks. She had normal brain imaging and her labs were negative for other causes of dementia. Her EEG findings revealed polyspikes with normal background activity. She was treated with antiepileptic drugs (AEDs) for control of seizures.

Myoclonic Disorders

Few movement disorders seem to make a straightforward approach to diagnosis and treatment more difficult and frustrating than myoclonus, due to its plethora of causes and its variable classifications. Nevertheless, in recent years, exciting advances have been made in the elucidation of the pathophysiology and genetic basis of many disorders presenting with myoclonus. Here, we provide a review of all of the important types of myoclonus encountered in pediatric and adult neurology, with an emphasis on the recent developments that have led to a deeper understanding of this intriguing phenomenon. An up-to-date list of the genetic basis of all major myoclonic disorders is presented. Randomized studies are scarce in myoclonus therapy, but helpful pragmatic approaches at diagnosis as well as treatment have been recently suggested.

Complex discharge-affecting networks in juvenile myoclonic epilepsy: A simultaneous EEG-fMRI study

Juvenile myoclonic epilepsy (JME) is a common subtype of idiopathic generalized epilepsies (IGEs) and is characterized by myoclonic jerks, tonic-clonic seizures and infrequent absence seizures. The network notion has been proposed to better characterize epilepsy. However, many issues remain not fully understood in JME, such as the associations between discharge-affecting networks and the relationships among resting-state networks. In this project, eigenspace maximal information canonical correlation analysis (emiCCA) and functional network connectivity (FNC) analysis were applied to simultaneous EEG-fMRI data from JME patients. The main findings of our study are as follows: discharge-affecting networks comprising the default model (DMN), self-reference (SRN), basal ganglia (BGN) and frontal networks have linear and nonlinear relationships with epileptic discharge information in JME patients; the DMN, SRN and BGN have dense/specific associations with discharge-affecting networks as well as resting-state networks; and compared with controls, significantly increased FNCs between the salience network (SN) and resting-state networks are found in JME patients. These findings suggest that the BGN, DMN and SRN may play intermediary roles in the modulation and propagation of epileptic discharges. These roles further tend to disturb the switching function of the SN in JME patients. We also postulate that emiCCA and FNC analysis may provide a potential analysis platform to provide insights into our understanding of the pathophysiological mechanism of epilepsy subtypes such as JME. Hum Brain Mapp 37:3515-3529, 2016. © 2016 Wiley Periodicals, Inc.

Chromosome loci vary by juvenile myoclonic epilepsy subsyndromes: linkage and haplotype analysis applied to epilepsy and EEG 3.5-6.0 Hz polyspike waves

Juvenile myoclonic epilepsy (JME), the most common genetic epilepsy, remains enigmatic because it is considered one disease instead of several diseases. We ascertained three large multigenerational/multiplex JME pedigrees from Honduras with differing JME subsyndromes, including Childhood Absence Epilepsy evolving to JME (CAE/JME; pedigree 1), JME with adolescent onset pyknoleptic absence (JME/pA; pedigree 2), and classic JME (cJME; pedigree 3). All phenotypes were validated, including symptomatic persons with various epilepsies, asymptomatic persons with EEG 3.5-6.0 Hz polyspike waves, and asymptomatic persons with normal EEGs. Two-point parametric linkage analyses were performed with 5185 single-nucleotide polymorphisms on individual pedigrees and pooled pedigrees using four diagnostic models based on epilepsy/EEG diagnoses. Haplotype analyses of the entire genome were also performed for each individual. In pedigree 1, haplotyping identified a 34 cM region in 2q21.2-q31.1 cosegregating with all affected members, an area close to 2q14.3 identified by linkage (Z max = 1.77; pedigree 1). In pedigree 2, linkage and haplotyping identified a 44 cM cosegregating region in 13q13.3-q31.2 (Z max = 3.50 at 13q31.1; pooled pedigrees). In pedigree 3, haplotyping identified a 6 cM cosegregating region in 17q12. Possible cosegregation was also identified in 13q14.2 and 1q32 in pedigree 3, although this could not be definitively confirmed due to the presence of uninformative markers in key individuals. Differing chromosome regions identified in specific JME subsyndromes may contain separate JME disease-causing genes, favoring the concept of JME as several distinct diseases. Whole-exome sequencing will likely identify a CAE/JME gene in 2q21.2-2q31.1, a JME/pA gene in 13q13.3-q31.2, and a cJME gene in 17q12.

Epilepsy and bipolar disorder

It is well recognized that mood disorders and epilepsy commonly co-occur. Despite this, our knowledge regarding the relationship between epilepsy and bipolar disorder is limited. Several shared features between the two disorders, such as their episodic nature and potential to run a chronic course, and the efficacy of some antiepileptic medications in the prophylaxis of both disorders, are often cited as evidence of possible shared underlying pathophysiology. The present paper aims to review the bidirectional associations between epilepsy and bipolar disorder, with a focus on epidemiological links, evidence for shared etiology, and the impact of these disorders on both the individual and wider society. Better recognition and understanding of these two complex disorders, along with an integrated clinical approach, are crucial for improved evaluation and management of comorbid epilepsy and mood disorders.

Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis

Objective

Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas.

Methods

Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients.

Results

Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance.

Conclusions

Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.

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Subnetwork of hyperconnectivity in juvenile myoclonic epilepsy

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Hyperconnectivity in primary motor, parietal and subcortical regions

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Network-based statistics is a valuable tool for predicting functional cognitive deficits.