Juvenile Myoclonic Epilepsy Shows Potential Structural White Matter Abnormalities: A TBSS Study

Diffusion tensor imaging in photosensitive and nonphotosensitive juvenile myoclonic epilepsy

INTRODUCTION/BACKGROUND

Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method.

METHODS

This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas.

RESULTS

In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS.

CONCLUSIONS

Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.

Multi-spectral diffusion MRI mega-analysis in genetic generalized epilepsy: Relation to outcomes

BACKGROUND AND OBJECTIVES

Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes.

METHODS

We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM).

RESULTS

Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy.

DISCUSSION

We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.

Facial Emotion Recognition in Patients with Juvenile Myoclonic Epilepsy

Previous studies have found facial emotion recognition (FER) impairments in individuals with epilepsy. While such deficits have been extensively explored in individuals with focal temporal lobe epilepsy, studies on individuals with generalized epilepsies are rare. However, studying FER specifically in individuals with juvenile myoclonic epilepsy (JME) is particularly interesting since they frequently suffer from social and neuropsychological difficulties in addition to epilepsy-specific symptoms. Furthermore, recent brain imaging studies have shown subtle microstructural alterations in individuals with JME. FER is considered a fundamental social skill that relies on a distributed neural network, which could be disturbed by network dysfunction in individuals with JME. This cross-sectional study aimed to examine FER and social adjustment in individuals with JME. It included 27 patients with JME and 27 healthy controls. All subjects underwent an Ekman-60 Faces Task to examine FER and neuropsychological tests to assess social adjustment as well as executive functions, intelligence, depression, and personality traits. Individuals with JME performed worse in global FER and fear and surprise recognition than healthy controls. However, probably due to the small sample size, no significant difference was found between the two groups. A potential FER impairment needs to be confirmed in further studies with larger sample size. If so, patients with JME could benefit from addressing possible deficits in FER and social difficulties when treated. By developing therapeutic strategies to improve FER, patients could be specifically supported with the aim of improving social outcomes and quality of life.

A tract-based spatial statistics study of white matter integrity in epilepsy

OBJECTIVE

To explore the changes of cerebral white matter diffusion tensor in epilepsy.

METHODS

This study was a retrospective study based on diffusion tensor imaging (DTI). Twenty-six epileptic patients and 42 normal controls matched for sex, age and handedness were enrolled in our research. Based on the method of tract-based spatial statistics (TBSS), we analyzed the changes of each relevant parameter index of DTI in white matter of the brain in all subjects, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD).

RESULTS

In comparison with the control group, epileptic patients had decreased FA and elevated MD, AD, and RD in the anterior thalamic radiation, corticospinal tract, forceps major, forceps minor, cingulum, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus and uncinate fasciculus (P < 0.05).

CONCLUSION

Widespread white matter integrity was observed in epileptic patients, which may be the structural basis for the development of affective disorders, impaired cognition, and motor abnormalities.

Characteristics of Microstructural Changes Associated with Glioma Related Epilepsy: A Diffusion Tensor Imaging (DTI) Study

(1) Background: Glioma is the most common primary tumor in the central nervous system, and glioma-related epilepsy (GRE) is one of its common symptoms. The abnormalities of white matter fiber tracts are involved in attributing changes in patients with epilepsy (Rudà, R, 2012).This study aimed to assess frontal lobe gliomas’ effects on the cerebral white matter fiber tracts. (2) Methods: Thirty patients with frontal lobe glioma were enrolled and divided into two groups (Ep and nEep). Among them, five patients were excluded due to apparent insular or temporal involvement. A set of 14 age and gender-matched healthy controls were also included. All the enrolled subjects underwent preoperative conventional magnetic resonance images (MRI) and diffusion tensor imaging (DTI). Furthermore, we used tract-based spatial statistics to analyze the characteristics of the white matter fiber tracts. (3) Results: The two patient groups showed similar patterns of mean diffusivity (MD) elevations in most regions; however, in the ipsilateral inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and superior corona radiata, the significant voxels of the EP group were more apparent than in the nEP group. No significant fractional anisotropy (FA) elevations or MD degenerations were found in the current study. (4) Conclusions: Gliomas grow and invade along white matter fiber tracts. This study assessed the effects of GRE on the white matter fiber bundle skeleton by TBSS, and we found that the changes in the white matter skeleton of the frontal lobe tumor-related epilepsy were mainly concentrated in the IFOF, SLF, and superior corona radiata. This reveals that GRE significantly affects the white matter fiber microstructure of the tumor.

Visually sensitive seizures: An updated review by the Epilepsy Foundation

Light flashes, patterns, or color changes can provoke seizures in up to 1 in 4000 persons. Prevalence may be higher because of selection bias. The Epilepsy Foundation reviewed light-induced seizures in 2005. Since then, images on social media, virtual reality, three-dimensional (3D) movies, and the Internet have proliferated. Hundreds of studies have explored the mechanisms and presentations of photosensitive seizures, justifying an updated review. This literature summary derives from a nonsystematic literature review via PubMed using the terms "photosensitive" and "epilepsy." The photoparoxysmal response (PPR) is an electroencephalography (EEG) phenomenon, and photosensitive seizures (PS) are seizures provoked by visual stimulation. Photosensitivity is more common in the young and in specific forms of generalized epilepsy. PS can coexist with spontaneous seizures. PS are hereditable and linked to recently identified genes. Brain imaging usually is normal, but special studies imaging white matter tracts demonstrate abnormal connectivity. Occipital cortex and connected regions are hyperexcitable in subjects with light-provoked seizures. Mechanisms remain unclear. Video games, social media clips, occasional movies, and natural stimuli can provoke PS. Virtual reality and 3D images so far appear benign unless they contain specific provocative content, for example, flashes. Images with flashes brighter than 20 candelas/m² at 3-60 (particularly 15-20) Hz occupying at least 10 to 25% of the visual field are a risk, as are red color flashes or oscillating stripes. Equipment to assay for these characteristics is probably underutilized. Prevention of seizures includes avoiding provocative stimuli, covering one eye, wearing dark glasses, sitting at least two meters from screens, reducing contrast, and taking certain antiseizure drugs. Measurement of PPR suppression in a photosensitivity model can screen putative antiseizure drugs. Some countries regulate media to reduce risk. Visually-induced seizures remain significant public health hazards so they warrant ongoing scientific and regulatory efforts and public education.

A systematic review of resting-state and task-based fmri in juvenile myoclonic epilepsy

Functional neuroimaging modalities have enhanced our understanding of juvenile myoclonic epilepsy (JME) underlying neural mechanisms. Due to its non-invasive, sensitive and analytical nature, functional magnetic resonance imaging (fMRI) provides valuable insights into relevant functional brain networks and their segregation and integration properties. We systematically reviewed the contribution of resting-state and task-based fMRI to the current understanding of the pathophysiology and the patterns of seizure propagation in JME Altogether, despite some discrepancies, functional findings suggest that corticothalamo-striato-cerebellar network along with default-mode network and salience network are the most affected networks in patients with JME. However, further studies are required to investigate the association between JME's main deficiencies, e.g., motor and cognitive deficiencies and fMRI findings. Moreover, simultaneous electroencephalography-fMRI (EEG-fMRI) studies indicate that alterations of these networks play a role in seizure modulation but fall short of identifying a causal relationship between altered functional properties and seizure propagation. This review highlights the complex pathophysiology of JME, which necessitates the design of more personalized diagnostic and therapeutic strategies in this group.

White matter structural connectivity as a biomarker for detecting juvenile myoclonic epilepsy by transferred deep convolutional neural networks with varying transfer rates

Objective. By detecting abnormal white matter changes, diffusion magnetic resonance imaging (MRI) contributes to the detection of juvenile myoclonic epilepsy (JME). In addition, deep learning has greatly improved the detection performance of various brain disorders. However, there is almost no previous study effectively detecting JME by a deep learning approach with diffusion MRI.Approach. In this study, the white matter structural connectivity was generated by tracking the white matter fibers in detail based on Q-ball imaging and neurite orientation dispersion and density imaging. Four advanced deep convolutional neural networks (CNNs) were deployed by using the transfer learning approach, in which the transfer rate searching strategy was proposed to achieve the best detection performance.Main results. Our results showed: (a) Compared to normal control, the white matter' neurite density of JME was significantly decreased. The most significantly abnormal fiber tracts between the two groups were found to be cortico-cortical connection tracts. (b) The proposed transfer rate searching approach contributed to find each CNN's best performance, in which the best JME detection accuracy of 92.2% was achieved by using the Inception_resnet_v2 network with a 16% transfer rate.Significance. The results revealed: (a) Through detection of the abnormal white matter changes, the white matter structural connectivity can be used as a useful biomarker for detecting JME, which helps to characterize the pathophysiology of epilepsy. (b) The proposed transfer rate, as a new hyperparameter, promotes the CNNs transfer learning performance in detecting JME.

Neuropsychiatric comorbidities in genetic/idiopathic generalized epilepsies and their effects on psychosocial outcomes

INTRODUCTION

Idiopathic/genetic generalized epilepsy (GGE) accounts for 15-20% of all epilepsy cases. Neuropsychiatric comorbidities and disorders, such as attention-deficit hyperactivity disorder (ADHD), academic failure, and poor social competence, are present at a higher rate in patients with epilepsy compared with the general population. In this study, we aimed to determine the frequency of neuropsychiatric comorbidities in GGE subgroups, and to reveal the risk factors in the patient group with neuropsychiatric comorbidities.

MATERIAL AND METHOD

This hospital-based, cross-sectional study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Patients with seizure-controlled GGE were invited to a semi-structured interview at the hospital. Variables [photosensitivity, valproic acid (VPA) resistance, timing of the neuropsychiatric comorbidities Attention deficit and hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and low academic performance), seizure control, and timing of the antiepileptic treatment] were statistically analyzed and evaluated in terms of their association with GGE subgroups [Generalized tonic-clonic seizures alone (EGTGS), juvenile myoclonic epilepsy (JME), and juvenile absence epilepsy (JAE)], RESULTS: Total 101 patients with GGE were included in the study and the mean age was 13.94 ± 1.66 years. A total of 12.9% (n = 13) of the patients had EGTGS, 49.5% (n = 50) had JME, and 37.6% (n = 38) had JAE. VPA resistance, photosensitivity, and the presence of neuropsychiatric symptoms before the starting of epilepsy were found to be risk factors in the GGE group with neuropsychiatric comorbidities compared with the group without neuropsychiatric comorbidities (p < 0.001). The subgroups of GGE did not show any relationship with psychiatric disorders, including ADHD, ODD, and low academic performance (neuropsychiatric comorbidities) (p > 0.005). No correlation was found between seizure control and decline in neuropsychiatric symptoms (p > 0.05).

CONCLUSION

In this study, the onset of psychiatric symptoms prior to the onset of epilepsy, photosensitivity, and VPA resistance were the most important factors affecting neuropsychiatric comorbidities. The JME, JAE, and EGTCS subgroups, early initiation of antiepileptic treatment, and seizure control were found to have no effect on poor psychosocial outcome and neuropsychiatric comorbidities.

Automated Detection of Juvenile Myoclonic Epilepsy using CNN based Transfer Learning in Diffusion MRI

Epilepsy is one of the largest neurological diseases in the world, and juvenile myoclonic epilepsy (JME) usually occurs in adolescents, giving patients tremendous burdens during growth, which really needs the early diagnosis. Advanced diffusion magnetic resonance imaging (MRI) could detect the subtle changes of the white matter, which could be a non-invasive early diagnosis biomarker for JME. Transfer learning can solve the problem of insufficient clinical samples, which could avoid overfitting and achieve a better detection effect. However, there is almost no research to detect JME combined with diffusion MRI and transfer learning. In this study, two advanced diffusion MRI methods, high angle resolved diffusion imaging (HARDI) and neurite orientation dispersion and density imaging (NODDI), were used to generate the connectivity matrix which can describe tiny changes in white matter. And three advanced convolutional neural networks (CNN) based transfer learning were applied to detect JME. A total of 30 participants (15 JME patients and 15 normal controls) were analyzed. Among the three CNN models, Inception_resnet_v2 based transfer learning is better at detecting JME than Inception_v3 and Inception_v4, indicating that the "short cut" connection can improve the ability to detect JME. Inception_resnet_v2 achieved to detect JME with the accuracy of 75.2% and the AUC of 0.839. The results support that diffusion MRI and CNN based transfer learning have the potential to improve the automated detection of JME.

Subtle Brain Developmental Abnormalities in the Pathogenesis of Juvenile Myoclonic Epilepsy

Juvenile myoclonic epilepsy (JME), a lifelong disorder that starts during adolescence, is the most common of genetic generalized epilepsy syndromes. JME is characterized by awakening myoclonic jerks and myoclonic-tonic-clonic (m-t-c) grand mal convulsions. Unfortunately, one third of JME patients have drug refractory m-t-c convulsions and these recur in 70-80% who attempt to stop antiepileptic drugs (AEDs). Behavioral studies documented impulsivity, but also impairment of executive functions relying on organization and feedback, which points to prefrontal lobe dysfunction. Quantitative voxel-based morphometry (VBM) revealed abnormalities of gray matter (GM) volumes in cortical (frontal and parietal) and subcortical structures (thalamus, putamen, and hippocampus). Proton magnetic resonance spectroscopy (MRS) found evidence of dysfunction of thalamic neurons. White matter (WM) integrity was disrupted in corpus callosum and frontal WM tracts. Magnetic resonance imaging (MRI) further unveiled anomalies in both GM and WM structures that were already present at the time of seizure onset. Aberrant growth trajectories of brain development occurred during the first 2 years of JME diagnosis. Because of genetic origin, disease causing variants were sought, first by positional cloning, and most recently, by next generation sequencing. To date, only six genes harboring pathogenic variants (GABRA1, GABRD, EFHC1, BRD2, CASR, and ICK) with Mendelian and complex inheritance and covering a limited proportion of the world population, are considered as major susceptibility alleles for JME. Evidence on the cellular role, developmental and cell-type expression profiles of these six diverse JME genes, point to their pathogenic variants driving the first steps of brain development when cell division, expansion, axial, and tangential migration of progenitor cells (including interneuron cortical progenitors) sculpture subtle alterations in brain networks and microcircuits during development. These alterations may explain "microdysgenesis" neuropathology, impulsivity, executive dysfunctions, EEG polyspike waves, and awakening m-t-c convulsions observed in JME patients.