Sarcopenia and anti-seizure medication response in juvenile myoclonic epilepsy.

INTRODUCTION: This study aimed to investigate the presence of sarcopenia in patients with juvenile myoclonic epilepsy (JME) and the association between sarcopenia and response to anti-seizure medication (ASM) in patients with JME. METHODS: We enrolled 42 patients with JME and 42 healthy controls who underwent brain magnetic resonance imaging with three-dimensional T1-weighted imaging. We measured the temporal muscle thickness (TMT), a radiographic marker for sarcopenia, using T1-weighted imaging. We compared the TMT between patients with JME and healthy controls and analyzed it according to the ASM response in patients with JME. We also performed a receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated the groups. RESULTS: The TMT in patients with JME did not differ from that in healthy controls (9.630 vs. 9.956 mm, p = .306); however, ASM poor responders had a lower TMT than ASM good responders (9.109 vs. 10.104 mm, p = .023). ROC curve analysis revealed that the TMT exhibited a poor performance in differentiating patients with JME from healthy controls, with an area under the ROC curve of .570 (p = .270), but good performance in differentiating between ASM good and poor responders, with an area under the ROC curve of .700 (p = .015). CONCLUSION: The TMT did not differ between patients with JME and healthy controls; however, it was reduced in ASM poor responders compared to ASM good responders, suggesting a link between ASM response and sarcopenia in patients with JME. TMT can be used to investigate sarcopenia in various neurological disorders.

Increased Thalamic Connectivity in Juvenile Myoclonic Epilepsy Based on Electroencephalography Source-Level Analysis.

Background: This study investigated alterations in the intrinsic thalamic network of patients with juvenile myoclonic epilepsy (JME) based on an electroencephalography (EEG) source-level analysis. Materials and Methods: We enrolled patients newly diagnosed with JME as well as healthy controls. The assessments were conducted in the resting state. We computed sources based on the scalp electrical potentials using a minimum-norm imaging method and a standardized, low-resolution, brain electromagnetic tomography approach. To create a functional connectivity matrix, we used the Talairach atlas to define thalamic nodes and applied the coherence method to measure brain synchronization as edges. We then calculated the intrinsic thalamic network using graph theory. We compared the intrinsic thalamic network of patients with JME with those of healthy controls. Results: This study included 67 patients with JME and 66 healthy controls. EEG source-level analysis revealed significant differences in the intrinsic thalamic networks between patients with JME and healthy controls. The measures of functional connectivity (radius, diameter, and characteristic path length) were significantly lower in patients with JME than in healthy controls (radius: 2.769 vs. 3.544, p = 0.015; diameter: 4.464 vs. 5.443, p = 0.024; and characteristic path length: 2.248 vs. 2.616, p = 0.046). Conclusions: We demonstrated alterations in the intrinsic thalamic network in patients with JME compared with those in healthy controls based on the EEG source-level analysis. These findings indicated increased thalamic connectivity in the JME group. These intrinsic thalamic network changes may be related to the pathophysiology of JME.

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.

Development and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy.

OBJECTIVE: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. MATERIALS AND METHODS: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. RESULTS: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. CONCLUSION: Radiomic models using MRI were able to differentiate JME from HCs.

Altered dynamic functional connectivity of striatal-cortical circuits in Juvenile Myoclonic Epilepsy.

OBJECTIVE: To investigate whether the dynamic functional connectivity (dFC) of striatal-cortical circuits changes in juvenile myoclonic epilepsy (JME). METHODS: The resting-state EEG-fMRI and the sliding-window approach were adopted to explore the dynamic striatal-cortical circuitry in thirty JME patients compared with 30 well-matched health controls (HCs). Six pairs of striatal seeds were selected as regions of interests. The correlation analysis was performed to reveal the relationship between the altered dFC variability and clinical variables in JME group. RESULTS: JME patients exhibited increased dFC variability mainly involved in fronto-striatal and striatal-thalamic networks; decreased dFC variability between striatum subdivisions and default mode network (DMN) regions compared with HCs (p<0.05, GRF corrected). In addition, the hypervariability between left ventral-rostral putamen and left medial superior frontal gyrus was positively (r= 0.493, p=0.008) correlated with the mean frequency score of myoclonic seizures in JME group. CONCLUSION: JME presented altered dFC variability in striatal-cortical circuits. The pattern of altered circuits showed increased variability in fronto-striatal and striatal-thalamic networks and decreased variability in striatal-DMN. These results provide novel information about the dynamic neural striatal-cortical circuitry of JME.

Distributed source localization of epileptiform discharges in juvenile myoclonic epilepsy: Standardized low-resolution brain electromagnetic tomography (sLORETA) Study.

Juvenile myoclonic epilepsy (JME) is a common generalized epilepsy syndrome considered the prototype of idiopathic generalized epilepsy. To date, generalized and focal seizures have been the fundamental concepts for classifying seizure types. In several studies, focal features of JME have been reported predominantly in the frontal lobe. However, results in previous studies are inconsistent. Therefore, we investigated the origin of epileptiform discharges in JME. We performed electroencephalography source localization using a distributed model with standardized low-resolution brain electromagnetic tomography. In 20 patients with JME, standardized low-resolution brain electromagnetic tomography images corresponding to the midpoint of the ascending phase and the negative peak of epileptiform discharges were obtained from a total of 362 electroencephalography epochs (181 epochs at each timepoint). At the ascending phase, the maximal current source density was located in the frontal lobe (58.6%), followed by the parietal (26.5%) and occipital lobes (8.8%). At the negative peak, the maximal current source density was located in the frontal lobe (69.1%), followed by the parietal (11.6%) and occipital lobes (9.4%). In the ascending phase, 41.4% of discharges were located outside the frontal lobe, and 30.9% were in the negative peak. Frontal predominance of epileptiform discharges was observed; however, source localization extending to various cortical regions also was identified. This widespread pattern was more prominent in the ascending phase (P = .038). The study results showed that JME includes widespread cortical regions over the frontal lobe. The current concept of generalized epilepsy and pathophysiology in JME needs further validation.

[Structural changes to the brain in drug-resistant juvenile myoclonic epilepsy]. / Cambios estructurales cerebrales en la epilepsia mioclónica juvenil farmacorresistente.

INTRODUCTION: The aim of this research is to determine the changes in brain structures, both cortical and subcortical, in patients with drug-resistant juvenile myoclonic epilepsy (JME), in order to contribute to the understanding of the characteristics of the drug-resistant syndrome and to offer possible answers and hypotheses for further studies and more adequate treatments. SUBJECTS AND METHODS: Observational case-control study. A convenience sample size of four cases and 16 healthy controls was defined to ensure the feasibility of the project (ratio of 4:1). The data collected for patients with drug-resistant JME came from 1.5T MRI equipment. FreeSurfer software was used to determine cortical and subcortical areas in both drug-resistant JME patients and healthy controls. RESULTS: A total of 20 participants were included in the study, of whom four (20%) were drug-resistant JME patients and 16% (80%) were healthy controls. The clusters with statistically significant differences in cortical thickness are located in the precentral gyrus, superior temporal gyrus, transverse temporal gyrus, medial temporal gyrus and supramarginal gyrus, predominantly in the left hemisphere. CONCLUSIONS: Structural brain changes are observed in patients with drug-resistant JME that may go undetected by the conventional processing techniques used in magnetic resonance imaging.

TITLE: Cambios estructurales cerebrales en la epilepsia mioclónica juvenil farmacorresistente.Introducción. El objetivo de la investigación es determinar los cambios en las estructuras cerebrales, tanto corticales como subcorticales, de pacientes con epilepsia mioclónica juvenil (EMJ) farmacorresistente, para aportar al conocimiento de las características del síndrome farmacorresistente y brindar posibles respuestas e hipótesis para nuevos estudios y tratamientos más adecuados. Sujetos y métodos. Estudio observacional de casos y controles. Se define un tamaño de muestra a conveniencia de cuatro casos y 16 controles sanos para garantizar la viabilidad del proyecto (relación 4:1). Los datos recolectados para los pacientes con EMJ farmacorresistentes provienen de un equipo de resonancia magnética de 1,5 T. Para determinar las áreas corticales y subcorticales, tanto en la EMJ farmacorresistente como en los controles sanos, se usó el software FreeSurfer. Resultados. Se incluyó a un total de 20 participantes en el estudio, de los cuales cuatro (20%) corresponden a EMJ farmacorresistentes y 16 (80 %) a controles sanos. La localización de los clústeres con diferencias estadísticamente significativas en el grosor cortical se encuentra en el giro precentral, el giro temporal superior, el giro temporal transverso, el giro temporal medial y el giro supramarginal, con predominancia en el hemisferio izquierdo. Conclusiones. Se evidencian cambios estructurales cerebrales en pacientes con EMJ farmacorresistente, cambios que pueden pasar desapercibidos por las técnicas convencionales en el procesamiento de las imágenes de resonancia magnética.

Aberrant dynamic structure-function relationship of rich-club organization in treatment-naïve newly diagnosed juvenile myoclonic epilepsy.

Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich-club organization-a core feature of the brain networks. Moreover, it is unclear how structure-function relationships dynamically change over time in JME. Here, we quantify the anatomical rich-club organization and dynamic structural and functional connectivity (SC-FC) coupling in 47 treatment-naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC-FC coupling were also calculated to examine the supporting of structure-function relationship to brain information transfer. The results showed that the anatomical rich-club organization was disrupted in the patient group, along with decreased connectivity strength among rich-club hub nodes. Furthermore, reduced SC-FC coupling in rich-club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor-cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC-FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC-FC coupling of rich-club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.

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.

Glymphatic system dysfunction in patients with juvenile myoclonic epilepsy.

OBJECTIVE: The glymphatic system is a glial cell-dependent waste clearance pathway in the brain that is essential for the maintenance of brain homeostasis. In this study, we evaluated glymphatic system function in patients with juvenile myoclonic epilepsy (JME) compared with healthy controls. METHODS: Patients with JME and healthy controls were retrospectively enrolled in this study. All the participants underwent brain diffusion tensor imaging (DTI). The "DTI-analysis along the perivascular space (ALPS)"-index was calculated to evaluate the glymphatic system function of the participants. The ALPS-indices of the patients with JME were compared with those of the healthy controls. In addition, the correlations between ALPS-index and the clinical characteristics of the patients with JME were analyzed to validate changes in glymphatic system function. RESULTS: A total of 39 patients with JME and 38 healthy controls were enrolled in this study. The mean ALPS- index of the patients with JME was significantly lower than that of the healthy controls (1.541 vs. 1.653, p = 0.041). ALPS-index was negatively correlated with age in patients with JME (r = -0.375, p = 0.018). However, ALPS-index was not correlated with age at onset, duration of epilepsy, or anti-seizure medication load in patients with JME. CONCLUSION: This study is the first in which the ALPS method was used to demonstrate that patients with JME have significant glymphatic system dysfunction. The results also show that glymphatic system index is negatively correlated with age in patients with JME, a finding which demonstrates that the glymphatic system function of patients with JME gradually declines with age. The ALPS-index might be a potential biomarker for monitoring glymphatic system function in patients with epilepsy.

Gray Matter Changes in Juvenile Myoclonic Epilepsy. A Voxel-Wise Meta-Analysis.

Background and Objectives. Juvenile myoclonic epilepsy (JME) is an idiopathic generalized epileptic syndrome, with a genetic basis clinically identified by myoclonic jerks of the upper limbs upon awaking, generalized tonic-clonic seizures and less frequent absences. Although the brain magnetic resonance imaging (MRI) is by definition normal, computer-based Voxel-Based morphometry studies have shown a number of volumetric changes in patients with juvenile myoclonic epilepsy. Thus, the aim of the present Voxel-Wise Meta-Analysis was to determine the most consistent regional differences of gray matter volume between JME patients and healthy controls. Materials and Methods. The initial search returned 31 studies. After excluding reviews and studies without control groups or without detailed peak coordinates, 12 studies were finally included in the present meta-analysis. The total number of JME patients was 325, and that of healthy controls was 357. Results. Our study showed a statistically significant increase of the gray matter in the left median cingulate/paracingulate gyri, the right superior frontal gyrus, the left precentral gyrus, the right supplementary motor area and left supplementary motor area. It also showed a decrease in the gray matter volume in the left thalamus, and in the left insula. Conclusions. Our findings could be related to the functional deficits and changes described by previous studies in juvenile myoclonic epilepsy. In this way, the volumetric changes found in the present study could be related to the impaired frontal lobe functions, the emotional dysfunction and impaired pain empathy, and to the disrupted functional connectivity of supplementary motor areas described in JME. It additionally shows changes in the volume of the left thalamus, supporting the theory of thalamocortical pathways being involved in the pathogenesis of juvenile myoclonic epilepsy.

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.

Identifying juvenile myoclonic epilepsy via diffusion tensor imaging using machine learning analysis.

The aim of this study was to evaluate the feasibility of using a machine learning approach based on diffusion tensor imaging (DTI) to identify patients with juvenile myoclonic epilepsy. We analyzed the usefulness of combining conventional DTI measures and structural connectomic profiles. This retrospective study was conducted at a tertiary hospital. We enrolled 55 patients with juvenile myoclonic epilepsy. All of the subjects underwent DTI from January 2017 to March 2020. We also enrolled 58 healthy subjects as a normal control group. We extracted conventional DTI measures and structural connectomic DTI profiles. We employed the support vector machines (SVM) algorithm to classify patients with juvenile myoclonic epilepsy and healthy subjects based on the conventional DTI measures and structural connectomic profiles. The SVM classifier based on conventional DTI measures had an accuracy of 68.1% and an area under the curve (AUC) of 0.682. Another SVM classifier based on the structural connectomic profiles demonstrated an accuracy of 72.7% and an AUC of 0.727. The SVM classifier based on combining the conventional DTI measures and structural connectomic profiles had an accuracy of 81.8% and an AUC of 0.818. DTI using machine learning is useful for classifying patients with juvenile myoclonic epilepsy and healthy subjects. Combining both the conventional DTI measures and structural connectomic profiles results in a better classification performance than using conventional DTI measures or the structural connectomic profiles alone to identify juvenile myoclonic epilepsy.

Alterations of the intrinsic amygdala-hippocampal network in juvenile myoclonic epilepsy.

INTRODUCTION: Several lines of evidence suggest that the amygdala-hippocampus is involved in the epileptogenic network of juvenile myoclonic epilepsy (JME). The aim of this study was to investigate the alterations in the individual nuclei of the amygdala and hippocampal subfields, and the intrinsic amygdala-hippocampal network of patients with JME compared to healthy controls. METHODS: This retrospective study conducted at a single tertiary hospital involved 35 patients with newly diagnosed JME, and 34 healthy subjects. We calculated the individual structural volumes of 18 nuclei in the amygdala, and 38 hippocampal subfields using three-dimensional volumetric T1-weighted imaging and FreeSurfer program. We also performed an analysis of the intrinsic amygdala-hippocampal global and local network based on these volumes using a graph theory and Brain Analysis using Graph Theory (BRAPH) program. We investigated the differences in these volumes and network measures between patients with JME and healthy controls. RESULTS: There were no significant volume differences in the nuclei of the amygdala and hippocampal subfields between patients with JME and healthy controls. However, we found significant differences in the global network between patients with JME and healthy controls. The mean clustering coefficient was significantly decreased in patients with JME compared to healthy controls (0.473 vs. 0.653, p = .047). In addition, specific regions in the hippocampal subfields showed significant differences in the local network between the two groups. The betweenness centrality of the right CA1-head, right hippocampus-amygdala-transition area, left hippocampal fissure, left fimbria, and left CA3-head, was increased in patients with JME compared to healthy controls. CONCLUSION: The intrinsic amygdala-hippocampal global and local networks differed in patients with JME compared to healthy controls, which may be related to the pathogenesis of JME, and memory dysfunction in patients with JME.

Functional connectivity disturbances of ascending reticular activating system and posterior thalamus in juvenile myoclonic epilepsy in relation with photosensitivity: A resting-state fMRI study.

OBJECTIVE: Juvenile myoclonic epilepsy (JME) is typified by the occurrence of myoclonic seizures after awakening, though another common trait is myoclonic seizures triggered by photic stimulation. We aimed to investigate the functional connectivity (FC) of nuclei in the ascending reticular activating system (ARAS), thalamus and visual cortex in JME with and without photosensitivity. METHODS: We examined 29 patients with JME (16 photosensitive (PS), 13 non- photosensitive-(NPS)) and 28 healthy controls (HCs) using resting-state functional magnetic resonance imaging (rs-fMRI). Seed-to-voxel FC analyses were performed using 25 seeds, including the thalamus, visual cortex, and ARAS nuclei. RESULTS: Mesencephalic reticular formation seed revealed significant hyperconnectivity between the bilateral paracingulate gyrus and anterior cingulate cortex in JME group, and in both JME-PS and JME-NPS subgroups compared to HCs (pFWE-corr < 0.001; pFWE-corr < 0.001; pFWE-corr = 0.002, respectively). Locus coeruleus seed displayed significant hyperconnectivity with the bilateral lingual gyri, intracalcarine cortices, occipital poles and left occipital fusiform gyrus in JME-PS group compared to HCs (pFWE-corr <0.001). Additionally, locus coeruleus seed showed significant hyperconnectivity in JME-PS group compared to JME-NPS group with a cluster corresponding to the bilateral lingual gyri and right intracalcarine cortex (pFWE-corr < 0.001). Lastly, the right posterior nuclei of thalamus revealed significant hyperconnectivity with the right superior lateral occipital cortex in JME-PS group compared to HCs (pFWE-corr < 0.002). CONCLUSIONS: In JME, altered functional connectivity of the arousal networks might contribute to the understanding of myoclonia after awakening, whereas increased connectivity of posterior thalamus might explain photosensitivity.

Surround inhibition in patients with juvenile myoclonic epilepsy.

OBJECTIVE: In healthy subjects, there is a reduction in the amplitudes of somatosensory-evoked potentials (SEPs) after the simultaneous stimulation of two nerves compared to the sum of separate stimulations. This reduction is due to the inhibition of one area in the cortex after stimulation of the neighboring area, which results from the surround inhibition (SI) phenomenon. In this study, we aimed to investigate whether there was a decrease in SI of SEP in patients with juvenile myoclonic epilepsy (JME). METHODS: We included 17 patients with JME and 18 healthy subjects. Groups were similar in terms of age and gender. We recorded SEPs after stimulating (i) median nerve (mSEP), (ii) ulnar nerve (uSEP), (iii) median and ulnar nerves simultaneously (muSEP) at wrist. The arithmetic sum (aSEP) of amplitudes of mSEP and uSEP was compared with the amplitudes of muSEP. We also calculated SI%. RESULTS: The amplitudes of SEPs were significantly higher in the JME group than in the healthy subjects (mSEP, p = 0.005; uSEP, p = 0.032; muSEP, p = 0.014). In healthy subjects and the JME group, the amplitude of muSEP was significantly lower than the aSEP (p = 0.014; p = 0.001, respectively). However, SI% was significantly higher in the JME group (p = 0.010). SIGNIFICANCE: Although the SI is maintained in JME patients, the higher SI% indicates an impairment relative to healthy subjects.

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.

Motor hyperactivation during cognitive tasks: An endophenotype of juvenile myoclonic epilepsy.

OBJECTIVE: Juvenile myoclonic epilepsy (JME) is the most common genetic generalized epilepsy syndrome. Myoclonus may relate to motor system hyperexcitability and can be provoked by cognitive activities. To aid genetic mapping in complex neuropsychiatric disorders, recent research has utilized imaging intermediate phenotypes (endophenotypes). Here, we aimed to (a) characterize activation profiles of the motor system during different cognitive tasks in patients with JME and their unaffected siblings, and (b) validate those as endophenotypes of JME. METHODS: This prospective cross-sectional investigation included 32 patients with JME, 12 unaffected siblings, and 26 controls, comparable for age, sex, handedness, language laterality, neuropsychological performance, and anxiety and depression scores. We investigated patterns of motor system activation during episodic memory encoding and verb generation functional magnetic resonance imaging (fMRI) tasks. RESULTS: During both tasks, patients and unaffected siblings showed increased activation of motor system areas compared to controls. Effects were more prominent during memory encoding, which entailed hand motion via joystick responses. Subgroup analyses identified stronger activation of the motor cortex in JME patients with ongoing seizures compared to seizure-free patients. Receiver-operating characteristic curves, based on measures of motor activation, accurately discriminated both patients with JME and their siblings from healthy controls (area under the curve: 0.75 and 0.77, for JME and a combined patient-sibling group against controls, respectively; P < .005). SIGNIFICANCE: Motor system hyperactivation represents a cognitive, domain-independent endophenotype of JME. We propose measures of motor system activation as quantitative traits for future genetic imaging studies in this syndrome.