Source localization of epileptiform discharges in juvenile myoclonic epilepsy (JME) using magnetoencephalography (MEG)

2017 ◽  
Vol 129 ◽  
pp. 67-73 ◽  
Author(s):  
Veeranna Gadad ◽  
Sanjib Sinha ◽  
Narayanan Mariyappa ◽  
Ganne Chaithanya ◽  
Velmurugan Jayabal ◽  
...  
Keyword(s):  
Source Localization ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Epileptiform Discharges

scholarly journals EEG recording after sleep deprivation in a series of patients with juvenile myoclonic epilepsy

2005 ◽  
Vol 63 (2b) ◽  
pp. 383-388 ◽  
Author(s):  
Nise Alessandra de Carvalho Sousa ◽  
Patrícia da Silva Sousa ◽  
Eliana Garzon ◽  
Américo C. Sakamoto ◽  
Nádia I.O. Braga ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Diagnostic Delay ◽  
Quantitative Eeg ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Mean Delay ◽  
Epileptiform Discharges ◽  
Number Of Patients ◽  
Syndromic Diagnosis ◽  
Eeg Recordings

Seizures in Juvenile Myoclonic Epilepsy (JME) are dependent on the sleep-wake cycle and precipitant factors, among which sleep deprivation (SD) is one of the most important. Still an under diagnosed syndrome, misinterpretation of the EEGs contributes to diagnostic delay. Despite this, a quantitative EEG investigation of SD effects has not been performed. We investigated the effect of SD on EEGs in 41 patients, aged 16-50 yr. (mean 25.4), who had not yet had syndromic diagnosis after a mean delay of 8.2 yr. Two EEG recordings separated by a 48-hour interval were taken at 7 a.m. preceded by a period of 6 hours of sleep (routine EEG) and after SD (sleep-deprived EEG). The same protocol was followed and included a rest wakefulness recording, photic stimulation, hyperventilation and a post-hyperventilation period. The EEGs were analyzed as to the effect of SD on the number, duration, morphology, localization and predominance of abnormalities in the different stages. A discharge index (DI) was calculated. Out of the 41 patients, 4 presented both normal EEG recordings. In 37 (90.2%) there were epileptiform discharges (ED). The number of patients with ED ascended from 26 (70.3%) in the routine EEG to 32 (86.5%) in the sleep-deprived exam. The presence of generalized spike-wave and multispike-wave increased from 20 (54.1%) and 13 (35.1%) in the first EEG to 29 (78.4%) and 19 (51.4%) in the second, respectively (p<0.05 and p<0.01). As to localization, the number of generalized, bilateral and synchronous ED increased from 21 (56.8%) to 30 (81.1%) (p<0.01). The DI also increased; while 8 patients (21.6%) presented greater rate in the routine EEG, 25 (67.6%) did so in the sleep-deprived EEG mainly during somnolence and sleep (p<0.01). Moreover, the paroxysms were also longer in the sleep-deprived EEG. Sleep-deprived EEG is a powerful tool in JME and can contribute significantly to the syndromic characterization of this syndrome.

scholarly journals Effect of Wrist Tapping on Interhemispheric Coherence in Patients with Juvenile Myoclonic Epilepsy

2021 ◽  
Vol 11 (1) ◽  
pp. 73-77
Author(s):  
Ekaterina Narodova ◽  
Natalia Shnayder ◽  
Vladislav Karnaukhov ◽  
Olesya Bogomolova ◽  
Kirill Petrov ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Epileptic Seizures ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Control Group ◽  
Alpha Band ◽  
Epileptiform Discharges ◽  
Interhemispheric Coherence ◽  
Before And After ◽  
The Brain

The aim of this study was to assess the dynamics of interhemispheric coherence (IC) as an indicator of integration of different areas of the brain and their participation in the performance of certain functions before and after wrist tapping (WT), using the author's method in juvenile myoclonic epilepsy (JME). Methods and Results: The study included 81 subjects of working age, including 51 clinically healthy volunteers (median age of 39[21;56] years) and 30 patients (median age of 27[23;38] years) with JME. Analysis of IC in the electrode pairs Fp1-Fp2, F3-F4, C3-C4, T3-T4 was performed using a computer encephalographic complex. A coherent EEG analysis was used to identify and evaluate the relationships between different areas of the brain. Based on the change in the coherence coefficients (CCs), the level of integrative activity of brain structures was quantified. In healthy volunteers, before and after WT, we observed a statistically significant decrease in CCs for the beta-1 band in the pairs Fp1-Fp2, F3-F4, and C3-C4 (P<0.05), while in the pair T3-T4, changes in CCs were not statistically significant (P>0.05). At the same time, a statistically significant decrease in CCs in the alpha band was found only in the frontal regions in the pairs Fp1-Fp2 and F3-F4 (P<0.05). No statistically significant changes were found in all the studied pairs in the theta band. When comparing CCs in JME patients in beta–1 and theta bands, before and after WT, we did not find statistically significant changes in CCs in all the studied electrode pairs. However, in the alpha band, we found a statistically significant decrease in CCs in the frontal region in the F3-F4 (P=0.0038) and C3-C4 electrode pairs (P=0.034). The results of the study of interhemispheric integration showed statistically significant differences between patients with JME and the control group. Conclusion: WT according to the author's method does not provoke the occurrence of interictal epileptiform discharges on the EEG and epileptic seizures in patients with JME. Coherent analysis showed positive changes in interhemispheric integrations of neurons in the beta–1 and alpha frequency ranges, mainly in the anterior hemispheres.

FV 837. Early Withdrawal of Antiepileptic Medication in Patients with Juvenile Myoclonic Epilepsy Is Justifiable!

2018 ◽  
Author(s):  
Gerhard Kurlemann ◽  
Jana Krois-Neudenberger ◽  
Oliver Schwartz ◽  
Beate Jensen ◽  
Jürgen Althaus ◽  
...  
Keyword(s):  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Early Withdrawal ◽  
Antiepileptic Medication

Behavior problems in patients with juvenile myoclonic epilepsy (JME)

2005 ◽  
Vol 36 (02) ◽  
Author(s):  
B Plattner ◽  
J Kindler ◽  
G Pahs ◽  
L Urak ◽  
H Mayer ◽  
...  
Keyword(s):  
Behavior Problems ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy

Surround inhibition in patients with juvenile myoclonic epilepsy

2020 ◽  
pp. 1-6
Author(s):  
Bengi Gul Turk ◽  
Naz Yeni ◽  
Aysegul Gunduz ◽  
Ceren Alis ◽  
Meral Kiziltan
Keyword(s):  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Surround Inhibition

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

2021 ◽  
pp. 088307382110195
Author(s):  
Sabrina Pan ◽  
Alan Wu ◽  
Mark Weiner ◽  
Zachary M Grinspan
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Juvenile Myoclonic Epilepsy ◽  
Myoclonic Epilepsy ◽  
Hypoxic Ischemic Encephalopathy ◽  
Pediatric Epilepsy ◽  
Health Record ◽  
Treatment Resistant ◽  
Ischemic Encephalopathy ◽  
F Measure

Introduction: Computable phenotypes allow identification of well-defined patient cohorts from electronic health record data. Little is known about the accuracy of diagnostic codes for important clinical concepts in pediatric epilepsy, such as (1) risk factors like neonatal hypoxic-ischemic encephalopathy; (2) clinical concepts like treatment resistance; (3) and syndromes like juvenile myoclonic epilepsy. We developed and evaluated the performance of computable phenotypes for these examples using electronic health record data at one center. Methods: We identified gold standard cohorts for neonatal hypoxic-ischemic encephalopathy, pediatric treatment-resistant epilepsy, and juvenile myoclonic epilepsy via existing registries and review of clinical notes. From the electronic health record, we extracted diagnostic and procedure codes for all children with a diagnosis of epilepsy and seizures. We used these codes to develop computable phenotypes and evaluated by sensitivity, positive predictive value, and the F-measure. Results: For neonatal hypoxic-ischemic encephalopathy, the best-performing computable phenotype (HIE ICD-9 /10 and [brain magnetic resonance imaging (MRI) or electroencephalography (EEG) within 120 days of life] and absence of commonly miscoded conditions) had high sensitivity (95.7%, 95% confidence interval [CI] 85-99), positive predictive value (100%, 95% CI 95-100), and F measure (0.98). For treatment-resistant epilepsy, the best-performing computable phenotype (3 or more antiseizure medicines in the last 2 years or treatment-resistant ICD-10) had a sensitivity of 86.9% (95% CI 79-93), positive predictive value of 69.6% (95% CI 60-79), and F-measure of 0.77. For juvenile myoclonic epilepsy, the best performing computable phenotype (JME ICD-10) had poor sensitivity (52%, 95% CI 43-60) but high positive predictive value (90.4%, 95% CI 81-96); the F measure was 0.66. Conclusion: The variable accuracy of our computable phenotypes (hypoxic-ischemic encephalopathy high, treatment resistance medium, and juvenile myoclonic epilepsy low) demonstrates the heterogeneity of success using administrative data to identify cohorts important for pediatric epilepsy research.

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