Valproate decreases EEG synchronization in a use-dependent manner in idiopathic generalized epilepsy

Spectral changes in electroencephalography linked to neuroactive medications: A computational pipeline for data mining and analysis

BACKGROUND AND OBJECTIVES

The increasing amount of open-access medical data provides new opportunities to gain clinically relevant information without recruiting new patients. We developed an open-source computational pipeline, that utilizes the publicly available electroencephalographic (EEG) data of the Temple University Hospital to identify EEG profiles associated with the usage of neuroactive medications. It facilitates access to the data and ensures consistency in data processing and analysis, thus reducing the risk of errors and creating comparable and reproducible results. Using this pipeline, we analyze the influence of common neuroactive medications on brain activity.

METHODS

The pipeline is constructed using easily controlled modules. The user defines the medications of interest and comparison groups. The data is downloaded and preprocessed, spectral features are extracted, and statistical group comparison with visualization through a topographic EEG map is performed. The pipeline is adjustable to answer a variety of research questions. Here, the effects of carbamazepine and risperidone were statistically compared with control data and with other medications from the same classes (anticonvulsants and antipsychotics).

RESULTS

The comparison between carbamazepine and the control group showed an increase in absolute and relative power for delta and theta, and a decrease in relative power for alpha, beta, and gamma. Compared to antiseizure medications, carbamazepine showed an increase in alpha and theta for absolute powers, and for relative powers an increase in alpha and theta, and a decrease in gamma and delta. Risperidone compared with the control group showed a decrease in absolute and relative power for alpha and beta and an increase in theta for relative power. Compared to antipsychotic medications, risperidone showed a decrease in delta for absolute powers. These results show good agreement with state-of-the-art research. The database allows to create large groups for many different medications. Additionally, it provides a collection of records labeled as "normal" after expert assessment, which is convenient for the creation of control groups.

CONCLUSIONS

The pipeline allows fast testing of different hypotheses regarding links between medications and EEG spectrum through ecological usage of readily available data. It can be utilized to make informed decisions about the design of new clinical studies.

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy

Objective.Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy.Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting.Main results.OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band.Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Objectives

An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.

Materials

Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).

Methods

EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.

Results

The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.

Discussion

FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.

Conclusion

Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

Shared genetic basis between genetic generalized epilepsy and background electroencephalographic oscillations

Objective

Paroxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.

Methods

Confounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome‐wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses.

Results

Our analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations.

Significance

Our results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy.

Spectral power of interictal EEG in the diagnosis and prognosis of idiopathic generalized epilepsies

INTRODUCTION

Idiopathic generalized epilepsies (IGE) are characterized by generalized interictal epileptiform discharges (IEDs) on a normal background electroencephalography (EEG). However, the yield of IEDs can be low. Approximately 20% of patients with IGE fail to achieve seizure control with antiepileptic drug (AED) treatment. Currently, there are no reliable prognostic markers for early identification of drug-resistant epilepsy (DRE). We examined spectral power of the interictal EEG in patients with IGE and healthy controls, to identify potential diagnostic and prognostic biomarkers of IGE.

METHODS

A 64-channel EEG was recorded under standard conditions in patients with well-controlled IGE (WC-IGE, n = 19), drug-resistant IGE (DR-IGE, n = 18), and age-matched controls (n = 20). After preprocessing, fast Fourier transform was performed to obtain 1D frequency spectra for each EEG channel. The 1D spectra (averaged over channels) and 2D topographic maps (averaged over canonical frequency bands) were computed for each participant. Power spectra in the 3 cohorts were compared using one-way analysis of variance (ANOVA), and power spectra images were compared using T-contrast tests. A post hoc analysis compared peak alpha power between the groups.

RESULTS

Compared with controls, participants with IGE had higher interictal EEG spectral power in the delta band in the midline central region, in the theta band in the midline, in the beta band over the left hemisphere, and in the gamma band over right hemisphere and left central regions. There were no differences in spectral power between cohorts with WC-IGE and DR-IGE. Peak alpha power was lower in WC-IGE and DR-IGE than controls.

CONCLUSIONS

Electroencephalography spectral power analysis could form part of a clinically useful diagnostic biomarker for IGE; however, it did not correlate with response to AED in this study.

Deep Brain Stimulation Increases Seizure Threshold by Altering REM Sleep and Delta Powers During NREM Sleep

We previously demonstrated that seizure occurrences at different zeitgeber times alter sleep and circadian rhythm differently. On the other hand, the synchronized delta wave of electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep facilitates seizure, while the desynchronized EEG of rapid eye movement (REM) sleep suppresses it. We also elucidated that unilateral deep brain stimulation (DBS) of the anterior nucleus of thalamus (ANT) suppresses seizure recurrence. In the present study, we intraperitoneally injected pentylenetetrazol (PTZ, 40 mg/kg) for 14 consecutive days (PTZ kindling) to induce spontaneous seizure in rats, and a 30-min (delivered 10 min before each PTZ injection) or a 3-h DBS of unilateral ANT (delivered 1 h before each PTZ injection) was applied to suppress seizure. The frequency of DBS stimulation was 200 Hz and the electrical current consisted of biphasic square pulses with 50-μA intensity, 100-μs pulse width, and 4.1-ms stimulation interval. Our results found that PTZ-induced spontaneous seizure did not cause a significant change in the quantity of NREM sleep but suppressed the amount of REM sleep. Unilateral ANT DBS prolonged the onset latency of ictal seizure, decreased the spontaneous seizure duration, and increased the survival rate but did not change the amplitude of epileptiform EEGs during ictal period. Unilateral ANT DBS did not significantly alter NREM sleep but increased the amount of REM sleep. An analysis of the spectrograms of fast Fourier transform indicated that the intensities of all frequencies were enhanced during the PTZ-induced ictal period and the subsequent spontaneous seizure. Thirty minutes of unilateral ANT DBS suppressed the augmentation of low-frequency (<10 Hz) intensities during the spontaneous seizure induced by PTZ kindling. We further found that consecutive injections of PTZ progressively increased the enhancement of the delta powers during NREM sleep, whereas unilateral ANT DBS inhibited this progressive enhancement. It was also noticed that 30 min of ANT DBS exhibited a better efficacy in epilepsy suppression than 3 h of ANT DBS. These results elucidated that unilateral ANT DBS enhanced the seizure threshold by increasing the amount of REM sleep and decreasing the progressive enhancement of delta power during NREM sleep to suppress spontaneous seizure recurrences in PTZ kindling-induced epileptic rats.

Juvenile myoclonic epilepsy shows increased posterior theta, and reduced sensorimotor beta resting connectivity

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We investigated whole brain source space connectivity in JME using across standard MEG frequency bands.

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Connectivity was increased in posterior theta and alpha bands in JME, and decreased in sensorimotor beta band.

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Our findings highlight altered interactions between posterior networks of arousal and attention and the motor system in JME.

Background

Widespread structural and functional brain network changes have been shown in Juvenile Myoclonic Epilepsy (JME) despite normal clinical neuroimaging. We sought to better define these changes using magnetoencephalography (MEG) and source space connectivity analysis for optimal neurophysiological and anatomical localisation.

Methods

We consecutively recruited 26 patients with JME who underwent resting state MEG recording, along with 26 age-and-sex matched controls. Whole brain connectivity was determined through correlation of Automated Anatomical Labelling (AAL) atlas source space MEG timeseries in conventional frequency bands of interest delta (1−4 Hz), theta (4−8 Hz), alpha (8−13 Hz), beta (13−30 Hz) and gamma (40−60 Hz). We used a Linearly Constrained Minimum Variance (LCMV) beamformer to extract voxel wise time series of ‘virtual sensors’ for the desired frequency bands, followed by connectivity analysis using correlation between frequency- and node-specific power fluctuations, for the voxel maxima in each AAL atlas label, correcting for noise, potentially spurious connections and multiple comparisons.

Results

We found increased connectivity in the theta band in posterior brain regions, surviving statistical correction for multiple comparisons (corrected p < 0.05), and decreased connectivity in the beta band in sensorimotor cortex, between right pre- and post- central gyrus (p < 0.05) in JME compared to controls.

Conclusions

Altered resting-state MEG connectivity in JME comprised increased connectivity in posterior theta – the frequency band associated with long range connections affecting attention and arousal - and decreased beta-band sensorimotor connectivity. These findings likely relate to altered regulation of the sensorimotor network and seizure prone states in JME.

Postoperative oscillatory brain activity as an add-on prognostic marker in diffuse glioma

Introduction

Progression-free survival (PFS) in glioma patients varies widely, even when stratifying for known predictors (i.e. age, molecular tumor subtype, presence of epilepsy, tumor grade and Karnofsky performance status). Neuronal activity has been shown to accelerate tumor growth in an animal model, suggesting that brain activity may be valuable as a PFS predictor. We investigated whether postoperative oscillatory brain activity, assessed by resting-state magnetoencephalography is of additional value when predicting PFS in glioma patients.

Methods

We included 27 patients with grade II–IV gliomas. Each patient’s oscillatory brain activity was estimated by calculating broadband power (0.5–48 Hz) in 56 epochs of 3.27 s and averaged over 78 cortical regions of the Automated Anatomical Labeling atlas. Cox proportional hazard analysis was performed to test the predictive value of broadband power towards PFS, adjusting for known predictors by backward elimination.

Results

Higher broadband power predicted shorter PFS after adjusting for known prognostic factors (n = 27; HR 2.56 (95% confidence interval (CI) 1.15–5.70); p = 0.022). Post-hoc univariate analysis showed that higher broadband power also predicted shorter overall survival (OS; n = 38; HR 1.88 (95% CI 1.00–3.54); p = 0.038).

Conclusions

Our findings suggest that postoperative broadband power is of additional value in predicting PFS beyond already known predictors.

Electronic supplementary material

The online version of this article (10.1007/s11060-019-03386-7) contains supplementary material, which is available to authorized users.

Musical hallucinations and their relation with epilepsy

Musical hallucinations are poorly understood phenomena. Their relation with epilepsy was first described over a century ago, but never systematically explored. We, therefore, reviewed the literature, and assessed all descriptions of musical hallucinations attributed to epileptic activity. Our search yielded 191 articles, which together describe 983 unique patients, with 24 detailed descriptions of musical hallucinations related to epilepsy. We also describe six of our own patients. Based on the phenomenological descriptions and neurophysiological data, we distinguish four subgroups of epilepsy-related musical hallucination, comprising auras/ictal, inter-ictal and post-ictal phenomena, and phenomena related to brain stimulation. The case descriptions suggest that musical hallucinations in epilepsy can be conceptualised as lying on a continuum with other auditory hallucinations, including verbal auditory hallucinations, and—notably—tinnitus. To account for the underlying mechanism we propose a Bayesian model involving top-down and bottom-up prediction errors within the auditory network that incorporates findings from EEG and MEG studies. An analysis of phenomenological characteristics, pharmacological triggers, and treatment effects suggests wider ramifications for understanding musical hallucinations. We, therefore, conclude that musical hallucinations in epilepsy open a window to understanding these phenomena in a variety of conditions.

Inter-ictal network of focal epilepsy and effects of clinical factors on network activity

OBJECTIVE

Aim of the study was to explore the inter-ictal, resting-state EEG network in patients with focal epilepsy (FE) and to specify clinical factors that influence network activity.

METHODS

Functional EEG connectivity (EEGfC) differences were computed between 232 FE patients (FE group) and 77 healthy controls. EEGfC was computed among 23 cortical regions within each hemisphere, for 25 very narrow bands from 1 to 25 Hz. We computed independent effects for six clinical factors on EEGfC in the FE group, by ANOVA and post-hoc t-statistics, corrected for multiple comparisons by false discovery rate method.

RESULTS

Robust, statistically significant EEGfC differences emerged between the FE and the healthy control groups. Etiology, seizure type, duration of the illness and antiepileptic treatment were independent factors that influenced EEGfC. Statistically significant results occurred selectively in one or a few very narrow bands and outlined networks. Most abnormal EEGfC findings occurred at frequencies that mediate integrative and motor activities.

CONCLUSIONS

FE patients have abnormal resting-state EEGfC network activity. Clinical factors significantly modify EEGfC.

SIGNIFICANCE

Delineation of the FE network and modifying factors can open the way for targeted investigations and introduction of EEGfC into epilepsy research and practice.

Efficacy of levetiracetam for reducing rolandic discharges in comparison with carbamazepine and valproate sodium in rolandic epilepsy

PURPOSE

The main purpose of this study was to compare the efficacy of levetiracetam (LEV) with the older antiepileptic drugs (AEDs) for preventing atypical evolution in children with Rolandic epilepsy (RE). Accordingly, the present study compared the efficacy of older AEDs (carbamazepine (CBZ) and valproate sodium (VPA)) with LEV in reducing rolandic discharges (RDs) on interictal electroencephalogram (EEG) in children with RE.

METHODS

Patients in this heterogenous study were subdivided into CBZ, VPA and LEV groups in accordance with the initial monotherapy. The CBZ and VPA groups were studied retrospectively, but the LEV group was studied prospectively. Appearances of discharges were counted and these rates were computed. In comparison with the baseline RD frequency, EEG response to AED treatment was classified such as complete disappearance and response (≥50% reduction in RD frequency). The time taken to attain complete disappearance or response in EEG responders was assessed for each AED treatment group.

RESULTS

Responders comprised 10 (11.2%) of the 89 patients treated with CBZ, 41 (56.2%) of the 73 patients with VPA, and 25 (71.4%) of the 35 patients with LEV. Mean interval to achievement of EEG response in the CBZ, VPA, and LEV groups were 36.3, 23.1, and 14.7 months, respectively. EEG response was achieved significantly more rapidly with LEV than with CBZ (p < 0.001) or VPA (p < 0.005). Seizure control was not significantly different in all 3 investigated drugs.

CONCLUSIONS

LEV seems to be superior to CBZ and VPA in its ability to suppress RDs in children with RE.

Prolonged epileptiform EEG runs are associated with persistent seizures in juvenile myoclonic epilepsy

OBJECTIVE

In juvenile myoclonic epilepsy (JME), various EEG characteristics have been suggested as poor prognostic signs, but their significance is unclear. The aim of this study was to assess the influence of EEG variables on seizure and psychosocial outcome after a follow-up exceeding 20 years.

METHODS

396 EEG recordings were available for assessment in 40 patients (42 complete digital, 330 paper segments and 24 written reports only). Mean follow-up was 31 years (range 20-68). The number of EEGs per patient ranged from 2 to 23 (mean 9). Twenty-one patients were in remission for >5 years, whereas 19 had persistent seizures. Favorable psychosocial outcome was found in 14 of 37. EEGs were retrospectively categorized into four main groups; normal, slowing, epileptiform discharges or both slowing and epileptiform discharges, with further sub-classification. Hyperventilation and photoparoxysmal responses were also evaluated. Scoring of EEG findings was blinded to seizure and psychosocial outcome.

RESULTS

Significant associations were found between poor seizure control and prolonged ≥3s epileptiform runs, p=0.03 (8/19 vs 2/21), long ≥3s photoparoxysmal runs, p=0.04 (6/19 vs 1/21) and long ≥3s hyperventilation-induced epileptiform runs, p=0.02 (5/19 vs 0/21). The strongest association between persistent seizures and EEG was found when all epileptiform runs ≥3s were combined (p=0.007), with a positive predictive value equal to 79% and a negative predictive value equal to 69%. Fast (4-5c/s) spike-wave runs were also more frequent in patients with persistent seizures compared to the remission group, p=0.04 (9/19 vs 3/21). Other epileptiform elements occurred equally in the two prognostic groups. Psychosocial outcome was not influenced by EEG findings. Prolonged runs within 6 months from first recording did also predict clinical outcome, p=0.03; (8/19 vs 2/21), with a positive predictive value equal to 80% and a negative predictive value equal to 63%.

SIGNIFICANCE

Fast spike-wave runs and prolonged (≥3s) epileptiform runs, including photoparoxysmal and hyperventilation-induced runs were associated with persistent seizures in JME. Focal EEG abnormalities were not associated with clinical outcome. Conceivably, the duration of epileptiform bursts reflects the degree of deficient intracortical inhibition. Prolonged runs may represent an essential predictive feature for poor seizure control in JME.

Comparative analysis of background EEG activity in childhood absence epilepsy during valproate treatment: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study

Valproate (VPA) is an antiepileptic drug (AED) used for initial monotherapy in treating childhood absence epilepsy (CAE). EEG might be an alternative approach to explore the effects of AEDs on the central nervous system. We performed a comparative analysis of background EEG activity during VPA treatment by using standardized, low-resolution, brain electromagnetic tomography (sLORETA) to explore the effect of VPA in patients with CAE. In 17 children with CAE, non-parametric statistical analyses using sLORETA were performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between the untreated and treated condition. Maximum differences in current density were found in the left inferior frontal gyrus for the delta frequency band (log-F-ratio = -1.390, P > 0.05), the left medial frontal gyrus for the theta frequency band (log-F-ratio = -0.940, P > 0.05), the left inferior frontal gyrus for the alpha frequency band (log-F-ratio = -0.590, P > 0.05), and the left anterior cingulate for the beta frequency band (log-F-ratio = -1.318, P > 0.05). However, none of these differences were significant (threshold log-F-ratio = ±1.888, P < 0.01; threshold log-F-ratio = ±1.722, P < 0.05). Because EEG background is accepted as normal in CAE, VPA would not be expected to significantly change abnormal thalamocortical oscillations on a normal EEG background. Therefore, our results agree with currently accepted concepts but are not consistent with findings in some previous studies.

Photosensitive epilepsy is associated with reduced inhibition of alpha rhythm generating networks

See Hamandi (doi:10.1093/awx049) for a scientific commentary on this article.Photosensitivity is a condition in which lights induce epileptiform activities. This abnormal electroencephalographic response has been associated with hyperexcitability of the visuo-motor system. Here, we evaluate if intrinsic dysfunction of this network is present in brain activity at rest, independently of any stimulus and of any paroxysmal electroencephalographic activity. To address this issue, we investigated the haemodynamic correlates of the spontaneous alpha rhythm, which is considered the hallmark of the brain resting state, in photosensitive patients and in people without photosensitivity. Second, we evaluated the whole-brain functional connectivity of the visual thalamic nuclei in the various populations of subjects under investigation. Forty-four patients with epilepsy and 16 healthy control subjects underwent an electroencephalography-correlated functional magnetic resonance imaging study, during an eyes-closed condition. The following patient groups were included: (i) genetic generalized epilepsy with photosensitivity, 16 subjects (mean age 25 ± 10 years); (ii) genetic generalized epilepsy without photosensitivity, 13 patients (mean age 25 ± 11 years); (iii) focal epilepsy, 15 patients (mean age 25 ± 9 years). For each subject, the posterior alpha power variations were convolved with the standard haemodynamic response function and used as a regressor. Within- and between-groups second level analyses were performed. Whole brain functional connectivity was evaluated for two thalamic regions of interest, based on the haemodynamic findings, which included the posterior thalamus (pulvinar) and the medio-dorsal thalamic nuclei. Genetic generalized epilepsy with photosensitivity demonstrated significantly greater mean alpha-power with respect to controls and other epilepsy groups. In photosensitive epilepsy, alpha-related blood oxygen level-dependent signal changes demonstrated lower decreases relative to all other groups in the occipital, sensory-motor, anterior cingulate and supplementary motor cortices. Coherently, the same brain regions demonstrated abnormal connectivity with the visual thalamus only in epilepsy patients with photosensitivity. As predicted, our findings indicate that the cortical-subcortical network generating the alpha oscillation at rest is different in people with epilepsy and visual sensitivity. This difference consists of a decreased alpha-related inhibition of the visual cortex and sensory-motor networks at rest. These findings represent the substrate of the clinical manifestations (i.e. myoclonus) of the photoparoxysmal response. Moreover, our results provide the first evidence of the existence of a functional link between the circuits that trigger the visual sensitivity phenomenon and those that generate the posterior alpha rhythm.

EEG resting state analysis of cortical sources in patients with benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECTS) is the most common idiopathic childhood epilepsy, which is often associated with developmental disorders in children. In the present study, we analyzed resting state EEG spectral changes in the sensor and source spaces in eight BECTS patients compared with nine age-matched controls. Using high-resolution scalp EEG data, we assessed statistical differences in spatial distributions of EEG power spectra and cortical sources of resting state EEG rhythms in five frequency bands: δ (0.5–3.5 Hz), θ (4–8 Hz), α (8.5–13 Hz), β₁ (13.5–20 Hz) and β₂ (20.5–30 Hz) under the eyes-closed resting state condition. To further investigate the impact of centrotemporal spikes on EEG spectra, we split the EEG data of the patient group into EEG portions with and without spikes. Source localization demonstrated the homogeneity of our population of BECTS patients with a common epileptic zone over the right centrotemporal region. Significant differences in terms of both spectral power and cortical source densities were observed between controls and patients. Patients were characterized by significantly increased relative power in θ, α, β₁ and β₂ bands in the right centrotemporal areas over the spike zone and in the right temporo-parieto-occipital junction. Furthermore, the relative power in all bands significantly decreased in the bilateral frontal and parieto-occipital areas of patients regardless of the presence or absence of spikes in EEG segments. However, the spectral differences between patients and controls were more pronounced in the presence of spikes. This observation emphasized the impact of benign epilepsy on cortical source power, especially in the right centrotemporal regions. Spectral changes in bilateral frontal and parieto-occipital areas may also suggest alterations in the default mode network in BECTS patients.

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BECTS patients exhibited enhanced θ activity over all cortical regions.

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BECTS patients displayed reduced α activity at the occipital regions.

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Patients showed increased power in θ, α, β in right temporo-parieto-occipital pole.

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EEG spectral changes in BECTS patients indicate dysfunction at the epileptic zone.

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EEG spectral changes in BECTS patients may show alteration in default mode network.

What graph theory actually tells us about resting state interictal MEG epileptic activity

Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suitable to characterize these networks. To address these issues, we recorded magnetoencephalographic (MEG) data using two orthogonal planar gradiometers from 45 subjects from three groups (15 healthy controls (7 males, 24 ± 6 years), 15 frontal focal (8 male, 32 ± 16 years) and 15 generalized epileptic (6 male, 27 ± 7 years) patients) during interictal resting state with closed eyes. Then, we estimated the total and relative spectral power of the largest principal component of the gradiometers, and the degree of phase synchronization between each sensor site in the frequency range [0.5–40 Hz]. We further calculated a comprehensive battery of 15 graph-theoretic measures and used the affinity propagation clustering algorithm to elucidate the minimum set of them that fully describe these functional brain networks. The results show that differences in spectral power between the control and the other two groups have a distinctive pattern: generalized epilepsy presents higher total power for all frequencies except the alpha band over a widespread set of sensors; frontal focal epilepsy shows higher relative power in the beta band bilaterally in the fronto-central sensors. Moreover, all network indices can be clustered into three groups, whose exemplars are the global network efficiency, the eccentricity and the synchronizability. Again, the patterns of differences were clear: the brain network of the generalized epilepsy patients presented greater efficiency and lower eccentricity than the control subjects for the high frequency bands, without a clear topography. Besides, the frontal focal epileptic patients showed only reduced eccentricity for the theta band over fronto-temporal and central sensors. These outcomes indicate that functional epileptic brain networks are different to those of healthy subjects during interictal stage at rest, with a unique pattern of dissimilarities for each type of epilepsy. Further, when properly selected, three network indices suffice to provide a comprehensive description of these differences. Yet, since such uniqueness in the pattern of differences is also evident in the power spectrum, we conclude that the added value of the graph theory approach in this context should not be overestimated.

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We study MEG activity during interictal resting state with closed eyes.

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Generalized epilepsy presents higher total power over a widespread set of sensors.

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Frontal epilepsy shows higher relative power in beta band on fronto-central sensors.

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We also found altered functional brain networks in epilepsy using graph theory.

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The pattern of differences from control subjects is unique for each type of epilepsy.

Valproate treatment normalizes EEG functional connectivity in successfully treated idiopathic generalized epilepsy patients

AIM

To investigate the effect of chronic VPA treatment of EEG functional connectivity in successfully treated idiopathic generalized epilepsy (IGE) patients.

PATIENTS AND METHODS

19-channel waking, resting-state EEG records of 26 IGE patients were analyzed before treatment (IGE) and after the 90th day of treatment (VPA), in seizure-free condition. Three minutes of artifact-free EEG background activity (without epileptiform potentials) was analyzed for each patient in both conditions. A group of 26 age-matched healthy normative control persons (NC) was analyzed in the same way. All the EEG samples were processed to LORETA (Low Resolution Electromagnetic Tomography) to localize multiple distributed sources of EEG activity. Current source density time series were generated for 33 regions of interest (ROI) in each hemisphere for four frequency bands. Pearson correlation coefficients (R) were computed between all ROIs in each hemisphere, for four bands across the investigated samples. R values corresponded to intrahemispheric, cortico-cortical functional EEG connectivity (EEGfC). Group and condition differences were analyzed by statistical parametric network method.

MAIN RESULTS

p<0.05, corrected for multiple comparisons: (1) The untreated IGE group showed increased EEGfC in the delta and theta bands, and decreased EEGfC in the alpha band (as compared to the NC group); (2) VPA treatment normalized EEGfC in the delta, theta and alpha bands; and (3) degree of normalization depended on frequency band and cortical region.

CONCLUSIONS

VPA treatment normalizes EEGfC in IGE patients.

QPEEG analysis of the effects of sodium valproate on adult Chinese patients with generalized tonic-clonic seizures

Objectives EEG effects of the sustained-release form of sodium valproate (SR-VPA) are unknown, although it is widely used in Chinese patients with generalized tonicclonic seizures (GTCS). Methods Fourteen newly diagnosed, untreated GTCS patients were recruited and treated with SR-VPA. Waking EEG was recorded and analyzed by way of quantitative pharmaco-electroencephalogram (QPEEG) analysis during the three-month follow-up. Results There was a statistically significant decrease in the absolute power of the delta band (P < 0.05), theta band (P < 0.03) and partial alpha-1 band (p < 0.05) with treatment compared to before treatment, while there was no significantly different absolute power between one-month and three-months after treatment. There was a strong correlation between the decrease in absolute power and the degree of the initial abnormality in all frequency bands. Two of 14 patients experienced seizures during the second month after initiation of SR-VPA therapy. Conclusions SR-VPA selectively decreased the activity of the abnormal EEG synchronization in a use-dependent manner. The reduced theta, delta, and partial alpha-1 absolute power may reflect or confirm the efficacy of SR-VPA on patients with GTCS.

Juvenile myoclonic epilepsy: Clinical characteristics, standard and quantitative electroencephalography analyses

OBJECTIVES

Most studies comparing inter-ictal background quantitative electroencephalography (EEG) measures in generalized epilepsies with normal controls do not specifically determine patients with juvenile myoclonic epilepsy (JME) as a separate group. The study aims at comparing absolute spectral power and global field coherence in various frequency bands between patients diagnosed exclusively with JME and 10 healthy controls, and correlating significantly different quantitative EEG measures with various clinical characteristics and standard EEG abnormalities.

MATERIALS AND METHODS

Clinical and EEG data were collected from 10 patients with JME and 10 healthy controls. Spectral power and global field spectral coherence were calculated using Welch's averaged periodogram method. The data was analyzed using descriptive statistics, Fisher's exact test and t-test.

RESULTS

Statistically significant (or trend level) higher power (global α and θ, frontal α and θ, left temporal θ, right occipital α, δ and γ1, and central δ, θ, α, β, and γ2) and coherence (global α and γ1) was found in JME patients when compared to controls. Significant correlation of left frontal and central θ-power with presence of absence seizures (negative), central δ-, and θ-power with the presence of psychiatric comorbidity and central θ-power with frequency of myoclonic seizures was found.

CONCLUSION

Findings on global-frontal and temporal-occipital power support mild diffuse epileptogenic state and θ-activity as an endophenotype concepts in JME patients, respectively; findings suggest future studies on JME to include psychiatric comorbidity while selecting the sample; some spectral measures (e.g., central θ-power) do relate to progression of JME while some do not.

EEG-LORETA endophenotypes of the common idiopathic generalized epilepsy syndromes

OBJECTIVE

We tested the hypothesis that the cortical areas with abnormal local EEG synchronization are dissimilar in the three common idiopathic generalized epilepsy (IGE) phenotypes: IGE patients with absence seizures (ABS), juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic-clonic seizures exclusively (EGTCS).

PATIENTS AND METHODS

Groups of unmedicated ABS, JME and EGTCS patients were investigated. Waking EEG background activity (without any epileptiform potentials) was analyzed by a source localization method, LORETA (Low Resolution Electromagnetic Tomography). Each patient group was compared to a separate, age-matched group of healthy control persons. Voxel-based, normalized broad-band (delta, theta, alpha, and beta) and very narrow band (VNB, 1Hz bandwidth, from 1 to 25Hz) LORETA activity (=current source density, A/m(2)) were computed for each person. Group comparison included subtraction (average patient data minus average control data) and group statistics (multiple t-tests, where Bonferroni-corrected p<0.05 values were accepted as statistically significant).

RESULTS

Statistically not significant main findings were: overall increased delta and theta broad band activity in the ABS and JME groups; decrease of alpha and beta activity in the EGTCS group. Statistically significant main findings were as follows. JME group: bilaterally increased theta activity in posterior (temporal, parietal, and occipital) cortical areas; bilaterally increased activity in the medial and basal prefrontal area in the 8Hz VNB; bilaterally decreased activity in the precuneus, posterior cingulate and superior parietal lobule in the 11Hz and 21-22Hz VNBs. ABS group: bilaterally increased theta activity emerged in the basal prefrontal and medial temporal limbic areas. Decreased activity was found at 19-21Hz in the right postcentral gyrus and parts of the right superior and medial temporal gyri. EGTCS group: decreased activity was found in the frontal cortex and the postcentral gyrus at 10-11Hz, increased activity in the right parahippocampal gyrus at 16-18Hz.

DISCUSSION

Increased theta activity in the posterior parts of the cortex is the endophenotype for JME. Increased theta activity in the fronto-temporal limbic areas is the endophenotype for ABS. Statistically not significant findings might indicate diffuse biochemical abnormality of the cortex in JME and ABS.

SIGNIFICANCE

EEG-LORETA endophenotypes may correspond to the selective propensity to generate absence and myoclonic seizures in the ABS and JME syndromes.

Antiepileptic drugs modify power of high EEG frequencies and their neural generators

BACKGROUND

The clinical and molecular effects of antiepileptic drugs (AEDs) have been extensively investigated. Much less is known about their effects on human electrophysiology.

METHODS

Topographic analysis in the frequency domain has been used to analyze 104 electroencephalogram (EEG) epochs of 52 patients presenting with first-ever generalized seizure, with normal MRI and EEG. Patients were treated with valproate, arbamazepine, or lamotrigine in monotherapy (each group n = 13). Thirteen patients without medication served as a control group.

RESULTS

Carbamazepine and lamotrigine, both sodium-channel modulators, altered brain topography in the gamma range in the same frequency bands (50-60 Hz). Valproate, which has multiple actions on sodium and calcium channels as well as GABA turnover, modified brain topography in the low gamma range (30-40 Hz). No such changes were found in the control group. For all AEDs, the neural generators were shifted more anteriorly in medial temporal through to inferior frontal regions.

CONCLUSION

Decreased gamma-power and anterior shift of neural generators after AED introduction reflect AED influence on human electrophysiology.

Coexistence of idiopathic generalized epilepsy among surgically treated patients with drug-resistant temporal lobe epilepsy

INTRODUCTION

Failure to identify the association antiepileptic drug (AED)-resistant temporal lobe epilepsy (TLE) with idiopathic generalized epilepsy (IGE) can interfere with decision for anterior temporal lobectomy (ATL) and prediction of post-ATL seizure outcome.

METHODS

Out of the 664 consecutive patients who underwent ATL between March 1995 and December 2007, 12 (1.8%) had coexisting IGE. The decision for ATL was made after a thorough discussion in the multidisciplinary patient management conference based upon the concordance between the clinical, electroencephalographic and magnetic resonance imaging data. All of them underwent epilepsy surgery for AED-resistant TLE.

RESULTS

In seven of the 12 patients, IGE was not identified until post-ATL. During a median follow-up period of 8.5 years, 8 of our 12 patients were seizure-free; the remaining 4 patients only had infrequent myoclonus. In two them, AEDs were discontinued; others were on montherapy for IGE.

CONCLUSIONS

Our study highlights the rare association of IGE with TLE, the most common AED-resistant focal epilepsy syndrome. As the seizure outcome following ATL is similar in AED-resistant TLE patients with and without IGE, their co-existence is not a contraindication for ATL. Future studies should explore the molecular genetic basis of the rare association between these two epilepsy syndromes.

Reduced movement-related β desynchronisation in juvenile myoclonic epilepsy: a MEG study of task specific cortical modulation

OBJECTIVE

We investigated differences in task induced responses in occipital and sensorimotor cortex between patients with juvenile myclonic epilepsy (JME) and healthy controls .

METHODS

Twelve patients with JME and 12 age-matched non-epilepsy volunteers performed visual and motor tasks during MEG. We used synthetic aperture magnetometry to localise areas of task-related oscillatory modulations, performed time-frequency analyses on the locations of peak task related power changes and compared power and frequency modulation at these locations between patients and controls.

RESULTS

Patients with JME had significantly reduced pre-movement beta event-related desynchronisation in the motor task compared to controls. No significant differences were seen in other motor-related responses, or visual oscillatory responses.

CONCLUSIONS

Altered beta event-related desynchronisation may represent network specific dysfunction in JME possibly through GABAergic dysfunction.

SIGNIFICANCE

Characterising task specific cortical responses in epilepsy offers the potential to understand the patho-physiological basis of seizures and provide a window on disease and treatment effects.

A preliminary study of the neural mechanisms of frustration in pediatric bipolar disorder using magnetoencephalography

BACKGROUND

Irritability is prevalent and impairing in pediatric bipolar disorder (BD) but has been minimally studied using neuroimaging techniques. We used magnetoencephalography (MEG) to study theta band oscillations in the anterior cingulate cortex (ACC) during frustration in BD youth. ACC theta power is associated with attention to emotional stimuli, and the ACC may mediate responses to frustrating stimuli.

METHODS

We used the affective Posner task, an attention paradigm that uses rigged feedback to induce frustration, to compare 20 medicated BD youth (14.9+/-2.0 years; 45% male) and 20 healthy controls (14.7+/-1.7 years; 45% male). MEG measured neuronal activity after negative and positive feedback; we also compared groups on reaction time, response accuracy, and self-reported affect. Patients met strict DSM-IV BD criteria and were euthymic. Controls had no psychiatric history.

RESULTS

BD youth reported more negative affective responses than controls. After negative feedback, BD subjects, relative to controls, displayed greater theta power in the right ACC and bilateral parietal lobe. After positive feedback, BD subjects displayed lower theta power in the left ACC than did controls. Correlations between MEG, behavior, and affect were nonsignificant.

CONCLUSION

In this first MEG study of BD youth, BD youth displayed patterns of theta oscillations in the ACC and parietal lobe in response to frustration-inducing negative feedback that differed from healthy controls. These data suggest that BD youth may display heightened processing of negative feedback and exaggerated self-monitoring after frustrating emotional stimuli. Future studies are needed with unmedicated bipolar youth, and comparison ADHD and anxiety groups.

The role of NMDA receptor subtypes in short-term plasticity in the rat entorhinal cortex

We have previously shown that spontaneous release of glutamate in the entorhinal cortex (EC) is tonically facilitated via activation of presynaptic NMDA receptors (NMDAr) containing the NR2B subunit. Here we show that the same receptors mediate short-term plasticity manifested by frequency-dependent facilitation of evoked glutamate release at these synapses. Whole-cell patch-clamp recordings were made from layer V pyramidal neurones in rat EC slices. Evoked excitatory postsynaptic currents showed strong facilitation at relatively low frequencies (3 Hz) of activation. Facilitation was abolished by an NR2B-selective blocker (Ro 25-6981), but unaffected by NR2A-selective antagonists (Zn(2+), NVP-AAM077). In contrast, postsynaptic NMDAr-mediated responses could be reduced by subunit-selective concentrations of all three antagonists. The data suggest that NMDAr involved in presynaptic plasticity in layer V are exclusively NR1/NR2B diheteromers, whilst postsynaptically they are probably a mixture of NR1/NR2A, NR1/NR2B diheteromers and NR1/NR2A/NR2B triheteromeric receptors.

Effects of levetiracetam on EEG abnormalities in juvenile myoclonic epilepsy

PURPOSE

A multicenter, prospective, long-term, open-label study to evaluate the effects of levetiracetam on electroencephalogram (EEG) abnormalities and photoparoxysmal response (PPR) of patients affected by juvenile myoclonic epilepsy (JME).

METHODS

Forty-eight patients with newly diagnosed JME (10) or resistant/intolerant (38) to previous antiepileptic drugs (AEDs) were enrolled. After an 8-week baseline period, levetiracetam was titrated in 2 weeks to 500 mg b.i.d. and then increased to up to 3,000 mg/day. Efficacy parameters were based on the comparison and analysis of EEG interictal abnormalities classified as spikes-and-waves, polyspikes-and-waves, and presence of PPR. Secondary end point was evaluation of EEG and PPR changes as predictive factors of 12-month seizure freedom.

RESULTS

Overall, mean dose of levetiracetam was 2,208 mg/day. Mean study period was 19.3 +/- 11.5 months (range 0.3-38). During the baseline period, interictal EEG abnormalities were detected in 44/48 patients (91.6%) and PPR was determined in 17/48 (35.4%) of patients. After levetiracetam treatment, 27/48 (56.2%) of patients compared to 4/48 (8.3%) in the baseline period (p < 0.0001) had a normal EEG. Thirteen of 17 (76.4%) (p < 0.0003) patients showed suppression of PPR. Cumulative probability of days with myoclonia (DWM) 12-month remission was significantly higher (p < 0.05) in patients with a normal (normalized) EEG after levetiracetam treatment compared to those with an unchanged EEG.

CONCLUSIONS

Levetiracetam appeared to be effective in decreasing epileptiform EEG abnormalities, and suppressing the PPR in JME patients. This effect, along with a good efficacy and tolerability profile in this population further supports a first-line role for levetiracetam in the treatment of JME.