Cognitive side-effects of antiepilpetic drugs in children

Although the causes of cognitive impairment in patients with epilepsy have not been completely elucidated, three factors are clearly involved: the underlying etiology of epilepsy, the effects of seizures or the epileptiform EEG discharges themselves, and the central nervous system effects of antiepileptic drugs (AEDs). All commonly used AEDs have some effect on cognitive function, and the effect may be substantial when crucial functions are involved, such as learning in children. With phenobarbital, there is a high risk for serious cognitive effects impacting attention and memory. Phenytoin may affect mental speed, mainly in higher dosing and polytherapy. Moderate monotherapy doses do not seem to induce much effect. Valproate does not seem to impair cognition if sufficiently controlled for hyperammonemia. For carbamazepine, there are conflicting reports, which may be due to selection bias or dosing. For oxcarbazepine, there is no evidence for any detrimental change compared to valproate but mild improvements on attentional tests. For topiramate, there is clear evidence for topiramate-induced cognitive impairment (attention, memory, and language function) in adults and children. Although data is sketchy, levetiracetam does not seem to have a negative impact on cognition. For lamotrigine, there is evidence of a cognitive-enhancing effect on attention. No evidence for cognitive side-effects has been found for vigabatrin. Ethosuximide is not associated with cognitive impairment although the evidence is sketchy. For gabapentin, tiagabine, zonisamide, and rufinamide no studies in children are available

The relative influence of epileptic EEG discharges, short nonconvulsive seizures, and type of epilepsy on cognitive function

\u3cp\u3ePURPOSE: This study addressed whether cognitive impairment in children with epilepsy is caused by disease-related stable factors, such as the type of epilepsy, or by acute effects of paroxysmal epileptic activity such as epileptic EEG discharges. We studied a nonselected group with short nonconvulsive seizures, as these seizures may elude detection and may therefore persist over a longer period. In this group, the diagnostic issue is to differentiate between the combined effects of several epilepsy-related factors on cognition.\u3c/p\u3e\u3cp\u3eMETHODS: All children were assessed with 32-channel EEG, synchronized with a computerized cognitive test system and a video-monitoring system. Recording time was 2 h. The primary inclusion criteria were unclear seizures and fluctuations in cognitive performance and/or frequent epileptic EEG discharges in a recent EEG.\u3c/p\u3e\u3cp\u3eRESULTS: One hundred fifty-two patients met the inclusion criteria; 31 patients appeared not to have a diagnosis of epilepsy and were used as a nonepilepsy control group. Our results show that type of epilepsy has an impact on stable cognitive functions, such as educational achievement. Paroxysmal epileptic activity (acute effects of seizures and epileptic EEG discharges) affects primarily transient mechanistic cognitive processes (alertness, mental speed).\u3c/p\u3e\u3cp\u3eCONCLUSIONS: These results suggest that the effects of paroxysmal epileptic activity on transient cognitive mechanisms may accumulate over time and consequently affect the more stable aspects of cognitive function such as educational achievement. The clinical relevance is that early detection of the cognitive impact of seizure-related activity and subsequent treatment may prevent its detrimental impact on cognitive and educational development.\u3c/p\u3

Wavelet coherence-based classifier : a resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism

\u3cbr/\u3eBackground and Objective\u3cbr/\u3eThe autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI.\u3cbr/\u3eMethods\u3cbr/\u3eWe investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset.\u3cbr/\u3eResults\u3cbr/\u3eWe distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity).\u3cbr/\u3eConclusions\u3cbr/\u3eThis study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.\u3cbr/\u3

Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?

Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.\u3cbr/\u3e

The cognitive impact of epileptiform EEG-discharges : relationship with type of cognitive task

\u3cp\u3eIn this study we analyzed the effect of differing task dimensions (high vs. low information demand; short vs. long testing duration) on the occurrence of epileptiform EEG-discharges and the cognitive impact of such discharges. We performed this study only in patients with focal discharges as this appears to be the most complicated group to assess any relationship between epileptiform EEG-discharges and cognitive impairment. Seventeen patients with focal discharges in the EEG and an established diagnosis of localization-related (partial) epilepsy were included. The following tasks were used: Low information demand: auditory and visual RT; high information demand: BCRT and CVST. Short testing duration: Arithmetic and Reading; long testing duration: Vocabulary and Block Design. The percentage of patients with epileptiform EEG-discharge and EEG-related cognitive impact were compared using Chi-square testing. The occurrence of epileptiform EEG-discharges was not associated with one of the experimental conditions introduced in our study, that is, high/low information demand or short/long testing period. Also the difference between computerized reaction-time measurement and more traditional 'paper and pencil tasks' such as reading was not statistically significant. The only statistical significant difference was the more frequent occurrence of epileptiform EEG-discharges during tasks that used the visual input mode. In addition, we could identify one test that appeared to be particularly sensitive to direct cognitive effects of epileptiform EEG-discharges. Only for the CVST, the computerized visual searching task, the relationship with epilepsy-related cognitive impact is statistically significant. This test is the most mentally demanding test of the tests presented in our study and measures speed of visual information processing, using complex stimulus patterns and has a long testing duration. Our results do not confirm that any of the investigated task dimensions (high vs. low information demand; short vs. long testing duration) have a dominant effect on the occurrence of epileptiform EEG-discharges and the cognitive impact of such discharges. The effect found for the CVST suggest that three factors combined are necessary to assess the impact of epileptiform EEG-discharges on cognition: visual input mode, longer testing duration and high information processing demand.\u3c/p\u3

Finding predictive EEG complexity features for classification of epileptic and psychogenic nonepileptic seizures using imperialist competitive algorithm

\u3cp\u3eIn this study, the imperialist competitive algorithm (ICA) is applied for classification of epileptic seizure and psychogenic nonepileptic seizure (PNES). For this purpose, after decomposing the EEG signal into five sub-bands and extracting some complexity features of EEG, the ICA is applied to find the predictive feature subset that maximizes the classification performance in the frequency spectrum. Results show that the spectral entropy and Renyi entropy are the most important EEG features as they are always appeared in the best feature subsets when applying different classifiers. Also, it is observed that the SVM-RBF and SVM-linear models are the best classifiers resulting in highest performance metrics compared to other classifiers. Our study shows that the reported algorithm is able to classify the epileptic seizure and PNES with a very high classification metrics.\u3c/p\u3

Antiepileptic drugs and high prevalence of low bone mineral density in a group of inpatients with chronic epilepsy

Purpose Long-term antiepileptic drug use is associated with low bone mineral density (BMD), fractures and abnormalities in bone metabolism. We aimed at determining the prevalence of bone mineral disorders in patients with refractory epilepsy treated with antiepileptic drugs. Methods A cross-sectional survey was conducted in adult patients (n = 205) from a residential unit of a tertiary epilepsy centre. Screening for bone mineral disorders was performed with dual-energy X-ray absorptiometry (DXA) scan of spine and hip (including bone mineral density and vertebral fracture assessment) and laboratory measurements. Patient information regarding demography, epilepsy characteristics and medication use was recorded. Based on DXA T-scores, prevalence of bone mineral disorders (osteopenia and osteoporosis) was calculated. Correlations between DXA T-scores and epilepsy parameters were explored. Results Of the 205 patients, there were 10 dropouts. 80% (n = 156/195) of the patients had low BMD: 48.2% had osteopenia and 31.8% had osteoporosis. Of those having low BMD, 51.9% (n = 81/195) was between 18 and 50 years. The T-score of the femoral neck correlated significantly with total duration of epilepsy, cumulative drug load and history of fractures. Linear regression analysis showed that of the epilepsy-related parameters, only cumulative drug load significantly predicted low femoral neck T-score (P = 0.001). Conclusion In this high-risk population, we obtained a very high prevalence of 80% of low BMD. Both men and women were affected as well as patient

Improving BOLD sensitivity with real-time multi-echo echo-planar imaging - Towards a cleaner neurofeedback signal

Real-time functional magnetic resonance imaging (rtfMRI) suffers from known issues related to T2*-weighted single-echo echo-planar imaging (EPI). These include image dropout in areas with increased local magnetic susceptibility susceptibility gradients; suboptimal whole-brain blood oxygen level-dependent (BOLD) contrast due to average T2*-weighting; and confounders like subject motion and physiology. During fMRI neurofeedback a metric calculated from real-time brain activity is presented visually to the subject in the scanner. To prevent sham feedback, new methods should focus on improving BOLD signal quality in real-time. In this work, presented as a poster at the 11th annual meeting of the ISMRM Benelux chapter (17 January 2019), we present our work on real-time multi-echo fMRI and its usefulness in increasing the temporal signal-to-noise ratio (tSNR) of rtfMRI

Bone disease during chronic antiepileptic drug therapy: general versus specific risk factors

An increasing number of studies suggest a direct effect of antiepileptic drug (AED) therapy on bone health: Patients on chronic AED therapy may have an increased risk of fractures, reduced bone mineral density, osteopenia, and osteoporosis. In an attempt to distinguish general and specific risk factors, this review examines the available empirical research. The pathophysiology is discussed and guidelines for early detection and treatment options are proposed