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01.07.2017 | Original Article

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

Erschienen in: Neurological Sciences | Ausgabe 7/2017

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Abstract

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.
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Metadaten
Titel
Comparative analysis of background EEG activity in childhood absence epilepsy during valproate treatment: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study
Publikationsdatum
01.07.2017
Erschienen in
Neurological Sciences / Ausgabe 7/2017
Print ISSN: 1590-1874
Elektronische ISSN: 1590-3478
DOI
https://doi.org/10.1007/s10072-017-2955-0

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