ObjectiveChildhood absence epilepsy (CAE) is a common syndrome of idiopathic generalized epilepsy.However, little is known about the brain structural changes in this type of epilepsy, especially in the default mode network (DMN) regions.Diffusion tensor imaging (DTI) is a noninvasive techniques that can be used to quantitatively explore structural characteristics of brain.This study aims at using the DTI technique to quantify structural abnormalities of DMN nodes in CAE patients.MethodDTI data were obtained in 14 CAE patients and 13 age-and gender-matched healthy controls.The data were analyzed using voxel-based analysis (VBA) and statistically compared between patients and controls.For the regions with significant difference in group comparison, their DTI metrics were further analyzed with clinical symptoms using Pearson's correlation.ResultsPatients showed significant increase of apparent diffusion coefficient (ADC) in left medial prefrontal cortex (MPFC) (P=0.042), while fractional anisotropy (FA) value was significantly decreased in left precuneus (P=0.010).In correlation analysis, ADC value from left MPFC was positively associated with duration of epilepsy.Neither the disease duration nor the seizure frequency showed significant correlation with FA values.ConclusionThe findings indicate that structural impairments exist in DMN regions in children suffering from absence epilepsy.This may contribute to understanding the pathological mechanisms and chronic neurological deficits of this disorder.
The integral and individual-scale wavelet entropy of electroencephalogram (EEG) were employed to investigate the information complexity in EEG and to explore the dynamic mechanism of child absence epilepsy (CAE). The digital EEG signals were collected from patients with CAE and normal controls. Time-frequency features were extracted by continuous wavelet transformation. Individual scale power spectrum characteristics were represented by wavelet-transform. The integral and individual-scale wavelet entropy of EEG were computed on the basis of individual scale power spectrum. The evolutions of wavelet entropy across ictal EEG of CAE were investigated and compared with normal controls. The integral wavelet entropy of ictal EEG is lower than inter-ictal EEG for CAE, and it also lower than normal controls. The individual-scale wavelet entropies of 12th scale (centered at 3 Hz) of ictal EEG in CAE was significantly higher than normal controls. The individual-scale wavelet entropies for α band (centered at 10 Hz) of ictal EEG in CAE were much lower than normal controls. The integral wavelet entropy of EEG can be considered as a quantitative parameter of complexity for EEG signals. The complexity of ictal EEG for CAE is obviously declined in CAE. The wavelet entropies declined could become quantitative electrophysiological parameters for epileptic seizures, and it also could provide a theoretical basis for the study of neuromodulation techniques in epileptic seizures.