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.
Objective To investigate biological markers that differentiate states during various seizure periods of childhood absence epilepsy (CAE) by examining the spatiotemporal dynamics of magnetoencephalographic (MEG) signals from Default Mode Network (DMN) nodes, revealing the neurophysiological mechanisms underlying changes in consciousness during CAE seizures. MethodsThirty-six drug-native children diagnosed with CAE were recruited. The interictal data, ictal data of CAE children were collected using a CTF-225 channel MEG system. Conduct temporal homogeneity partitioning for all seizure period data, co-registering 14 distinct seizure states. Identify 12 brain regions associated with the default mode network (DMN) as regions of interest (ROI); employ minimum norm estimation in conjunction with the Welch method to compute the power spectral density (PSD) of the ROI; conduct differential analysis on the relative PSD values; and use a random forest model to identify significant PSD features that differentiate between states of epilepsy. ResultsPower changes in DMN-related brain regions across various frequency bands show significant synchrony. During a seizure, the power in the δ band rapidly increases at the onset and quickly decreases at the end. Meanwhile, the power in the θ-γ2 bands decreases at the beginning and gradually recovers after the seizure. During the O+2 phase following seizure onset, the power in the β band peaks briefly before rapidly declining. The medial frontal cortex has lower power in the δ frequency band during seizures compared to other DMN brain regions, but higher power in the α frequency band. The random forest model's feature importance analysis reveals that the precuneus, lateral temporal lobe and medial temporal lobe play important roles in identifying seizure states. Power changes in the precuneus in the α and δ frequency bands improve the model's classification accuracy. ConclusionsThis study investigated the dynamic spatiotemporal characteristics of the DMN during CAE seizures, revealing the typical manifestations of power changes in specific brain regions and frequency bands at the onset, maintenance, and termination of seizures. It was discovered that power of the precuneus can act as an important feature to distinguish between different stages of CAE seizures. These findings shed new light on the pathophysiological mechanisms underlying changes in consciousness states in CAE.