Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures.

Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.