Alterations of multilayer network correlated with cognitive impairment and gene expression profiles in children with idiopathic generalized epilepsy.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563000, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. [email protected].
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563000, China. [email protected].
- Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563000, China. [email protected].
- Bijie Medical College, Bijie, 551700, China. [email protected].
Abstract
This study investigated dynamic brain network changes and their genetic correlations in children with idiopathic generalized epilepsy (IGE). We included 26 children with IGE and 35 healthy controls, all participants underwent resting-state functional magnetic resonance imaging and cognitive assessments. Modular variability (MV) in time-varying networks was compared, and correlations with cognition and clinical variables were analyzed, we also explored classification problems using machine learning. Gene sets associated with IGE-related network remodeling were identified using the Allen Human Brain Atlas and gene enrichment analysis tools. The results showed that children with IGE exhibited reduced MV in sensorimotor and frontoparietal networks and increased MV in the default mode network (DMN). MV changes in the left prefrontal and right orbitofrontal cortices correlated with verbal and full-scale IQ scores, respectively. MV changes in the left precuneus/posterior cingulate cortex correlated with performance IQ scores. Transcriptomic analysis revealed 985 genes (FDR < 0.05) whose spatial expression patterns covaried with network alterations, prominently enriched for synaptic signaling and neuroactive ligand-receptor interactions, including GABA receptor subunits (GABRE) and neurodevelopmental regulators (BCL11A). Machine learning confirmed MV as a significant predictor of verbal IQ (permutation P = 0.041), with DMN and frontoparietal regions contributing most to prediction. Dynamic brain network abnormalities in children with IGE were significantly associated with cognitive function and gene expression, providing new insights into the neural mechanisms underlying network dysfunction and cognitive impairment in epilepsy.