Identification of topological alterations using microstate dynamics in patients with infantile epileptic spasms syndrome

Abstract

Infantile epileptic spasm syndrome (IESS) is characterized by clustered epileptic spasms and hypsarrhythmia on electroencephalography (EEG). This study aimed to investigate the temporal dynamics and dynamic synchronization of neural networks in IESS using EEG microstate analysis of interictal recordings from 49 healthy controls (HC) and 42 patients with IESS. Five microstate maps were identified, and features including the global explained variance (GEV), mean correlation, occurrence, time coverage, mean time duration, and transition probabilities were extracted. Significant differences were observed in patients with IESS compared to HCs, with increased microstate features and transition probabilities in microstates A and B, and reduced values in microstates D and E. Furthermore, in patients with structural/metabolic etiologies, microstate A demonstrated heightened microstate features and transition probabilities compared to genetic/unknown etiologies. These microstate characteristics enabled accurate classification of IESS versus HCs and differentiation between structural/metabolic and genetic/unknown etiologies. The altered microstate topologies in IESS reflect disruptions in brain network dynamics, suggesting that specific microstate features and transition probabilities could serve as potential diagnostic biomarkers. This study underscores the potential of EEG microstate analysis in understanding neural dysfunction, particularly in structural/metabolic subtypes of IESS.