Predictive modeling based on functional connectivity of interictal scalp EEG for infantile epileptic spasms syndrome

Abstract

ObjectiveThis study aims to delineate the electrophysiological variances between patients with infantile epileptic spasms syndrome (IESS) and healthy controls and to devise a predictive model for long-term seizure outcomes. MethodsThe cohort consisted of 30 individuals in the seizure-free group, 23 in the seizure-residual group, and 20 in the control group. We conducted a comprehensive analysis of pretreatment electroencephalography, including the relative power spectrum (rPS), weighted phase-lag index (wPLI), and network metrics. Follow-up EEGs at 2 years of age were also analyzed to elucidate physiological changes among groups. ResultsInfants in the seizure-residual group exhibited increased rPS in theta and alpha bands at IESS onset compared to the other groups (all p < 0.0001). The control group showed higher rPS in fast frequency bands, indicating potentially enhanced cognitive function. The seizure-free group presented increased wPLI across all frequency bands (all p < 0.0001). Our predictive model utilizing wPLI anticipated long-term outcomes at IESS onset (area under the curve 0.75). ConclusionOur findings demonstrated an initial “hypersynchronous state” in the seizure-free group, which was ameliorated following successful treatment. SignificanceThis study provides a predictive model utilizing functional connectivity and insights into the diverse electrophysiology observed among outcome groups of IESS.