Prediction of treatment response in infantile epileptic spasms syndrome using EEG phase-amplitude coupling

Treatment selection for infantile epileptic spasms syndrome (IESS) is complex and multifaceted, and currently no electroencephalogram (EEG) biomarkers can guide this decision by predicting treatment response. We tested the predictive value of phase-amplitude coupling (PAC) as IESS patients are known to have elevated PAC.

We analyzed retrospective EEG recordings from 40 IESS patients, before and after treatment, and 20 healthy controls. Patients were classified as responders (n = 25) or nonresponders (n = 15) based on short-term treatment outcomes. We measured PAC in each EEG using modulation index (MI) and mean vector length (MVL) and analyzed the relationship between pre- and posttreatment values and the ability of pretreatment values to predict response.

MI and MVL values decreased with treatment in almost all subjects. However, nonresponders had significantly higher pretreatment MI than responders (p < 0.05), suggesting utility for predicting treatment response. Logistic regression modeling suggested that a 0.5-unit decrease in ln(MI), which is approximately one IQR of the pretreatment ln(MI) values, results in a 3.5-fold increase in odds of positive treatment response. MI reflects short-term treatment response and is a candidate predictive EEG biomarker for IESS.

MI may offer individualized insights for treatment selection and management strategies for IESS.

We examined brain activity in infants with infantile epileptic spasms syndrome (IESS) to predict which infants' seizures would stop with standard medication. Specifically, we measured coupling between slow and fast brain waves in both healthy infants and those with IESS. Infants for whom medication failed had stronger coupling, even before the treatment was started, while those with weaker coupling (more similar to healthy infants) were more likely to have their seizures stop after treatment. These findings suggest that brain wave coupling could help doctors predict which infants will need more aggressive treatment.