6th Advanced Residential Course in Neonatal Epilepsy

Course description

Neonatal epilepsy constitutes a paradigmatic condition of early-life brain dysfunction, in which alterations at the molecular and genetic level converge to disrupt the emergence and organization of neuronal networks during critical phases of neurodevelopment.

Over the past decade, neonatal epileptology has undergone a substantial conceptual evolution, transitioning from a predominantly phenotype-driven framework toward an integrated, data-intensive model grounded in precision medicine.

This shift has been enabled by the convergence of multiple technological and methodological advances, including high-resolution next-generation sequencing (NGS), sophisticated EEG analytics with continuous neurocritical monitoring, and functional validation platforms spanning in vitro and in silico systems. Within this landscape, the introduction of Digital Twin paradigms represents a further transformative step, offering the potential to computationally model individual disease trajectories.

Clinically, neonatal seizures are frequently characterized by electroclinical dissociation, subtle semiology, and marked etiological heterogeneity. Such complexity inherently limits the effectiveness of traditional diagnostic approaches and necessitates a multidimensional strategy that integrates genomic, electrophysiological, and clinical data. Central to this approach is the early identification of actionable molecular targets, enabling the timely initiation of mechanism-based, precision anti-seizure therapies. In this context, neonatal epileptology can no longer be considered a purely descriptive discipline; rather, it is evolving into a predictive, model-driven, and intervention-oriented field. The present course, as a natural progression of the scientific and educational pathway developed in previous editions, is designed to introduce a translational framework that systematically links multiple levels of biological organization: a multilevel biological cascade integrating genomic information, protein structure, cellular excitability, and network dynamics (EEG), ultimately shaping clinical

phenotype and informing therapeutic response. Such an integrated pipeline provides the conceptual and operational basis for the development of patient-specific Digital Twins. These computational models hold the potential to simulate disease evolution, predict pharmacological

responsiveness, and ultimately support real-time clinical decision-making within the neonatal intensive care setting.

Course Directors

Raffaele Falsaperla

Agnese Suppiej