Thesis Project Form

Dravet syndrome (DS) is a severe developmental epileptic encephalopathy caused predominantly by loss-of-function mutations in the SCN1A gene, encoding the Nav1.1 voltage-gated sodium channel. Patients present with drug-resistant seizures, pronounced cognitive impairment, and elevated risk of sudden unexpected death in epilepsy. A critical limitation in current DS research is the lack of human-relevant in vitro platforms capturing network-level electrophysiological dysfunction. Human induced pluripotent stem cell (hiPSC)-derived neuronal networks recorded on multiwell microelectrode arrays (multiwell MEAs) represent a powerful and translationally relevant platform to study disease-specific activity patterns. In particular, fragmented network bursts have recently emerged as a candidate biomarker of network dysfunction in DS, but their detection depends critically on the choice of burst detection algorithm and parameters, an aspect not yet systematically investigated.

• Characterise spontaneous electrophysiological activity (mean firing rate, network burst rate, fragments per burst) across four hiPSC-derived cell lines (DD1C, DD5A, TUBA, C10902) at multiple maturation timepoints (DIV 46–90).
• Compare two network burst detection algorithms (Tampere vs Doorn) and two pipeline versions (V1 vs V2) in terms of NB rate, duration, and fragmentation metrics.
• Develop and validate a MATLAB statistical pipeline for the comparison of electrophysiological metrics across cell lines, detectors, and pipeline versions.

• Advanced MATLAB programming for electrophysiological signal processing and statistical analysis of neuronal data.
• Implementation and benchmarking of network burst and fragmentation detection algorithms for MEA data.