Thesis Project Form

Radiofrequency thermocoagulation (RFTC) is a minimally invasive surgical procedure used in patients with drug-resistant focal epilepsy. During stereoelectroencephalography (SEEG), clinicians can apply small, targeted thermal lesions to dysfunctional brain regions in an attempt to reduce seizure frequency.
Despite its promising outcomes, RFTC remains highly empirical: there are no standardized criteria for selecting the contacts to coagulate, and the relationship between local lesions and global changes in network dynamics is still poorly understood. This thesis focuses on analysing SEEG recordings collected before and after RFTC to explore how the procedure modifies epileptic network activity. By combining signal processing, network neuroscience, and computational analysis, the project aims to support a deeper understanding of therapeutic mechanisms and contribute to more data-driven surgical planning.

The goal of this thesis is to develop a computational workflow for assessing how RFTC alters brain dynamics in patients with drug-resistant epilepsy.
The student will work with SEEG datasets acquired during clinical monitoring and will:
- Preprocess SEEG recordings, including artefact rejection and quality checks.
- Extract dynamical metrics (e.g., spectral power, Phase-Locking Value, excitation–inhibition balance, bistability indices).
- Compare pre- and post-RFTC activity at both channel and network levels.
- Analyse spatial patterns by linking each electrode contact to its anatomical location.
- Correlate electrophysiological changes with clinical outcomes, such as reduction in seizure frequency.

During the thesis, the student will gain:
- Practical experience in intracranial EEG analysis and handling complex biomedical datasets.
- Skills in Python, signal processing, and quantitative neuroscience methods.
- Experience with network metrics, temporal–dynamical analyses, and multimodal data integration.