dc.description.abstract |
The use of computational brain models in epilepsy constitutes a powerful tool not
only to improve our understanding of the disease, but also to optimize and personalize
treatments in silico. In this direction, properly extracting functional connectivity
(FC) measures from epileptogenic networks is crucial for a proper personalization of
brain models. This thesis focuses on investigating the effectiveness of three connectivity
metrics—cross-correlation, phase locking value (PLV), and h2 correlation—for
extracting FC values from empirical and simulated stereo-electroencephalography
(SEEG) data. The main goal was to identify the most suitable metric for fitting
the epileptogenic computational models used at Neuroelectrics in the framework
of the Galvani project. To achieve this goal, a broad analysis was performed on
empirical SEEG recordings from epilepsy patients, and the simulated SEEG data
obtained from their personalized models. We computed the cross-correlation, PLV,
and h2 correlation between signal pairs, and their respective ability to capture typical
epileptogenic FC patterns was assessed and tested using bivariate surrogates.
Although the findings revealed substantial variability in the connectivity results
obtained with each metric and in each patient, the h2 correlation shows a better
performance, especially in the epileptogenic zone, which is the most critical area
in our modelling pipeline. This superior performance of the h2 correlation fits the
state-of-the-art literature and suggests its potential as a valuable tool for personalized
modelling, ultimately leading to more effective, personalized and life-changing
treatments for epilepsy patients. This work introduces a novel aspect by incorporating
surrogates in the analysis of simulated epileptogenic SEEG data, and provides
empirical evidence of the efficacy of connectivity metrics in functionally characterizing
epileptogenic networks and in supporting the development of personalized and
accurate brain models. |