Abstract

Using resting-state functional Magnetic Resonance Imaging (rs-fMRI), we can non-invasively measure functional connectivity (FC) between brain regions using the blood-oxygen-level-dependent (BOLD) signal which characterizes slow fluctuations of hemodynamic processes.Through various FC analysis techniques, we can extract the resting state networks (RSNs) of the brain, and identify highly connected regions called hubs, which are important for long-range and efficient communication within the brain. In this thesis, we applied, adapted, and carefully investigated the method entitled SParsity-based Analysis of Reliable k-hubness (SPARK),
introduced by our group, to identify and quantify in a reliable manner the reorganization of connector hubs for patients with frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE),
when compared to healthy controls (HC). In this work, we used several metrics to characterize reorganization of connector hubs, aiming at generating whole-brain fingerprint models to
characterize patients with epilepsy. We considered the following metrics, estimated on different parcellations of the brain in either nineteen anatomical regions or eleven functional networks: the
hub disruption index (HDI), the hub emergence index (HEI), the hierarchical segregation index (HSI) and regional k-hubness. Our results are suggesting that we found more significant
reorganization of hubness assessed using HDI and HEI when considering a segmentation in functional networks, as opposed to a segmentation in anatomical regions. We also reported
significant decreases in regional k in epilepsy patients when compared to controls. In addition, we reported a significant decrease in HSI between epilepsy and controls as well. These preliminary results should be confirmed when applied on larger epilepsy cohorts, where we can use our proposed methodology and metrics combined with other non-invasive imaging modalities to
discover potential biomarkers that could predict the postsurgical outcome of these patients.