When: 10:00 pm
In the recent years, there has been a great amount of work developing new investigation methods of the brain connectivity based on fMRI. The exploration of brain networks with resting-state fMRI (rs-fMRI) combined with graph theoretical approaches has become popular, with the perspective of finding network graph metrics as biomarkers in the context of clinical studies. A preliminary requirement for such findings is to assess the reliability of the graph based connectivity metrics in healthy subjects. Taking advantage of a large test-retest database provided by the Human Connectome Project, we quantified the reliability of the graph metrics computed both at global and regional level depending, at optimal cost, on two key parameters, the sample size (number of subjects) and the number of time points (or scan duration). We also explored how other factors, such as the parcellation scheme, the connectivity measure or the filtering method may influence this reliability. In a clinical context, stroke is one of the leading causes of mortality and disability worldwide. Resulting in focal structural damage, it induces changes in brain function at both local and global levels. Following stroke, cerebral networks present structural and functional reorganization to compensate for the functional impairment provoked by the lesion itself and its remote effects. Here, we studied the role of the contralesional hemisphere in the reorganization of brain function of stroke patients using rs-fMRI and graph theory and we explored the relation between graph metrics and cognitive scores to predict stroke recovery.