Functional connectivity in resting state MRI. Graph analysisnew biomarkers of functional recovery in stroke

Abstract

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. In this thesis, 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. In this thesis, we studied the role of the contralesional hemisphere in the reorganization of brain function of stroke patients using resting state fMRI and graph theory. We explored this reorganization using the ’hub disruption index’ (κ), a global index sensitive to the reorganization of nodes within the graph. We explored the relation between κ and behavioral clinical scores to assess whether κ could be used as a surrogate biomarker of stroke recovery.