Evaluation of hierarchy transitions and directed functional connectivity in large-scale cortical network models
Large-scale cortical network models simulate the dynamical behavior of several coupled brain areas. Models based on connectomes are more biologically plausible by considering the structural connectivity, and their results can be extended to experimental and clinical applications. In this thesis, we present two studies using large-scale network models for the mouse cortex. In the first study, we analyze the relationship between structural and directed functional connectivity. We show that the correlation between structural and directed functional connectivity depends on the number of areas considered and measure this impact. The results indicate that directed functional connectivity estimates provide statistical information on structural connections, but individual connection estimates are not reliable. In the second study, we investigate hierarchy transitions in cortical excitability. We observed that the hierarchy transitions are dependent on the stimulus intensity and the stimulated primary area. Our main conclusion was that the system heterogeneities play a crucial role in the hierarchy transitions in cortical excitability.