FIGURE 1.

Whole-brain modeling steps to create simulated functional time series fitted to empirical BOLD data. Using a whole-brain network model such as the dynamic mean field model allows us to accurately create time series data at different temporal scales. Local dynamics of each region given by a parcellation are generated by a dynamic mean field model and coupled through the structural connectome (as provided by the numbers of fiber tracts estimated from diffusion-weighted imaging). To fit the resulting neuronal time series to the empirical BOLD time series, we employ a Balloon-Windkessel hemodynamic model to create simulated BOLD time series. The simulated time series are fitted to the empirical time series using metrics of metastability and phase similarity matrix distributions.