Figure 14.

Interaction between the different computational stages in a neural mass model simulation. The raw connectivity from sources such as diffusion tensor imaging is pre-processed to produce a connectivity map between brain regions defined by a parcellation scheme. The connectivity map is fed into a network kernel composed of a coupling, neural mass and integration kernel. The coupling kernel combines the simulated system's current state with the connectivity data to compute the input to each node for the current time step. For each node, the NMM kernel computes the state changes which are fed into the integration kernel to compute the final state at the end of the current time step. The resulting observables are fed back to the coupling kernel and forward to a post-processing kernel to compute derived signals such as BOLD or EEG data for comparison to experimental results. Since there are no dependencies between distinct instances of the network kernel, this data flow can be parallelized over each set of model parameters.