Before revision:  We determined the desynchronziation behavior , by solving this model via the 4th order Runge-Kutta algorithm on a large, weighted human connectome network and compared the results with those of a two-dimensional lattice, with additional random, long-range links. In the latter case a mean-field critical transition is expected and here we provide numerical results for the synchonization/desynchonization duration distributions. We find power-law tails, characterized by a critical exponent \tau_d \simeq 1.6(1). In case of the connectome we assumed a homeostatic state, by the application of normalized incoming weights . Since this graph has a topological dimension d < 4  a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states.
After revision:  We determined the desynchronziation behavior , by solving this model via the 4th order Runge-Kutta algorithm on a large, weighted human connectome network , containing 804092 nodes, in an assumed homeostatic state . Since this graph has a topological dimension d < 4 a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states.