  We have extended the study of the Kuramoto model with additive Gaussian noise
running on the KKI-18 large human connectome graph. We determined the dynamical
behavior of this model by solving it numerically in an assumed homeostatic
state, below the synchronization crossover point we determined previously. The
de-synchronization duration distributions exhibit power-law tails,
characterized by the exponent in the range $1.1 < \tau_t < 2$, overlapping the
in vivo human brain activity experiments by Palva et al. We show that these
scaling results remain valid, by a transformation of the ultra-slow
eigen-frequencies to Gaussian with unit variance. We also compare the
connectome results with those, obtained on a regular cube with $N=10^6$ nodes,
related to the embedding space, and show that the quenched internal frequencies
themselves can cause frustrated synchronization scaling in an extended coupling
space.
