Neuronal morphology plays a fundamental role in determining the neuronal network's function. However, understanding the effect of single neuron morphology on whole-brain function is still an open challenge. Limited by resolution and time cost, current studies on the functional properties of whole-brain connectivity are mainly described with simulations from the mesoscale perspective, like those generated in The Virtual Mouse Brain (TVMB) platform. Whole mouse brain activity is simulated using connectivity weights between brain regions obtained from tracer injections in populations of neurons and correlated with functional Magnetic Resonance Imaging (fMRI) experiments. However, high-throughput tracing of full single neurons unravels intricate and extensive axonal arborization and collateral innervation throughout the brain. Our recent work showed that single-cell morphological details impact network topology. Hence, we hypothesized that single-cell morphology data could better model resting-state function with a refined inter-regional connectivity description. Specifically, we propose a method to define inter-region connection strength as the ratio between the number of putative axonal boutons in a post-synaptic brain region divided by the region volume, normalized by the density of neurons (number of neurons per unit of volume in the presynaptic region) traced in the presynaptic brain region. We used an extensive dataset of 1876 fully reconstructed neurons, revealing stronger and more heterogeneous connections than previous tracer injection-based brain connectivity measurements captured. We simulated the whole mouse brain's resting state using the Reduced Wong-Wang Model. After optimizing global coupling and background noise parameters, we tested the simulation's alignment with 20 available experiments of resting state fMRI, finding that simulations using single-cell connectivity have increased predictive power for Functional Connectivity Dynamics (FCD) compared to tracer-based connectomes. Our findings underscore the importance of incorporating detailed single-cell information to accurately model brain dynamics, offering insights into the mouse brain's functional architecture. Keywords: Full neuron morphology; mesoscale simulation; resting-state; synaptic boutons