I assume that human brains can be considered as directed graphs with neurons as nodes and synapses as edges. I explicitly don't want to consider the weights, the dynamics of neural activity (based on the weights), and the adjustment of weights (learning) - just brains as static unweighted finite directed graphs.
Sensor neurons may be those having in-degree 0, actor neurons may be those having out-degree 0. (0 meaning "essentially 0".)
Considering human brains as finite directed graphs, for each question concerning finite directed graphs there should be an answer with respect to human brains.
Such questions might be:
How long is the shortest path from a sensor to an actor neuron?
How long is the longest (direct) path from a sensor to an actor neuron?
What is the (global/local) layer structure (on different levels of granularity)?
What is the (global/local) cycle structure (on different levels of granularity)?
I find it hard to get answers to such questions considering human brains as directed graphs, because neuro-scientists don't think in terms of graphs, but for example in terms of signal paths and neuro-anatomy. But then - for them - "anything goes", and "everything is connected to everything" - which is not very helpful.
I would be very glad for any reference treating (formally) human brains as directed graphs.