In natural systems time delays are inevitable due to the finite speed of the signal transmission over a distance, and are not negligible if they are similar to the time scales of the observed dynamics. For example in the brain, where the delays are on the same scale as the signal operation, they affect the brain performances and need to be considered. On the other hand, many complex networks, such as the cortical networks, are hierarchical. It means that their communities may be further divided into sub-communities. Therefore, because of the above-mentioned reasons, the analysis of time-delayed dynamics on hierarchical networks may help to understand the interplay between brain structure and function. Based on extensive simulations in artificial and cortical networks with homogeneous time delays, we uncovered that for a fixed coupling strength, by changing time delay different regions of coherent, multistable and incoherent dynamics are revealed. We show that in a hierarchical network, by transition from incoherent to coherent states, different topological scales of the network are revealed respectively in a non transient behavior. In addition, we find that by considering bimodal distribution of the time delays the parameter regions corresponding to the multistabe dynamics will be extended. Finally we show that the results are connected to the global slow dynamics of the brain.