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Network control theory can be used to model how one should steer the brainbetween different states by driving a specific region with an input. The neededenergy to control a network is often used to quantify its controllability, andcontrolling brain networks requires diverse energy depending on the selectedinput region. We use the theory of how input node placement affects the longestcontrol chain (LCC) in the controllability of brain networks to study the roleof the architecture of white matter fibers in the required control energy. Weshow that the energy needed to control human brain networks is related to theLCC, i.e., the longest distance between the input region and other regions inthe network. We indicate that regions that control brain networks with lowerenergy have small LCCs. These regions align with areas that can steer the brainaround the state space smoothly. By contrast, regions that need higher energyto move the brain toward different target states have larger LCCs. We alsoinvestigate the role of the number of paths between regions in the controlenergy. Our results show that the more paths between regions, the lower costneeded to control brain networks. We evaluate the number of paths by countingspecific motifs in brain networks since determining all paths in graphs is adifficult problem.

The impact of input node placement in the controllability of brain networks