Controllability Of Structural Brain Networks In Dementia

The dynamics of large-scale neural circuits is known to play an important role in both aberrant and normal cognitive functioning. Describing these phenomena is extremely important when we want to get an understanding of the aging processes and for neurodegenerative disease evolution. Modern systems and control theory offers a wealth of methods and concepts that can be easily applied to facilitate an insight into the dynamic processes governing disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. Past research has shown that two types of controllability - the modal and average controllability - are key components when it comes to the mechanistic explanation of how the brain operates in different cognitive states. The average controllability describes the role of a brain network's node in driving the system to many easily reachable states. On the other hand, the modal controllability is employed to identify the states that are difficult to control. The first controllability type favors highly connected areas while the latter weakly connected areas of the brain. Certain areas of the brain or nodes in the connectivity graph (structural or functional) can act as drivers and move the system (brain) into specific states of action. To determine these areas we apply the novel concept of exact controllability and determine the minimum set and the location of driver nodes for dementia networks. Our results applied on structural brain networks in dementia suggest that this novel technique can accurately describe the different node roles in controlling trajectories of brain networks, and show the transition of some driver nodes and the conservation of others in the course of this disease.