Controlling the dynamics of large-scale neural circuits might play an important role in aberrant cognitive functioning as found in Alzheimer's disease (AD). Analyzing the disease trajectory changes is of critical relevance when we want to get an understanding of the neurodegenerative disease evolution. Advanced control theory offers a multitude of techniques and concepts that can be easily translated into the dynamic processes governing disease evolution at the patient level, treatment response evaluation and revealing some central mechanisms in brain connectomic networks that drive alterations in these diseases. Two types of controllability - the modal and average controllability - have been applied in brain research to provide the mechanistic explanation of how the brain operates in different cognitive states. In this paper, we apply the concept of target controllability to structural (MRI) connectivity graphs for control (CN), mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. In target controllability, only a subset of the network states are steered towards a desired objective. We show the graph-theoretic necessary and sufficient conditions for the structural target controllability of the above-mentioned brain networks and demonstrate that only local topological information is needed for its verification. Certain areas of the brain and corresponding to nodes in the brain network graphs can act as drivers and move the system (brain) into specific states of action. We select first the drivers that ensures the controllability of these networks and since they do not represent the smallest set, we employ the concept of structural target controllability to determine those nodes that can steer a collection of states being representative for the transitions between CN, MCI and AD 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 being relevant for disease evolution.