Large-Scale Dynamical Graph Networks Applied to Brain Cancer Image Data Processing

摘要

Brain tumor patients frequently experience tumor-induced alterations in cognitive functions. The early detection of such alterations becomes imperative in the clinical environment and with this the need for computational tools that are capable of quantitatively characterizing functional connectivity changes observed in brain imaging data. This paper presents the application of a novel modern control concept, pinning controllability, to determine intervention points (driver nodes) in the brain tumor-bearing resting-state connectome. The theoretical frameworks provides us with the minimal number of "driver nodes", and their location to determine the full control over the obtained graph network in order to provide a change in the network's dynamics from an initial state (disease) to a desired state (non-disease). Thus we are able to quantify the tumor-induced alterations in different brain regions and the differences in brain connectivity and dynamics. The achieved results will provide clinicians with techniques to identify more tumor-affected regions and biological pathways for brain cancer, to design and test novel therapeutic solutions.