5413 Local Brain Connectivity Dynamics Using a Graph-Theoretical Approach

Introduction: Graphs form a good abstraction and representation for brain connectivity where the nodes represent neural elements (neurons, brain regions etc.) and the edges represent some measure of structural or functional connectivity between nodes. While a number of graph based metrics such as the clustering coefficient capture connectivity at a global level (integrated view), often it is necessary to examine changes at the local level (segregated view). One example of alteration in brain connectivity is the development of re-routings that restore functionality typically after a local injury [1]. Scholz et al. report changes in white matter fiber connectivity following behavioral training of a complex skill [3]. While these changes take place over a length of time, brain connectivity could be quite dynamic even in a short time scale. While graph theoretical methods have been extensively used in neuroimaging, the dynamics aspect of brain connectivity is relatively less explored. In this work we investigate the short term dynamics of connectivity within a graph based representation. We propose to quantify the dynamics at the local level by quantifying changes in the neighborhood connectivity across time.