Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles

Segregation and integration are two general principles of the brain's functional architecture; therefore brain network analysis is of significant importance in understanding brain function. Critical to brain network analysis and construction is the identification of reliable, reproducible and accurate network nodes, or Regions of Interest (ROIs). In this paper, based on functional ROIs derived from task-based fMRI, we propose a novel framework to optimize the location and size of the ROIs which ensures that the difference of structural connectivity profiles among a group of subjects is minimized. In order to facilitate the optimization procedure, we present a new approach to quantitatively describe and measure the fiber bundle similarity within and across subjects. This framework has been extensively evaluated and our experimental results suggest the promise of our approaches. This capability of accurately localizing brain network ROIs would open up many applications in brain imaging that rely on identification of functional ROIs.