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 construction and analysis is the identification of reliable, reproducible, and accurate network nodes, or Regions of Interest (ROIs). Task-based fMRI has been widely considered as a reliable approach to identify functionally meaningful ROIs in the brain. However, recent studies have shown that factors such as spatial smoothing could considerably shift the locations of detected activation peaks. As a result, structural and functional connectivity patterns can be significantly altered. Here, we propose a novel framework by which to optimize ROI sizes and locations, ensuring that differences between the structural connectivity profiles among a group of subjects is minimized. This framework is based on functional ROIs derived from task-based fMRI and diffusion tensor imaging (DTI) data. Accordingly, we present a new approach to describe and measure the fiber bundle similarity quantitatively within and across subjects which will facilitate the optimization procedure. Experimental results demonstrated that this framework improved the localizations of fMRI-derived ROIs. Through our optimization procedure, structural and functional connectivities were more consistent across different individuals. Overall, the ability to accurately localize network ROIs could facilitate many applications in brain imaging that rely on the accurate identification of ROIs.