Hierarchical structural mapping for globally optimized estimation of functional networks.

In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct "N-step reachable structural maps". Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals.

A framework for quantifying node-level community structure group differences in brain connectivity networks.

We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures.