Recent advances in magnetic resonance imaging (MRI) are allowing neuroscientists to gain
critical insights into the neural networks mediating a variety of cognitive processes. This …

Graph analysis has become an increasingly popular tool for characterizing topological
properties of brain connectivity networks. Within this approach, the brain is modeled as a …

The brain is a large-scale complex network whose workings rely on the interaction between
its various regions. In the past few years, the organization of the human brain network has …

Structural magnetic resonance (MR) connectomics holds promise for the diagnosis, outcome
prediction, and treatment monitoring of many common neurodevelopmental, psychiatric, and …

Brain connectomics research has rapidly expanded using functional MRI (fMRI) and
diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a …

Graph-based computational network analysis has proven a powerful tool to quantitatively
characterize functional architectures of the brain. However, the test-retest (TRT) reliability of …

There is a growing interest in exploring the connectivity patterns of the human brain.
Specifically, the utility of noninvasive neuroimaging data and graph theoretical analysis …

Graph theoretical analysis has become an important tool in the examination of brain
dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in …

The reliability of graph metrics calculated in network analysis is essential to the
interpretation of complex network organization. These graph metrics are used to deduce the …

Previous studies have investigated both structural and functional brain networks via graph‐
theoretical methods. However, there is an important issue that has not been adequately …