Brain graphs provide a useful way to computationally model the network structure of the
connectome, and this has led to increasing interest in the use of graph theory to quantitate …

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 …

Graph theory-based analysis has been widely employed in brain imaging studies, and
altered topological properties of brain connectivity have emerged as important features of …

Graph theoretical analysis is a powerful tool for quantitatively evaluating brain connectivity
networks. Conventionally, brain connectivity is assumed to be temporally stationary …

Understanding the human brain remains the holy grail in biomedical science, and arguably
in all of the sciences. Our brains represent the most complex systems in the world (and some …

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful
tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and …

Interest in understanding the organization of the brain has led to the application of graph
theory methods across a wide array of functional connectivity studies. The fundamental …

Human brain connectivity is complex. Graph-theory-based analysis has become a powerful
and popular approach for analyzing brain imaging data, largely because of its potential to …

Over the past two decades, resting-state functional connectivity (RSFC) methods have
provided new insights into the network organization of the human brain. Studies of brain …

The large-scale organization of the brain has features of complex networks that can be
quantified using network measures from graph theory. However, many network measures …