<p class="first" id="P1">Major neuropsychiatric disorders such as psychosis are increasingly
acknowledged to
be disorders of brain connectivity. Yet, tools to map, model, predict, and change
connectivity are difficult to develop, largely due to the complex, dynamic, and multivariate
nature of interactions between brain regions. Network neuroscience provides a theoretical
framework and mathematical toolset to address these difficulties. Building on areas
of mathematics such as graph theory, network neuroscience in its simplest form summarizes
neuroimaging data by treating brain regions as nodes in a graph, and by treating interactions
or connections between nodes as edges in the graph. Network metrics can then be used
to quantitatively describe the architecture of the graph, which in turn reflects the
network’s function. Here we review evidence supporting the utility of network neuroscience
in understanding psychiatric disorders, with a particular focus on normative brain
network development and abnormalities associated with psychosis. We also emphasize
relevant methodological challenges such as motion artifact correction, which are particularly
important to consider when applying network tools to developmental neuroimaging data.
We close with a discussion of several emerging frontiers of network neuroscience in
psychiatry, including generative network modeling and network control theory. We aim
to offer an accessible introduction to this emerging field, and motivate further work
that uses network neuroscience to better understand the normative development of brain
networks, and alterations in that development that accompany or foreshadow psychiatric
disease.
</p>