  As the field of neuroimaging grows, it can be difficult for scientists within
the field to gain and maintain a detailed understanding of its ever-changing
landscape. While collaboration and citation networks highlight important
contributions within the field, the roles of and relations among specific areas
of study can remain quite opaque. Here, we apply techniques from network
science to map the landscape of neuroimaging research documented in the journal
NeuroImage over the past decade. We create a network in which nodes represent
research topics, and edges give the degree to which these topics tend to be
covered in tandem. The network displays small-world architecture, with
communities characterized by common imaging modalities and medical
applications, and with bridges that integrate these distinct subfields. Using
node-level analysis, we quantify the structural roles of individual topics
within the neuroimaging landscape, and find high levels of clustering within
the structural MRI subfield as well as increasing participation among topics
related to psychiatry. The overall prevalence of a topic is unrelated to the
prevalence of its neighbors, but the degree to which a topic becomes more or
less popular over time is strongly related to changes in the prevalence of its
neighbors. Broadly, this work presents a cohesive model for understanding the
landscape of neuroimaging research across the field, in broad subfields, and
within specific topic areas.
