The landscape of NeuroImage-ing research

TL;DR

This study maps the neuroimaging research landscape using network science, revealing community structures, key bridging topics, and evolving participation patterns over a decade.

Contribution

It introduces a network-based model to analyze the structure and dynamics of neuroimaging research topics within the journal NeuroImage.

Findings

The network exhibits small-world properties.

Communities are organized by imaging modalities and medical applications.

Topic popularity is influenced by neighboring topics' trends.

Abstract

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.