NeuroImage-ing研究的前景
The landscape of NeuroImage-ing research
摘要：
随着神经成像领域的发展，该领域的科学家很难获得并保持对其不断变化的景观的详细了解。虽然协作和引文网络突出了该领域的重要贡献，但特定研究领域之间的作用和关系仍然非常不透明。在这里，我们应用网络科学的技术来绘制过去十年《神经影像》杂志上记录的神经影像研究的图景。我们创建了一个网络，其中节点代表研究主题，边给出了这些主题倾向于串联覆盖的程度。该网络显示了小世界架构，社区的特点是共同的成像模式和医疗应用，并与桥梁，整合这些不同的子领域。使用节点级分析，我们量化了神经影像学景观中各个主题的结构作用，并发现结构MRI子字段中的高水平聚类以及与精神病学相关的主题中的参与度增加。一个话题的总体流行程度与其邻近话题的流行程度无关，但随着时间的推移，一个话题变得越来越流行或越来越不流行的程度与其邻近话题的流行程度密切相关。从广义上讲，这项工作提出了一个有凝聚力的模型，了解景观的神经影像学研究领域，在广泛的子领域，并在特定的主题领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
