Knowledge atlas of white matter microstructure: a bibliometric analysis.

Background: White matter microstructure is valued for being an indicator of neural network integrity, which plays an indispensable role in the execution of advanced brain functions. Although the number of publications has increased in the past 10 years, no comprehensive analysis has yet been conducted of this field. Therefore, this study aimed to identify the research hotspots and trends in research on white matter microstructure using a bibliometric analysis of the related literature published from 2013 to 2022.

Methods: VOSviewer and CiteSpace were used to objectively analyze the research articles concerning white matter microstructure, which were retrieved from the Web of Science Core Collection (WoSCC).

Results: A total of 5,806 publications were obtained, with the number of published articles increasing annually over the past decade. The United States, China, the United Kingdom, and Canada maintained the top positions worldwide and had strong cooperative relationships. The top institution and journal were Harvard Medical School and Neuroimage, respectively. Alexander Leemans, Marek Kubicki, and Martha E Shenton were the most productive authors. Thematic keywords mainly included "diffusion tensor imaging" (DTI), "white matter integrity", and "connectivity". The keyword analysis revealed that DTI has a critical role in detecting white matter microstructure integrity and that fractional anisotropy is the main parameter in the assessment process. Keyword burst detection identified four research hotspots: movement, distortion correction, voxelwise analysis, and fixel-based analysis.

Conclusions: This bibliometric analysis provided a systematic understanding of the research on white matter microstructure and identified the current frontiers. This may help clinicians and researchers comprehensively identify hotspots and trends in this field.