Research Trends and Hotspots on Connectomes from 2005 to 2021: A Bibliometric and Latent Dirichlet Allocation Application Study

Abstract

Background This study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm. Methods Documents were retrieved from the Web of Science Core Collection (WoSCC) database and analyzed in Rstudio 1.3.1. Through quantitative and qualitative methods, the top 20 productive and impact academic journals and authors in the field of connectomes were compared in terms of the total number of publications and h_inde over time. Meanwhile, the countries/regions and institutions involved in connectome research were compared, as well as their scientific collaboration. The study analyzed topics and research trends by R package “bibliometrix”. The major topics of connectomes were classified by Latent Dirichlet allocation (LDA). Results The entire criteria were met by 6617 publications. Neuroimage outperformed all other relevant journals in terms of publication volume (680 articles) and impact factor (h index:98). The majority of articles were published by developed countries, with the United States having the most. The University of Pennsylvania and Beijing Normal University were the two most productive institutions. Neuroimaging analysis technology and brain functions and diseases were the two major topics of connectome research. The combination with machining learning or deep learning, as well as the focus on deep brain stimulation and hippocampus has been the recent trend in connectome research. Conclusions This study offers a comprehensive overview of connectome research and provides researchers with critical information for understanding the recent trends and hotspots of connectomes.