Mapping Trends in Moyamoya Angiopathy Research: A 10-Year Bibliometric and Visualization-Based Analyses of the Web of Science Core Collection (WoSCC)

Abstract

Background: Moyamoya angiopathy (MMA), which includes moyamoya disease (MMD) and moyamoya syndrome (MMS), is an uncommon cerebrovascular condition characterized by recurrent stroke. We carried out a bibliometric analysis to examine the development of and research trends in MMA research. Methods: Studies published between 2010 and 2019 on MMA were retrieved from the Web of Science Core Collection (WoSCC) on August 14, 2020, and bibliometric and visualization-based analyses were performed by using three different scientometric tools: HistCite, VOSviewer, and CiteSpace. Results: A total of 1,896 publications published in 384 journals by 6,744 authors, 1,641 institutions and 56 countries/regions were included in the analyses. Annual publication outputs increased from 2010 to 2019. The USA, Japan and China were three key contributors to this study field. Capital Medical University, Seoul National University, and Stanford University were three major institutions with larger numbers of publications. Zhang D, World Neurosurgery, Kuroda S, and STROKE were the most prolific author, prolific journal, top co-cited author and top co-cited journal, respectively. The top five keywords during this period were moyamoya disease, revascularization, stroke, children and surgery, while revascularization surgery and RNF213 were the most common frontier topics. Conclusions: In this study, the research trends of global scientific research on MMA over the past decade were systematically analyzed. The study can provide guidance for scholars who want to understand current trends in research in this area and new research frontiers.

Keywords: CiteSpace; HistCite; VOSviewer; Web of Science; bibliometrics; emerging topics; moyamoya; visualization.

Figure 1

A frame flow diagram. The diagram showed detailed search strategies and selection criteria for MMA publications from WoSCC database and the steps of bibliometric analysis.

Figure 2

(A,B) The annual output, citations and growth rate on a year-on-year basis with regard to MMA during 2010–2019. (C) The linear model of the upward trend in the field of MMA (R² = 0.9654, P < 0.0001). Recs: Number of Records, namely the number of publications for a given year; TGCS: Total Global Citation Score, namely the total count of citations.

Figure 3

The co-authorship network visualization map of countries/regions related to MMA. Lines between clusters represented occurrence and the size of node was positively associated with the number of publications.

Figure 4

The co-authorship network visualization map of institutions for MMA research.

Figure 5

(A) Co-authorship network visualization map of authors on MMA research. (B) Co-citation network visualization map of authors on MMA research.

Figure 6

The visualization map of subject categories on MMA. The tree ring-shaped nodes represented different subject categories. The lines between two nodes meant co-occurrence. The area of the nodes referred to the number of publications. Nodes with high centrality (purple ring) were deemed as the hot field.

Figure 7

(A) The co-occurrence network visualization map of keywords in the MMA research field from 2010 to 2019. Keywords in the same color represent were sorted into the same cluster. (B) The top 46 keywords with the strongest citation bursts on MMA research between 2010 and 2019. The red segment of the blue line denoted the burst duration of a keyword.

Figure 8

(A) The Co-citation network visualization map of references on MMA research between 2010 and 2019. (B) The top 25 references with the strongest citation bursts on MMA research between 2010 and 2019. The red segment of the blue line denoted the burst duration of a keyword.