05 March 2024: Database Analysis
Bibliometric Analysis of Brain Stimulation Technologies in Sleep Disorders
Chao Shen1BCDEF, Zhao-Di Wang
1BCDEF, Wen-Jun Qian1CDE, Cheng-Fan Wu1BDF, Su-Rong Qian1ACDEG*, Tong Tang 1ACDEG
DOI: 10.12659/MSM.942687
Med Sci Monit 2024; 30:e942687
Abstract
BACKGROUND: Sleep disorders are a common disease faced by people today and can lead to fatigue, lack of concentration, impaired memory, and even death. In recent years, the development of brain stimulation techniques has provided a new perspective for the treatment of sleep disorders. However, there is a lack of bibliometric analyses related to sleep disorders and brain stimulation techniques. Therefore, this study analyzed the application status and trend of brain stimulation technology in sleep disorder research.
MATERIAL AND METHODS: Articles and reviews published between 1999 and 2023 were retrieved from the Web of Science. CiteSpace was used to visually analyze the publications, countries, institutions, journals, authors, references, and keywords.
RESULTS: A total of 459 publications were obtained. The number of studies was shown to be on a general upward trend. The country with the largest number of publications was the United States; UDICE-French Research Universities had the highest number of publications; Neurology had the highest citation frequency; 90% of the top 10 references cited were from Journal Citation Reports Q1; Brigo was the author with the highest number of publications; and the most frequent keywords were “transcranial magnetic stimulation”, “deep brain stimulation”, and “Parkinson disease”.
CONCLUSIONS: Our study used CiteSpace software to analyze 459 studies published since 1999 on brain stimulation techniques for the treatment of sleep disorders, revealing research trends and the current state of the field. Our results will help researchers to understand the existing research quickly and provide direction for future research.
Keywords: Bibliometrics, Data Visualization, Electric Stimulation Therapy, Sleep Wake Disorders
Background
Sleep disorders are defined as conditions that interfere with a person’s sleep and prevent them from getting restful sleep [1]. According to the International Classification of Sleep Disorders, sleep disorders include insomnia, sleep-related breathing disorders, central hypersomnolence disorders, circadian rhythm sleep-wake disorders, parasomnias, sleep-related movement disorders, and other sleep disorders [2]. Sleep is essential for growth, development, metabolism, and regulation of the immune system [3]. Short-term sleep disorders can contribute to tiredness, poor concentration, and memory impairment [4]. Long-term sleep disorders can lead to a range of illnesses and even death [5]. Thus, sleep disorders are a health issue that deserves more attention.
Generally, treatments for sleep disorders are divided into pharmacological and non-pharmacological therapies [6,7]. Medication can cause adverse effects, such as drug dependence, cognitive decline, and decreased respiratory function [8]. Therefore, an increasing number of researchers are investigating the efficacy of non-pharmacological treatments to improve sleep disorders [9,10]. The development of brain stimulation techniques has provided a new perspective for the treatment of sleep disorders. Transcranial direct current stimulation, repetitive transcranial magnetic stimulation (rTMS), and deep brain stimulation (DBS) are common brain stimulation techniques. In addition, transcranial random noise stimulation and transcranial alternating current stimulation are being gradually adopted into the field of sleep disorders.
Currently, brain stimulation technologies have various applications in different causes of sleep disorders. DBS is used to improve sleep quality and nighttime motor symptoms in Parkinson disease [11]. rTMS can be an effective treatment for primary insomnia, restless leg syndrome, obstructive sleep apnea syndrome, and narcolepsy [12]. Transcranial direct current stimulation can improve sleep efficiency, thus improving sleep quality [13]. Transcranial random noise stimulation and transcranial focused ultrasound stimulation are used more often to study sleep disorders caused by other causes [14,15]. Many articles have been published on the therapeutic effects and mechanisms of brain stimulation technology in sleep disorders. They are mostly published in the form of research articles, reviews, and meta-analyses, lacking a systematic and comprehensive evaluation of articles in the entire field. Thus, we considered it necessary to publish an article that visualizes the overall development trends and research hotspots in this field.
Bibliometric analysis uses computer imaging techniques to convert data into intuitive graphics. It can visualize data on countries, institutions, cited journals, authors, references, and keywords to reveal hotspots and trends in specific research areas. At present, there are bibliometric analyses of sleep in the elderly [16], transcranial direct current stimulation [17], and transcranial magnetic stimulation (TMS) in the treatment of pain [18]; however, bibliometric analyses of the application of brain stimulation techniques in sleep disorders has not been found.
In this study, utilizing the Web of Science (WoS) database and CiteSpace software, we used bibliometric methods to investigate the basic situation, research hotspots, and development trends of brain stimulation techniques for sleep disorders, through visualization analysis.
Material and Methods
DATA SOURCES AND RETRIEVAL STRATEGIES:
The data source was the Web of Science Core Collection (WoSCC) database, which is the most suitable database for bibliometric analysis [19]. The search was conducted from the database establishment date to July 19, 2023, with the keywords “sleep disorders” and “brain stimulation technology” (Table 1). The search method was set as an advanced search, and specific details of the search strategy can be found in the tables and figures. A preliminary search yielded 3246 articles.
INCLUSION AND EXCLUSION CRITERIA:
The inclusion criteria were (1) English publications and (2) papers or reviews.
The exclusion criteria were as follows: (1) repeated publications; (2) meeting abstracts, letters, revisions, editorial materials, case studies, or books; (3) publications with incomplete information; and (4) publications unrelated to sleep disorders and brain stimulation techniques.
Two researchers imported the retrieved literature into EndNote X9 file management software. They independently read the titles and abstracts of the publications, excluded irrelevant publications, and cross checked. When there were differences of opinion, a third researcher assisted in judgment. Finally, 459 articles were included (Figure 1).
DATA EXTRACTION:
We selected 459 eligible references as “fully recorded and cited references”, exported them in “plain text file format”, and renamed them to “download_. Txt” to ensure that the CiteSpace (6.2.R4) software could read the records correctly.
DATA ANALYSIS:
The software used for the analysis in this article was CiteSpace (6.2.R4). CiteSpace is a Java application for visualizing and analyzing trends and hotspots of disciplines and fields. It can support various types of bibliometric research, including collaborative network analysis (country/institution/author), co-occurrence analysis (keyword), and co-citation analysis (journal/reference/author) [20]. Citespace uses knowledge graph technology to show historical trends and current research hotspots across disciplines and fields [21].
Visually, the size of the nodes in the graph is positively correlated with the frequency of the analyzed objects [22]. The lines between nodes represent the co-occurrence or referenced relationship between 2 objects [23], and the thickness of the lines represents the strength of the relationship between the objects [24]. The color of the colored rings and lines around the nodes represents the year in which the object or relationship first appeared in the literature [25]. The purple circles around certain nodes represent the between-ness centrality (BC), which is a measure of the importance of nodes in a network [26]. Nodes with BC ≥0.1 (generally considered critical nodes) are marked with a purple ring, and the thickness of the purple ring is proportional to the BC value [24]. The mean silhouette, also known as the contour value, represents the homogeneity of the clustering graph [27]. The closer its value is to 1, the more efficient the clustering [28].
The CiteSpace parameters were set as follows: Time Slice (1999–2023); Year per Slice (1); Term Source (Title/Abstract/Author Keywords/Keywords Plus); Node Type (Author/Institution/Country/Keyword/Reference/Cited Author/Cited Journal); Top N (50); Pruning (Pathfinder/Pruning sliced networks); Visualization (Cluster View-Static/Show Merged Network).
Results
ANNUAL QUANTITATIVE DISTRIBUTION OF PUBLICATIONS:
The number of papers published per year reflects the speed and trend of development in the field of research. As shown in Figure 2, the number of studies on brain stimulation techniques for sleep disorders fluctuated between 1999 and 2023, but it has been on a general upward trend. There was a significant decrease from 2006 to 2008 and from 2013 to 2014. However, there was a rapid growth trend from 2012 to 2013 and from 2019 to 2022 (Figure 2).
COUNTRIES:
Researchers from 48 countries published articles related to the application of brain stimulation techniques in sleep disorders in the included articles. Among them, the top 5 countries for the number of publications were the United States (138), China (114), Italy (87), Germany (49), and England (41) (Table 2). It is noted that the United States had the highest total number of publications and the highest centrality (BC=0.53), far exceeding England (BC=0.23). Although France, Spain, Switzerland, Brazil, and Canada were not in the top 5 in terms of publication volume, their BC values were high (Figure 3).
INSTITUTIONS:
In the included articles, a total of 295 institutions participated in the study of brain stimulation techniques for sleep disorders. As shown in Table 3, the institution with the highest number of publications was UDICE-French Research Universities (23). Four of the top 7 institutions were French. In terms of centrality, Harvard University (BC=0.20) was first. Capital Medical University (BC=0.13) was second, along with Institute National de la Sante et de la Recherche Medical (BC=0.13). They were in a leading position in collaboration (Figure 4).
JOURNALS:
As shown in Table 4, among the top 10 journals ranked by common citation frequency, Neurology had the highest citation frequency (257), with most of the journals coming from the United States and England. The impact factor of the 8 journals exceeded 5 points, and all journals were included in the Journal Citation Reports Q1. However, the BC values of the top 10 journals were all below 0.1, except for Clin Neurophysiol (BC=0.11) (Figure 5).
REFERENCES:
Reference co-citation analysis was a significant approach to exploring the research frontiers and key points in this field. We set the k value to 6 through the g indicator parameter of CiteSpace software. As shown in Table 5, 90% of the top 10 articles were from Journal Citation Reports Q1, and the top-ranked literature had an impact factor of 4.7 points. Half of the top 10 articles focused on the use of rTMS in patients with sleep disorders. Reviews and clinical trials each accounted for 5 of the top 10 articles.
COLLABORATIVE NETWORK ANALYSIS: Among the included articles, a total of 298 authors participated in the study of brain stimulation techniques in sleep disorders. As shown in Table 6, the top 3 authors in terms of publication volume were Brigo (9), Ashkan (8), and Nardone (8). Meanwhile, the top 7 authors were all from Europe. In terms of centrality, the above authors were all below 0.1.
CO-CITATION ANALYSIS: When the articles of 2 authors are simultaneously cited by the articles of a third author, there is a common citation relationship between these 2 authors. Table 7 shows the top 10 authors in terms of co-citation frequency, among which Buysse ranked first for 66 citations. Notably, Nardone was in the top 10 in both the co-citation analysis and the collaborative network analysis. In terms of centrality, Chaudhuri (BC=0.21) and Arnulf (BC=0.16) were relatively leading (Figure 6).
KEYWORD CO-OCCURRENCE ANALYSIS: A total of 343 keywords appeared in the included articles. As shown in Table 8, among the top 10 keywords with the highest co-occurrence frequency, “transcranial magnetic stimulation” (116) and “deep brain stimulation” (112) appeared more than 100 times. Key nodes for keyword co-occurrence analysis (BC ≥0.1) included “transcranial magnetic stimulation”, “Parkinson disease”, “depression”, and “double blind” (Figure 7).
KEYWORD CLUSTERING ANALYSIS: Cluster keywords using the log-likelihood rate algorithm model draw a timeline graph and export the clustering data. It is evident from the visual time-zone mapping (Figure 8) that 8 keyword clusters were present. The top 3 research fields in the cluster were “Parkinson disease” (47), “sleep deprivation” (32), and “transcranial magnetic stimulation” (30) (Table 9).
KEYWORD BURST ANALYSIS: Keyword burst analysis indicates a sudden increase in the frequency of the keyword within a certain period of time. The red area indicates the duration of the keyword after its sudden appearance. Saliency analysis obtained 25 keywords. Figure 9 shows that symbols (6.81), cortical index (5.86), human motor core (5.62), and validation (5.07) had high saliency. For human motor cortex, the outbreak period was up to 10 years. Until July 19, 2023, keywords that were still in the explosive period included “direct current simulation”, “dorsolatel prefrontal core”, “validation”, “efficiency”, and “non-invasive brain simulation”.
Discussion
STUDY STRENGTHS AND LIMITATIONS:
This study is the first to use bibliometric analysis to summarize and analyze the development trends and research hotspots of brain stimulation techniques for sleep disorders. However, there are several limitations to this study. First, due to the limitations of CiteSpace software and databases, this study analyzed only the WoSCC database. Although the majority of the literature is included in the WoSCC database, it is possible that the literature included in our study is not exhaustive. Second, this study was limited to English-language publications, which may have overlooked publications in other languages.
Conclusions
We used CiteSpace software to analyze 459 studies published from January 1, 1999, to July 19, 2023, on brain stimulation techniques for the treatment of sleep disorders, revealing research trends and the current state of the field. In recent years, studies about the relationship between brain stimulation techniques and sleep disorders have continued to grow, in which Parkinson disease, TMS, and DBS have become hot topics in the field. Currently, researchers are investigating the therapeutic effects of the dorsolateral prefrontal cortex, direct current stimulation, and noninvasive brain stimulation on sleep disorders. In the future, extensive research is required on the mechanisms and effectiveness of brain stimulation techniques to present quality and valid medical evidence for patients experiencing sleep disorders.
Figures
Figure 1. Database search flow chart. (Software: WPS Office 2023, Kingsoft, China). 
Figure 2. Annual number of publications. Microsoft Excel 2021 software was used for graphing the annual circulation of publications. (Software: Microsoft Excel 2021, Microsoft, USA). 
Figure 3. Country collaborative network analysis. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: country, g-index(K=25); pruning: pathfinder, N=4, E=145. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). 
Figure 4. Institution collaborative network analysis. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: institution; g-index(K=20); pruning: pathfinder, N=293, E=727. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). 
Figure 5. Journals ranked by co-cited frequency. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: cited journal; g-index(K=12); pruning: pathfinder; N=291, E=1596. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). 
Figure 6. Author co-cited analysis. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). 
Figure 7. Keyword co-occurrence knowledge map. Keyword co-occurrence analysis. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: keyword; g-index(K=12); pruning: pathfinder; N=283, E=1239. (Software: CiteSpace 6.2.R4 Drexel University, Philadelphia, PA, USA). 
Figure 8. Keyword clustering and visual time-zone mapping. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). 
Figure 9. Top 25 keyword bursts. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). Tables
Table 1. Search strategy from Web of Science core collection.
Table 2. Top 10 countries by publications. The ranking was based on the number of publications. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 3. Top 7 institutions by publications. The ranking was based on the number of publications. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 4. Top 10 journals ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 5. Top 10 references ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 6. Top 7 authors by publications. The ranking was based on the number of publications published by the authors. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 7. Top 10 authors ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 8. Top 10 keywords co-occurrence frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
Table 9. List of keyword clusters. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.
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Figures
Figure 1. Database search flow chart. (Software: WPS Office 2023, Kingsoft, China).Figure 2. Annual number of publications. Microsoft Excel 2021 software was used for graphing the annual circulation of publications. (Software: Microsoft Excel 2021, Microsoft, USA).Figure 3. Country collaborative network analysis. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: country, g-index(K=25); pruning: pathfinder, N=4, E=145. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA).Figure 4. Institution collaborative network analysis. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: institution; g-index(K=20); pruning: pathfinder, N=293, E=727. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA).Figure 5. Journals ranked by co-cited frequency. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: cited journal; g-index(K=12); pruning: pathfinder; N=291, E=1596. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA).Figure 6. Author co-cited analysis. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA).Figure 7. Keyword co-occurrence knowledge map. Keyword co-occurrence analysis. Time slicing: January 1999 to December 2023; slice length: 1 year; node types: keyword; g-index(K=12); pruning: pathfinder; N=283, E=1239. (Software: CiteSpace 6.2.R4 Drexel University, Philadelphia, PA, USA).Figure 8. Keyword clustering and visual time-zone mapping. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA).Figure 9. Top 25 keyword bursts. (Software: CiteSpace 6.2.R4, Drexel University, Philadelphia, PA, USA). Tables
Table 1. Search strategy from Web of Science core collection.Table 2. Top 10 countries by publications. The ranking was based on the number of publications. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 3. Top 7 institutions by publications. The ranking was based on the number of publications. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 4. Top 10 journals ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 5. Top 10 references ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 6. Top 7 authors by publications. The ranking was based on the number of publications published by the authors. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 7. Top 10 authors ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 8. Top 10 keywords co-occurrence frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 9. List of keyword clusters. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 1. Search strategy from Web of Science core collection.Table 2. Top 10 countries by publications. The ranking was based on the number of publications. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 3. Top 7 institutions by publications. The ranking was based on the number of publications. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 4. Top 10 journals ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 5. Top 10 references ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 6. Top 7 authors by publications. The ranking was based on the number of publications published by the authors. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 7. Top 10 authors ranked by co-cited frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 8. Top 10 keywords co-occurrence frequency. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023.Table 9. List of keyword clusters. The data were derived from analysis using CiteSpace 6.2.R4, of the 459 publications retrieved on July 19, 2023. In Press
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