Trends in Neuroscience in Iran: A Scientometric Analysis for Mapping and Clustering Neuroscience Literature from 2000 to 2019

Background and Aim : Neuroscience as an interdisciplinary field devoted to the scientific study of the nervous system, is rapidly expanding. With the explosive growth of neuroscience research, there is a need to evaluate the publication output in order to be used in policy and management contexts. Scientometric analyses is a powerful tool for identifying research trends and gaps to suggest future priorities. The purpose of the current scientometric study was to extract the trends in neuroscience publications in Iran from 2000-2019 via a rigorous analysis on the published articles.Methods : A publication set broadly covering neuro/brain science was constructed in two steps: First, Medical Subject Heading (MeSH) descriptors related to neuro/brain sciences were identified and used for an inclusive query in PubMed database to identify the relevant publications. Subsequently, the corresponding publications’ characteristics were extracted from the web of Science (WoS) citation database. Using this approach 15204 articles published under the neuroscience category were included. This publication set was investigated for estimating annual growth rate. Furthermore, algorithmic methods were conducted to cluster the publications set. Using Vosviewer software, the MeSH descriptors were mapped according to their co-occurrence, their frequency, and and an algorithmically produced clustering of overall topics. Similarly, international collaboration network for the publication set was identified.Results : The Iranian neuroscience publication output was increasing over the examined years with an average of 25% (SD=18 %) relative annual growth. The average annual number of publications was 316 for the first decade while it surged above 1500 in the second decade. However, the number of publications decreased dramatically in 2019 which needs to be further investigated. The co-MeSH descriptor network singled out seven clusters that described the MeSH topicality of the Iranian set of neuroscience publications. The cluster emerged around: Human behavioural studies, Cell signal transduction studies, animal studies, clinical studies, brain studies, neurogenetic studies and mental disorders. In each main cluster, the highly trendy research topics were identified which gives an overall and comprehensive view of the research types. For instance, in the brain cluster, MRI and EEG studies are bold whereas researches focused on hippocampus and dose-response relationship are dominant in the animal studies. In the mental disorder cluster, schizophrenia and autism are the significant topics. Mapping the international network illustrated that USA, Germany, Canada, England, Australia, Italy, Switzerland, Sweden and France are the main nodes for Iranian neuroscience collaboration.Conclusion : The MeSH-centered approach enabled us to identify the neuroscience publication set according to their subject indexing at individual publication level. Therefore, we overcome the limitation of journal classification systems in traditional scientometric analyses which generally leads to exclusion of articles in multidisciplinary journals. Our results showed an expanding growth of neuroscience publication over the examined years with growing proportion for international collaborations. Both the trendy neuroscience research topics and those that need to be improved in Iran compared to the global trends were identified. The Iranian set somewhat resembles the global set with less emphasis on topics such as dementia.

کلیدواژه ها:

scientometric ، MeSH ، mapping ، literature ، analysis