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Abstract

Introduction

Neural regeneration remains a highly debated topic, yet it lacks a systematic bibliometric analysis. The objective of this study is to utilize bibliometric methods to identify research trends and significant topics within this domain, thereby providing a comprehensive overview of the current state of knowledge in this field.

Methods

The Web of Science Core Collection (January 1, 2015 to October 3, 2024) served as the basis for analyzing 3,941 documents using CiteSpace and VOSviewer. The analysis focused on country/institution collaboration networks, keyword co-occurrence, and hotspot evolution.

Results

Between 2015 and 2024, the number of publications in this field demonstrated an upward trend, characterised by fluctuations. China and the United States were the leading contributors to global research output, with China contributing 1,387 papers, accounting for 35.19% of the total, and boasting an H-index of 62. In contrast, the United States contributed 1,047 papers, with an h-index of 74. In recent years, research has been concentrated on four major technological directions, including neural electrical stimulation, biomaterial scaffolds, gene editing, and neural modulation.

Discussion

This transformation in scholarly focus reflects the convergence of multiple catalytic factors, which have enabled the sophisticated simulation of neural systems, provided unprecedented analytical tools for neuroscience inquiry, and intensified societal demands for artificial intelligence applications and neurotechnology innovations, thereby stimulating accelerated research investment.

Conclusion

Over the past decade, researchers worldwide have focused on neural regeneration. Bibliometric analyses have assessed scholarship, identified research hotspots, summarized core concepts, and provided valuable insights for future research in this field.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-10-28
2025-12-15

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Global Emerging Trends of Neural Regeneration Knowledge Structures Using Bibliometrics and Visual Analytics based on 3941 Studies from 2015 to 2024
Bentham Science Publishers ; https://doi.org/10.2174/011570159X394299251006114128
/content/journals/cn/10.2174/011570159X394299251006114128
/content/journals/cn/10.2174/011570159X394299251006114128
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  • Article Type:     Review Article
Keywords: VOSviewer ; CiteSpace ; clinical studies ; bibliometric analysis ; Neuronal regeneration ; simulation of neural systems

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