Functional magnetic resonance imaging in neuropathic pain: a bibliometric analysis incorporating machine learning
- 1College of Acupuncture and Massage, Changchun University of Chinese Medicine, Changchun , ,
- 2Department of Spleen and Gastric Diseases, Liaoyuan Hospital of Traditional Chinese Medicine, Liaoyuan , ,
Abstract
Background: Neuropathic pain (NP) affects 6.9%–10% of the global population. Functional magnetic resonance imaging (fMRI) has become central to NP investigation, yet few studies have systematically mapped this literature. We used bibliometric and machine-learning methods to chart the fMRI-NP research landscape.
Methods: We retrieved 1,866 fMRI-NP articles (2005–2025) from Web of Science, PubMed, and Scopus. Following PRISMA guidelines, we analyzed publication trends, citation metrics, and collaborative networks with CiteSpace and VOSviewer, complemented by exploratory topic modeling based on term frequency–inverse document frequency (TF-IDF) and non-negative matrix factorization (NMF), and by community detection combining GraphSAGE node embeddings with density-based spatial clustering (DBSCAN).
Results: The corpus amassed 94,160 citations, with an average of 50.46 per article and a corpus h-index of 139. Annual output rose from 35 articles in 2005 to 156 in 2025, following a power-law trend (R ² = 0.862). The United States and China were the most productive countries. Harvard Medical School and Massachusetts General Hospital were the most prolific institutions. NeuroImage and Pain were the most productive journals, and NeuroImage, the Journal of Neuroscience, and Pain were the most frequently co-cited. Text-mining of titles and abstracts indicated that stimulus-evoked activation, brain-connectivity/default-mode, and post-herpetic/regional themes were the most prevalent, with the limbic and sensorimotor systems the most frequently mentioned pathways.
Conclusion: This study provides a comprehensive bibliometric mapping of the fMRI-NP research landscape, characterizing its growth trajectory, the most productive countries, institutions and journals, the most co-cited references, and the recurrent themes in the literature. The mapping indicates increasing attention to brain connectivity, multimodal imaging, and computational text analysis. These bibliometric patterns delineate active and emerging areas of research activity and may help orient future literature-based inquiry and collaboration in the field.
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BibTeX
@article{sun2026functional,
title = {{Functional magnetic resonance imaging in neuropathic pain: a bibliometric analysis incorporating machine learning}},
author = {Ning Sun and Yu-Xin Zhu and Yi-Dan Cui and Shi-Qi Lu and Tian-Ran Gu and Ao Li and Jing-Zhi Wang and Yan Zou and Zhen Zhong and Hai-Peng Huang},
journal = {Frontiers in neuroscience},
year = {2026},
month = sep,
doi = {10.3389/fnins.2026.1963495},
url = {https://europepmc.org/article/PMC/PMC13661907},
note = {CC BY},
}
RIS
TY - JOUR
TI - Functional magnetic resonance imaging in neuropathic pain: a bibliometric analysis incorporating machine learning
AU - Ning Sun
AU - Yu-Xin Zhu
AU - Yi-Dan Cui
AU - Shi-Qi Lu
AU - Tian-Ran Gu
AU - Ao Li
AU - Jing-Zhi Wang
AU - Yan Zou
AU - Zhen Zhong
AU - Hai-Peng Huang
PY - 2026
DA - 2026/09/30
JO - Frontiers in neuroscience
DO - 10.3389/fnins.2026.1963495
UR - https://europepmc.org/article/PMC/PMC13661907
AB - Background: Neuropathic pain (NP) affects 6.9%–10% of the global population. Functional magnetic resonance imaging (fMRI) has become central to NP investigation, yet few studies have systematically mapped this literature. We used bibliometric and machine-learning methods to chart the fMRI-NP research landscape. Methods: We retrieved 1,866 fMRI-NP articles (2005–2025) from Web of Science, PubMed, and Scopus. Following PRISMA guidelines, we analyzed publication trends, citation metrics, and collaborative networks with CiteSpace and VOSviewer, complemented by exploratory topic modeling based on term frequency–inverse document frequency (TF-IDF) and non-negative matrix factorization (NMF), and by community detection combining GraphSAGE node embeddings with density-based spatial clustering (DBSCAN). Results: The corpus amassed 94,160 citations, with an average of 50.46 per article and a corpus h-index of 139. Annual output rose from 35 articles in 2005 to 156 in 2025, following a power-law trend (R ² = 0.862). The United States and China were the most productive countries. Harvard Medical School and Massachusetts General Hospital were the most prolific institutions. NeuroImage and Pain were the most productive journals, and NeuroImage, the Journal of Neuroscience, and Pain were the most frequently co-cited. Text-mining of titles and abstracts indicated that stimulus-evoked activation, brain-connectivity/default-mode, and post-herpetic/regional themes were the most prevalent, with the limbic and sensorimotor systems the most frequently mentioned pathways. Conclusion: This study provides a comprehensive bibliometric mapping of the fMRI-NP research landscape, characterizing its growth trajectory, the most productive countries, institutions and journals, the most co-cited references, and the recurrent themes in the literature. The mapping indicates increasing attention to brain connectivity, multimodal imaging, and computational text analysis. These bibliometric patterns delineate active and emerging areas of research activity and may help orient future literature-based inquiry and collaboration in the field.
SN - 1662-4548
SN - 1662-453X
ER -