Functional magnetic resonance imaging in neuropathic pain: a bibliometric analysis incorporating machine learning

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.