Bibliometric Analysis of White Matter Disease and Novel Drug Therapy.

Abstract

The rapid expansion of therapeutic research in white matter diseases, particularly Multiple Sclerosis (MS), necessitates a structured analysis of the field. This study aimed to systematically map the knowledge structure and thematic evolution of drug therapies from 2005 to 2024. A total of 9,041 articles were retrieved from the Web of Science Core Collection. This study employed bibliometric tools (Bibliometrix and CiteSpace) and Latent Dirichlet Allocation (LDA) to model temporal trends, research networks, and topic evolution. From 2005 to 2024, annual publications on white matter disease therapy have risen 3.6-fold from 175 to 631, and are projected to reach 800 by 2030. Regionally, the U.S. leads research output, and China has shown the fastest growth. Core journals like NEUROLOGY and STROKE (h-index=63 each) led the field. Top institutions, such as Harvard University, are key producers, and international collaboration networks are growing tighter. LDA topic analysis revealed core clusters, such as Progres-sive Multifocal Leukoencephalopathy (PML), MS demyelination, and small vessel disease, reflecting that research was evolving from single-disease to integrated multidisease frameworks. PML mentions surged from 24 in 2005 to 1850 in 2024, linked to natalizumab-related risks, becoming a core safety focus. MS treatment has shifted from broad immunosuppression to precision targeting and risk stratification, such as anti-JCV testing and extended dosing, forming a mature safety framework. PML management has moved to active intervention, such as pembrolizumab, for antiviral immunity and endogenous repair (myelin regeneration, microglia regulation)-marking innovation. Research has transitioned from clinical description to mechanistic exploration (microglial activation, neuroinflam-mation) and quantitative imaging (MRI susceptibility mapping), driving deeper, systematic progress. This study revealed trends in drug therapy for white matter disease through bibliometric analysis, guiding research priorities and evidence-based decisions, including mechanism-oriented precision medicine, risk algorithm optimization, and international collaboration to accelerate thera-peutic innovation.