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Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Web of Science Bibliometrics Analysis of Magnetic Resonance Imaging Research Advances in Multiple Sclerosis
Xiaoxing Li1,2,3, Dingbang Peng4, Xiao Liang1,2,3
1Department of Radiology, Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, 17 Yongwaizheng Street, Nanchang, Jiangxi 330006, People's Republic of China.
Introduction:
Comprehensive bibliometric analysis of magnetic resonance imaging applications in multiple sclerosis research remains scarce despite exponential growth. This study maps 25-year global MS-MRI trends (2000-2024) to identify transformative shifts.
Methods:
We analyzed 8,038 publications from the Web of Science Core Collection using VOSviewer, Bibliometrix, and CiteSpace. Machine learning clustering quantified collaboration networks, while dual-map overlays and burst detection quantified interdisciplinary bridges and paradigm shifts.
Results:
Publication growth showed three phases: steady (2005-2011, +6.2%/year), accelerated (2011-2021, peak 480 publications), and stabilization (2022-2024), with recent decline linked to diagnostic criteria simplification and artificial intelligence-driven consolidation. The USA dominated total output (24.2%), while the UK led international collaboration (44.2% multi-country publications). China’s unique focus on psychoneuroimmunology contrasts with Western clinical-translational priorities. The strongest interdisciplinary link connected Neurology/Sports/Ophthalmology and Molecular/Biology/Genetics fields (Z-score = 5.3). Artificial intelligence drove paradigm shifts, with deep learning showing the highest keyword burst strength (413.27). Central authors (e.g., Massimo Filippi, Frederik Barkhof) bridged magnetic resonance imaging biomarkers and therapeutic innovation.
Discussion:
MS-MRI research is evolving from descriptive observations to AI-driven precision medicine. Future success relies on a closed-loop paradigm integrating ultra-high-field MRI and multi-omics.
Conclusion:
This analysis reveals: (1) Magnetic resonance imaging-artificial intelligence-biomarker integration resolves clinical-radiological paradoxes, enabling dynamic patient stratification; (2) ultra-high-field magnetic resonance imaging and multi-omics provide a roadmap for precision neurology in therapy personalization; (3) global collaboration synergies may democratize advanced multiple sclerosis care.
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