Front Neurol. 2026 Sep 4;17:1891094. doi: 10.3389/fneur.2026.1891094. eCollection 2026.
ABSTRACT
BACKGROUND: Post-Stroke Cognitive Impairment (PSCI) is a common complication after stroke, significantly affecting patients' quality of life and long-term prognosis. Although PSCI has been extensively studied, most research combines ischemic and hemorrhagic strokes. Whether the distinct inflammatory mechanisms and vascular injury underlying ischemic stroke-specific cognitive dysfunction (ISCD) have followed an independent research trajectory remains unknown from a bibliometric perspective.
METHODS: Using the Web of Science Core Collection (2004-2024), our study identified 1,062 articles focused on ISCD for bibliometric analyses. Following cross-validation with PubMed confirming complete literature concordance, bibliometric tools (CiteSpace, VOSviewer, the R-based bibliometrix package) were employed to analyze publication trends, contributions and collaborations among countries, institutions, journals, and authors. Subgroup analyses quantified trends in inflammatory biomarkers, assessment tools and prediction models.
RESULTS: Annual publication peaked in 2022 (n = 133), followed by a decline in 2023 and 2024. China dominated in productivity (31.4%), while the United States exhibited the highest collaborative centrality (0.30). Keyword bursts identified ischemic-specific hotspots, including "model" (2021, burst strength = 4.66), "validation" (2022, burst strength = 4.96), and "inflammation" (2021, burst strength = 3.83). Inflammatory biomarkers and assessment tools have consistently been research hotspots in ISCD, recent efforts are shifting toward the development of prediction models for cognitive outcomes following acute ischemic stroke.
CONCLUSION: This ischemic-specific bibliometric analysis confirms that inflammatory biomarkers, assessment tools, and prediction models are persistent hotspots in ISCD, with a clear ongoing shift from descriptive studies toward predictive model development and validation. These findings provide a quantitative literature-based rationale for guiding future research directions.
PMID:42760929 | PMC:PMC13585521 | DOI:10.3389/fneur.2026.1891094