ArticleFrontiers in aging neuroscience2022
Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Clinical value of structural cranial magnetic resonance imaging analysis in assessing brain aging in ischemic stroke patients.BMC neurology · 2026Article
- Brain Age as a Biomarker in Alzheimer's Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential.Brain sciences · 2025Review
- Review
- Interaction Between DHCR24 and hsa_circ_0015335 Facilitates Cognitive Impairment in Cerebral Small Vessel Disease Patients.CNS neuroscience & therapeutics · 2024Article
- Macrophage Migration Inhibitory Factor as a Potential Plasma Biomarker of Cognitive Impairment in Cerebral Small Vessel Disease.ACS omega · 2024Article
- Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.Human brain mapping · 2024Article
- Association between behavioral and psychological symptoms and cerebral small vessel disease MRI findings in memory clinic patients.Frontiers in aging neuroscience · 2023Article
- Alteration and clinical potential in gut microbiota in patients with cerebral small vessel disease.Frontiers in cellular and infection microbiology · 2023Article
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD. Materials and methods: A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers. Results: The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD ( Conclusion: Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.
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