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Background <p>As the global population ages, the decline in cognitive and physical functions presents significant challenges for individuals and healthcare systems. In older adults, conventional assessment methods are often subjective, time-consuming, and influenced by external factors, highlighting the need for objective and efficient evaluation tools. Neuroimaging biomarkers, particularly diffusion tensor imaging (DTI) metrics, offer promising insights into brain structure and function, potentially serving as reliable indicators of functional decline.</p> Methods <p>This study examines the relationship between DTI-derived metrics and cognitive and physical functions in older adults (<i>n</i> = 106). Four primary diffusion metrics, such as fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were analyzed to assess their strength of association with functional decline. To enhance this association, principal component analysis (PCA) was applied, integrating multiple diffusion features. Age, sex, and educational level were included as covariates to control for their potential confounding effects.</p> Results <p>Neuroimaging biomarkers were significantly associated with both cognitive and physical functions in older adults. Key neural pathways, including the corpus callosum, anterior and retrolenticular internal capsule, fornix, and superior fronto-occipital fasciculus, showed strong associations across domains. PCA combining metrics enhanced these associations, highlighting integrated patterns of white matter contributions. Models selecting multiple neural tracts demonstrated increased predictive accuracy, especially when adjusting for age, sex, and education. Distinct tract-function relationships were observed across physical and cognitive subdomains, emphasizing the complex and domain-specific roles of white matter in functional outcomes.</p> Conclusions <p>The findings highlight the potential of neuroimaging biomarkers as objective tools for evaluating functional decline in aging. Identifying key neural pathways linked to cognitive and physical functions may contribute to early diagnosis and targeted interventions. The integration of multiple neuroimaging features enhances the strength of associations, suggesting that advanced neuroimaging techniques could play a crucial role in aging research and clinical applications.</p>

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Diffusion tensor imaging biomarkers for assessing cognitive and physical function in aging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月10日

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  • Jungsoo Lee,
  • Woohee Han,
  • Hyunjin Kim

摘要

Background

As the global population ages, the decline in cognitive and physical functions presents significant challenges for individuals and healthcare systems. In older adults, conventional assessment methods are often subjective, time-consuming, and influenced by external factors, highlighting the need for objective and efficient evaluation tools. Neuroimaging biomarkers, particularly diffusion tensor imaging (DTI) metrics, offer promising insights into brain structure and function, potentially serving as reliable indicators of functional decline.

Methods

This study examines the relationship between DTI-derived metrics and cognitive and physical functions in older adults (n = 106). Four primary diffusion metrics, such as fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were analyzed to assess their strength of association with functional decline. To enhance this association, principal component analysis (PCA) was applied, integrating multiple diffusion features. Age, sex, and educational level were included as covariates to control for their potential confounding effects.

Results

Neuroimaging biomarkers were significantly associated with both cognitive and physical functions in older adults. Key neural pathways, including the corpus callosum, anterior and retrolenticular internal capsule, fornix, and superior fronto-occipital fasciculus, showed strong associations across domains. PCA combining metrics enhanced these associations, highlighting integrated patterns of white matter contributions. Models selecting multiple neural tracts demonstrated increased predictive accuracy, especially when adjusting for age, sex, and education. Distinct tract-function relationships were observed across physical and cognitive subdomains, emphasizing the complex and domain-specific roles of white matter in functional outcomes.

Conclusions

The findings highlight the potential of neuroimaging biomarkers as objective tools for evaluating functional decline in aging. Identifying key neural pathways linked to cognitive and physical functions may contribute to early diagnosis and targeted interventions. The integration of multiple neuroimaging features enhances the strength of associations, suggesting that advanced neuroimaging techniques could play a crucial role in aging research and clinical applications.