| Title: | Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples |
| Journal: | Research |
| Published: | 1 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41020157/ |
| DOI: | https://doi.org/10.34133/research.0887 |
| URL: | https://spj.science.org/doi/pdf/10.34133/research.0887 |
Publication 16605
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Abstract
Aging has important impacts on both the function and structure of the brain, yet the interplay between these changes remains unclear. Here, we present a unified framework including both single-modal and multimodal age predictions using a large UK Biobank dataset (27,793 healthy subjects, 49 to 76 years) to identify and validate brain functional network connectivity (FNC) and gray matter volume (GMV) changes associated with aging, then propose a novel analysis method to reveal various joint aging patterns, and finally investigate the association between joint function-structure changes and cognitive declines. Multimodality outperforms single modality in the age prediction, underscoring the significance of multimodal aging-related changes. Aging primarily induces synergistic changes, with both FNC and GMV decreased in the cerebellum, frontal pole, paracingulate gyrus, and precuneus cortex, indicating consistent degeneration in motor control, sensory processing, and emotional regulation, and contradictory changes with increased FNC magnitude but decreased GMV in the occipital pole, lateral occipital cortex, and frontal pole, acting as a compensatory mechanism as one ages to preserve visual acuity, cognitive ability, and behavioral modulation. Particularly, joint changes, with both FNC and GMV decreased in the crus I cerebellum and the paracingulate gyrus, show a strong Pearson correlation with the reaction time. In summary, our study unveils diverse joint function-structure changes, providing strong evidence for understanding distinct cognitive deteriorations during aging.</p>
4 Authors
- Yuhui Du
- Ruotong Li
- Ying Xing
- Vince D. Calhoun
1 Application
| Application ID | Title |
|---|---|
| 34175 | Identify biomarkers for distinguishing different mental disorders using brain images and their associations with genetic risk |
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