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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\u2013structure 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\u2013structure changes, providing strong evidence for understanding distinct cognitive deteriorations during aging.<\/jats:p>","DOI":"10.34133\/research.0887","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T04:47:46Z","timestamp":1755578866000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":3,"title":["Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples"],"prefix":"10.34133","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0079-8177","authenticated-orcid":true,"given":"Yuhui","family":"Du","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, \rShanxi University, Taiyuan, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruotong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, \rShanxi University, Taiyuan, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, \rShanxi University, Taiyuan, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, \rEmory University, Atlanta, GA, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"issue":"9","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1162\/jocn_a_01685","article-title":"Contributions of brain function and structure to three different domains of cognitive control in normal aging","volume":"33","author":"Rieck JR","year":"2021","unstructured":"Rieck JR, Baracchini G, Grady CL. 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