Large-scale study reveals common functional and structural patterns of brain aging

Large-scale study reveals common functional and structural patterns of brain aging

Healthy aging leads to parallel changes in brain functional activity and structural morphology, but the interplay between these changes remains unclear. Prof. Yuhui Du's team at the College of Computer and Information Technology, Shanxi University, in collaboration with Prof. Vince D. Calhoun (Georgia State University), analyzed multimodal neuroimaging data from 27,793 healthy volunteers (aged 49-76 years) in the UK Biobank. They proposed a unified framework for single-modal and multimodal brain age prediction and joint functional-structural aging analysis, which systematically characterizes various synergistic and contradictory aging patterns between functional network connectivity (FNC) and gray matter volume (GMV). Importantly, these joint patterns were also associated with specific cognitive decline. The study, titled “Joint Aging Patterns in Brain Function and Structure Revenue Using 27,793 Samples,” was published in Research (2025, 8:0887; DOI: 10.34133/research.0887).

background

Age-related cognitive decline is closely linked to changes in brain structure and functional interactions. Most previous neuroimaging studies have examined aging using a single modality, either structural MRI (sMRI) or functional connectivity derived from resting-state fMRI (rs-fMRI). However, brain function and structure are linked and develop together as we age. Studying only one modality makes it difficult to decipher the true mechanisms of cognitive aging. Furthermore, many multimodal brain aging studies simply link functional and structural features, often resulting in the stronger structural features overshadowing more subtle functional contributions. This can cause real joint aging patterns to be missed.

Research progress

To address these challenges, the authors developed a unified multimodal framework for brain age prediction and joint aging analysis (Fig. 1). Specifically, age was predicted separately from whole-brain FNC and GMV using a nested, two-tier, 10-fold cross-validated lasso regression, resulting in robust age-related functional and structural characteristics. Key FNC and GMV features identified from individual modalities were merged and evaluated under the same nested cross-validation scheme to ensure fair multimodal comparison and prevent structural dominance. Finally, each reliable FNC was paired with the GMVs of its two connected regions to form common aging changes, allowing systematic characterization of synergistic (or contradictory) functional-structural changes and their cognitive relevance.

The study confirmed that GMV-based models outperformed FNC-based models in age prediction, indicating stronger structural sensitivity to aging. Crucially, the multimodal model combining FNC and GMV functions achieved the highest prediction accuracy (Fig. 2), highlighting the need for an integrated analysis for a comprehensive understanding of brain aging.

Further analysis of the common FNC-GMV changes revealed two primary aging patterns (Fig. 3):
Synergistic changes: Simultaneous decreases in FNC strength and GMV, predominantly observed in the cerebellum, frontal pole, paracingulate gyrus and precuneus cortex. This pattern suggests coordinated functional and structural degeneration in regions that control motor control and higher-order cognition.

Conflicting changes: Increased FNC coupled with GMV reduction occurring primarily in visual areas such as the occipital pole and lateral occipital cortex. This suggests adaptive functional improvement to counteract structural decline.

Notably, certain joint changes were strongly associated with deteriorations in certain cognitive domains (Fig. 4). Conflicting changes in visual areas correlated most strongly with fluid intelligence and numerical memory, reflecting adaptive maintenance of visual information processing. In contrast, the synergistic decline between cerebellar crus I and paracingulate gyrus was associated with slower reaction time, suggesting direct effects of sensorimotor and attentional circuitry deterioration.

Significance and future prospects

This large-scale study provides direct evidence for common functional-structural changes in healthy brain aging and uncovers a complex dynamic process involving both widespread synergistic degeneration and localized compensatory adaptation. The results not only expand our understanding of the neurobiological mechanisms underlying differential cognitive decline, but also lay a foundation for the development of multimodal neuroimaging biomarkers and targeted early intervention strategies.

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