Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples.

IF 10.7 1区 综合性期刊 Q1 Multidisciplinary
Research Pub Date : 2025-09-25 eCollection Date: 2025-01-01 DOI:10.34133/research.0887
Yuhui Du, Ruotong Li, Ying Xing, Vince D Calhoun
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引用次数: 0

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.

使用27,793个样本揭示脑功能和结构的关节老化模式。
衰老对大脑的功能和结构都有重要影响,但这些变化之间的相互作用尚不清楚。在此,我们提出了一个统一的框架,包括单模态和多模态的年龄预测,使用大型UK Biobank数据集(27,793名健康受试者,49至76岁)来识别和验证与衰老相关的脑功能网络连接(FNC)和灰质体积(GMV)变化,然后提出了一种新的分析方法来揭示各种关节衰老模式,最后探讨关节功能结构变化与认知能力下降之间的关系。多模态在年龄预测方面优于单模态,强调了多模态老龄化相关变化的重要性。衰老主要诱发协同性变化,小脑、额极、扣带旁回和楔前叶皮层的FNC和GMV均下降,表明运动控制、感觉加工和情绪调节的一致性退行,而枕极、枕外侧皮层和额极的FNC量增加而GMV下降的矛盾变化,作为一种代偿机制,随着年龄的增长,保持视力、认知能力。行为调节。尤其是关节变化,小脑小腿和扣带回的FNC和GMV均下降,与反应时间呈很强的Pearson相关性。总之,我们的研究揭示了不同的关节功能结构变化,为理解衰老过程中不同的认知退化提供了有力的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Research
Research Multidisciplinary-Multidisciplinary
CiteScore
13.40
自引率
3.60%
发文量
0
审稿时长
14 weeks
期刊介绍: Research serves as a global platform for academic exchange, collaboration, and technological advancements. This journal welcomes high-quality research contributions from any domain, with open arms to authors from around the globe. Comprising fundamental research in the life and physical sciences, Research also highlights significant findings and issues in engineering and applied science. The journal proudly features original research articles, reviews, perspectives, and editorials, fostering a diverse and dynamic scholarly environment.
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