Shen Chenye, Liu Chaoqiang, Chen Nanguang, Qiu Anqi
Department of Biomedical Engineering, National University of Singapore, Singapore.
Department of Biomedical Engineering, National University of Singapore, Singapore; Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong; Department of Biomedical Engineering, the Johns Hopkins University, USA.
Neuroimage. 2025 May 1;311:121183. doi: 10.1016/j.neuroimage.2025.121183. Epub 2025 Apr 1.
Brain functional dedifferentiation, marked by reduced specificity of brain activity or greater similarity of functional connectivity (FC) among networks, is a hallmark of aging. Traditionally, task functional magnetic resonance imaging studies have explored functional dedifferentiation within specific cognitive domains, while FC-based approaches have focused on regional connectivity patterns. Here, we leverage the principal functional gradient to provide a macro-scale and integrative perspective on functional dedifferentiation in aging, offering a novel framework for understanding its relationship with aging, cognition, and disease. We utilized brain images and clinical data from the UK Biobank, comprising 23,578 participants aged 44-82. Linear regression was employed to assess relationships between the network dedifferentiation along the principal functional gradient and age, and cognitive performance across six domains in a normal aging population. We tested interactions between age, sex, and education to assess their influence on age-related dedifferentiation. Logistic regression was applied to classify stroke in participants with stroke and matched normal aging participants. Our findings revealed a reduced principal functional gradient range with age, indicating reduced FC variability of all brain regions. At the network level, the dedifferentiation between the frontoparietal and other networks was strongly linked to aging and cognitive performance. Males exhibited faster dedifferentiation than females across multiple networks. The somatomotor network was most affected by stroke-related dedifferentiation. Validation via covariate-matched subgroups confirmed the robustness of these findings. This research provides macro-scale insights into age-related brain functional changes, highlighting dedifferentiation along the principal gradient as a network-sensitive indicator of aging and the development of stroke.
脑功能去分化以脑活动特异性降低或网络间功能连接（FC）的更大相似性为特征，是衰老的一个标志。传统上，任务功能磁共振成像研究探索了特定认知领域内的功能去分化，而基于FC的方法则专注于区域连接模式。在这里，我们利用主要功能梯度来提供一个关于衰老中功能去分化的宏观和综合视角，为理解其与衰老、认知和疾病的关系提供了一个新的框架。我们使用了来自英国生物银行的脑图像和临床数据，包括23578名年龄在44-82岁之间的参与者。采用线性回归来评估沿着主要功能梯度的网络去分化与年龄之间的关系，以及正常衰老人群中六个领域的认知表现。我们测试了年龄、性别和教育之间的相互作用，以评估它们对与年龄相关的去分化的影响。应用逻辑回归对患有中风的参与者和匹配的正常衰老参与者进行中风分类。我们的研究结果显示，随着年龄的增长，主要功能梯度范围减小，表明所有脑区的FC变异性降低。在网络水平上，额顶叶网络与其他网络之间的去分化与衰老和认知表现密切相关。在多个网络中，男性的去分化速度比女性快。躯体运动网络受中风相关去分化的影响最大。通过协变量匹配亚组的验证证实了这些发现的稳健性。这项研究提供了关于与年龄相关的脑功能变化的宏观见解，强调沿着主要梯度的去分化是衰老和中风发展的网络敏感指标。
