Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Mathematics, Dartmouth College, Hanover, NH, USA.
Commun Biol. 2024 Jun 7;7(1):701. doi: 10.1038/s42003-024-06392-2.
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
大脑随着年龄的增长会发生重大的拓扑结构变化。大脑减轻与年龄相关的拓扑结构变化以维持大脑网络稳健控制的机制尚不清楚。在这里,我们使用来自认知正常的参与者(n=480,年龄 40-90 岁)的弥散 MRI 数据来研究与年龄相关的结构大脑网络平均可控性差异、可能缓解这些差异的拓扑特征,以及对认知功能的整体影响。我们发现,在控制枢纽和大规模网络中,与年龄相关的平均可控性下降,尤其是在前顶叶控制和默认模式网络中。此外,我们发现冗余,一种通过评估网络内多步路径来量化的储备假设机制,缓解了拓扑差异对平均网络可控性的影响。最后,我们发现,平均网络可控性、冗余和灰质体积,各自都为认知功能的预测模型做出了独特的贡献。总之,我们的研究结果强调了冗余对于大脑网络稳健控制和健康衰老中认知功能的重要性。