Wang Yin, Zhang Yinghui, Zheng Weihao, Liu Xia, Zhao Ziyang, Li Shan, Chen Nan, Yang Lin, Fang Lei, Yao Zhijun, Hu Bin
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Guangyuan Mental Health Center, Guangyuan, China.
J Magn Reson Imaging. 2023 Feb;57(2):434-443. doi: 10.1002/jmri.28318. Epub 2022 Aug 3.
Healthy aging is usually accompanied by alterations in brain network architecture, influencing information processing and cognitive performance. However, age-associated coordination patterns of morphological networks and cognitive variation are not well understood.
To investigate the age-related differences of cortical topology in morphological brain networks from multiple perspectives.
Prospective, observational multisite study.
A total of 1427 healthy participants (59.1% female, 51.75 ± 19.82 years old) from public datasets.
FIELD STRENGTH/SEQUENCE: 1.5 T/3 T, T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) sequence.
The multimodal parcellation atlas was used to define regions of interest (ROIs). The Jensen-Shannon divergence-based individual morphological networks were constructed by estimating the interregional similarity of cortical thickness distribution. Graph-theory based global network properties were then calculated, followed by ROI analysis (including global/nodal topological analysis and hub analysis) with statistical tests.
Chi-square test, Jensen-Shannon divergence-based similarity measurement, general linear model with false discovery rate correction. Significance was set at P < 0.05.
The clustering coefficient (q = 0.016), global efficiency (q = 0.007), and small-worldness (q = 0.006) were significantly negatively quadratic correlated with age. The group-level hubs of seven age groups were found mainly distributed in default mode network, visual network, salient network, and somatosensory motor network (the sum of these hubs' distribution in each group exceeds 55%). Further ROI-wise analysis showed significant nodal trajectories of intramodular connectivities.
These results demonstrated the age-associated reconfiguration of morphological networks. Specifically, network segregation/integration had an inverted U-shaped relationship with age, which indicated age-related differences in transmission efficiency.
2 TECHNICAL EFFICACY: Stage 1.
健康衰老通常伴随着脑网络结构的改变，影响信息处理和认知表现。然而，与年龄相关的形态网络协调模式和认知变化尚未得到很好的理解。
从多个角度研究形态学脑网络中皮质拓扑结构的年龄相关差异。
前瞻性、观察性多中心研究。
来自公共数据集的1427名健康参与者（女性占59.1%，年龄51.75±19.82岁）。
场强/序列：1.5T/3T，T1加权磁化准备快速梯度回波（MP-RAGE）序列。
使用多模态分割图谱定义感兴趣区域（ROI）。通过估计皮质厚度分布的区域间相似性，构建基于 Jensen-Shannon 散度的个体形态网络。然后计算基于图论的全局网络属性，接着进行ROI分析（包括全局/节点拓扑分析和枢纽分析）并进行统计检验。
卡方检验、基于Jensen-Shannon散度的相似性测量、采用错误发现率校正的一般线性模型。显著性设定为P<0.05。
聚类系数（q = 0.016）、全局效率（q = 0.007）和小世界特性（q = 0.006）与年龄呈显著负二次相关。发现七个年龄组的组水平枢纽主要分布在默认模式网络、视觉网络、突显网络和体感运动网络（每组中这些枢纽的分布总和超过55%）。进一步的逐ROI分析显示了模块内连接性的显著节点轨迹。
这些结果表明了形态网络与年龄相关的重新配置。具体而言，网络分离/整合与年龄呈倒U形关系，这表明了传输效率的年龄相关差异。
2 技术效能：1级
