School of Artificial Intelligence, Beijing Normal University, Beijing, China.
Strategic Support Force Medical Center, Beijing, China.
J Magn Reson Imaging. 2024 May;59(5):1841-1851. doi: 10.1002/jmri.28984. Epub 2023 Sep 13.
Many studies have shown topological alterations associated with age in population-based brain morphological networks. However, it is not clear how individual brain morphological networks change with age across the lifespan.
To characterize age-related topological changes in individual networks and investigate the relationships between individual- and group-based brain networks at the nodal, modular, and connectome levels.
Retrospective analysis.
One hundred seventy-nine healthy subjects (108 males and 71 females), aged 6-85 years with a median age of 32 years and an inter-quartile range (IQR) of 26 years.
FIELD STRENGTH/SEQUENCE: T1-weighted images using the magnetization-prepared rapid gradient echo (MPRAGE) sequences.
Two nodal-level indicators (nodal similarity and node matching), five modular-level indicators (modularity, intra/inter-module similarity, adjusted mutual information [AMI], and module variation), and five connectome-level indicators (global efficiency, characteristic path length, clustering coefficient, local efficiency, and individual contribution) were calculated in brain morphological networks. Regression models for different indicators were built to examine their lifetime trajectory patterns.
Single-sample t-test, Mantel's test, Pearson correlation coefficient. A P value <0.05 was considered statistically significant.
Among 68 nodes, 34 nodes showed significant age-related patterns (all P < 0.05, FDR-corrected) in nodal similarity, including linear decline and quadratic trends. The lifespan trajectory of the connectome-level topological attributes of the individual networks presented U-shaped or inverse U-shaped trends with age. Between the individual- and group-based brain networks, the average nodal similarity was 0.67 and the average AMI of module partitions was 0.57.
The lifespan trajectories of the nodal similarity mainly followed linear decreasing and nonlinear trends, whereas the modularity and the global topological attributes exhibited nonlinear patterns. There was a high degree of consistency at both nodal similarity and modular division between the individual and group networks.
1 TECHNICAL EFFICACY: Stage 1.
许多研究表明，人群脑部形态网络与年龄相关的拓扑结构改变。然而，个体脑部形态网络在整个生命周期中如何随年龄变化尚不清楚。
描述个体网络与年龄相关的拓扑变化，并在节点、模块和连接体水平上研究个体和群体脑网络之间的关系。
回顾性分析。
179 名健康受试者（108 名男性，71 名女性），年龄 6-85 岁，中位年龄 32 岁，四分位间距（IQR）为 26 岁。
场强/序列：使用磁化准备快速梯度回波（MPRAGE）序列的 T1 加权图像。
计算了脑形态网络中的两个节点水平指标（节点相似性和节点匹配）、五个模块水平指标（模块性、内/模块间相似性、调整互信息[AMI]和模块变异性）以及五个连接体水平指标（全局效率、特征路径长度、聚类系数、局部效率和个体贡献）。建立了不同指标的回归模型，以检查其终生轨迹模式。
单样本 t 检验、Mantel 检验、Pearson 相关系数。P 值<0.05 被认为具有统计学意义。
在 68 个节点中，34 个节点的节点相似性显示出与年龄相关的显著模式（均 P<0.05，经 FDR 校正），包括线性下降和二次趋势。个体网络的连接体水平拓扑属性的寿命轨迹随年龄呈 U 形或反 U 形趋势。在个体和群体脑网络之间，平均节点相似性为 0.67，模块划分的平均 AMI 为 0.57。
节点相似性的寿命轨迹主要遵循线性下降和非线性趋势，而模块性和全局拓扑属性则呈现非线性模式。个体和群体网络在节点相似性和模块划分方面具有高度一致性。
1 技术功效：阶段 1。
