Age‐Related Differences of Cortical Topology Across the Adult Lifespan: Evidence From a Multisite MRI Study With 1427 Individuals

Abstract

Background 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. Purpose To investigate the age‐related differences of cortical topology in morphological brain networks from multiple perspectives. Study Type Prospective, observational multisite study. Population 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. Assessment 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. 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. Results 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. Data Conclusion 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. Evidence Level 2 Technical Efficacy Stage 1

Single‐subject cortical morphological brain networks across the adult lifespan

“…This is consistent with previous studies of single‐subject morphological brain networks based on gray matter volume, which found a negative correlation for clustering coefficient and local efficiency with age (Kong et al, 2015 ; Shigemoto et al, 2023 ). However, using the same method as this work, a recent study found that clustering coefficient of CT networks showed an inverted U‐shaped age‐related trajectory (Wang et al, 2022 ). This discrepancy may be due to differences between the current study and previous one in the statistical model used (linear, quadratic, and cubic vs. quadratic) and/or in the data homogeneity with respect to site (single vs. multiple) and field strength (3 T vs. 1.5/3 T).…”

“…Using the newly developed approaches, two previous studies have explored early cortical development of the neonatal brain (Fenchel et al, 2020 ; Galdi et al, 2020 ). More recently, two large sample studies further examined single‐subject morphological brain networks across the adult lifespan (Shigemoto et al, 2023 ; Wang et al, 2022 ). However, these studies constructed single‐subject morphological brain networks either based on a single morphological feature (i.e., gray matter volume) or by integrating multiple features into a single model.…”

Individual‐level brain morphological similarity networks: Current methodologies and applications

“…Aging in healthy populations is also commonly accompanied by changes in brain network topologies, leading to decreased information processing and cognitive performance. By exploring the trajectory of changes in cortical topology throughout the life cycle, Wang et al 45 confirmed the age‐related morphological network reconfiguration, and the findings may have implications for understanding age‐associated cognitive decline and for developing interventions to support healthy brain aging.…”

Aging effects on dual‐route speech processing networks during speech perception in noise

“…Thus, a sparsity threshold 0.25 < S < 0.49 with an interval of 0.015 was applied to binarize adjacency networks in the theoretical analysis graph. Previous studies had found that a measure of the graph‐theoretical network was able to reveal developmental changes in the functional organization in patients with age‐related hearing loss (Wang et al, 2022; Yong et al, 2022). We therefore focused on the measures of global network parameters of small‐world: $$$ L $$$ (characteristic path length), $$$ C $$$ (clustering coefficient), $$$ \gamma $$$ (normalized clustering coefficient), $$$ \lambda $$$ (normalized characteristic path length), $$$ \delta $$$ (small‐worldness) and the network efficiency parameters including $$$ {E}_g $$$ (global efficiency) and $$$ {E}_l $$$ (local efficiency) to characterize the global topological structure.…”