Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102206, China.
Chinese Institute for Brain Research, Beijing, Beijing, 102206, China.
BMC Med. 2024 Nov 25;22(1):556. doi: 10.1186/s12916-024-03784-3.
The spatial layout of large-scale functional brain networks exhibits considerable inter-individual variability, especially in the association cortex. Research has demonstrated a link between early socioeconomic status (SES) and variations in both brain structure and function, which are further associated with cognitive and mental health outcomes. However, the extent to which SES is associated with individual differences in personalized functional network topography during childhood remains largely unexplored.
We used a machine learning approach-spatially regularized non-negative matrix factorization (NMF)-to delineate 17 personalized functional networks in children aged 9-10 years, utilizing high-quality functional MRI data from 6001 participants in the Adolescent Brain Cognitive Development study. Partial least square regression approach with repeated random twofold cross-validation was used to evaluate the association between the multivariate pattern of functional network topography and three SES factors, including family income-to-needs ratio, parental education, and neighborhood disadvantage.
We found that individual variations in personalized functional network topography aligned with the hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicted the three SES factors from unseen individuals. The associations between functional topography and SES factors were also hierarchically organized along the sensorimotor-association cortical axis, exhibiting stronger positive associations in the higher-order association cortex. Additionally, we have made the personalized functional networks publicly accessible.
These results offer insights into how SES influences neurodevelopment through personalized functional neuroanatomy in childhood, highlighting the cortex-wide, hierarchically organized plasticity of the functional networks in response to diverse SES backgrounds.
大规模功能大脑网络的空间布局表现出相当大的个体间可变性，尤其是在联合皮层。研究表明，早期社会经济地位（SES）与大脑结构和功能的变化之间存在联系，而这些变化进一步与认知和心理健康结果相关。然而，SES 与儿童时期个性化功能网络拓扑个体差异之间的关联在很大程度上仍未得到探索。
我们使用机器学习方法——空间正则化非负矩阵分解（NMF）——在 6001 名青少年大脑认知发育研究参与者的高质量功能磁共振成像数据中，描绘了 9-10 岁儿童的 17 个个性化功能网络。采用重复随机二折交叉验证的偏最小二乘回归方法，评估功能网络拓扑的多元模式与三个 SES 因素（家庭收入需求比、父母教育程度和邻里劣势）之间的关联。
我们发现，个性化功能网络拓扑的个体差异与皮质上的层次感觉运动-联合轴一致。此外，我们观察到功能网络拓扑可以显著预测来自未知个体的三个 SES 因素。功能拓扑与 SES 因素之间的关联也沿着感觉运动-联合皮质轴进行层次组织，在更高阶的联合皮层中表现出更强的正相关。此外，我们已经公开了个性化功能网络。
这些结果提供了有关 SES 如何通过儿童时期的个性化功能神经解剖学影响神经发育的见解，强调了功能网络对不同 SES 背景的皮质广泛、层次组织的可塑性。
