State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
Hum Brain Mapp. 2024 Feb 1;45(2):e26575. doi: 10.1002/hbm.26575.
Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.
功能信号源于结构网络，通过潜在的分子机制支持多种认知过程。人类大脑结构与功能之间的联系在整个新皮层中具有区域特异性和层次结构。然而，层次结构-功能解耦与个体行为和认知表现之间的关系，以及涉及的功能系统的重要性，以及结构-功能解耦背后的具体分子机制仍未得到充分描述。在这里，我们使用结构解耦指数（SDI）来量化功能信号对结构连接组的依赖关系，使用了一个明显更大的健康受试者队列。典型相关分析（CCA）用于评估整个大脑中特定区域 SDI 之间的多变量相关模式与多种认知特征。然后，我们分别使用主要网络、关联网络和所有网络中的 SDI 预测多元分析得到的五个综合认知分数。最后，我们通过研究 SDI 的遗传因素及其与神经递质受体/转运体的关系，探索了与 SDI 相关的分子机制。我们证明了结构-功能解耦在新皮层中具有层次结构，从主要网络扩展到关联网络。我们揭示了使用高级层次结构 SDI 可以更好地实现认知预测，不同脑区在预测认知过程方面具有不同的重要性。我们发现 SDI 与几个受体相关术语的基因表达水平有关，我们还发现四个受体/转运体的空间分布与 SDI 显著相关，即 D2、NET、MOR 和 mGluR5，它们在神经元功能的灵活性中起着重要作用。总的来说，我们的研究结果证实了层次宏观结构-功能解耦与个体认知之间的关联，并为理解结构-功能解耦的分子机制提供了启示。临床医生要点：结构-功能解耦在新皮层中具有层次结构，从主要网络扩展到关联网络。高级层次结构-功能解耦对个体认知的贡献比低级解耦更大。结构-功能解耦可以通过与关键受体相关的基因来调节，这些受体对于神经元功能的灵活性至关重要。
