Oh Younghyun, Ann Yejin, Lee Jaejoong, Ito Takuya, Froudist-Walsh Sean, Paquola Casey, Milham Michael, Spreng R Nathan, Margulies Daniel, Bernhardt Boris, Woo Choong-Wan, Hong Seok-Jun
bioRxiv. 2025 Jun 25:2025.06.24.660962. doi: 10.1101/2025.06.24.660962.
Understanding the principle of information flow across distributed brain networks is of paramount importance in neuroscience. Here, we introduce a novel neuroimaging framework, leveraging integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven discovery of the human cerebral functional hierarchy. Simulation and empirical validation demonstrated the high fidelity of iEC in recovering connectome directionality and its potential relationship with histologically defined feedforward and feedback pathways. Notably, the iEC-derived hierarchy revealed a monotonically increasing level along the axis where the sensorimotor, association, and paralimbic areas are sequentially ordered - a pattern supported by the Structural Model of laminar connectivity. This hierarchy was further demonstrated to flexibly reorganize across brain states: flattening during an externally oriented condition, evidenced by a reduced slope in the hierarchy, and steepening during an internally focused condition, reflecting heightened engagement of interoceptive regions. Our study highlights the unique role of macroscale directed functional connectivity in uncovering a biologically interpretable state-dependent signal flow hierarchy.
理解分布式脑网络中的信息流原理在神经科学中至关重要。在此，我们引入了一种新颖的神经成像框架，利用整合有效连接性（iEC）和无约束信号流映射来进行数据驱动的人类大脑功能层次结构发现。模拟和实证验证表明，iEC在恢复连接组方向性及其与组织学定义的前馈和反馈通路的潜在关系方面具有高保真度。值得注意的是，由iEC得出的层次结构显示，沿着感觉运动、联合和边缘旁区域依次排列的轴，水平单调增加——这种模式得到了层状连接结构模型的支持。该层次结构进一步被证明可在不同脑状态下灵活重组：在外部定向条件下变平，表现为层次结构中的斜率降低，而在内部聚焦条件下变陡，反映出内感受区域的参与度提高。我们的研究强调了宏观定向功能连接性在揭示生物学上可解释的状态依赖信号流层次结构中的独特作用。
