We appreciate the thoughtful Correspondence from A. Turnbull, F. V. Lin and Z. Zhang about our recent Review (Fotiadis, P. et al. Structure–function coupling in macroscale human brain networks. Nat. Rev. Neurosci. 25, 688–704; 2024)¹, in which we synthesized recent work assessing the dynamic relationship between structural and functional connectivity in the human brain, commonly referred to as structure–function coupling (SFC). We focused in the Review on studies that quantify this relationship by correlating each brain region’s structural and functional connectivity towards all other brain regions. In their Correspondence, the authors emphasize an important methodological limitation of this approach, which is that it depends on the definition of brain regions — and by extension, the brain parcellation — used (Turnbull, A., Lin, F. V. & Zhang, Z. Issues of parcellation in the calculation of structure–function coupling. Nat. Rev. Neurosci. https://doi.org/10.1038/s41583-024-00877-z; 2024)². To address this limitation, they propose an alternative methodology to quantify SFC that does not rely upon a pre-defined parcellation scheme. The proposed surface-based approach instead projects the structural and functional connectivity to the grey matter–white matter boundary, allowing the computation of SFC at a high spatial resolution³.

我们感谢 A. Turnbull、F. V. Lin 和 Z. Zhang 关于我们最近的评论（Fotiadis， P. et al. 宏观人脑网络中的结构-功能耦合。Nat. Rev. 神经科学。25， 688–704;2024）¹，其中我们综合了最近的工作，评估了人脑中结构和功能连接之间的动态关系，通常称为结构-功能耦合 （SFC）。在综述中，我们重点介绍了通过将每个大脑区域的结构和功能连接与所有其他大脑区域相关联来量化这种关系的研究。在他们的通信中，作者强调了这种方法的一个重要方法论局限性，即它取决于所使用的大脑区域的定义 - 以及引申开来，大脑的分割 - 使用（Turnbull， A.， Lin， F. V. & Zhang， Z. 结构-功能耦合计算中的分割问题。Nat. Rev. 神经科学。https://doi.org/10.1038/s41583-024-00877-z;2024）².为了解决这一限制，他们提出了一种不依赖于预定义分包方案的替代方法来量化 SFC。相反，所提出的基于表面的方法将结构和功能连接投影到灰质-白质边界，从而允许以高空间分辨率计算 SFC³。