Radiology, Weill Cornell Medical College, New York, NY, USA.
Radiology, Weill Cornell Medical College, New York, NY, USA.
Neuroimage. 2018 May 15;172:728-739. doi: 10.1016/j.neuroimage.2018.02.016. Epub 2018 Feb 14.
How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses.
结构连接（SC）如何产生功能连接（FC）尚不完全清楚。在这里，我们从扩散张量成像中测量 SC，并从静息状态 fMRI 中测量 FC，通过数学方法推导出它们之间的简单关系。我们确定 SC 和 FC 是通过（结构）拉普拉斯谱相关的，其中 FC 和 SC 共享特征向量，它们的特征值呈指数关系。这首次在结构和功能网络的图频谱之间建立了简单的解析关系。拉普拉斯特征向量被证明是功能特征向量的良好预测因子，并且基于功能时间序列的独立成分分析构建网络。拉普拉斯特征模式的数量很少就足以重建 FC 矩阵，作为基函数。这种方法速度快，不需要耗时的模拟。我们在两个经验性的 SC/FC 数据集上进行了测试，发现它明显优于耦合神经质量的生成模型模拟。
