Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure

Neuroimage. 2018 May 15;172:728-739. doi: 10.1016/j.neuroimage.2018.02.016. Epub 2018 Feb 14.

Abstract

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.

Keywords: Eigen decomposition; Functional network; Graph theory; Laplacian; Networks; Structural network.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain / physiology*
  • Brain Mapping / methods
  • Diffusion Tensor Imaging / methods
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods
  • Models, Neurological*
  • Nerve Net / physiology*
  • Neural Pathways / physiology*