Journal Article

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches

Hillebrand, Arjan, Tewarie, Prejaas, Stam, Cornelis J., Douw, Linda, Meier, Jil M., Van Mieghem, Piet, Prasse, Bastian, Santos, Fernando A.N., Schoonheim, Menno M.

2020

Overview

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks. •Two prominent theories on mappings between structural and functional networks are:•Functional networks can be explained by all possible walks in the structural network.•Functional networks can be explained by the eigenmodes of the structural network.•We show that these two approaches are equivalent using empirical and simulated data.•We provide explicit expressions for model coefficients for both approaches.

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: Adult / Brain - anatomy & histology / Brain - diagnostic imaging / Brain - physiology / Brain mapping / Brain Mapping - methods / Datasets as Topic / Diffusion tensor imaging (DTI) / Diffusion Tensor Imaging - methods / Functional anatomy / Functional magnetic resonance imaging / Functional MRI (fMRI) / Functional network / Honey / Humans / Models, Statistical / Nerve Net - anatomy & histology / Nerve Net - diagnostic imaging / Nerve Net - physiology / Neural mass / Neuroimaging / Simultaneous diagonalisation / Structural connectome / Structural network / Structure-function relationships