The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales

Abstract Conventional connectome edge weights, such as number of streamlines (NOS) or diffusion tensor imaging (DTI) metrics, lack specificity to microstructural details which may hold relevance for macroscale brain organisation. Since biophysical diffusion modelling offers greater specificity to microstructure, we investigated whether parameters from the Standard Model of white matter diffusion provide informative alternatives for connectome weights – namely the intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity (De⟂). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, De⟂, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only De⟂ and normalised NOS captured non-random properties of local organisation at all spatial scales. We then correlated each weighted connectome with resting-state fMRI functional connectivity and intracranial measurements of conduction velocity. At both the whole-brain and regional level, NOS gave strong coupling with fMRI functional connectivity. Connectomes weighted by De⟂, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, De⟂ and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from De⟂ capture meaningful aspects of brain network organisation with functional relevance.