The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales
- 1Lausanne University Hospital (CHUV)
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 (DePerp). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, DePerp, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only DePerp 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 DePerp, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, DePerp and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from DePerp capture meaningful aspects of brain network organisation with functional relevance.
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BibTeX
@misc{spencer2026microstructureweighted,
title = {{The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales}},
author = {A. P. C. Spencer and S. Asadi and Y. Aleman-Gomez and Q. Wang and M. Jedynak and C. H. M. Chan and A. Cionca and D. Van De Ville and O. David and P. Hagmann and I. Jelescu},
howpublished = {bioRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.19.726180},
url = {https://www.biorxiv.org/content/10.64898/2026.05.19.726180v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales
AU - A. P. C. Spencer
AU - S. Asadi
AU - Y. Aleman-Gomez
AU - Q. Wang
AU - M. Jedynak
AU - C. H. M. Chan
AU - A. Cionca
AU - D. Van De Ville
AU - O. David
AU - P. Hagmann
AU - I. Jelescu
PY - 2026
DA - 2026/05/19
PB - bioRxiv
DO - 10.64898/2026.05.19.726180
UR - https://www.biorxiv.org/content/10.64898/2026.05.19.726180v2
AB - 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 (DePerp). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, DePerp, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only DePerp 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 DePerp, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, DePerp and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from DePerp capture meaningful aspects of brain network organisation with functional relevance.
ER -