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Home
The Microstructure-weighted Human Connectome: Network Properties And Structure-function Correlations Across Spatial Scales.

Home
The Microstructure-weighted Human Connectome: Network Properties And Structure-function Correlations Across Spatial Scales.

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

Arthur P C Spencer¹, Saina Asadi^1,2, Yasser Alemán-Gómez^1,2

¹Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.

Biorxiv : the Preprint Server for Biology

|June 4, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

New brain connectome weights using biophysical diffusion modeling reveal more specific microstructural details. These novel metrics, intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity (D⊥), better capture structure-function relationships in the brain.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroimaging
Computational Neuroscience
Biophysics

Background:

Conventional connectome edge weights (e.g., number of streamlines) lack microstructural specificity.
Biophysical diffusion modeling offers greater detail about white matter microstructure.

Purpose of the Study:

Investigate if biophysical diffusion model parameters can serve as informative connectome weights.
Assess the specificity and functional relevance of novel connectome metrics.

Main Methods:

Used diffusion MRI data from healthy adults.
Constructed structural networks weighted by intra-axonal signal fraction (f), perpendicular extra-axonal diffusivity (D⊥), number of streamlines (NOS), fractional anisotropy (FA), and radial diffusivity (RD).
Correlated weighted connectomes with resting-state fMRI and intracranial conduction velocity measurements.

Main Results:

All weights showed small-world network properties.
Intra-axonal signal fraction (f), perpendicular extra-axonal diffusivity (D⊥), and normalized NOS captured non-random local organization.
Only D⊥ demonstrated significant structure-function coupling across all scales and modalities.
f and radial diffusivity (RD) showed high consistency in regional structure-function coupling.

Conclusions:

Connectome weights derived from biophysical diffusion modeling, particularly D⊥, capture meaningful aspects of brain network organization.
These novel metrics offer improved specificity for understanding macroscale brain organization and function.