Dissecting whole-brain conduction delays through MRI microstructural measures

Matteo Mancini,Mara Cercignani,Susie Y Huang,Qiyuan Tian,Qiuyun Fan

doi:10.1007/s00429-021-02358-w

Abstract

Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.

Highlights

Network models offer a powerful framework to study structure–function relationships in the human brain
We estimated a rich set of structural connectivity patterns in a group of healthy volunteers combining tractography and several microstructural measures: the axonal diameter, estimated from ultra-high gradient diffusion data using a spherical mean model (Fan et al, 2020); the myelin volume fraction, estimated as the macromolecular tissue volume (MTV)
2018; Mezer et al, 2013); the g-ratio, computed using the approach proposed by Stikov and colleagues (Stikov et al, 2015); and the conduction velocity, computed from the axonal diameter and the g-ratio using the Rushton model (Rushton, 1951)

Summary

Introduction

Network models offer a powerful framework to study structure–function relationships in the human brain. In applications to brain dynamics, the Kuramoto model has been used both for slow oscillations as in metabolic activity (as estimated by functional magnetic resonance imaging), and fast ones as in electrical activity (as measured for example through magnetoencephalography) (Breakspear, 2017)

Objectives

Methods

Results

Discussion

Conclusion