FVT4DWI: FVTool for Diffusion-weighted Imaging

Quantification of Structural Brain Connectivity via a Conductance Model

This is a toolbox to study structural brain connectivity using a combination of differential Maxwell’s equations and Kirchhoff’s circuit laws, resulting in an equation similar to the heat equation. By solving this partial differential equation for a certain current configuration between 2 voxels, we find the potential map for that specific configuration. We further compute the electric conductance between each pair of voxels from potential maps, to which all diffusion paths between the pair contribute.
The same measure can also be computed between a pair of regions of interest (ROIs) instead of voxels, by distributing the currents among the ROI voxels.

How do I use it?

You can find

• toy examples with different shapes, and plot conductance interactively here.
• an example of how to interactively plot conductance using the Fibercup data here.
• a generic example for structural brain connectivity in Examples/Connectivity/run_conductance_model.m

Reference

This work has been published in NeuroImage:

Aina Frau-Pascual, Morgan Fogarty, Bruce Fischl, Anastasia Yendiki, and Iman Aganj, “Quantification of structural brain connectivity via a conductance model,” In NeuroImage 189, pp. 485-496, 2019, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2019.01.033.

Inspiration

This toolbox is a fork of FVTool, and was extended for dealing with tensors and modified for its use in structural connectivity settings.