Embedding of Functional Human Brain Networks on a Sphere

TL;DR

This paper introduces a method to embed complex human brain networks onto a spherical surface to improve visualization and analysis of high-dimensional connectivity data from fMRI studies.

Contribution

The paper proposes a novel spherical embedding technique for brain networks, facilitating better visualization and understanding of large-scale connectivity data.

Findings

Spherical embedding simplifies visualization of high-dimensional brain networks

Provides a new tool for analyzing functional connectivity data

Code implementation available for researchers

Abstract

Human brain activity is often measured using the blood-oxygen-level dependent (BOLD) signals obtained through functional magnetic resonance imaging (fMRI). The strength of connectivity between brain regions is then measured as a Pearson correlation matrix. As the number of brain regions increases, the dimension of matrix increases. It becomes extremely cumbersome to even visualize and quantify such weighted complete networks. To remedy the problem, we propose to embed brain networks onto a sphere, which is a Riemannian manifold with constant positive curvature. The Matlab code for the spherical embedding is given in https://github.com/laplcebeltrami/sphericalMDS.