Brain Functional Connectivity Estimation Utilizing Diffusion Kernels on a Structural Connectivity Graph

TL;DR

This paper introduces a novel, efficient method for estimating brain functional connectivity by integrating diffusion tensor imaging with graphical models, revealing new insights into brain interactions during motor tasks.

Contribution

The authors develop a scalable, diffusion kernel-based approach that improves accuracy and speed in brain connectivity estimation using multi-modal imaging data.

Findings

Method performs comparably or better than existing approaches in simulations.

Application to HCP data reveals new brain interaction insights during motor tasks.

Approach offers greater transparency and flexibility in network estimation.

Abstract

Functional connectivity (FC) refers to the investigation of interactions between brain regions to understand integration of neural activity in several regions. FC is often estimated using functional magnetic resonance images (fMRI). There has been increasing interest in the potential of multi-modal imaging to obtain robust estimates of FC in high-dimensional settings. We develop novel algorithms adapting graphical methods incorporating diffusion tensor imaging (DTI) to estimate FC with computational efficiency and scalability. We propose leveraging a graphical random walk on DTI to define a new measure of structural connectivity highlighting spurious connected components. Our proposed approach is based on finding appropriate subnetwork topology using permutation testing before selection of subnetwork components comprising FC. Extensive simulations demonstrate that the performance of our methods is comparable to or better than currently used approaches in estimation accuracy, with the advantage of greater speed and simpler implementation. We analyze task-based fMRI data obtained from the Human Connectome Project database using our proposed methods and reveal novel insights into brain interactions during performance of a motor task. We expect that the transparency and flexibility of our approach will prove valuable as further understanding of the structure-function relationship informs future network estimation.