The Resolution Matrix for Visualizing Functional Network Connectivity

TL;DR

This paper introduces the resolution matrix as a tool for analyzing the accuracy and robustness of functional network connectivity estimates in fMRI data, providing new insights into network interactions.

Contribution

It applies the resolution matrix to fMRI data, demonstrating its utility in quantifying network resolution and revealing novel relationships between brain networks.

Findings

Resolution metrics can identify networked activity.

New insights into default mode and frontoparietal networks.

Quantifies robustness of network estimates.

Abstract

The resolution matrix is a mathematical tool for analyzing inverse problems such as computational imaging systems. When treating network connectivity estimation as an inverse problem, the resolution matrix describes the degree to which network nodes and edges can be resolved. This is useful both for quantifying robustness of the network estimate, as well as identifying correlated activity. In this report we analyze the resolution matrix for functional MRI data from the Human Connectome project. We find that common metrics of the resolution metric can be used to identify networked activity, though with a new twist on the relationship between default mode network and the frontoparietal attention network.