A Novel Metric Shows the Robustness of the Graph Communities to Brain-Tractography False-Positives

TL;DR

This paper introduces two new metrics to evaluate the robustness of brain connectivity graph communities against false positives in tractography data, demonstrating that community detection remains reliable despite overestimations.

Contribution

The paper presents novel metrics for assessing tractogram quality and shows that graph community estimation is robust to false positives in brain tractography.

Findings

Community detection is robust to high false-positive rates.

Proposed metrics effectively rank tractogram quality.

Results validated on ISMRM-2015 dataset.

Abstract

We study the impact of the brain tractography false positives in the brain connectivity graphs. The representative input database for the analysis is the set of tractograms from the participants on the ISMRM-2015 Tractography Challenge. We propose 2 novel metrics to rank the quality of a tractogram when it is compared with known ground truth. The results of this study indicate that the estimation of graph communities is robust to high levels of overestimation in the connectivity.