Threshold Selection for Brain Connectomes

Abstract

Introduction:

Structural and functional brain connectomes represent macroscale data collected through techniques such as magnetic resonance imaging (MRI). Connectomes may contain noise that contributes to false-positive edges, thereby obscuring structure-function relationships and data interpretation. Thresholding procedures can be applied to reduce network density by removing low-signal edges, but there is limited consensus on appropriate selection of thresholds. This article compares existing thresholding methods and introduces a novel alternative “objective function” thresholding method.

Methods:

The performance of thresholding approaches, based on percolation and objective functions, is assessed by (1) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and by (2) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. An application to empirical data is provided.

Results:

Our proposed objective function-based threshold exhibits the best performance in terms of resulting in high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks.

Discussion:

Existing network thresholding methods yield widely different results when graph metrics are subsequently computed. Thresholding based on the objective function maintains a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides a proof of principle that objective function thresholding could offer a useful approach to reducing the network density of functional connectivity data.

Impact statement

Network thresholding refers to removing edges between node pairs in a functional network that have weak edge-weights potentially arising from unwanted variability or noise. Since edge-weight cutoffs used to generate a binary network can be sensitive to the thresholding method, we introduce a novel thresholding algorithm. We find that when applied to networks derived via perturbations, namely through simulated functional connectivity of a known network, our approach yields a binary network that is more similar to the known network compared with using existing thresholding approaches. Our algorithm is a competitive candidate for thresholding brain connectomes.