Evaluating Network Threshold Selection for Structural and Functional Brain Connectomes

Structural and functional brain connectomes represent macroscale neurophysical data collected through methods such as magnetic resonance imaging (MRI). Such data may contain noise that contribute to false positive edges, which motivates the use of edge-wise thresholding. Thresholding procedures are useful for reducing network density in graphs to retain only the most informative, non-noisy edges. Nevertheless, limited consensus exists on selecting appropriate threshold levels. We compare existing thresholding methods and introduce a novel thresholding approach in the context of MRI-derived and simulated brain connectomes. Performance is measured using normalized mutual information (NMI), a quantity robust to arbitrary changes in partition labeling, and describes the similarity of community structure between two node-matched networks. We found that the percolation-based threshold and our newly proposed objective function-based threshold exhibited the best performance in terms of NMI. We show an application of these two thresholding methods to real data that showed that both percolation-based and objective function-based thresholding provide statistically similar NMI values between real world FC networks and structural connectivity (SC) counterparts, where shared modular structure is assumed. The two thresholding methods, however, achieve these NMI values at significantly different thresholds (p<0.0001) in both simulated and real networks. Moreover, the threshold obtained from the objective function gives a more accurate estimate of the number of modules present in the network and includes more flexibility in threshold selection, suggesting that this method may represent a useful option for graph thresholding.