Weighting the Structural Connectome: Exploring its Impact on Network Properties and Predicting Cognitive Performance in the Human Brain

Abstract Brain function does not emerge from isolated activity, but rather from the interactions and exchanges between neural elements which form a network known as the connectome. The human connectome consists of structural and functional aspects. The structural connectome (SC) represents the anatomical connections and the functional connectome represents the resulting dynamics which emerge from this arrangement of structures. As there are different ways of weighting these connections, it is important to consider how such different approaches impact study conclusions. Here, we propose that different weighted connectomes result in varied network properties and while neither superior the other, selection might affect interpretation and conclusions in different study cases. We present three different weighting models, namely, Number of Streamlines (NOS), Fractional Anisotropy (FA), and Axon-Diameter Distribution (ADD), to demonstrate these differences. The later, is extracted using recently published AxSI method, and is first compared to commonly used weighting methods. Moreover, we explore the functional relevance of each weighted SC, using the HCP database. By analyzing intelligencerelated data, we develop a predictive model for cognitive performance based on graph properties and the NIH toolbox. Results demonstrate that the ADD SC, combined with a functional subnetwork model, outperforms other models in estimating cognitive performance.