Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.21.053702v1?rss=1 Authors: Seguin, C., Tian, Y., Zalesky, A. Abstract: The structure and function of the human connectome are coupled, but the correspondence is far from exact. We aimed to establish whether accounting for polysynaptic (multi-hop) paths in structural brain networks would improve prediction of interindividual variation in behavior as well as the strength of coupling with functional brain networks. Diffusion-weighted MRI and tractography were used to map structural connectomes for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic transmission, paths between unconnected pairs of regions were identified using each of 15 candidate models of brain network communication, giving rise to 15 communication matrices for each individual. Communication matrices were (i) used to perform predictions of five data-driven behavioral dimensions and (ii) correlated to interregional resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, network communication models improved the performance of structural connectivity. Communicability and navigation typically led to the most accurate behavioral predictions amongst the explored communication models. Accounting for polysynaptic communication in structural brain networks also significantly strengthened structure-function coupling in the human connectome. We observed that parcellation resolution and whether analyses were performed on individual- or population-level structural connectivity matrices had marked influence on the strength of associations to FC. Despite these effects, navigation and shortest paths produced consistently top-ranking FC predictions, leading to 35-65% improvements in structure-function coupling. Combining behavioral and functional results into a single ranking of communication models positioned navigation as the top model, suggesting that it may more faithfully recapitulate underlying neural signaling patterns. We conclude that network communication models augment the functional and behavioral predictive utility of the human structural connectome and contribute to narrowing the gap between brain structure and function. Copy rights belong to original authors. Visit the link for more info