RT Journal Article SR Electronic T1 Multidimensional encoding of brain connectomes JF bioRxiv FD Cold Spring Harbor Laboratory SP 107607 DO 10.1101/107607 A1 Cesar F. Caiafa A1 Franco Pestilli YR 2017 UL http://biorxiv.org/content/early/2017/02/10/107607.abstract AB The ability to map brain networks at the macroscale in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance data into multidimensional arrays (tensors). The framework overcomes current limitations in building connectomes; it prevents information loss by integrating the relation between connectome nodes, edges, fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing. The framework reduces dramatically storage requirements for connectome evaluation methods, with up to 40x compression factors. We apply the framework to evaluate 1,980 connectomes, thirteen tractography methods, and three data sets. We describe a general equation to predicts connectome resolution (number of fascicles) given data quality and tractography model parameters. Finally, we provide open-source software implementing the method and data to reproduce the results.