Multidimensional encoding of brain connectomes

Cesar F Caiafa,Franco Pestilli

doi:10.1038/s41598-017-09250-w

Abstract

The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.

Highlights

The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease
We show that connectome edges is an ensemble of fascicles that can be represented by a set of frontal slices in (Fig. 1b bottom)
Blue and yellow frontal volumes in Fig. 1b bottom correspond to the encoded representation of the Arcuate Fasciculus and Corticospinal Tract, reproduced in Fig. 1b top in their natural brain space

Summary

Introduction

The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. The recent increase in availability of neuroimaging data and large scale projects has the potential to empower new ways of discovery by studying large populations of human brains[5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] Exploiting these large-scale data sets will require convergent efforts in advancing measurement methods, data representation frameworks, as well as computational algorithms and theory[24,25]. Tractography and diffusion-weighted magnetic resonance imaging (dMRI) are the primary methods for mapping structural brain networks and white matter tissue properties in living human brains.

Methods

Results

Conclusion