Fast-Marching Tractography for Connection Matrix (Fast-TraC)

Although high angular resolution diffusion MRI techniques are able to solve multiple intra-voxel fiber orientations, the usual streamline Diffusion Spectrum Imaging (DSI) tractography algorithms present some limitations in their ability to map complex fiber-crossings in the brain white matter because they select locally only the most linear trajectories. In this work, we present a fast marching tractography algorithm for DSI, called Fast-TraC, which 1) is able to efficiently address this issue, 2) creates fiber trajectories between 1000 small cortical ROIs covering the entire brain and 3) builds a whole brain connection matrix. We also see selected tracts that are accurately reconstructed

A Method to Study Alterations in Networks of Structural Connectivity

The global structural connectivity of the brain can be explored in vivo with a connectivity matrix derived from diffusion MRI tractography [1]. In such a matrix, M, every index i or j represents a small region of interest (ROI) at the white-gray matter (WGM) interface and every entry M(i,j) provides a measure of connectivity derived from tractography. Once the matrix computed, it is easy to obtain connectional information betwee

Mapping Human Whole-Brain Structural Networks with Diffusion MRI

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world

Adhesion of aluminium alloys : morphology, surface chemistry and adhesive bond durability

International audienc

MR connectomics: Principles and challenges.

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches