Prix de la Fondation Dimitris N. Chorafas 2006 - Patric Hagmann
31.12.06 - From Diffusion MRI to Brain Connectomics. Thèse EPFL, n° 3230 (2005). Dir.: Prof. J.-Ph. Thiran.
In his thesis, Dr. Patric Hagmann pioneered the field of brain connectivity analysis by Diffusion MRI. His scientific contributions range from fundamental Diffusion MR Physics to algorithmic developments in tractography, and to the study of the global brain connectivity by network and graph theories.
From Diffusion MRI to Brain Connectomics
This work is not only a journey that takes us from essential diffusion MRI physics to an investigation of the brain neuronal circuitry, but also a thesis aiming at demonstrating the power of large scale analysis of brain connectivity.
After a short introduction on diffusion NMR, we start by showing that diffusion contrast is positive. This key issue, that was only postulated up to now, allows us to justify why diffusion can be computed accurately with the only signal modulus. Accordingly, various emerging MRI techniques that map non-Gaussian diffusion have found a sound justification.
Tractography is the necessary link that from diffusion MRI provides us with nerve fiber trajectories and maps of brain axonal connectivity. In this thesis two algorithms are proposed designed for diffusion tensor and for high angular resolution diffusion MRI. We also investigate whether tractography can be formulated as a segmentation problem in a high dimensional non Euclidean space, i.e. position-orientation space.
Based on diffusion tensor MRI data of 32 healthy volunteers, language networks are investigated. It is shown that men and women as well as left and right handers have different connectivity patterns. Diffusion Spectrum MRI data of a single subject is also collected and tractography performed in order to analyze the global connectivity pattern of the human brain. For this purpose we propose to model this large scale architecture by an abstract graph. It is shown that the long-range axonal network exhibits a "small world" and a hierarchical topology.