Tractography (also called fiber tracking or fiber tracing) is a general term for methods to reconstruct fiber pathways in the white matter based on diffusion MR imaging. It offers a unique possibility to gain insight into the structure of the human brain non invasively and in vivo. The information won in this manner is not only of high value for visualization of the brain connectivity and segmentation of the brain into different functional areas, it also has the potential to provide essential information that can be exploited e.g. for neurosurgical planning or for better understanding major diseases such as multiple sclerosis, epilepsy, schizophrenia, brain plasticity after strokes etc.
Tractography is thus the central tool in MR-based brain connectivity analysis, i.e. in MR connectomics. Although largely developed, tractography still mostly relies on local optimization methods, and remains therefore quite unstable and unreliable, especially for mapping non-dominant pathways and when used with advanced diffusion MR acquisition schemes such as High Angular Resolution Diffusion Imaging (HARDI) or Diffusion Spectrum Imaging (DSI).
Recently, global tractography methods appeared. Although being promising, they suffer from major limitations, including prohibitive computational time. Moreover they are not adapted to be used with the most advanced acquisition scheme, namely DSI.
In this project, we will develop a new global tractography algorithm, fully adapted to DSI and based on a new parametrization of the fiber curves, to reduce the number of parameters to estimate, and on advanced optimization schemes using Gibbs sampling, to find the optimal solution of the tractography. With this algorithm, we expect to improve significantly the accuracy and the stability of the brain connectivity analysis, and to be able to map subtle changes in the brain connectome.
In terms of application, our global tractography method will be used to study global and local variation in brain connectivity in epileptic patients. It will allow the detection of subtle abnormalities and the tracking of neuronal fibers between any two cortical regions of the brain. Moreover, because the same patients are also investigated with electric source imaging and with EEG-fMRI, the connectivity maps will be directly compared to the functional data, providing a unique possibility to study structure and function in the same brain.
This 2-year project has to be seen as a methodological complement and a direct contribution to the SNF SPUM project entitled “Imaging large scale neuronal networks in epilepsy”(SNF project nbr 33CM30-124089), coordinated by Prof. Margitta Seeck (Univ. Hospital of Geneva). While the clinical research will be performed in the SPUM project, our project will develop new methodological tools towards a better understanding of the neuronal network functioning in epilepsy, and far beyond, as a major contribution to brain connectivity analysis, i.e. to MR connectomics.