Measuring and modeling of diffusion and white-matter tracts

Ariel Rokem

(Stanford University)

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Date: May 16, 2014


This talk will present a general methodological overview of diffusion MRI (dMRI), with a special focus on methods used to image connectivity and tissue properties in the human visual system. We will start by describing the principles of dMRI measurements. We will then provide an overview of models that are used to describe the signal and make inferences about the properties of the tissue and the trajectories of fiber fascicles in white-matter. We will focus on the classical Diffusion Tensor Model (DTM), which is used in many applications, and on the more recent development of Sparse Fascicle Models (SFM), which are more realistic representations of the signal as a combination of signals from different fascicles. Using cross-validation, we have found that DTM provides an accurate representation of the data, better than the reliability of a repeated measurement. SFM provide even more accurate models of the data, and particularly in regions where different fiber tracts cross. In the second part of the talk, we will focus on tractography. With special emphasis on probabilistic and deterministic tractography. We will introduce ideas about validation of white-matter trajectories and to perform statistical inferences about connectivity between different parts of the visual system. A major problem of the field is that different algorithms provide different estimates of connectivity. This problem is solved by choosing the fiber estimates that best account for the data in a repeated measurement (cross-validation).

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Co-Authors: Franco Pestilli

Created: Friday, May 23rd, 2014