Background image of Rolex Learning Center @ Alain Herzog / EPFL

Diffusion MRI

MR physics by Ruud van Heeswijk

This lecture will give a general overview of the physics and engineering behind MR imaging. After introducing the hardware components of an MR scanner, the source of the MR signal will be discussed, followed by the mechanisms that generate contrast between tissues. This will then be followed by spatial encoding, basic pulse sequences, and an introduction to sampling schemes. At the end of the lecture, students should be able to explain the practical basics of how MRI works and how an image is created.

By Patric Hagmann

Diffusion MR imaging of the living brain allows mapping tissue microstructure and axonal fiber bundles connecting different cortical regions. As such it has become an essential neuroimaging tool that is largely used in clinical and basic neuroscience research. My talk will focus on the physical principles governing the diffusion process in living tissue and the way the MR signal can be made sensitive to diffusion related molecular displacements.

By Marco Pizzolato & Gabriel Girard

Reconstruction & Tractography. We will go through the representation of the diffusion directional signal for the purpose of reconstructing the three-dimensional anatomical fiber pathways of the brain’s white matter. Different techniques, from diffusion tensor imaging to more advanced non-parametric fiber orientation descriptions, will be used to reconstruct the fiber pathways by means of tractography algorithms. An overview of such algorithms will be presented to illustrate their differences, advantages, and the current challenges in characterizing the brain white matter connectivity.

By Alessandra Griffa

The human brain is a complex network composed of structurally connected and dynamically interacting nervous regions. Mental functions (and dysfunctions) crucially relate to connectivity patterns in the brain. In this class you will learn the fundamentals of brain network analysis, including the concepts of integration, segregation, scale-free and small-world network properties. At the end of course you will also be able to identify brain hubs and cores, and to compare brain network properties between clinical populations, while controlling for possible biases in the analyses. We will discuss the possible biological interpretations of the different network measures, and how they have been related to human cognition and diseases.