Outline: In this course, the students will learn on some
advanced imaging techniques used in neuroscience and clinical research to
non-invasively explore the connectivity underlying the morphological and
functional structure of the human brain. We will cover theoretical and
practical materials necessary for analyzing data from various imaging
modalities: MEG, functional MRI (resting-state and activation) and diffusion
MRI. For each of these modalities, we will go from the basis to advanced
image processings with examples of applications in neuroscience and clinical
research. The current limitations of each technique will be presented as
well as the state-o-the art method to overcome these issues (artefact
correction method, biases modelization). During practicals, the students
will manipulate real and simulated data in a python-based environment, and
will acquire basic skills in programming in order to manage the full
potential of these tools. In the end, the students will be able to analyze
various imaging datasets while being aware of the potentialities and
limitations of each acquisition and processing technique.
Previous UE providing basics about neuroimaging and data analysis (i.e. Medical Image Analysis 1&2 (UE 3.3 & 3.10b)).
Notions of Python coding is a plus
Lecture 1: Connectivity for active-state fMRI.
Lecture 2-3: Functional brain Imaging with MEG and EEG: Source reconstruction
Lecture 4: Tractography: From Diffusion-weighted MRI to brain anatomical connectivity.
Lecture 5-6: Resting-state functional connectivity.
Evaluation: Notebook iPython on practicals
Sylvain Charron, Centre Hospitalier Sainte Anne / Paris Descartes
Alexandre Gramfort, Telecom ParisTech, Neurospin, INRIA
Pauline Roca, Centre Hospitalier Sainte Anne / Paris Descartes
Gaël Varoquaux, INRIA/Neurospin