The research developed within the spinal cord theme in the SNC team aims to improve the characterization by multiparametric MRI of spinal cord alterations encountered in various pathological conditions (neuro-degeneration, compression, ..).
These approaches are specifically designed to quantify the breakdown of tissues as well as the resulting malfunctions.
While current multimodal MRI methods allow a relatively good characterization of the white matter, gray matter remains poorly characterized.
In this context, this internship proposal aims to better determine the morphology of the spinal gray matter and its substructures.
This project is based on the acquisition and analysis ultra high-field images in humans (whole-body 7T imager, newly installed at the CRMBM-CEMEREM), with improved spatial resolution and contrasts.
The subsequent identification of medullary subregions is relevant to the regional analysis of changes occurring in various diseases studied in the laboratory in collaboration with AP-HM services (myelopathy, multiple sclerosis, amyotrophic lateral sclerosis…).
This identification will also help build an atlas (such as AMU40 atlas (1) or Poly-MNI-AMU (2)), prepared in the laboratory in collaboration with NeuroPoly (J.Cohen-Adad, Ecole Polytechnique de Montréal).
In this context, the proposed internship at CRMBM-CEMEREM can be performed in connection with trainings at NeuroPoly on the development of the Spinal Cord Toolbox (3).
7T MRI , multi-parametric MRI, spinal cord, spinal substructures
High and Ultra High Field MRI (3T , 7T)
(1) – A reliable spatially normalized template of the human spinal cord – Applications to automated white matter/gray matter segmentation and Tensor-Based Morphometry (TBM) mapping of gray matter alterations occurring with age, M. Taso, A. Le Troter, M. Sdika, J. Cohen-Adad, PJ. Arnoux, M. Guye, JP. Ranjeva, V. Callot, Neuroimage, 117:20-8, 2015.
(2) – Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template, V. Fonov, A. Le Troter, M. Taso, G. Leveque, M. Benhamou, M. Sdika, H. Benali, PF. Pradat, L. Collins, V. Callot, J. Cohen-Adad, Neuroimage, 102 Pt 2:817-27, 2014.
(3) – SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data, B. De Leener, S. Lévy, V.S. Fonov, N. Stikov, L.D. Collins, V. Callot, J. Cohen-Adad Neuroimage, en révision, 2016.