Project

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New methods for mapping and analysing large scale structural brain connectivity with MRI

English title New methods for mapping and analysing large scale structural brain connectivity with MRI
Applicant Thiran Jean-Philippe
Number 121945
Funding scheme Project funding (Div. I-III)
Research institution Laboratoire de traitement des signaux 5 EPFL - STI - IEL - LTS5
Institution of higher education EPF Lausanne - EPFL
Main discipline Information Technology
Start/End 01.08.2009 - 31.07.2012
Approved amount 158'101.00
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Keywords (11)

image analysis; image segmentation; medical imaging; diffusion MR image processing; tractography; fiber tracking; surface registration; network analysis; brain connectivity analysis; magnetic resonance imaging;

Lay Summary (English)

Lead
Lay summary
Diffusion Magnetic Resonance Imaging is a recent medical imaging modality that allows obtaining non-invasive information about the ability of water molecules to diffuse in their microenvironement in a given direction at every point (voxel) of an organ like the brain. As this ability to diffuse is closely related to the orientation of the neural fibers in the brain, by processing these diffusion images, it is possible to infer the neural connectivity between different regions in the brain as well as to segment the major fiber tracts in vivo. This new domain is called MRI tractography. We propose in this project a new framework for analyzing large-scale structural brain connectivity with MRI. This framework relies on the concept of a structural connection matrix derived from MRI tractography that we introduced recently. This connection matrix is obtained by combining the result of a standard tractography experiment with a partition of the cortex into small regions of interest identified with labels. This way the connectivity between each pair of regions on interest can be assessed and the result displayed as a connection matrix.This very powerful and promising technique needs however considerable additional work. In this project, we will tackle three of the most important methodological developments that are needed to achieve this goal.- The parcellation of the cerebral cortex into many small regions of interest has to be robust and coherent between different subjects: regions with the same label have to correspond to the same cortical areas on different brains. - To be effective, our framework has to use superior tractography algorithms. In this project, we will use the most advanced type of diffusion MR images, namely Diffusion Spectrum Images, partially developed in our group. We will develop new tractography algorithms adapted to such data, based on advanced anisotropic front propagation techniques.- Finally, a connection matrix is the raw material, basis for individual and group studies. Network analysis tools will be developed to make sense out of these complex networks..At the end of this project we will thus have developed a new framework and associated image processing and network analysis techniques for mapping and analyzing large scale structural brain connectivity with diffusion MRI, ready to be used for individual or group studies. This framework will be general enough to be used in many other neuroscientific research domains, to investigate a large range of pathologies where brain connectivity is involved, including neurodegenerative disorders such as Alzheimer's disease and neuro-developmental diseases like schizophrenia or autism.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
A new early and automated MRI-based predictor of motor improvement after stroke
Granziera Cristina, Daducci Alessandro, Meskaldji Djalel, Roche Alexis, Maeder Philippe, Michel Patrick, Hadjikhani Nouchine, Sorensen A. Gregory, Frackowiak Richard S., Thiran Jean-Philippe, Meuli Reto, Krueger Gunnar, A new early and automated MRI-based predictor of motor improvement after stroke, in Neurology, 79(1), 39-46.
Adaptive strategy for the statistical analysis of connectomes
Meskaldji DE, Ottet M-C, Cammoun L, Hagmann P, Meuli R, Eliez S, Thiran JP, Morgenthaler S, Adaptive strategy for the statistical analysis of connectomes, in PLoS ONE, 6(8), 23009-23009.
Diffusion Spectrum Imaging after stroke shows structural changes in the contra-­‐lateral motor network correlating with functional recovery
Granziera Cristina, Daducci Alessandro, Gigandet Xavier, Cammoun Leila, Meskaldji Djalel Eddine, Michel Patrik, Maeder Philippe, Sorensen Alma Gregory, Thiran Jean-­Philippe, Meuli Reto, Krüger Gunnar, Diffusion Spectrum Imaging after stroke shows structural changes in the contra-­‐lateral motor network correlating with functional recovery, in 19th International Society for Magnetic Resonance in Medicine (ISMRM) conference, Montreal19th International Society for Magnetic Resonance in Medicine (ISMRM) conference, Montreal, Quebec, Canada.
Mapping the human connectome at multiple scales with diffusion spectrum MRI
Cammoun Leila, Gigandet Xavier, Meskaldji Djalel, Thiran Jean-Philippe, Sporns Olaf, Do Kim Q., Maeder Philippe, Meuli Reto, Hagmann Patric, Mapping the human connectome at multiple scales with diffusion spectrum MRI, in Journal of Neuroscience Methods, 203(2), 386-397.
MR connectomics: Principles and challenges
Hagmann P, Cammoun L, Gigandet X, Gerhard S, Ellen Grant P, Wedeen V, Meuli R, Thiran J-P, Honey CJ, Sporns O, MR connectomics: Principles and challenges, in Journal of Neuroscience Methods, 194(1), 34-45.
Testing the variability of Diffusion Spectrum Imaging (DSI): Inter-­‐ and intra-­‐site comparison on “identical” 3T scanners.
Lemkaddem Alia, Daducci Alessandro, Vulliémoz Serge, Seeck Margitta, Lazeyras François, Meuli Reto, Krüger Gunnar, Thiran Jean-Philippe, Testing the variability of Diffusion Spectrum Imaging (DSI): Inter-­‐ and intra-­‐site comparison on “identical” 3T scanners., in 19th International Society for Magnetic Resonance in Medicine (ISMRM) conference, Montreal19th International Society for Magnetic Resonance in Medicine (ISMRM) conference, Montreal, Quebec, Canada.
The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.
Gerhard Stephan, Daducci Alessandro, Lemkaddem Alia, Meuli Reto, Thiran Jean-Philippe, Hagmann Patric, The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes., in Frontiers in neuroinformatics, 5, 3-3.
White matter maturation reshapes structural connectivity in the late developing human brain
Hagmann P, Sporns O, Madan N, Cammoun L, Pienaar R, Wedeen VJ, Meuli R, Thiran J-P, Grant PE, White matter maturation reshapes structural connectivity in the late developing human brain, in Proceedings of the National Academy of Sciences of the United States of America, 107(44), 19067-19072.

Collaboration

Group / person Country
Types of collaboration
EPFL - Prof. St. Morgenthaler Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Associated projects

Number Title Start Funding scheme
144467 Statistical methods for brain connectivity analysis 01.10.2012 Project funding (Div. I-III)
144467 Statistical methods for brain connectivity analysis 01.10.2012 Project funding (Div. I-III)
144529 Fast Global Tractography for Sensitive MR Connectomics 01.10.2012 Project funding (Div. I-III)
103595 Brain connectivity analysis in High Angular Resolution Diffusion Imaging 01.04.2004 Project funding (Div. I-III)

Abstract

Diffusion Magnetic Resonance Imaging is a recent medical imaging modality that allows obtaining non-invasive information about the ability of water molecules to diffuse in their microenvironement in a given direction at every point (voxel) of an organ like the brain. As this ability to diffuse is closely related to the orientation of the neural fibers in the brain, by processing these diffusion images, it is possible to infer the neural connectivity between different regions in the brain as well as to segment the major fiber tracts in vivo. This new domain is called MRI tractography. Our group is among the pioneers of the processing of such images, leading to important scientific and medical results and publications. Based on those results, we propose in this project a new framework for analyzing large-scale structural brain connectivity with MRI. This framework relies on the concept of a structural connection matrix derived from MRI tractography that we introduced recently. This connection matrix is obtained by combining the result of a standard tractography experiment with a partition of the cortex into small regions of interest identified with labels. This way the connectivity between each pair of regions on interest can be assessed and the result displayed as a connection matrix.This very powerful and promising technique needs however considerable additional work to be used as a tool for comparing brain connectivity between subjects or patients, which is the ultimate goal of those technical developments.In this project, we will tackle three of the most important methodological developments that are needed to achieve this goal.1. The parcellation of the cerebral cortex into many small regions of interest has to be robust and coherent between different subjects: regions with the same label have to correspond to the same cortical areas on different brains. To do this, we will adopt an atlas-based approach and develop advanced surface registration algorithms, exploiting a multi-scale approach on the sphere.2. To be effective, our framework has to use superior tractography algorithms. In this project, we will use the most advanced type of diffusion MR images, namely Diffusion Spectrum Images, partially developed in our group. We will develop new tractography algorithms adapted to such data, based on advanced anisotropic front propagation techniques, to improve both the specificity and the sensitivity of the tractography.3. Finally, a connection matrix is the raw material, basis for individual and group studies. Network analysis tools will be developed to make sense out of these complex networks and allow the formulation of neuro-biological hypothesis that can be tested on populations.At the end of this project we will thus have developed a new framework and associated image processing and network analysis techniques for mapping and analyzing large scale structural brain connectivity with diffusion MRI, ready to be used for individual or group studies. This framework will be general enough to be used in many other neuroscientific research domains, to investigate a large range of pathologies where brain connectivity is involved, including neurodegenerative disorders such as Alzheimer’s disease and neuro-developmental diseases like schizophrenia or autism.
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