Section: Partnerships and Cooperations

International Initiatives

INRIA Associate Teams

CAPNEONATES

• Title: Analysis of structural MR and DTI in neonates

• INRIA principal investigator: Pierre Fillard

• International Partner:

  • Institution: University of Southern California (United States)

  • Laboratory: Image Lab at Children Hospital at Los Angeles

  • Researcher: Natasha Lepore

• International Partner:

  • Institution: University of Pennsylvania (United States)

  • Laboratory: Penn Image Computing and Science Laboratory

  • Researcher: Caroline Brun

• Duration: 2011 - 2013

• See also: http://www.capneonates.org/

• While survival is possible at increasingly lower gestational ages at birth, premature babies are at higher risk of developing mental disorders or learning disabilities than babies born at term. A precise identification of the developmental differences between premature and control neonates is consequently of utmost importance. Nowadays, the continuously improving quality and availability of MR systems makes it possible to precisely determine, characterize and compare brain structures such as cortical regions, or white matter fiber bundles. The objective of this project is to understand the developmental differences of premature versus normal neonates, using structural and diffusion MRI. This work will consist in identifying, characterizing and meticulously studying the brain structures that are different between the two groups. To do so, we propose to join forces between the Parietal team at INRIA and the University of Southern California. Parietal has a recognized expertise in medical image registration and in statistical analyses of groups of individuals. USC has a broad knowledge in MR image processing. In particular, the Children's Hospital at Los Angeles (CHLA), which is part of USC, is in the process of collecting a unique database of several hundreds of premature and normal neonates MR scans. This joint collaboration is consequently a unique chance of addressing key questions pertaining to neonatal and premature development. It will make it possible to elaborate new tools to analyze neonate MR images while tremendously increasing our knowledge of neuroanatomy at such an early stage in life.

INRIA International Partners

• LIAMA http://www.nlpr.ia.ac.cn/jiangtz/ : B.Thirion visited LIAMA (contact person: Shan Yu) in May and gave a presentation. We plan to develop come collaborations on fMRI data analysis and functional connectivity in the future.

• Donders institute https://sites.google.com/a/distrep.org/distrep/marcel-van-gerven : We share with M. van Gerven some interest on biological vision and on the use of fMRI to probe specific hypotheses related to computational models of vision. We hope to have a student in common in the future.

• Biomedical Image analysis group, Imperial College, London http://www.doc.ic.ac.uk/~dr/ : We have started some joint work on the comparison of functional and anatomical connectivity using machine learning tools. We showed preliminary common contributions at IPMI and MLINI 2011.

• MIT, CSAIL http://www.csail.mit.edu/ , P.Golland's group : we regularly visit each other and share common interests in the use of machine learning for neuroimaging, in the introduction of functional information into co-registration procedures, and in the study and comparison of anatomical and functional connectivity. We plan a common project and more visits for next year.

Visits of International Scientists

Bernard Ng, from Biomedical Image and Signal Computing Laboratory, British Columbia University http://bisicl.ece.ubc.ca/ , has visited Parietal from Sept 1st, 2010 to March 1st, 2011. The collaboration is about the introduction of functional connectivity into the analysis of fMRI activation data.

Participation In International Programs

Parietal has taken part to the program INRIA@SiliconValley, and had a 18-months post-doc funded to work on the comparison of anatomical and fuctional connectivity (18 months, 2011-2013):

In this project, we would like to build probabilistic models that relates quantitatively the observations in anatomical and functional connectivity. For instance given a set of brain regions, the level of functional integration might be predicted by the anatomical connectivity measurement derived from the fibers in a given population of subjects. More generally, we will seek to extract latent factors explaining both connectivity measures across the population. Such models require specifically that a generative model is proposed to explain the observations in either domain, so that a meaningful and testable link is built between the two modalities. The inference problem can then be formulated as learning the coupling parameters that are necessary to model the association between modalities, and tested e.g. by assessing the ability of the learned model to generalize to new subjects. The aim is then to provide the mathematical and algorithmic tools necessary to build a standardized model of brain connectivity informed by both modalities, associated with confidence intervals to take into account between subject variability. Such an atlas is a long-term project, that requires adequate validation on high-resolution data, but it will probably be tightly linked to this project.