Context and general aim
New representations from multimodal medical images

Network theoretic approaches to integrate heterogeneous brain networks

Spatio-temporal models to build trajectories of disease progression from longitudinal data
Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

Decision support systems for diagnosis, prognosis and design of clinical trials

External collaborations
Methodological collaborations
- King’s College London, UK (Sébastien Ourselin)
- Center for Medical Image Computing, University College London, UK (Daniel Alexander)
- Erasmus Medical Center (EMC), Rotterdam, Netherlands (Stefan Klein, Meike W. Vernooij, Wiro Niessen)
- Tandon School of Engineering, New York University, USA (Guido Gerig)
- Departement of Physics. Queen Mary University of London, UK (Vito Latora)
- Center for Magnetic Resonance Research, University of Minnesota, USA
(Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil) - Inria Epione, Sophia-Antipolis, France (Nicholas Ayache)
- Center for Applied Mathematics, ENS Paris-Saclay (Alain Trouvé)
- Université Paris-Descartes (Joan Glaunès)
- Laboratoire AMIS, Université Paul Sabatier, Toulouse (José Braga, Jean Dumoncel)
- Institut Pasteur, Paris (Roberto Toro)
- INSEAD, Fontainebleau (Theodoros Evgeniou)
- Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier)
Medical collaborations
- Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin)
- Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges)
- Fatebene Fratelli, Brescia, Italy (Giovanni Frisoni, Alberto Redolfi,Damiano Archetti)
Local collaborations
Methodological collaborations
- CENIR MRI core facility (Stéphane Lehéricy, Eric Bardinet, Romain Valabrègue)
- CENIR MEG/EEG core facility (Nathalie George, Denis Schwartz, Laurent Hugueville)
- ICM iCONICS Bioinformatics/biostatistics core facility (Ivan Moszer)
- Laboratoire d’Imagerie Biomédicale (Marie-Odile Habert)
Medical collaborations
- IM2A / ICM Bruno Dubois’s team (Bruno Dubois, Harald Hampel, Marc Teichmann, Isabelle Le Ber)
- ICM Alexis Brice’s team (Alexis Brice, Isabelle Le Ber, Christel Depienne, Jean-Christophe Corvol)
- ICM Marie Vidailhet / Stéphane Lehéricy’s team (Marie Vidailhet, Stéphane Lehéricy, Andreas Hartmann, Yulia Worbe)
- ICM Catherine Lubetzki / Bruno Stankoff’s team (Bruno Stankoff, Benedetta Bodini)
- ICM Brahim Nait Oumesmar / Anne Baron Van Evercooren’s team (Violetta Zujovic)
- Department of Neuroradiology, Pitié-Salpêtrière Hospital
Funding / main grants
- European Union H2020 program, project EuroPOND
- European Union H2020 program, project VirtualBrainCloud
- IHU ICM, Investissements d’avenir
- PRAIRIE 3IA Institute, Investissements d’avenir
- ERC Starting Grant, project LEASP (S. Durrleman)
- NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
- NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
- ANR, project PREVDEMALS
- ICM Big Brain Theory Program (project DYNAMO, project PredictICD, project SEMAPHORE, project Attack)
- Inria Project Lab Program (project Neuromarkers)
- Fondation pour la Recherche sur Alzheimer (project HistoMRI)
- Abeona Foundation (project Brain@Scale)
- Fondation Vaincre Alzheimer
- IDEX Sorbonne Universités, project LearnPETMR