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Learning spatiotemporal patterns in longitudinal image data sets of the aging brain

Total Cost €


EC-Contrib. €






 LEASP project word cloud

Explore the words cloud of the LEASP project. It provides you a very rough idea of what is the project "LEASP" about.

alterations    principles    iconic    tools    generation    elementary    aging    time    learning    clusters    statistical    clinical    bayesian    opportunity    estimate    yield    otherwise    framework    riemannian    introduce    series    progressive    computational    geometric    think    brain    sharing    align    models    paving    temporal    lesion    normal    anatomy    detected    adds    collection    scenario    exhibit    longitudinal    representation    treatments    construct    followed    function    paradigm    situation    medical    difficulty    spatiotemporal    propagation    statisticians    standard    lack    individual    profiles    trajectories    departure    pace    forms    virtual    highest    investigation    trajectory    methodological    dynamical    extraordinary    individuals    follows    chance    combine    personal    data    limiting    categories    anatomical    functional    multimodal    disease    unveil    care    suitable    patients    effect    patterns    images    mixed    geometry    dynamics    track    pathologic    bio    questions   

Project "LEASP" data sheet

The following table provides information about the project.


Organization address
postcode: 78153

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Project website
 Total cost 1˙499˙894 €
 EC max contribution 1˙499˙894 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-STG
 Funding Scheme ERC-STG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Time-series of multimodal medical images offer a unique opportunity to track anatomical and functional alterations of the brain in aging individuals. A collection of such time series for several individuals forms a longitudinal data set, each data being a rich iconic-geometric representation of the brain anatomy and function. These data are already extraordinary complex and variable across individuals. Taking the temporal component into account further adds difficulty, in that each individual follows a different trajectory of changes, and at a different pace. Furthermore, a disease is here a progressive departure from an otherwise normal scenario of aging, so that one could not think of normal and pathologic brain aging as distinct categories, as in the standard case-control paradigm.

Bio-statisticians lack a suitable methodological framework to exhibit from these data the typical trajectories and dynamics of brain alterations, and the effects of a disease on these trajectories, thus limiting the investigation of essential clinical questions. To change this situation, we propose to construct virtual dynamical models of brain aging by learning typical spatiotemporal patterns of alterations propagation from longitudinal iconic-geometric data sets.

By including concepts of the Riemannian geometry into Bayesian mixed effect models, the project will introduce general principles to average complex individual trajectories of iconic-geometric changes and align the pace at which these trajectories are followed. It will estimate a set of elementary spatiotemporal patterns, which combine to yield a personal aging scenario for each individual. Disease-specific patterns will be detected with an increasing likelihood.

This new generation of statistical and computational tools will unveil clusters of patients sharing similar lesion propagation profiles, paving the way to design more specific treatments, and care patients when treatments have the highest chance of success.


year authors and title journal last update
List of publications.
2019 Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois, Harald Hampel, Stanley Durrleman
Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis
published pages: 96228021882303, ISSN: 0962-2802, DOI: 10.1177/0962280218823036
Statistical Methods in Medical Research 2019-10-29
2017 Schiratti, Jean-Baptiste; Allassonniere, Stéphanie; Colliot, Olivier; Durrleman, Stanley
A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations
published pages: 1-33, ISSN: 1532-4435, DOI:
Journal of Machine Learning Research 18 2019-07-08
2017 James Fishbaugh, Stanley Durrleman, Marcel Prastawa, Guido Gerig
Geodesic shape regression with multiple geometries and sparse parameters
published pages: 1-17, ISSN: 1361-8415, DOI: 10.1016/
Medical Image Analysis 39 2019-07-08
2018 Harald Hampel, Nicola Toschi, Claudio Babiloni, Filippo Baldacci, Keith L. Black, Arun L.W. Bokde, René S. Bun, Francesco Cacciola, Enrica Cavedo, Patrizia A. Chiesa, Olivier Colliot, Cristina-Maria Coman, Bruno Dubois, Andrea Duggento, Stanley Durrleman, Maria-Teresa Ferretti, Nathalie George, Remy Genthon, Marie-Odile Habert, Karl Herholz, Yosef Koronyo, Maya Koronyo-Hamaoui, Foudil Lamari, Tod
Revolution of Alzheimer Precision Neurology Passageway of Systems Biology and Neurophysiology
published pages: 1-59, ISSN: 1387-2877, DOI: 10.3233/JAD-179932
Journal of Alzheimer\'s Disease 2019-07-08
2017 H. Hampel, S. E. O’Bryant, S. Durrleman, E. Younesi, K. Rojkova, V. Escott-Price, J-C. Corvol, K. Broich, B. Dubois, S. Lista
A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling
published pages: 107-118, ISSN: 1369-7137, DOI: 10.1080/13697137.2017.1287866
Climacteric 20/2 2019-07-08
2017 I. Koval, J.-B. Schiratti, A. Routier, M. Bacci, O. Colliot, S. Allassonnière, S. Durrleman
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
published pages: 451-459, ISSN: , DOI: 10.1007/978-3-319-66182-7_52
International Conference on Medical Image Computing and Computer-Assisted Intervention 2019-07-08
2017 Alexandre Bône, Maxime Louis, Alexandre Routier, Jorge Samper, Michael Bacci, Benjamin Charlier, Olivier Colliot, Stanley Durrleman
Prediction of the progression of subcortical brain structures in Alzheimer’s disease from baseline
published pages: 101-113, ISSN: , DOI: 10.1007/978-3-319-67675-3_10
International Workshop on Mathematical Foundations of Computational Anatomy 2019-07-08
2019 Junhao Wen, Hui Zhang, Daniel C Alexander, Stanley Durrleman, Alexandre Routier, Daisy Rinaldi, Marion Houot, Philippe Couratier, Didier Hannequin, Florence Pasquier, Jiaying Zhang, Olivier Colliot, Isabelle Le Ber, Anne Bertrand
Neurite density is reduced in the presymptomatic phase of C9orf72 disease
published pages: 387-394, ISSN: 0022-3050, DOI: 10.1136/jnnp-2018-318994
Journal of Neurology, Neurosurgery & Psychiatry 90/4 2019-05-22
2018 Maxime Louis, Benjamin Charlier, Paul Jusselin, Susovan Pal, Stanley Durrleman
A Fanning Scheme for the Parallel Transport along Geodesics on Riemannian Manifolds
published pages: 2563-2584, ISSN: 0036-1429, DOI: 10.1137/17m1130617
SIAM Journal on Numerical Analysis 56/4 2019-05-22
2018 Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, Stanley Durrleman
Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns
published pages: , ISSN: 1664-2295, DOI: 10.3389/fneur.2018.00235
Frontiers in Neurology 9 2019-05-22
2018 Jorge Samper-González, Ninon Burgos, Simona Bottani, Sabrina Fontanella, Pascal Lu, Arnaud Marcoux, Alexandre Routier, Jérémy Guillon, Michael Bacci, Junhao Wen, Anne Bertrand, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot
Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data
published pages: 504-521, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2018.08.042
NeuroImage 183 2019-05-22
2019 Claire Cury, Stanley Durrleman, David M. Cash, Marco Lorenzi, Jennifer M. Nicholas, Martina Bocchetta, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, James B. Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B. Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Sebastien Ourselin, Jonathan D. Rohrer, Marc Modat, Christin Andersson, Silvana Archetti, Andrea Arighi, Luisa Benussi, Sandra Black, Maura Cosseddu, Marie Fallstrm, Carlos Ferreira, Chiara Fenoglio, Nick Fox, Morris Freedman, Giorgio Fumagalli, Stefano Gazzina, Roberta Ghidoni, Marina Grisoli, Vesna Jelic, Lize Jiskoot, Ron Keren, Gemma Lombardi, Carolina Maruta, Lieke Meeter, Rick van Minkelen, Benedetta Nacmias, Linn ijerstedt, Alessandro Padovani, Jessica Panman, Michela Pievani, Cristina Polito, Enrico Premi, Sara Prioni, Rosa Rademakers, Veronica Redaelli, Ekaterina Rogaeva, Giacomina Rossi, Martin Rossor, Elio Scarpini, David Tang-Wai, Carmela Tartaglia, Hakan Thonberg, Pietro Tiraboschi, Ana Verdelho, Jason Warren
Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort
published pages: 282-290, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2018.11.063
NeuroImage 188 2019-05-22
2018 Arnaud Marcoux, Ninon Burgos, Anne Bertrand, Marc Teichmann, Alexandre Routier, Junhao Wen, Jorge Samper-González, Simona Bottani, Stanley Durrleman, Marie-Odile Habert, Olivier Colliot
An Automated Pipeline for the Analysis of PET Data on the Cortical Surface
published pages: , ISSN: 1662-5196, DOI: 10.3389/fninf.2018.00094
Frontiers in Neuroinformatics 12 2019-05-22
2018 Bône , Alexandre; Colliot , Olivier; Durrleman , Stanley
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
published pages: , ISSN: , DOI:
CVPR 2018 - Computer Vision and Pattern Recognition 2018, Jun 2018, Salt Lake City, United States 2 2019-05-22

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