Learning Spatiotemporal Patterns of the Aging Brain


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 digital 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.

LEASP is a research group funded by the European Research Council (ERC).


Professors and Researchers

  • Stanley Durrleman
  • Benjamin Charlier (funded by CNRS while working on the project)
  • Stephanie Allassonniere (funded by Paris Descartes university)
  • Bruno Jedynak (visiting professor from Portland state university)


  • Ninon Burgos

PhD students

  • Raphael Couronné
  • Maxime Louis (fellowship from Ecole Polytechnique)
  • Vianney Debavelaere (fellowship from Ecole Noramle Superieure)
  • Paul Vernhet

Software Engineers

  • Benoit Martin
  • Arnaud Valladier


Most of my publications since 2016 are related to the project. See my publication list.