Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Claire Cury, Stanley Durrleman, David M. Cash, Marco Lorenzi, Jennifer M. Nicholas, Martina Bocchetta, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Carmela Tartaglia, James B. Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B. Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Sebastien OurselinJonathan 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, Hakan Thonberg, Pietro Tiraboschi, Ana Verdelho, Jason Warren

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

Original languageEnglish (US)
Pages (from-to)282-290
Number of pages9
StatePublished - Mar 1 2019


  • Clustering
  • Computational anatomy
  • Parallel transport
  • Shape analysis
  • Spatiotemporal geodesic regression
  • Thalamus

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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