Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease

  • Claire Cury
  • Marco Lorenzi
  • David Cash
  • Jennifer M. Nicholas
  • Alexandre Routier
  • Jonathan Rohrer
  • Sebastien Ourselin
  • Stanley Durrleman
  • Marc Modat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)


The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug efficacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant differences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neurodegeneration and represents a valuable instrument to model presymptomatic anatomical changes occurring in specific brain regions.

In this study we propose a computational framework to model group-wise spatio-temporal shape differences, and to statistically evaluate the effects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specific “residual” transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape differences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.


Shape Thalamus Spatio-temporal geodesic regression FTD Parallel transport 



Claire Cury is supported by the EU-FP7 project VPH-DARE@IT (FP7-ICT-2011-9-601055). Marco Lorenzi received funding from the EPSRC (EP/J020990/1). Jennifer Nicholas is supported by UK Medical Research Council (grant MR/M023664/1). David Cash is supported by grants from the Alzheimer Society (AS-PG-15-025), Alzheimers Research UK (ARUK-PG2014-1946) and Medical Research Council UK (MR/M023664/1). Stanley Durrleman has received funding from the program Investissements d’avenir ANR-10-IAIHU-06 and the European Unions Horizon 2020 research and innovation programme EuroPOND under grant agreement No. 666992. Jonathan D. Rohrer is an MRC Clinician Scientist and has received funding from the NIHR Rare Diseases Translational Research Collaboration. Sebastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/K005278), the MRC (MR/J01107X/1), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative- Marc Modat is supported by the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575) and Alzheimers Society UK (AS-PG-15-025). We would like to thank the participants and their families for taking part in the GENFI study.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Claire Cury
    • 1
    • 2
  • Marco Lorenzi
    • 1
  • David Cash
    • 1
    • 2
  • Jennifer M. Nicholas
    • 2
    • 3
  • Alexandre Routier
    • 4
  • Jonathan Rohrer
    • 2
  • Sebastien Ourselin
    • 1
    • 2
  • Stanley Durrleman
    • 4
  • Marc Modat
    • 1
    • 2
  1. 1.Translational Imaging Group, Centre for Medical Image Computing, Medical Physics and Biomedical Engineering DepartmentUniversity College LondonLondonUK
  2. 2.Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
  3. 3.Department of Medical Statistics, London School of Hygiene & Tropical MedicineLondonUK
  4. 4.Inria Aramis project-team Centre Paris-Rocquencourt, Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICMParisFrance

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