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Article Dans Une Revue NeuroImage Année : 2019

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

1 UCL - Department of Medical Physics and Biomedical Engineering
2 DRC - Dementia Research Centre [London]
3 Empenn - Neuroimagerie: méthodes et applications
4 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
5 EPIONE - E-Patient : Images, données & mOdèles pour la médeciNe numériquE
6 LSHTM - London School of Hygiene and Tropical Medicine
7 Erasmus MC - Erasmus University Medical Center [Rotterdam]
8 UniBs - Università degli Studi di Brescia = University of Brescia
9 Centro Dino Ferrari [Milano]
10 SRI - Sunnybrook Research Institute [Toronto]
11 Tanz Center Research in Neurodegenerative Diseases [Toronto]
12 CAM - University of Cambridge [UK]
13 Karolinska Institutet [Stockholm]
14 Karolinska University Hospital [Stockholm]
15 Fondazione IRCCS Istituto Neurologico "Carlo Besta"
16 IRCCS - Centro San Giovanni di Dio, Fatebenefratelli, Brescia
17 ULaval - Université Laval [Québec]
18 UWO - University of Western Ontario
19 Faculdade de Medicina [Lisboa]
20 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
21 Imaging Sciences and Biomedical Engineering Division [London]
22 Civic Hospital of Brescia
23 Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico
24 IRCCS Fatebenefratelli - Brescia
25 GeM - Institut de Recherche en Génie Civil et Mécanique
26 UCL Queen Square Institute of Neurology
27 University of Toronto
28 Toronto Western Hospital
29 LENITEM - Neuroimaging and Telemedicine
30 Mayo Clinic [Jacksonville]
31 ULISBOA - Universidade de Lisboa = University of Lisbon
James Rowe
  • Fonction : Auteur
  • PersonId : 887931
Sandra Black
  • Fonction : Auteur
  • PersonId : 901341
Michela Pievani
Ekaterina Rogaeva
  • Fonction : Auteur
  • PersonId : 921847

Résumé

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.
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Dates et versions

inserm-01958916 , version 1 (16-01-2019)

Identifiants

Citer

Claire Cury, Stanley Durrleman, David Cash, Marco Lorenzi, Jennifer M Nicholas, et al.. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort. NeuroImage, 2019, 188, pp.282-290. ⟨10.1016/j.neuroimage.2018.11.063⟩. ⟨inserm-01958916⟩
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