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

Claire Cury 1, 2, 3, * Stanley Durrleman 4 David Cash 1, 2 Marco Lorenzi 1, 5 Jennifer M Nicholas 2, 6 Martina Bocchetta 2 John C. van Swieten 7 Barbara Borroni 8 Daniela Galimberti 9 Mario Masellis 10 Maria Carmela Tartaglia 11 James Rowe 12 Caroline Graff 13, 14 Fabrizio Tagliavini 15 Giovanni B. Frisoni 16 Robert Laforce 17 Elizabeth Finger 18 Alexandre de Mendonça 19 Sandro Sorbi 20 Sébastien Ourselin 1, 2, 21 Jonathan Rohrer 2 Marc Modat 1, 2, 21 Christin Andersson 13 Silvana Archetti 22 Andrea Arighi 23 Luisa Benussi 24 Sandra Black 10 Maura Cosseddu 25 Marie Fallstrm 14 Carlos G. Ferreira 26 Chiara Fenoglio 23 Nick Fox 27 Morris Freedman 28 Giorgio Fumagalli 23 Stefano Gazzina 25 Robert Ghidoni 16 Marina Grisoli 15 Vesna Jelic 13 Lize Jiskoot 7 Ron Keren 29 Gemma Lombardi 20 Carolina Maruta 19 Lieke Meeter 7 Rick van Minkelen 7 Benedetta Nacmias 20 Linn Ijerstedt 13 Alessandro Padovani 25 Jessica Panman 7 Michela Pievani 30 Cristina Polito 20 Enrico Premi 25 Sara Prioni 15 Rosa Rademakers 31 Veronica Redaelli 15 Ekaterina Rogaeva 28 Giacomina Rossi 15 Martin Rossor 27 Elio Scarpini 23 David Tang-Wai 29 Hakan Thonberg 13 Pietro Tiraboschi 15 Ana Verdelho 32 Jason Warren 27 
* Corresponding author
3 Empenn
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
4 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau = Paris Brain Institute
Abstract : 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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Submitted on : Wednesday, January 16, 2019 - 11:07:57 AM
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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, Elsevier, 2019, 188, pp.282-290. ⟨10.1016/j.neuroimage.2018.11.063⟩. ⟨inserm-01958916⟩



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