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)
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
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 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
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 University of Brescia
26 GeM - Institut de Recherche en Génie Civil et Mécanique
27 UCL, Institute of Neurology [London]
28 University of Toronto
29 Toronto Western Hospital
30 LENITEM - Neuroimaging and Telemedicine
31 Mayo Clinic [Jacksonville]
32 ULISBOA - Universidade de Lisboa = University of Lisbon
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
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 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
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 University of Brescia
26 GeM - Institut de Recherche en Génie Civil et Mécanique
27 UCL, Institute of Neurology [London]
28 University of Toronto
29 Toronto Western Hospital
30 LENITEM - Neuroimaging and Telemedicine
31 Mayo Clinic [Jacksonville]
32 ULISBOA - Universidade de Lisboa = University of Lisbon
Claire Cury
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- Function : Correspondent author
- PersonId : 6774
- IdHAL : claire-cury
- ORCID : 0000-0002-9903-7940
- IdRef : 184327512
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Stanley Durrleman
- Function : Author
- PersonId : 739344
- IdHAL : stanley-durrleman
- ORCID : 0000-0002-9450-6920
- IdRef : 150299540
Marco Lorenzi
- Function : Author
- PersonId : 178572
- IdHAL : marco-lorenzi
- ORCID : 0000-0003-0521-2881
- IdRef : 168153335
Caroline Graff
- Function : Author
- PersonId : 889097
Fabrizio Tagliavini
- Function : Author
- PersonId : 957675
Marc Modat
- Function : Author
- PersonId : 764549
- ORCID : 0000-0002-5277-8530
Michela Pievani
- Function : Author
- PersonId : 768263
- ORCID : 0000-0002-1794-8987
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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