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, Carmela Tartaglia, Hakan Thonberg, Pietro Tiraboschi, Ana Verdelho, Jason Warren, Genetic FTD Initiative, GENFI1

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)282-290
Number of pages9
JournalNeuroImage
Volume188
Early online dateDec 1 2018
DOIs
Publication statusPublished - Mar 2019

Fingerprint

Spatio-Temporal Analysis
Neurodegenerative Diseases
Atrophy
Thalamus
Frontotemporal Dementia
Brain
Mutation
Frontal Lobe
Disease Progression
Healthy Volunteers
Alzheimer Disease
Biomarkers
Magnetic Resonance Imaging
Population

Keywords

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

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases : Initial application to the GENFI cohort. / Cury, Claire; Durrleman, Stanley; Cash, David M.; Lorenzi, Marco; Nicholas, Jennifer M.; Bocchetta, Martina; van Swieten, John C.; Borroni, Barbara; Galimberti, Daniela; Masellis, Mario; Tartaglia, Carmela; Rowe, James B.; Graff, Caroline; Tagliavini, Fabrizio; Frisoni, Giovanni B.; Laforce, Robert; Finger, Elizabeth; de Mendonça, Alexandre; Sorbi, Sandro; Ourselin, Sebastien; Rohrer, Jonathan D.; Modat, Marc; Andersson, Christin; Archetti, Silvana; Arighi, Andrea; Benussi, Luisa; Black, Sandra; Cosseddu, Maura; Fallstrm, Marie; Ferreira, Carlos; Fenoglio, Chiara; Fox, Nick; Freedman, Morris; Fumagalli, Giorgio; Gazzina, Stefano; Ghidoni, Roberta; Grisoli, Marina; Jelic, Vesna; Jiskoot, Lize; Keren, Ron; Lombardi, Gemma; Maruta, Carolina; Meeter, Lieke; van Minkelen, Rick; Nacmias, Benedetta; ijerstedt, Linn; Padovani, Alessandro; Panman, Jessica; Pievani, Michela; Polito, Cristina; Premi, Enrico; Prioni, Sara; Rademakers, Rosa; Redaelli, Veronica; Rogaeva, Ekaterina; Rossi, Giacomina; Rossor, Martin; Scarpini, Elio; Tang-Wai, David; Tartaglia, Carmela; Thonberg, Hakan; Tiraboschi, Pietro; Verdelho, Ana; Warren, Jason; GENFI1, Genetic FTD Initiative,.

In: NeuroImage, Vol. 188, 03.2019, p. 282-290.

Research output: Contribution to journalArticle

Cury, C, Durrleman, S, Cash, DM, Lorenzi, M, Nicholas, JM, Bocchetta, M, van Swieten, JC, Borroni, B, Galimberti, D, Masellis, M, Tartaglia, C, Rowe, JB, Graff, C, Tagliavini, F, Frisoni, GB, Laforce, R, Finger, E, de Mendonça, A, Sorbi, S, Ourselin, S, Rohrer, JD, Modat, M, Andersson, C, Archetti, S, Arighi, A, Benussi, L, Black, S, Cosseddu, M, Fallstrm, M, Ferreira, C, Fenoglio, C, Fox, N, Freedman, M, Fumagalli, G, Gazzina, S, Ghidoni, R, Grisoli, M, Jelic, V, Jiskoot, L, Keren, R, Lombardi, G, Maruta, C, Meeter, L, van Minkelen, R, Nacmias, B, ijerstedt, L, Padovani, A, Panman, J, Pievani, M, Polito, C, Premi, E, Prioni, S, Rademakers, R, Redaelli, V, Rogaeva, E, Rossi, G, Rossor, M, Scarpini, E, Tang-Wai, D, Tartaglia, C, Thonberg, H, Tiraboschi, P, Verdelho, A, Warren, J & GENFI1, GFTDI 2019, 'Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort', NeuroImage, vol. 188, pp. 282-290. https://doi.org/10.1016/j.neuroimage.2018.11.063
Cury, Claire ; Durrleman, Stanley ; Cash, David M. ; Lorenzi, Marco ; Nicholas, Jennifer M. ; Bocchetta, Martina ; van Swieten, John C. ; Borroni, Barbara ; Galimberti, Daniela ; Masellis, Mario ; Tartaglia, Carmela ; Rowe, James B. ; Graff, Caroline ; Tagliavini, Fabrizio ; Frisoni, Giovanni B. ; Laforce, Robert ; Finger, Elizabeth ; de Mendonça, Alexandre ; Sorbi, Sandro ; Ourselin, Sebastien ; Rohrer, Jonathan D. ; Modat, Marc ; Andersson, Christin ; Archetti, Silvana ; Arighi, Andrea ; Benussi, Luisa ; Black, Sandra ; Cosseddu, Maura ; Fallstrm, Marie ; Ferreira, Carlos ; Fenoglio, Chiara ; Fox, Nick ; Freedman, Morris ; Fumagalli, Giorgio ; Gazzina, Stefano ; Ghidoni, Roberta ; Grisoli, Marina ; Jelic, Vesna ; Jiskoot, Lize ; Keren, Ron ; Lombardi, Gemma ; Maruta, Carolina ; Meeter, Lieke ; van Minkelen, Rick ; Nacmias, Benedetta ; ijerstedt, Linn ; Padovani, Alessandro ; Panman, Jessica ; Pievani, Michela ; Polito, Cristina ; Premi, Enrico ; Prioni, Sara ; Rademakers, Rosa ; Redaelli, Veronica ; Rogaeva, Ekaterina ; Rossi, Giacomina ; Rossor, Martin ; Scarpini, Elio ; Tang-Wai, David ; Tartaglia, Carmela ; Thonberg, Hakan ; Tiraboschi, Pietro ; Verdelho, Ana ; Warren, Jason ; GENFI1, Genetic FTD Initiative,. / Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases : Initial application to the GENFI cohort. In: NeuroImage. 2019 ; Vol. 188. pp. 282-290.
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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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T2 - Initial application to the GENFI cohort

AU - Cury, Claire

AU - Durrleman, Stanley

AU - Cash, David M.

AU - Lorenzi, Marco

AU - Nicholas, Jennifer M.

AU - Bocchetta, Martina

AU - van Swieten, John C.

AU - Borroni, Barbara

AU - Galimberti, Daniela

AU - Masellis, Mario

AU - Tartaglia, Carmela

AU - Rowe, James B.

AU - Graff, Caroline

AU - Tagliavini, Fabrizio

AU - Frisoni, Giovanni B.

AU - Laforce, Robert

AU - Finger, Elizabeth

AU - de Mendonça, Alexandre

AU - Sorbi, Sandro

AU - Ourselin, Sebastien

AU - Rohrer, Jonathan D.

AU - Modat, Marc

AU - Andersson, Christin

AU - Archetti, Silvana

AU - Arighi, Andrea

AU - Benussi, Luisa

AU - Black, Sandra

AU - Cosseddu, Maura

AU - Fallstrm, Marie

AU - Ferreira, Carlos

AU - Fenoglio, Chiara

AU - Fox, Nick

AU - Freedman, Morris

AU - Fumagalli, Giorgio

AU - Gazzina, Stefano

AU - Ghidoni, Roberta

AU - Grisoli, Marina

AU - Jelic, Vesna

AU - Jiskoot, Lize

AU - Keren, Ron

AU - Lombardi, Gemma

AU - Maruta, Carolina

AU - Meeter, Lieke

AU - van Minkelen, Rick

AU - Nacmias, Benedetta

AU - ijerstedt, Linn

AU - Padovani, Alessandro

AU - Panman, Jessica

AU - Pievani, Michela

AU - Polito, Cristina

AU - Premi, Enrico

AU - Prioni, Sara

AU - Rademakers, Rosa

AU - Redaelli, Veronica

AU - Rogaeva, Ekaterina

AU - Rossi, Giacomina

AU - Rossor, Martin

AU - Scarpini, Elio

AU - Tang-Wai, David

AU - Tartaglia, Carmela

AU - Thonberg, Hakan

AU - Tiraboschi, Pietro

AU - Verdelho, Ana

AU - Warren, Jason

AU - GENFI1, Genetic FTD Initiative,

PY - 2019/3

Y1 - 2019/3

N2 - 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.

AB - 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.

KW - Clustering

KW - Computational anatomy

KW - Parallel transport

KW - Shape analysis

KW - Spatiotemporal geodesic regression

KW - Thalamus

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