Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease.

Journal

Neurology. Genetics
ISSN: 2376-7839
Titre abrégé: Neurol Genet
Pays: United States
ID NLM: 101671068

Informations de publication

Date de publication:
Oct 2021
Historique:
received: 07 04 2021
accepted: 06 07 2021
entrez: 18 10 2021
pubmed: 19 10 2021
medline: 19 10 2021
Statut: epublish

Résumé

Longitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD). We use data from the 2 largest cohort imaging studies in HD-284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)-to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control. Our model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years). Our findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

Sections du résumé

BACKGROUND AND OBJECTIVES OBJECTIVE
Longitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD).
METHODS METHODS
We use data from the 2 largest cohort imaging studies in HD-284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)-to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control.
RESULTS RESULTS
Our model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years).
DISCUSSION CONCLUSIONS
Our findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

Identifiants

DOI: 10.1212/NXG.0000000000000617 PMID: 34660889 PMC: PMC8515202
pubmed: 34660889
doi: 10.1212/NXG.0000000000000617
pii: NG2021017035
pmc: PMC8515202
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e617

Subventions

Organisme : Medical Research Council
ID : MR/T027770/1
Pays : United Kingdom
Organisme : NINDS NIH HHS
ID : U01 NS103475
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS105509
Pays : United States

Informations de copyright

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

Références

Neuroimage. 2014 Aug 15;97:333-48
pubmed: 24742919
Clin Genet. 2004 Apr;65(4):267-77
pubmed: 15025718
Neuroimage. 2019 Apr 15;190:56-68
pubmed: 29079521
Neurology. 2004 Jul 13;63(1):66-72
pubmed: 15249612
Lancet Neurol. 2010 Jan;9(1):119-28
pubmed: 20083042
Am J Med Genet B Neuropsychiatr Genet. 2010 Mar 5;153B(2):397-408
pubmed: 19548255
Neurology. 2001 Sep 25;57(6):1025-8
pubmed: 11571328
Cereb Cortex. 2005 Jun;15(6):846-53
pubmed: 15459079
Nature. 2000 Feb 3;403(6769):544-9
pubmed: 10676962
JAMIA Open. 2019 Jan 07;2(1):123-130
pubmed: 31984350
Lancet Neurol. 2020 Jun;19(6):502-512
pubmed: 32470422
Front Neurosci. 2013 Feb 05;6:197
pubmed: 23386806
Lancet Neurol. 2013 Jul;12(7):637-49
pubmed: 23664844
Lancet Neurol. 2013 Apr;12(4):357-67
pubmed: 23477989
Ann Neurol. 2020 May;87(5):751-762
pubmed: 32105364
Brain. 2018 Sep 1;141(9):2545-2560
pubmed: 30137209
Ageing Res Rev. 2016 Sep;30:25-48
pubmed: 26827786
Lancet Neurol. 2009 Sep;8(9):791-801
pubmed: 19646924
EBioMedicine. 2015 Aug 04;2(10):1420-9
pubmed: 26629536
Curr Opin Neurol. 2017 Aug;30(4):371-379
pubmed: 28520598
Neuroimage. 2002 Oct;17(2):825-41
pubmed: 12377157
Neuroimage. 2012 Nov 15;63(3):1478-86
pubmed: 22885136
Hum Brain Mapp. 2013 Jul;34(7):1625-35
pubmed: 22359398
J Neurol Neurosurg Psychiatry. 2008 Aug;79(8):874-80
pubmed: 18096682
Handb Clin Neurol. 2017;144:247-261
pubmed: 28947121
Ann Clin Transl Neurol. 2018 Apr 02;5(5):570-582
pubmed: 29761120
Biol Psychiatry. 2021 Apr 15;89(8):807-816
pubmed: 33500176
Brain. 2018 May 1;141(5):1529-1544
pubmed: 29579160
IEEE Trans Med Imaging. 2015 Sep;34(9):1976-88
pubmed: 25879909
Nat Rev Dis Primers. 2015 Apr 23;1:15005
pubmed: 27188817
JAMA Neurol. 2019 Nov 1;76(11):1375-1385
pubmed: 31403680
Alzheimers Dement (N Y). 2019 Jul 18;5:308-318
pubmed: 31367671

Auteurs

Peter A Wijeratne (PA)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0002-4885-6241

Sara Garbarino (S)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0002-3583-3630

Sarah Gregory (S)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0001-9864-8240

Eileanoir B Johnson (EB)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0002-5271-0747

Rachael I Scahill (RI)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0002-1832-106X

Jane S Paulsen (JS)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0002-6536-9138

Sarah J Tabrizi (SJ)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0003-2716-2045

Marco Lorenzi (M)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0003-0521-2881

Daniel C Alexander (DC)

Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d'Azur (M.L.), Inria, Epione Research Project, Valbonne, France.
ORCID: 0000-0003-2439-350X

Classifications MeSH