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October 2021; 7 (5) ArticleOpen Access

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

View ORCID ProfilePeter A. Wijeratne, View ORCID ProfileSara Garbarino, View ORCID ProfileSarah Gregory, View ORCID ProfileEileanoir B. Johnson, View ORCID ProfileRachael I. Scahill, View ORCID ProfileJane S. Paulsen, View ORCID ProfileSarah J. Tabrizi, View ORCID ProfileMarco Lorenzi, View ORCID ProfileDaniel C. Alexander; on behalf of the PREDICT-HD investigators and the TRACK-HD investigators.
First published October 12, 2021, DOI: https://doi.org/10.1212/NXG.0000000000000617
Peter A. Wijeratne
From the 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.
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Sara Garbarino
From the 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.
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  • For correspondence: garbarin@dima.unige.it
Sarah Gregory
From the 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.
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  • For correspondence: s.gregory@ucl.ac.uk
Eileanoir B. Johnson
From the 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.
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  • For correspondence: eileanoir.johnson@alumni.ucl.ac.uk
Rachael I. Scahill
From the 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.
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  • For correspondence: r.scahill@ucl.ac.uk
Jane S. Paulsen
From the 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.
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  • For correspondence: jane-paulsen@uiowa.edu
Sarah J. Tabrizi
From the 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.
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  • For correspondence: s.tabrizi@ucl.ac.uk
Marco Lorenzi
From the 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.
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  • For correspondence: marco.lorenzi@inria.fr
Daniel C. Alexander
From the 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.
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  • For correspondence: d.alexander@ucl.ac.uk
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Citation
Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease
Peter A. Wijeratne, Sara Garbarino, Sarah Gregory, Eileanoir B. Johnson, Rachael I. Scahill, Jane S. Paulsen, Sarah J. Tabrizi, Marco Lorenzi, Daniel C. Alexander
Neurol Genet Oct 2021, 7 (5) e617; DOI: 10.1212/NXG.0000000000000617

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Abstract

Background and Objectives 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 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 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 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.

Glossary

AD=
Alzheimer disease;
CAG=
cytosine-adenine-guanine;
DCM=
dynamic causal model;
EBM=
event-based model;
GP=
Gaussian process;
GPPM=
GP progression model;
HC=
healthy control;
HD=
Huntington disease;
PreHD=
premanifest HD;
SDMT=
Symbol Digit Modalities Test;
SM=
survival model;
sMRI=
structural MRI;
SWRT=
Stroop Word Reading Test;
TMS=
Total Motor Score

Footnotes

  • ↵* These authors are co–senior authors.

  • Go to Neurology.org/NG for full disclosures. Funding information is provided at the end of the article.

  • The Article Processing Charge was funded by the MRC and Wellcome Trust.

  • PREDICT-HD and TRACK-HD coinvestigators are listed in Appendix 2 at links.lww.com/NXG/A473.

  • Received April 7, 2021.
  • Accepted in final form July 6, 2021.
  • Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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