Differences in topological progression profile among neurodegenerative diseases from imaging data

  1. Sara Garbarino  Is a corresponding author
  2. Marco Lorenzi
  3. Neil P Oxtoby
  4. Elisabeth J Vinke
  5. Razvan V Marinescu
  6. Arman Eshaghi
  7. M Arfan Ikram
  8. Wiro J Niessen
  9. Olga Ciccarelli
  10. Frederik Barkhof
  11. Jonathan M Schott
  12. Meike W Vernooij
  13. Daniel C Alexander
  1. Inria Centre de Recherche Sophia Antipolis Méditerranée, France
  2. University College London, United Kingdom
  3. Erasmus Medical Center, Netherlands

Abstract

The spatial distribution of atrophy in neurodegenerative diseases suggests that brain connectivity mediates disease propagation. Different descriptors of the connectivity graph potentially relate to different underlying mechanisms of propagation. Previous approaches for evaluating the influence of connectivity on neurodegeneration consider each descriptor in isolation and match predictions against late-stage atrophy patterns. We introduce the notion of a topological profile — a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. Experimental results comparing topological profiles for Alzheimer's disease, multiple sclerosis and normal ageing show that topological profiles explain the observed data better than single descriptors. Within each condition, most individual profiles cluster around the cohort-level profile, and individuals whose profiles align more closely with other cohort-level profiles show features of that cohort. The cohort-level profiles suggest new insights into the biological mechanisms underlying pathology propagation in each disease.

Data availability

AD data set from ADNI. ADNI is a public-private partnership. All ADNI data are shared without embargo through the LONI Image and Data Archive (https://ida.loni.usc.edu/login.jsp) a secure research data repository. Interested scientists may obtain access to ADNI imaging, clinical, genomic, and biomarker data for the purposes of scientific investigation, teaching, or planning clinical research studies. Access is contingent on adherence to the ADNI Data Use Agreement. For up-to-date information please see http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_DSP_Policy.pdf.PPMS data set from UCLH. Data can be obtained upon request, directed the management team of the data at the Institute of Neurology, UCL: uclh.qsmsc@nhs.net.HA data set from the Rotterdam Study. Data can be obtained upon request. Requests should be directed towards the management team of the Rotterdam Study (secretariat.epi@erasmusmc.nl), which has a protocol for approving data requests. Because of restrictions based on privacy regulations and informed consent of the participants, data cannot be made freely available in a public repository. The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalogue number NTR6831. All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians.HCP data are from the Human Connectome Project. Open Access Data (all imaging data and most of the behavioral data) is available to those who register an account at ConnectomeDB and agree to the Open Access Data Use Terms. This includes agreement to comply with institutional rules and regulations. For up-to-date information please see https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms.AD, PPMS and HA processed, anonymized and unrecognizable data, useful to process the mechanistic weights can be obtained upon request to the corresponding author: sara.garbarino@inria.fr.

The following previously published data sets were used
    1. Alzheimer's Disease Neuroimaging Initiative
    (2003) ADNI
    Alzheimer's Disease Neuroimaging Initiative.

Article and author information

Author details

  1. Sara Garbarino

    Epione team-project, Inria Centre de Recherche Sophia Antipolis Méditerranée, Sophia Antipolis, France
    For correspondence
    sara.garbarino@inria.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3583-3630
  2. Marco Lorenzi

    Epione team-project, Inria Centre de Recherche Sophia Antipolis Méditerranée, Sophia Antipolis, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Neil P Oxtoby

    Centre for Medical Image Computing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0203-3909
  4. Elisabeth J Vinke

    Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  5. Razvan V Marinescu

    Centre for Medical Image Computing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Arman Eshaghi

    Institute of Neurology, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. M Arfan Ikram

    Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  8. Wiro J Niessen

    Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  9. Olga Ciccarelli

    Institute of Neurology, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  10. Frederik Barkhof

    Centre for Medical Image Computing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3543-3706
  11. Jonathan M Schott

    Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Meike W Vernooij

    Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  13. Daniel C Alexander

    Centre for Medical Image Computing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Funding

Horizon 2020 Framework Programme (666992)

  • Sara Garbarino
  • Marco Lorenzi
  • Neil P Oxtoby
  • Elisabeth J Vinke
  • Olga Ciccarelli
  • Frederik Barkhof
  • Jonathan M Schott
  • Meike W Vernooij
  • Daniel C Alexander

UCA Jedi (ANX 15 IDEX 01)

  • Sara Garbarino

Michael J Fox Foundation (BAND 15 368107 11042)

  • Neil P Oxtoby

Engineering and Physical Sciences Research Council (EP/M020533/1)

  • Neil P Oxtoby
  • Daniel C Alexander

Engineering and Physical Sciences Research Council (EP/J020990/01)

  • Neil P Oxtoby

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Floris P de Lange, Radboud University, Netherlands

Version history

  1. Received: June 13, 2019
  2. Accepted: December 2, 2019
  3. Accepted Manuscript published: December 3, 2019 (version 1)
  4. Accepted Manuscript updated: December 13, 2019 (version 2)
  5. Version of Record published: December 19, 2019 (version 3)

Copyright

© 2019, Garbarino et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Sara Garbarino
  2. Marco Lorenzi
  3. Neil P Oxtoby
  4. Elisabeth J Vinke
  5. Razvan V Marinescu
  6. Arman Eshaghi
  7. M Arfan Ikram
  8. Wiro J Niessen
  9. Olga Ciccarelli
  10. Frederik Barkhof
  11. Jonathan M Schott
  12. Meike W Vernooij
  13. Daniel C Alexander
(2019)
Differences in topological progression profile among neurodegenerative diseases from imaging data
eLife 8:e49298.
https://doi.org/10.7554/eLife.49298

Share this article

https://doi.org/10.7554/eLife.49298

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