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.

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.

Metrics

  • 2,109
    views
  • 320
    downloads
  • 13
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    A Sofia F Oliveira, Fiona L Kearns ... Adrian J Mulholland
    Short Report

    The spike protein is essential to the SARS-CoV-2 virus life cycle, facilitating virus entry and mediating viral-host membrane fusion. The spike contains a fatty acid (FA) binding site between every two neighbouring receptor-binding domains. This site is coupled to key regions in the protein, but the impact of glycans on these allosteric effects has not been investigated. Using dynamical nonequilibrium molecular dynamics (D-NEMD) simulations, we explore the allosteric effects of the FA site in the fully glycosylated spike of the SARS-CoV-2 ancestral variant. Our results identify the allosteric networks connecting the FA site to functionally important regions in the protein, including the receptor-binding motif, an antigenic supersite in the N-terminal domain, the fusion peptide region, and another allosteric site known to bind heme and biliverdin. The networks identified here highlight the complexity of the allosteric modulation in this protein and reveal a striking and unexpected link between different allosteric sites. Comparison of the FA site connections from D-NEMD in the glycosylated and non-glycosylated spike revealed that glycans do not qualitatively change the internal allosteric pathways but can facilitate the transmission of the structural changes within and between subunits.