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Differences in topological progression profile among neurodegenerative diseases from imaging data

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 c...

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Autores principales: Garbarino, Sara, Lorenzi, Marco, Oxtoby, Neil P, Vinke, Elisabeth J, Marinescu, Razvan V, Eshaghi, Arman, Ikram, M Arfan, Niessen, Wiro J, Ciccarelli, Olga, Barkhof, Frederik, Schott, Jonathan M, Vernooij, Meike W, Alexander, Daniel C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922631/
https://www.ncbi.nlm.nih.gov/pubmed/31793876
http://dx.doi.org/10.7554/eLife.49298
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author Garbarino, Sara
Lorenzi, Marco
Oxtoby, Neil P
Vinke, Elisabeth J
Marinescu, Razvan V
Eshaghi, Arman
Ikram, M Arfan
Niessen, Wiro J
Ciccarelli, Olga
Barkhof, Frederik
Schott, Jonathan M
Vernooij, Meike W
Alexander, Daniel C
author_facet Garbarino, Sara
Lorenzi, Marco
Oxtoby, Neil P
Vinke, Elisabeth J
Marinescu, Razvan V
Eshaghi, Arman
Ikram, M Arfan
Niessen, Wiro J
Ciccarelli, Olga
Barkhof, Frederik
Schott, Jonathan M
Vernooij, Meike W
Alexander, Daniel C
author_sort Garbarino, Sara
collection PubMed
description 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.
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spelling pubmed-69226312019-12-23 Differences in topological progression profile among neurodegenerative diseases from imaging data Garbarino, Sara Lorenzi, Marco Oxtoby, Neil P Vinke, Elisabeth J Marinescu, Razvan V Eshaghi, Arman Ikram, M Arfan Niessen, Wiro J Ciccarelli, Olga Barkhof, Frederik Schott, Jonathan M Vernooij, Meike W Alexander, Daniel C eLife Computational and Systems Biology 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. eLife Sciences Publications, Ltd 2019-12-13 /pmc/articles/PMC6922631/ /pubmed/31793876 http://dx.doi.org/10.7554/eLife.49298 Text en © 2019, Garbarino et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Garbarino, Sara
Lorenzi, Marco
Oxtoby, Neil P
Vinke, Elisabeth J
Marinescu, Razvan V
Eshaghi, Arman
Ikram, M Arfan
Niessen, Wiro J
Ciccarelli, Olga
Barkhof, Frederik
Schott, Jonathan M
Vernooij, Meike W
Alexander, Daniel C
Differences in topological progression profile among neurodegenerative diseases from imaging data
title Differences in topological progression profile among neurodegenerative diseases from imaging data
title_full Differences in topological progression profile among neurodegenerative diseases from imaging data
title_fullStr Differences in topological progression profile among neurodegenerative diseases from imaging data
title_full_unstemmed Differences in topological progression profile among neurodegenerative diseases from imaging data
title_short Differences in topological progression profile among neurodegenerative diseases from imaging data
title_sort differences in topological progression profile among neurodegenerative diseases from imaging data
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922631/
https://www.ncbi.nlm.nih.gov/pubmed/31793876
http://dx.doi.org/10.7554/eLife.49298
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