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Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease

Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a quest...

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Autores principales: Rasero, Javier, Amoroso, Nicola, La Rocca, Marianna, Tangaro, Sabina, Bellotti, Roberto, Stramaglia, Sebastiano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685585/
https://www.ncbi.nlm.nih.gov/pubmed/29135998
http://dx.doi.org/10.1371/journal.pone.0187281
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author Rasero, Javier
Amoroso, Nicola
La Rocca, Marianna
Tangaro, Sabina
Bellotti, Roberto
Stramaglia, Sebastiano
author_facet Rasero, Javier
Amoroso, Nicola
La Rocca, Marianna
Tangaro, Sabina
Bellotti, Roberto
Stramaglia, Sebastiano
author_sort Rasero, Javier
collection PubMed
description Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.
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spelling pubmed-56855852017-11-30 Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease Rasero, Javier Amoroso, Nicola La Rocca, Marianna Tangaro, Sabina Bellotti, Roberto Stramaglia, Sebastiano PLoS One Research Article Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex. Public Library of Science 2017-11-14 /pmc/articles/PMC5685585/ /pubmed/29135998 http://dx.doi.org/10.1371/journal.pone.0187281 Text en © 2017 Rasero et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rasero, Javier
Amoroso, Nicola
La Rocca, Marianna
Tangaro, Sabina
Bellotti, Roberto
Stramaglia, Sebastiano
Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease
title Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease
title_full Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease
title_fullStr Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease
title_full_unstemmed Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease
title_short Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease
title_sort multivariate regression analysis of structural mri connectivity matrices in alzheimer’s disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685585/
https://www.ncbi.nlm.nih.gov/pubmed/29135998
http://dx.doi.org/10.1371/journal.pone.0187281
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