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Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted an...

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Autores principales: Amoroso, Nicola, La Rocca, Marianna, Bellantuono, Loredana, Diacono, Domenico, Fanizzi, Annarita, Lella, Eufemia, Lombardi, Angela, Maggipinto, Tommaso, Monaco, Alfonso, Tangaro, Sabina, Bellotti, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538815/
https://www.ncbi.nlm.nih.gov/pubmed/31178715
http://dx.doi.org/10.3389/fnagi.2019.00115
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author Amoroso, Nicola
La Rocca, Marianna
Bellantuono, Loredana
Diacono, Domenico
Fanizzi, Annarita
Lella, Eufemia
Lombardi, Angela
Maggipinto, Tommaso
Monaco, Alfonso
Tangaro, Sabina
Bellotti, Roberto
author_facet Amoroso, Nicola
La Rocca, Marianna
Bellantuono, Loredana
Diacono, Domenico
Fanizzi, Annarita
Lella, Eufemia
Lombardi, Angela
Maggipinto, Tommaso
Monaco, Alfonso
Tangaro, Sabina
Bellotti, Roberto
author_sort Amoroso, Nicola
collection PubMed
description Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.
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spelling pubmed-65388152019-06-07 Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age Amoroso, Nicola La Rocca, Marianna Bellantuono, Loredana Diacono, Domenico Fanizzi, Annarita Lella, Eufemia Lombardi, Angela Maggipinto, Tommaso Monaco, Alfonso Tangaro, Sabina Bellotti, Roberto Front Aging Neurosci Neuroscience Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used. Frontiers Media S.A. 2019-05-22 /pmc/articles/PMC6538815/ /pubmed/31178715 http://dx.doi.org/10.3389/fnagi.2019.00115 Text en Copyright © 2019 Amoroso, La Rocca, Bellantuono, Diacono, Fanizzi, Lella, Lombardi, Maggipinto, Monaco, Tangaro and Bellotti. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Amoroso, Nicola
La Rocca, Marianna
Bellantuono, Loredana
Diacono, Domenico
Fanizzi, Annarita
Lella, Eufemia
Lombardi, Angela
Maggipinto, Tommaso
Monaco, Alfonso
Tangaro, Sabina
Bellotti, Roberto
Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
title Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
title_full Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
title_fullStr Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
title_full_unstemmed Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
title_short Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
title_sort deep learning and multiplex networks for accurate modeling of brain age
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538815/
https://www.ncbi.nlm.nih.gov/pubmed/31178715
http://dx.doi.org/10.3389/fnagi.2019.00115
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