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Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps
The trajectory of the developing brain is characterized by a sequence of complex, nonlinear patterns that occur at systematic stages of maturation. Although significant prior neuroimaging research has shed light on these patterns, the challenge of accurately characterizing brain maturation, and iden...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4418382/ https://www.ncbi.nlm.nih.gov/pubmed/25640476 http://dx.doi.org/10.1002/hbm.22671 |
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author | Dean, Douglas C O'Muircheartaigh, Jonathan Dirks, Holly Waskiewicz, Nicole Lehman, Katie Walker, Lindsay Piryatinsky, Irene Deoni, Sean CL |
author_facet | Dean, Douglas C O'Muircheartaigh, Jonathan Dirks, Holly Waskiewicz, Nicole Lehman, Katie Walker, Lindsay Piryatinsky, Irene Deoni, Sean CL |
author_sort | Dean, Douglas C |
collection | PubMed |
description | The trajectory of the developing brain is characterized by a sequence of complex, nonlinear patterns that occur at systematic stages of maturation. Although significant prior neuroimaging research has shed light on these patterns, the challenge of accurately characterizing brain maturation, and identifying areas of accelerated or delayed development, remains. Altered brain development, particularly during the earliest stages of life, is believed to be associated with many neurological and neuropsychiatric disorders. In this work, we develop a framework to construct voxel-wise estimates of brain age based on magnetic resonance imaging measures sensitive to myelin content. 198 myelin water fraction (VF(M)) maps were acquired from healthy male and female infants and toddlers, 3 to 48 months of age, and used to train a sigmoidal-based maturational model. The validity of the approach was then established by testing the model on 129 different VF(M) datasets. Results revealed the approach to have high accuracy, with a mean absolute percent error of 13% in males and 14% in females, and high predictive ability, with correlation coefficients between estimated and true ages of 0.945 in males and 0.94 in females. This work represents a new approach toward mapping brain maturity, and may provide a more faithful staging of brain maturation in infants beyond chronological or gestation-corrected age, allowing earlier identification of atypical regional brain development. Hum Brain Mapp 36:1233–1244, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. |
format | Online Article Text |
id | pubmed-4418382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-44183822015-05-06 Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps Dean, Douglas C O'Muircheartaigh, Jonathan Dirks, Holly Waskiewicz, Nicole Lehman, Katie Walker, Lindsay Piryatinsky, Irene Deoni, Sean CL Hum Brain Mapp Research Articles The trajectory of the developing brain is characterized by a sequence of complex, nonlinear patterns that occur at systematic stages of maturation. Although significant prior neuroimaging research has shed light on these patterns, the challenge of accurately characterizing brain maturation, and identifying areas of accelerated or delayed development, remains. Altered brain development, particularly during the earliest stages of life, is believed to be associated with many neurological and neuropsychiatric disorders. In this work, we develop a framework to construct voxel-wise estimates of brain age based on magnetic resonance imaging measures sensitive to myelin content. 198 myelin water fraction (VF(M)) maps were acquired from healthy male and female infants and toddlers, 3 to 48 months of age, and used to train a sigmoidal-based maturational model. The validity of the approach was then established by testing the model on 129 different VF(M) datasets. Results revealed the approach to have high accuracy, with a mean absolute percent error of 13% in males and 14% in females, and high predictive ability, with correlation coefficients between estimated and true ages of 0.945 in males and 0.94 in females. This work represents a new approach toward mapping brain maturity, and may provide a more faithful staging of brain maturation in infants beyond chronological or gestation-corrected age, allowing earlier identification of atypical regional brain development. Hum Brain Mapp 36:1233–1244, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. Blackwell Publishing Ltd 2015-04 2015-01-30 /pmc/articles/PMC4418382/ /pubmed/25640476 http://dx.doi.org/10.1002/hbm.22671 Text en © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Dean, Douglas C O'Muircheartaigh, Jonathan Dirks, Holly Waskiewicz, Nicole Lehman, Katie Walker, Lindsay Piryatinsky, Irene Deoni, Sean CL Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps |
title | Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps |
title_full | Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps |
title_fullStr | Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps |
title_full_unstemmed | Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps |
title_short | Estimating the Age of Healthy Infants From Quantitative Myelin Water Fraction Maps |
title_sort | estimating the age of healthy infants from quantitative myelin water fraction maps |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4418382/ https://www.ncbi.nlm.nih.gov/pubmed/25640476 http://dx.doi.org/10.1002/hbm.22671 |
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