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Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality
BACKGROUND: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patie...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3868601/ https://www.ncbi.nlm.nih.gov/pubmed/24367629 http://dx.doi.org/10.1371/journal.pone.0084093 |
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author | Dickie, David Alexander Job, Dominic E. Gonzalez, David Rodriguez Shenkin, Susan D. Ahearn, Trevor S. Murray, Alison D. Wardlaw, Joanna M. |
author_facet | Dickie, David Alexander Job, Dominic E. Gonzalez, David Rodriguez Shenkin, Susan D. Ahearn, Trevor S. Murray, Alison D. Wardlaw, Joanna M. |
author_sort | Dickie, David Alexander |
collection | PubMed |
description | BACKGROUND: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. MATERIALS AND METHODS: We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer’s disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age. RESULTS: In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5(th) percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects. CONCLUSIONS: While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease. |
format | Online Article Text |
id | pubmed-3868601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38686012013-12-23 Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality Dickie, David Alexander Job, Dominic E. Gonzalez, David Rodriguez Shenkin, Susan D. Ahearn, Trevor S. Murray, Alison D. Wardlaw, Joanna M. PLoS One Research Article BACKGROUND: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. MATERIALS AND METHODS: We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer’s disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age. RESULTS: In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5(th) percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects. CONCLUSIONS: While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease. Public Library of Science 2013-12-19 /pmc/articles/PMC3868601/ /pubmed/24367629 http://dx.doi.org/10.1371/journal.pone.0084093 Text en © 2013 Dickie 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Dickie, David Alexander Job, Dominic E. Gonzalez, David Rodriguez Shenkin, Susan D. Ahearn, Trevor S. Murray, Alison D. Wardlaw, Joanna M. Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality |
title | Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality |
title_full | Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality |
title_fullStr | Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality |
title_full_unstemmed | Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality |
title_short | Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality |
title_sort | variance in brain volume with advancing age: implications for defining the limits of normality |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3868601/ https://www.ncbi.nlm.nih.gov/pubmed/24367629 http://dx.doi.org/10.1371/journal.pone.0084093 |
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