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Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction

Background: Over the life span, the diffusion metrics in brain MRI show different, partly nonlinear changes. These age-dependent changes also seem to exhibit regional differences with respect to the brain anatomy. The age correction of a study cohort's diffusion metrics might thus require consi...

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Autores principales: Behler, Anna, Kassubek, Jan, Müller, Hans-Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239142/
https://www.ncbi.nlm.nih.gov/pubmed/34211389
http://dx.doi.org/10.3389/fnagi.2021.682109
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author Behler, Anna
Kassubek, Jan
Müller, Hans-Peter
author_facet Behler, Anna
Kassubek, Jan
Müller, Hans-Peter
author_sort Behler, Anna
collection PubMed
description Background: Over the life span, the diffusion metrics in brain MRI show different, partly nonlinear changes. These age-dependent changes also seem to exhibit regional differences with respect to the brain anatomy. The age correction of a study cohort's diffusion metrics might thus require consideration of age-related factors. Methods: Diffusion tensor imaging data sets were acquired from 219 healthy participants at ages between 19 and 81 years. Fractional anisotropy (FA), mean diffusivity (MD), and axial and radial diffusivity (AD and RD, respectively) maps were analyzed by a tract of interest-based fiber tracking approach. To describe diffusion metrics as a function of the participant age, linear splines were used to perform curve fitting in 21 specific tract systems covering different functional areas and diffusion directions. Results: In the majority of tracts, an interpolation with a change of alteration rate during adult life described the diffusion properties more accurately than a linear model. Consequently, the diffusion properties remained relatively stable until a decrease (of FA) or increase (of MD, AD, and RD) started at a region-specific time point, whereas a uniform change of diffusion properties was observed only in a few tracts. Single tracts, e.g., located in the cerebellum, remained nearly unaltered throughout the ages between 19 and 81 years. Conclusions: Age corrections of diffusion properties should not be applied to all white matter regions and all age spans in the same way. Therefore, we propose three different approaches for age correction based on fiber tracking techniques, i.e., no correction for areas that do not experience age-related changes and two variants of an age correction depending on the age range of the cohort and the tracts considered.
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spelling pubmed-82391422021-06-30 Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction Behler, Anna Kassubek, Jan Müller, Hans-Peter Front Aging Neurosci Neuroscience Background: Over the life span, the diffusion metrics in brain MRI show different, partly nonlinear changes. These age-dependent changes also seem to exhibit regional differences with respect to the brain anatomy. The age correction of a study cohort's diffusion metrics might thus require consideration of age-related factors. Methods: Diffusion tensor imaging data sets were acquired from 219 healthy participants at ages between 19 and 81 years. Fractional anisotropy (FA), mean diffusivity (MD), and axial and radial diffusivity (AD and RD, respectively) maps were analyzed by a tract of interest-based fiber tracking approach. To describe diffusion metrics as a function of the participant age, linear splines were used to perform curve fitting in 21 specific tract systems covering different functional areas and diffusion directions. Results: In the majority of tracts, an interpolation with a change of alteration rate during adult life described the diffusion properties more accurately than a linear model. Consequently, the diffusion properties remained relatively stable until a decrease (of FA) or increase (of MD, AD, and RD) started at a region-specific time point, whereas a uniform change of diffusion properties was observed only in a few tracts. Single tracts, e.g., located in the cerebellum, remained nearly unaltered throughout the ages between 19 and 81 years. Conclusions: Age corrections of diffusion properties should not be applied to all white matter regions and all age spans in the same way. Therefore, we propose three different approaches for age correction based on fiber tracking techniques, i.e., no correction for areas that do not experience age-related changes and two variants of an age correction depending on the age range of the cohort and the tracts considered. Frontiers Media S.A. 2021-06-15 /pmc/articles/PMC8239142/ /pubmed/34211389 http://dx.doi.org/10.3389/fnagi.2021.682109 Text en Copyright © 2021 Behler, Kassubek and Müller. https://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
Behler, Anna
Kassubek, Jan
Müller, Hans-Peter
Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction
title Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction
title_full Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction
title_fullStr Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction
title_full_unstemmed Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction
title_short Age-Related Alterations in DTI Metrics in the Human Brain—Consequences for Age Correction
title_sort age-related alterations in dti metrics in the human brain—consequences for age correction
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239142/
https://www.ncbi.nlm.nih.gov/pubmed/34211389
http://dx.doi.org/10.3389/fnagi.2021.682109
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