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Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes
The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or “BOLD(SD),” is an emerging metric of variability in neur...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422923/ https://www.ncbi.nlm.nih.gov/pubmed/30914944 http://dx.doi.org/10.3389/fnagi.2019.00046 |
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author | Pur, Daiana R. Eagleson, Roy A. de Ribaupierre, Anik Mella, Nathalie de Ribaupierre, Sandrine |
author_facet | Pur, Daiana R. Eagleson, Roy A. de Ribaupierre, Anik Mella, Nathalie de Ribaupierre, Sandrine |
author_sort | Pur, Daiana R. |
collection | PubMed |
description | The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or “BOLD(SD),” is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLD(SD) is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLD(SD) changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLD(SD) changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLD(SD) is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLD(SD) alterations. Our study highlights that CT changes should be considered when evaluating BOLD(SD) alternations in the lifespan. |
format | Online Article Text |
id | pubmed-6422923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64229232019-03-26 Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes Pur, Daiana R. Eagleson, Roy A. de Ribaupierre, Anik Mella, Nathalie de Ribaupierre, Sandrine Front Aging Neurosci Neuroscience The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or “BOLD(SD),” is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLD(SD) is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLD(SD) changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLD(SD) changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLD(SD) is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLD(SD) alterations. Our study highlights that CT changes should be considered when evaluating BOLD(SD) alternations in the lifespan. Frontiers Media S.A. 2019-03-12 /pmc/articles/PMC6422923/ /pubmed/30914944 http://dx.doi.org/10.3389/fnagi.2019.00046 Text en Copyright © 2019 Pur, Eagleson, de Ribaupierre, Mella and de Ribaupierre. 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 Pur, Daiana R. Eagleson, Roy A. de Ribaupierre, Anik Mella, Nathalie de Ribaupierre, Sandrine Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes |
title | Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes |
title_full | Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes |
title_fullStr | Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes |
title_full_unstemmed | Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes |
title_short | Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes |
title_sort | moderating effect of cortical thickness on bold signal variability age-related changes |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422923/ https://www.ncbi.nlm.nih.gov/pubmed/30914944 http://dx.doi.org/10.3389/fnagi.2019.00046 |
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