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Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults

Despite extensive research in the field of aging neuroscience, it still remains unclear whether age related cortical changes can be detected in different functional networks of younger adults and whether these networks respond identically to healthy aging. We collected high-resolution brain anatomic...

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Autores principales: Bajaj, Sahil, Alkozei, Anna, Dailey, Natalie S., Killgore, William D. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732192/
https://www.ncbi.nlm.nih.gov/pubmed/29321739
http://dx.doi.org/10.3389/fnagi.2017.00412
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author Bajaj, Sahil
Alkozei, Anna
Dailey, Natalie S.
Killgore, William D. S.
author_facet Bajaj, Sahil
Alkozei, Anna
Dailey, Natalie S.
Killgore, William D. S.
author_sort Bajaj, Sahil
collection PubMed
description Despite extensive research in the field of aging neuroscience, it still remains unclear whether age related cortical changes can be detected in different functional networks of younger adults and whether these networks respond identically to healthy aging. We collected high-resolution brain anatomical data from 56 young healthy adults (mean age = 30.8 ± 8.1 years, 29 males). We performed whole brain parcellation into seven functional networks, including visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode networks. We estimated intracranial volume (ICV) and averaged cortical thickness (CT), cortical surface area (CSA) and cortical volume (CV) over each hemisphere as well as for each network. Averaged cortical measures over each hemisphere, especially CT and CV, were significantly lower in older individuals compared to younger ones (one-way ANOVA, p < 0.05, corrected for multiple comparisons). There were negative correlations between age and averaged CT and CV over each hemisphere (p < 0.05, corrected for multiple comparisons) as well as between age and ICV (p = 0.05). Network level analysis showed that age was negatively correlated with CT for all functional networks (p < 0.05, corrected for multiple comparisons), apart from the limbic network. While age was unrelated to CSA, it was negatively correlated with CV across several functional networks (p < 0.05, corrected for multiple comparisons). We also showed positive associations between CV and CT and between CV and CSA for all networks (p < 0.05, corrected for multiple comparisons). We interpret the lack of association between age and CT of the limbic network as evidence that the limbic system may be particularly resistant to age-related declines during this period of life, whereas the significant age-related declines in averaged CT over each hemisphere as well as in all other six networks suggests that CT may serve as a reliable biomarker to capture the effect of normal aging. Due to the simultaneous dependence of CV on CT and CSA, CV was unable to identify such effects of normal aging consistently for the other six networks, but there were negative associations observed between age and averaged CV over each hemisphere as well as between age and ICV. Our findings suggest that the identification of early cortical changes within various functional networks during normal aging might be useful for predicting the effect of aging on the efficiency of functional performance even during early adulthood.
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spelling pubmed-57321922018-01-10 Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults Bajaj, Sahil Alkozei, Anna Dailey, Natalie S. Killgore, William D. S. Front Aging Neurosci Neuroscience Despite extensive research in the field of aging neuroscience, it still remains unclear whether age related cortical changes can be detected in different functional networks of younger adults and whether these networks respond identically to healthy aging. We collected high-resolution brain anatomical data from 56 young healthy adults (mean age = 30.8 ± 8.1 years, 29 males). We performed whole brain parcellation into seven functional networks, including visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal and default mode networks. We estimated intracranial volume (ICV) and averaged cortical thickness (CT), cortical surface area (CSA) and cortical volume (CV) over each hemisphere as well as for each network. Averaged cortical measures over each hemisphere, especially CT and CV, were significantly lower in older individuals compared to younger ones (one-way ANOVA, p < 0.05, corrected for multiple comparisons). There were negative correlations between age and averaged CT and CV over each hemisphere (p < 0.05, corrected for multiple comparisons) as well as between age and ICV (p = 0.05). Network level analysis showed that age was negatively correlated with CT for all functional networks (p < 0.05, corrected for multiple comparisons), apart from the limbic network. While age was unrelated to CSA, it was negatively correlated with CV across several functional networks (p < 0.05, corrected for multiple comparisons). We also showed positive associations between CV and CT and between CV and CSA for all networks (p < 0.05, corrected for multiple comparisons). We interpret the lack of association between age and CT of the limbic network as evidence that the limbic system may be particularly resistant to age-related declines during this period of life, whereas the significant age-related declines in averaged CT over each hemisphere as well as in all other six networks suggests that CT may serve as a reliable biomarker to capture the effect of normal aging. Due to the simultaneous dependence of CV on CT and CSA, CV was unable to identify such effects of normal aging consistently for the other six networks, but there were negative associations observed between age and averaged CV over each hemisphere as well as between age and ICV. Our findings suggest that the identification of early cortical changes within various functional networks during normal aging might be useful for predicting the effect of aging on the efficiency of functional performance even during early adulthood. Frontiers Media S.A. 2017-12-11 /pmc/articles/PMC5732192/ /pubmed/29321739 http://dx.doi.org/10.3389/fnagi.2017.00412 Text en Copyright © 2017 Bajaj, Alkozei, Dailey and Killgore. 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) or licensor 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
Bajaj, Sahil
Alkozei, Anna
Dailey, Natalie S.
Killgore, William D. S.
Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults
title Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults
title_full Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults
title_fullStr Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults
title_full_unstemmed Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults
title_short Brain Aging: Uncovering Cortical Characteristics of Healthy Aging in Young Adults
title_sort brain aging: uncovering cortical characteristics of healthy aging in young adults
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732192/
https://www.ncbi.nlm.nih.gov/pubmed/29321739
http://dx.doi.org/10.3389/fnagi.2017.00412
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