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Inter- and intra-individual variation in brain structural-cognition relationships in aging
The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively c...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393406/ https://www.ncbi.nlm.nih.gov/pubmed/35490915 http://dx.doi.org/10.1016/j.neuroimage.2022.119254 |
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author | Patel, Raihaan Mackay, Clare E. Jansen, Michelle G. Devenyi, Gabriel A. O’Donoghue, M. Clare Kivimäki, Mika Singh-Manoux, Archana Zsoldos, Enikő Ebmeier, Klaus P. Chakravarty, M. Mallar Suri, Sana |
author_facet | Patel, Raihaan Mackay, Clare E. Jansen, Michelle G. Devenyi, Gabriel A. O’Donoghue, M. Clare Kivimäki, Mika Singh-Manoux, Archana Zsoldos, Enikő Ebmeier, Klaus P. Chakravarty, M. Mallar Suri, Sana |
author_sort | Patel, Raihaan |
collection | PubMed |
description | The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ± 4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure. |
format | Online Article Text |
id | pubmed-9393406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93934062022-08-22 Inter- and intra-individual variation in brain structural-cognition relationships in aging Patel, Raihaan Mackay, Clare E. Jansen, Michelle G. Devenyi, Gabriel A. O’Donoghue, M. Clare Kivimäki, Mika Singh-Manoux, Archana Zsoldos, Enikő Ebmeier, Klaus P. Chakravarty, M. Mallar Suri, Sana Neuroimage Article The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ± 4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure. 2022-08-15 2022-04-28 /pmc/articles/PMC9393406/ /pubmed/35490915 http://dx.doi.org/10.1016/j.neuroimage.2022.119254 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ) |
spellingShingle | Article Patel, Raihaan Mackay, Clare E. Jansen, Michelle G. Devenyi, Gabriel A. O’Donoghue, M. Clare Kivimäki, Mika Singh-Manoux, Archana Zsoldos, Enikő Ebmeier, Klaus P. Chakravarty, M. Mallar Suri, Sana Inter- and intra-individual variation in brain structural-cognition relationships in aging |
title | Inter- and intra-individual variation in brain structural-cognition relationships in aging |
title_full | Inter- and intra-individual variation in brain structural-cognition relationships in aging |
title_fullStr | Inter- and intra-individual variation in brain structural-cognition relationships in aging |
title_full_unstemmed | Inter- and intra-individual variation in brain structural-cognition relationships in aging |
title_short | Inter- and intra-individual variation in brain structural-cognition relationships in aging |
title_sort | inter- and intra-individual variation in brain structural-cognition relationships in aging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393406/ https://www.ncbi.nlm.nih.gov/pubmed/35490915 http://dx.doi.org/10.1016/j.neuroimage.2022.119254 |
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