Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants
Quantifying the microstructural and macrostructural geometrical features of the human brain’s connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets tot...
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| Published in | Brain Structure and Function Vol. 227; no. 6; pp. 2111 - 2125 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.07.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1863-2653 1863-2661 1863-2661 0340-2061 |
| DOI | 10.1007/s00429-022-02503-z |
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| Abstract | Quantifying the microstructural and macrostructural geometrical features of the human brain’s connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50–97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions. |
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| AbstractList | Quantifying the microstructural and macrostructural geometrical features of the human brain’s connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50–97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions. Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions. Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions. |
| Author | Ramdass, Karthik Pechman, Kimberly R. Jefferson, Angela Yeh, Fang-Cheng Kang, Hakmook Archer, Derek Anderson, Adam W. Landman, Bennett A. Rheault, Francois Hohman, Timothy J. Cai, Leon Y. Resnick, Susan M. Schilling, Kurt G. Hansen, Colin Yang, Qi Gifford, Katherine A. Shafer, Andrea T. |
| AuthorAffiliation | 3 Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA 2 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA 5 Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA 8 Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA 11 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA 10 Department of Biostatistics, Vanderbilt University, Nashville, TN, USA 4 Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA 7 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 9 Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 1 Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA 6 Department of Neurological Surgery, University of Pittsburgh Medical Center, P |
| AuthorAffiliation_xml | – name: 11 Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA – name: 3 Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA – name: 6 Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA – name: 7 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA – name: 9 Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA – name: 2 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA – name: 1 Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA – name: 5 Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA – name: 8 Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA – name: 10 Department of Biostatistics, Vanderbilt University, Nashville, TN, USA – name: 4 Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA |
| Author_xml | – sequence: 1 givenname: Kurt G. orcidid: 0000-0003-3686-7645 surname: Schilling fullname: Schilling, Kurt G. email: kurt.g.schilling.1@vumc.org organization: Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center – sequence: 2 givenname: Derek surname: Archer fullname: Archer, Derek organization: Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Department of Neurology, Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Vanderbilt University School of Medicine – sequence: 3 givenname: Fang-Cheng surname: Yeh fullname: Yeh, Fang-Cheng organization: Department of Neurological Surgery, University of Pittsburgh Medical Center, Department of Bioengineering, University of Pittsburgh – sequence: 4 givenname: Francois surname: Rheault fullname: Rheault, Francois organization: Department of Electrical Engineering and Computer Science, Vanderbilt University – sequence: 5 givenname: Leon Y. surname: Cai fullname: Cai, Leon Y. organization: Department of Electrical Engineering and Computer Science, Vanderbilt University – sequence: 6 givenname: Colin surname: Hansen fullname: Hansen, Colin organization: Department of Electrical Engineering and Computer Science, Vanderbilt University – sequence: 7 givenname: Qi surname: Yang fullname: Yang, Qi organization: Department of Electrical Engineering and Computer Science, Vanderbilt University – sequence: 8 givenname: Karthik surname: Ramdass fullname: Ramdass, Karthik organization: Department of Electrical Engineering and Computer Science, Vanderbilt University – sequence: 9 givenname: Andrea T. surname: Shafer fullname: Shafer, Andrea T. organization: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health – sequence: 10 givenname: Susan M. surname: Resnick fullname: Resnick, Susan M. organization: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health – sequence: 11 givenname: Kimberly R. surname: Pechman fullname: Pechman, Kimberly R. organization: Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Department of Neurology, Vanderbilt University Medical Center – sequence: 12 givenname: Katherine A. surname: Gifford fullname: Gifford, Katherine A. organization: Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Department of Neurology, Vanderbilt University Medical Center – sequence: 13 givenname: Timothy J. surname: Hohman fullname: Hohman, Timothy J. organization: Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Department of Neurology, Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Vanderbilt University School of Medicine – sequence: 14 givenname: Angela surname: Jefferson fullname: Jefferson, Angela organization: Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Department of Neurology, Vanderbilt University Medical Center, Department of Medicine, Vanderbilt University Medical Center – sequence: 15 givenname: Adam W. surname: Anderson fullname: Anderson, Adam W. organization: Department of Biomedical Engineering, Vanderbilt University – sequence: 16 givenname: Hakmook surname: Kang fullname: Kang, Hakmook organization: Department of Biostatistics, Vanderbilt University – sequence: 17 givenname: Bennett A. surname: Landman fullname: Landman, Bennett A. organization: Department of Electrical Engineering and Computer Science, Vanderbilt University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35604444$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. |
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| Keywords | Aging Diffusion MRI Volume White matter Tractography |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 Author contributions All authors contributed to the study conception and design. Data collection was performed by the Baltimore Longitudinal Study of Aging at the National Institutes of Aging, and the Vanderbilt Memory & Aging Project (VMAP). All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. |
| ORCID | 0000-0003-3686-7645 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/9648053 |
| PMID | 35604444 |
| PQID | 2680446120 |
| PQPubID | 38983 |
| PageCount | 15 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9648053 proquest_miscellaneous_2668217940 proquest_journals_2680446120 pubmed_primary_35604444 crossref_primary_10_1007_s00429_022_02503_z crossref_citationtrail_10_1007_s00429_022_02503_z springer_journals_10_1007_s00429_022_02503_z |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-07-01 |
| PublicationDateYYYYMMDD | 2022-07-01 |
| PublicationDate_xml | – month: 07 year: 2022 text: 2022-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
| PublicationTitle | Brain Structure and Function |
| PublicationTitleAbbrev | Brain Struct Funct |
| PublicationTitleAlternate | Brain Struct Funct |
| PublicationYear | 2022 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
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| SubjectTerms | Age Aging Biomedical and Life Sciences Biomedicine Brain - diagnostic imaging Cell Biology Cross-Sectional Studies Diffusion Magnetic Resonance Imaging - methods Diffusion Tensor Imaging - methods Humans Longitudinal Studies Magnetic resonance imaging Nervous system Neuroimaging Neurology Neurosciences Original Article Substantia alba Thalamus White Matter - diagnostic imaging |
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| Title | Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants |
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