Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageing Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1002/hbm.26333
Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age‐associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age ( N = 35,749, 44.6–82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM‐age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/hbm.26333
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.26333
- OA Status
- gold
- Cited By
- 34
- References
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- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4376872252Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/hbm.26333Digital Object Identifier
- Title
-
Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-05-17Full publication date if available
- Authors
-
Max Korbmacher, Ann‐Marie G. de Lange, Dennis van der Meer, Dani Beck, Eli Eikefjord, Arvid Lundervold, Ole A. Andreassen, Lars T. Westlye, Ivan I. MaximovList of authors in order
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https://doi.org/10.1002/hbm.26333Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.26333Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.26333Direct OA link when available
- Concepts
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Fornix, White matter, Fractional anisotropy, Ageing, Diffusion MRI, Psychology, Magnetic resonance imaging, Neuroscience, Hippocampus, Medicine, Internal medicine, RadiologyTop concepts (fields/topics) attached by OpenAlex
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34Total citation count in OpenAlex
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2025: 14, 2024: 13, 2023: 7Per-year citation counts (last 5 years)
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104Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Human Brain Mapping |
| best_oa_location.landing_page_url | https://doi.org/10.1002/hbm.26333 |
| primary_location.id | doi:10.1002/hbm.26333 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S121666818 |
| primary_location.source.issn | 1065-9471, 1097-0193 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1065-9471 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Human Brain Mapping |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.26333 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Human Brain Mapping |
| primary_location.landing_page_url | https://doi.org/10.1002/hbm.26333 |
| publication_date | 2023-05-17 |
| publication_year | 2023 |
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| referenced_works_count | 104 |
| abstract_inverted_index.( | 52 |
| abstract_inverted_index.= | 54 |
| abstract_inverted_index.N | 53 |
| abstract_inverted_index.a | 12, 74, 130 |
| abstract_inverted_index.In | 20 |
| abstract_inverted_index.UK | 39 |
| abstract_inverted_index.WM | 31, 99 |
| abstract_inverted_index.We | 157 |
| abstract_inverted_index.an | 21 |
| abstract_inverted_index.as | 107, 123, 178 |
| abstract_inverted_index.in | 67, 118 |
| abstract_inverted_index.is | 11 |
| abstract_inverted_index.of | 4, 24, 30, 58, 98, 133, 161, 174, 181 |
| abstract_inverted_index.to | 83, 101, 120 |
| abstract_inverted_index.we | 37 |
| abstract_inverted_index.WM, | 169 |
| abstract_inverted_index.age | 26, 51, 80, 87, 113, 137, 150, 183 |
| abstract_inverted_index.and | 28, 49, 61, 146, 170, 176, 184 |
| abstract_inverted_index.for | 15, 111, 138, 151, 165 |
| abstract_inverted_index.the | 2, 17, 108, 159, 171 |
| abstract_inverted_index.was | 88, 105 |
| abstract_inverted_index.(WM) | 7 |
| abstract_inverted_index.age. | 70, 103 |
| abstract_inverted_index.best | 90 |
| abstract_inverted_index.dMRI | 63, 163 |
| abstract_inverted_index.data | 46 |
| abstract_inverted_index.from | 33, 81 |
| abstract_inverted_index.into | 168 |
| abstract_inverted_index.mean | 152 |
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| abstract_inverted_index.when | 91 |
| abstract_inverted_index.with | 78, 136, 149 |
| abstract_inverted_index.Brain | 86 |
| abstract_inverted_index.These | 127 |
| abstract_inverted_index.age). | 59 |
| abstract_inverted_index.ages. | 85 |
| abstract_inverted_index.brain | 25, 69, 102, 112, 182 |
| abstract_inverted_index.found | 106 |
| abstract_inverted_index.intra | 139 |
| abstract_inverted_index.minor | 122 |
| abstract_inverted_index.older | 50, 84 |
| abstract_inverted_index.water | 141 |
| abstract_inverted_index.white | 5 |
| abstract_inverted_index.years | 57 |
| abstract_inverted_index.(dMRI) | 45 |
| abstract_inverted_index.Fornix | 104 |
| abstract_inverted_index.across | 47, 115 |
| abstract_inverted_index.ageing | 10, 18 |
| abstract_inverted_index.axial, | 143 |
| abstract_inverted_index.axonal | 140 |
| abstract_inverted_index.brain. | 19 |
| abstract_inverted_index.fornix | 175 |
| abstract_inverted_index.matter | 6 |
| abstract_inverted_index.radial | 144 |
| abstract_inverted_index.region | 110 |
| abstract_inverted_index.steady | 75 |
| abstract_inverted_index.35,749, | 55 |
| abstract_inverted_index.Biobank | 40 |
| abstract_inverted_index.ageing. | 185 |
| abstract_inverted_index.another | 124 |
| abstract_inverted_index.aspects | 97 |
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| abstract_inverted_index.details | 3 |
| abstract_inverted_index.forceps | 121, 177 |
| abstract_inverted_index.further | 172 |
| abstract_inverted_index.general | 131 |
| abstract_inverted_index.imaging | 44 |
| abstract_inverted_index.midlife | 48, 82 |
| abstract_inverted_index.pattern | 132 |
| abstract_inverted_index.region. | 126 |
| abstract_inverted_index.regions | 128 |
| abstract_inverted_index.showing | 95 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.WM‐age | 71 |
| abstract_inverted_index.advanced | 62 |
| abstract_inverted_index.analyzed | 38 |
| abstract_inverted_index.detailed | 166 |
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| abstract_inverted_index.indicate | 73 |
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| abstract_inverted_index.positive | 134 |
| abstract_inverted_index.question | 14 |
| abstract_inverted_index.Unveiling | 1 |
| abstract_inverted_index.combining | 92 |
| abstract_inverted_index.different | 34, 96 |
| abstract_inverted_index.diffusion | 35, 41, 93, 116 |
| abstract_inverted_index.encourage | 158 |
| abstract_inverted_index.estimated | 89 |
| abstract_inverted_index.exhibited | 129 |
| abstract_inverted_index.extensive | 22 |
| abstract_inverted_index.important | 125 |
| abstract_inverted_index.kurtosis. | 156 |
| abstract_inverted_index.potential | 179 |
| abstract_inverted_index.resonance | 43 |
| abstract_inverted_index.approaches | 64, 117, 164 |
| abstract_inverted_index.biomarkers | 180 |
| abstract_inverted_index.comparison | 23 |
| abstract_inverted_index.complement | 119 |
| abstract_inverted_index.consistent | 66 |
| abstract_inverted_index.fractional | 154 |
| abstract_inverted_index.fractions, | 142 |
| abstract_inverted_index.increasing | 79 |
| abstract_inverted_index.maturation | 8 |
| abstract_inverted_index.predicting | 68 |
| abstract_inverted_index.throughout | 9 |
| abstract_inverted_index.44.6–82.8 | 56 |
| abstract_inverted_index.anisotropy, | 155 |
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| abstract_inverted_index.approaches, | 36, 94 |
| abstract_inverted_index.fundamental | 13 |
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| abstract_inverted_index.contributing | 100 |
| abstract_inverted_index.degeneration | 77 |
| abstract_inverted_index.investigation | 173 |
| abstract_inverted_index.relationships | 148 |
| abstract_inverted_index.understanding | 16 |
| abstract_inverted_index.diffusivities, | 145, 153 |
| abstract_inverted_index.microstructure | 76 |
| abstract_inverted_index.age‐associations | 29 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5037337043, https://openalex.org/A5040953129 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I1281400175, https://openalex.org/I179863766, https://openalex.org/I184942183 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.98492267 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |