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Cited by 78Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesCerebrovascular and genetic disorders

Aging and white matter microstructure and macrostructure: A longitudinal multi-site diffusion MRI study of 1218 participants

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Schilling, Kurt G., Archer, Derek, Yeh, Fang-Cheng, Rheault, Francois, Cai, Leon Y., Hansen, Colin, Yang, Qi, Ramdass, Karthik +9 more·2022·Brain Structure and Function
DOI 10.1007/s00429-022-02503-zPMC PMC9648053
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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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  • Multi-shell approaches, including NODDI, constrained NODDI (C-NODDI), and diffusion kurtosis imaging, provide model-dependent indices related to neurite organization, extracellular water, axonal density, and tissue integrity.

    “Multi-shell approaches, including NODDI, constrained NODDI (C-NODDI), and diffusion kurtosis imaging, provide model-dependent indices related to neurite organization, extracellular water, axonal density, and tissue integrity (Alsameen et al., 2023; Raghavan et al., 2021; Schilling et al., 2022).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Schilling et al., 2022

    The Discussion (Age-related microstructural decline; page unavailable) says advanced diffusion models can measure neurite density, extracellular diffusivity, axonal dispersion, and diffusion non-Gaussianity, and links advanced modeling to data with multiple diffusion weightings. It does not identify NODDI, C-NODDI, or diffusion kurtosis imaging by name, or establish the specific claim about extracellular water. The Methods (Processing; page unavailable) report that this study extracted DTI microstructural metrics rather than those named approaches. (LLM, conf=0.87)

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Research works printed but not linked: 55

  1. Abe O, Yamasue H, Aoki S (2008) Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data. Neurobiol Aging 29(1): 102– 116. 10.1016/j.neurobiolaging.2006.09.003 17023094 · DOI
  2. Beck D, de Lange AG, Maximov II (2021) White matter microstructure across the adult lifespan: a mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. Neuroimage 224: 117441. 10.1016/j.neuroimage.2020.117441 33039618 · DOI · Paper page
  3. Bergfield KL, Hanson KD, Chen K (2010) Age-related networks of regional covariance in MRI gray matter: reproducible multivariate patterns in healthy aging. Neuroimage 49(2): 1750– 1759. 10.1016/j.neuroimage.2009.09.051 19796692 PMC2789892 · DOI
  4. Bigham B, Zamanpour SA, Zare H (2022) Alzheimer’s disease neuroimaging I. Features of the superficial white matter as biomarkers for the detection of Alzheimer’s disease and mild cognitive impairment: a diffusion tensor imaging study. Heliyon 8(1): 08725. 10.1016/j.heliyon.2022.e08725 PMC8761704 35071808 · DOI
  5. Chamberland M, Tax CMW, Jones DK (2018) Meyer’s loop tractography for image-guided surgery depends on imaging protocol and hardware. Neuroimage Clin 20: 458– 465. 10.1016/j.nicl.2018.08.021 30128284 PMC6096050 · DOI
  6. Chandio BQ, Risacher SL, Pestilli F (2020) Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10(1): 17149. 10.1038/s41598-020-74054-4 33051471 PMC7555507 · DOI
  7. Chang YS, Owen JP, Pojman NJ (2015) White matter changes of neurite density and fiber orientation dispersion during human brain maturation. PLoS One 10(6): e0123656. 10.1371/journal.pone.0123656 26115451 PMC4482659 · DOI · Paper page
  8. Coutu JP, Chen JJ, Rosas HD, Salat DH (2014) Non-Gaussian water diffusion in aging white matter. Neurobiol Aging 35(6): 1412– 1421. 10.1016/j.neurobiolaging.2013.12.001 24378085 PMC3961541 · DOI
  9. Cox SR, Ritchie SJ, Tucker-Drob EM (2016) Ageing and brain white matter structure in 3513 UK Biobank participants. Nat Commun 7: 13629. 10.1038/ncomms13629 27976682 PMC5172385 · DOI · Paper page
  10. Daducci A, Canales-Rodríguez EJ, Descoteaux M (2014) Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI. IEEE Trans Med Imaging 33(2): 384– 399. 10.1109/TMI.2013.2285500 24132007 · DOI
  11. Fan Q, Tian Q, Ohringer NA (2019) Age-related alterations in axonal microstructure in the corpus callosum measured by high-gradient diffusion MRI. Neuroimage 191: 325– 336. 10.1016/j.neuroimage.2019.02.036 30790671 PMC6467051 · DOI
  12. Farrell JA, Landman BA, Jones CK (2007) Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. J Magn Reson Imaging 26(3): 756– 67. 10.1002/jmri.21053 17729339 PMC2862967 · DOI
  13. Fillard P, Descoteaux M, Goh A (2011) Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56(1): 220– 234. 10.1016/j.neuroimage.2011.01.032 21256221 · DOI · Paper page
  14. Fortin JP, Parker D, Tunç B (2017) Harmonization of multi-site diffusion tensor imaging data. Neuroimage 11(161): 149– 170. 10.1016/j.neuroimage.2017.08.047 PMC5736019 28826946 · DOI · Paper page
  15. Giorgio A, Santelli L, Tomassini V (2010) Age-related changes in grey and white matter structure throughout adulthood. Neuroimage 51(3): 943– 951. 10.1016/j.neuroimage.2010.03.004 20211265 PMC2896477 · DOI · Paper page
  16. Guevara P, Duclap D, Poupon C (2012) Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage 61(4): 1083– 1099. 10.1016/j.neuroimage.2012.02.071 22414992 · DOI
  17. Guo Y, Zhang Z, Zhou B (2014) Grey-matter volume as a potential feature for the classification of Alzheimer’s disease and mild cognitive impairment: an exploratory study. Neurosci Bull 30(3): 477– 489. 10.1007/s12264-013-1432-x 24760581 PMC5562611 · DOI
  18. Isaac Tseng WY, Hsu YC, Chen CL (2021) Microstructural differences in white matter tracts across middle to late adulthood: a diffusion MRI study on 7167 UK Biobank participants. Neurobiol Aging 98: 160– 172. 10.1016/j.neurobiolaging.2020.10.006 33290993 · DOI
  19. Jefferson AL, Gifford KA, Acosta LM (2016) The Vanderbilt memory & aging project: study design and baseline cohort overview. J Alzheimers Dis 52(2): 539– 559. 10.3233/JAD-150914 26967211 PMC4866875 · DOI
  20. Jones DK (2010) Diffusion MRI: theory, methods, and application. Oxford University Press, Oxford, p 767
  21. Jones DK, Basser PJ (2004) “ Squashing peanuts and smashing pumpkins”: how noise distorts diffusion-weighted MR data. Magn Reson Med 52(5): 979– 993. 10.1002/mrm.20283 15508154 · DOI
  22. Jorge L, Martins R, Canario N (2021) Investigating the spatial associations between amyloid-beta deposition, grey matter volume, and neuroinflammation in Alzheimer’s disease. J Alzheimers Dis 80(1): 113– 132. 10.3233/JAD-200840 33523050 PMC8075404 · DOI
  23. Kimmel CL, Alhassoon OM, Wollman SC (2016) Age-related parieto-occipital and other gray matter changes in borderline personality disorder: a meta-analysis of cortical and subcortical structures. Psychiatry Res Neuroimaging 251: 15– 25. 10.1016/j.pscychresns.2016.04.005 27107250 · DOI
  24. Landman BA, Farrell JA, Jones CK, Smith SA, Prince JL, Mori S (2007) Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. Neuroimage 36(4): 1123– 38. 10.1016/j.neuroimage.2007.02.056 17532649 PMC12008999 · DOI
  25. Lawrence KE, Nabulsi L, Santhalingam V (2021) Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: a UK biobank study. Brain Imaging Behav 15(6): 2813– 2823. 10.1007/s11682-021-00548-y 34537917 PMC8761720 · DOI
  26. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C (2012) Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage 60(1): 340– 352. 10.1016/j.neuroimage.2011.11.094 22178809 · DOI · Paper page
  27. Mirzaalian H, Ning L, Savadjiev P (2016) Inter-site and inter-scanner diffusion MRI data harmonization. Neuroimage 07(135): 311– 323. 10.1016/j.neuroimage.2016.04.041 PMC5367052 27138209 · DOI
  28. Molloy CJ, Nugent S, Bokde ALW (2021) Alterations in diffusion measures of white matter integrity associated with healthy aging. J Gerontol A Biol Sci Med Sci 76(6): 945– 954. 10.1093/gerona/glz289 31830253 · DOI
  29. Nicolas R, Hiba B, Dilharreguy B (2020) Changes over time of diffusion MRI in the white matter of aging brain, a good predictor of verbal recall. Front Aging Neurosci 12: 218. 10.3389/fnagi.2020.00218 32922282 PMC7456903 · DOI
  30. Ning L, Bonet-Carne E, Grussu F (2020) Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and results. Neuroimage 221: 117128. 10.1016/j.neuroimage.2020.117128 32673745 PMC10243465 · DOI
  31. Pfefferbaum A, Lim KO, Zipursky RB (1992) Brain gray and white matter volume loss accelerates with aging in chronic alcoholics: a quantitative MRI study. Alcohol Clin Exp Res 16(6): 1078– 1089. 10.1111/j.1530-0277.1992.tb00702.x 1471762 · DOI
  32. Ramanoel S, Hoyau E, Kauffmann L (2018) Gray matter volume and cognitive performance during normal aging. A Voxel-based morphometry study. Front Aging Neurosci 10: 235. 10.3389/fnagi.2018.00235 30123123 PMC6085481 · DOI
  33. Rheault F, De Benedictis A, Daducci A (2020) Tractostorm: the what, why, and how of tractography dissection reproducibility. Hum Brain Mapp 41(7): 1859– 1874. 10.1002/hbm.24917 31925871 PMC7267902 · DOI
  34. Schilling KG, Janve V, Gao Y, Stepniewska I, Landman BA, Anderson AW (2018) Histological validation of diffusion MRI fiber orientation distributions and dispersion. Neuroimage 01(165): 200– 221. 10.1016/j.neuroimage.2017.10.046 PMC5732036 29074279 · DOI · Paper page
  35. Schilling KG, Tax CMW, Rheault F (2021b) Fiber tractography bundle segmentation depends on scanner effects, acquisition, diffusion sensitization, and bundle segmentation workflow. bioRxiv. 10.1101/2021.03.17.435872 PMC9933001 34358660 · DOI
  36. Schilling KG, Tax CMW, Rheault F (2021c) Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow. Neuroimage 242: 118451. 10.1016/j.neuroimage.2021.118451 34358660 PMC9933001 · DOI
  37. Storsve AB, Fjell AM, Yendiki A, Walhovd KB (2016) Longitudinal changes in white matter tract integrity across the adult lifespan and its relation to cortical thinning. PLoS One 11(6): e0156770. 10.1371/journal.pone.0156770 27253393 PMC4890742 · DOI
  38. Taki Y, Thyreau B, Kinomura S (2011) Correlations among brain gray matter volumes, age, gender, and hemisphere in healthy individuals. PLoS One 6(7): e22734. 10.1371/journal.pone.0022734 21818377 PMC3144937 · DOI
  39. Tax CM, Grussu F, Kaden E (2019) Cross-scanner and cross-protocol diffusion MRI data harmonisation: a benchmark database and evaluation of algorithms. Neuroimage. 10.1016/j.neuroimage.2019.01.077 PMC6556555 30716459 · DOI
  40. Taylor JR, Williams N, Cusack R (2017) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144(Pt B): 262– 269. 10.1016/j.neuroimage.2015.09.018 26375206 PMC5182075 · DOI
  41. Terribilli D, Schaufelberger MS, Duran FL (2011) Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol Aging 32(2): 354– 368. 10.1016/j.neurobiolaging.2009.02.008 19282066 PMC3004040 · DOI
  42. Toschi N, Gisbert RA, Passamonti L, Canals S, De Santis S (2020) Multishell diffusion imaging reveals sex-specific trajectories of early white matter degeneration in normal aging. Neurobiol Aging 02(86): 191– 200. 10.1016/j.neurobiolaging.2019.11.014 31902522 · DOI
  43. Wang J, Knol MJ, Tiulpin A (2019) Gray matter age prediction as a biomarker for risk of dementia. Proc Natl Acad Sci USA 116(42): 21213– 21218. 10.1073/pnas.1902376116 31575746 PMC6800321 · DOI
  44. Warrington S, Bryant KL, Khrapitchev AA (2020) XTRACT—standardised protocols for automated tractography in the human and macaque brain. Neuroimage 217: 116923. 10.1016/j.neuroimage.2020.116923 32407993 PMC7260058 · DOI
  45. Wasserthal J, Neher P, Maier-Hein KH (2018) TractSeg—fast and accurate white matter tract segmentation. Neuroimage 183: 239– 253. 10.1016/j.neuroimage.2018.07.070 30086412 · DOI · Paper page
  46. Williams OA, An Y, Beason-Held L (2019) Vascular burden and APOE epsilon4 are associated with white matter microstructural decline in cognitively normal older adults. Neuroimage 188: 572– 583. 10.1016/j.neuroimage.2018.12.009 30557663 PMC6601608 · DOI
  47. Winter M, Tallantyre EC, Brice TAW, Robertson NP, Jones DK, Chamberland M (2021) Tract-specific MRI measures explain learning and recall differences in multiple sclerosis. Brain Commun. 10.1093/braincomms/fcab065 PMC8088789 33959710 · DOI
  48. Yap QJ, Teh I, Fusar-Poli P, Sum MY, Kuswanto C, Sim K (2013) Tracking cerebral white matter changes across the lifespan: insights from diffusion tensor imaging studies. J Neural Transm (Vienna) 120(9): 1369– 1395. 10.1007/s00702-013-0971-7 23328950 · DOI
  49. Yeh F-C (2020) Shape analysis of the human association pathways. bioRxiv. 10.1101/2020.04.19.049544 PMC7775618 32882375 · DOI
  50. Yeh FC, Wedeen VJ, Tseng WY (2010) Generalized q-sampling imaging. IEEE Trans Med Imaging 29(9): 1626– 1635. 10.1109/TMI.2010.2045126 20304721 · DOI
  51. Yeh FC, Verstynen TD, Wang Y, Fernandez-Miranda JC, Tseng WY (2013) Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One 8(11): e80713. 10.1371/journal.pone.0080713 24348913 PMC3858183 · DOI · Paper page
  52. Yeh FC, Panesar S, Fernandes D (2018) Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178: 57– 68. 10.1016/j.neuroimage.2018.05.027 29758339 PMC6921501 · DOI
  53. Yeh FC, Panesar S, Barrios J (2019) Automatic removal of false connections in diffusion MRI tractography using topology-informed pruning (TIP). Neurotherapeutics 16(1): 52– 58. 10.1007/s13311-018-0663-y 30218214 PMC6361061 · DOI
  54. Yendiki A, Panneck P, Srinivasan P (2011) Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front Neuroinform 5: 23. 10.3389/fninf.2011.00023 22016733 PMC3193073 · DOI
  55. Zuo N, Hu T, Liu H, Sui J, Liu Y, Jiang T (2021) Gray matter-based age prediction characterizes different regional patterns. Neurosci Bull 37(1): 94– 98. 10.1007/s12264-020-00558-8 32813179 PMC7811971 · DOI

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Authors
Schilling, K. G., Archer, D., Yeh, F.-C., Rheault, F., Cai, L. Y., Hansen, C., Yang, Q., Ramdass, K., Shafer, A. T., Resnick, S. M., Pechman, K. R., Gifford, K. A., Hohman, T. J., Jefferson, A., Anderson, A. W., Kang, H., & Landman, B. A.
Affiliations
  • Vanderbilt University Medical Center (US)
  • Vanderbilt University (US)
  • University of Pittsburgh (US)
  • National Institute on Aging (US)
Corresponding author
Schilling, Kurt G.
ORCID
Venue
Brain Structure and Function
Publisher
Springer Science and Business Media LLC
Published
2022-05-23
Volume
227
Issue
6
Pages
2111-2125
ISSN
  • 1863-2653 (ISSN-L)
  • 1863-2661
DOI
10.1007/s00429-022-02503-z
PMC
PMC9648053
OpenAlex
W4281288063
Cited by
78
References
7
Citations by year
2026: 15 · 2025: 26 · 2024: 18 · 2023: 18 · 2022: 1
FWCI
8.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Brain Structure and Function (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Science Foundation (#1452485)
  • Foundation for the National Institutes of Health (R01EB017230)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
Keywords
brain aging, white matter microstructure, white matter tracts, diffusion MRI, age-related decline, anisotropy, commissural tracts, association tracts, longitudinal change
MeSH terms
Aging, Brain, Cross-Sectional Studies, Humans, Longitudinal Studies, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, White Matter

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