Skip to main content

Advertisement

Log in

Effects of aging on brain volumes in healthy individuals across adulthood

  • Original Article
  • Published:
Neurological Sciences Aims and scope Submit manuscript

Abstract

In this retrospective study, we analyzed the effects of age on brain volumes in healthy brains across adulthood. We investigated the correlations between brain volumes and age in the brains of 563 healthy individuals (age range: 20–86, 55% female) whose MRI scans and related information were drawn from the IXI database (brain-development.org/ixi-dataset/). We conducted a regression analysis to assess the effect of age on whole-brain volumes as well as selected regional volumetric measures. The whole-brain analysis revealed a negative linear relationship between gray matter (GM) and age as well as nonlinear patterns of the relationship between age and the white matter (WM), cerebrospinal fluid (CSF), and the GM/WM ratio across adulthood. The regional volumetric analysis showed linear and non-linear age-related regional volumetric changes with aging. Our present findings contribute to the understanding of how structures in the human brain change over the adult years and will help address the pathological age-related neural changes in age-related neural disorders such as Parkinson disease and Alzheimer disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and materials

The dataset analyzed during this current study are available at http://brain-development.org/ixi-dataset/.

Abbreviations

GM:

gray matter

WM:

white matter

CSF:

cerebrospinal fluid

nGM:

normalize GM

nWM:

normalize WM

nCSF:

normalize CSF

MRI:

magnetic resonance imaging

VBM:

voxel-based morphometry

TIV:

total intracranial volume

ICV:

intracranial volume

CAT:

Computational Anatomy Toolbox

References

  1. Terribilli D, Schaufelberger MS, Duran FLS, Zanetti MV, Curiati PK, Menezes PR, Scazufca M, Amaro E Jr, Leite CC, Busatto GF (2011) Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol Aging 32(2):354–368

    Article  PubMed Central  PubMed  Google Scholar 

  2. Giorgio A, Santelli L, Tomassini V, Bosnell R, Smith S, de Stefano N, Johansen-Berg H (2010) Age-related changes in grey and white matter structure throughout adulthood. Neuroimage 51(3):943–951

    Article  PubMed  Google Scholar 

  3. Taki Y, Thyreau B, Kinomura S, Sato K, Goto R, Kawashima R, Fukuda H (2011) Correlations among brain gray matter volumes, age, gender, and hemisphere in healthy individuals. PLoS One 6(7):e22734

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Sowell ER, Thompson PM, Tessner KD, Toga AW (2001) mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J Neurosci 21(22):8819–8829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW (2003) Mapping cortical change across the human life span. Nat Neurosci 6(3):309–315

    Article  CAS  PubMed  Google Scholar 

  6. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C (2003) Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 23(8):3295–3301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Abe O, Yamasue H, Aoki S, Suga M, Yamada H, Kasai K, Masutani Y, Kato N, Kato N, Ohtomo K (2008) Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data. Neurobiol Aging 29(1):102–116

    Article  PubMed  Google Scholar 

  8. Kalpouzos G, Chételat G, Baron JC, Landeau B, Mevel K, Godeau C, Barré L, Constans JM, Viader F, Eustache F, Desgranges B (2009) Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiol Aging 30(1):112–124

    Article  CAS  PubMed  Google Scholar 

  9. Alexander GE, Chen K, Merkley TL, Reiman EM, Caselli RJ, Aschenbrenner M, Santerre-Lemmon L, Lewis DJ, Pietrini P, Teipel SJ, Hampel H, Rapoport SI, Moeller JR (2006) Regional network of magnetic resonance imaging gray matter volume in healthy aging. Neuroreport 17(10):951–956

    Article  PubMed  Google Scholar 

  10. Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (2001) A voxel-based morphometric study of ageing in 465 Normal adult human brains. Neuroimage 14(1):21–36

    Article  CAS  PubMed  Google Scholar 

  11. Smith CD, Chebrolu H, Wekstein DR, Schmitt FA, Markesbery WR (2007) Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly. Neurobiol Aging 28(7):1075–1087

    Article  PubMed  Google Scholar 

  12. Giorgio A, Watkins KE, Chadwick M, James S, Winmill L, Douaud G, de Stefano N, Matthews PM, Smith SM, Johansen-Berg H, James AC (2010) Longitudinal changes in grey and white matter during adolescence. Neuroimage 49(1):94–103

    Article  CAS  PubMed  Google Scholar 

  13. Matsuda H (2013) Voxel-based morphometry of brain MRI in Normal aging and Alzheimer’s disease. Aging Dis 4(1):29–37

    PubMed  Google Scholar 

  14. Potvin O, Dieumegarde L, Duchesne S, Initiative N (2017) NeuroImage Freesurfer cortical normative data for adults using Desikan-Killiany- Tourville and ex vivo protocols. Neuroimage 156:43–64

    Article  PubMed  Google Scholar 

  15. Potvin O, Mouiha A, Dieumegarde L, Duchesne S, Initiative ADN (2016) Normative data for subcortical regional volumes over the lifetime of the adult human brain. Neuroimage 137:9–20

    Article  PubMed  Google Scholar 

  16. Swerdlow RH (2011) Brain aging, Alzheimer’s disease, and mitochondria. Biochim Biophys Acta Mol basis Dis 1812(12):1630–1639

    Article  CAS  Google Scholar 

  17. Reeve A, Simcox E, Turnbull D (2014) Ageing and Parkinson’s disease: why is advancing age the biggest risk factor? Ageing Res Rev 14(1):19–30

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Gaser C and Dahnke R (2012), “CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data,” vol. 32, no. 7, p. 7743

  19. Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4):1402–1418

    Article  PubMed  Google Scholar 

  20. Reuter M, Rosas HD, Fischl B (2010) Highly accurate inverse consistent registration: a robust approach. Neuroimage 53(4):1181–1196

    Article  PubMed  Google Scholar 

  21. Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, Kennedy D, Schmitt F, Brown G, MacFall J, Fischl B, Dale A (Apr. 2006) Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30(2):436–443

    Article  PubMed  Google Scholar 

  22. Ge Y, Grossman R, Babb J (2002) Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. Am J Dent 23:1327–1333

    Google Scholar 

  23. Grieve SM, Clark CR, Williams LM, Peduto AJ, Gordon E (2005) Preservation of limbic and paralimbic structures in aging. Hum Brain Mapp 25(4):391–401

    Article  PubMed  PubMed Central  Google Scholar 

  24. Farokhian F, Yang C, Beheshti I, Matsuda H, Wu S (2018) Age-related gray and white matter changes in Normal adult brains. Aging Dis 9(1):1–11

    Article  Google Scholar 

  25. Kennedy KM, Erickson KI, Rodrigue KM, Voss MW, Colcombe SJ, Kramer AF, Acker JD, Raz N (2009) Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry. Neurobiol Aging 30(10):1657–1676

    Article  PubMed  Google Scholar 

  26. Hedden T, Gabrieli JDE (2004) Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci 5(2):87–96

    Article  CAS  PubMed  Google Scholar 

  27. Kruggel F, Turner J, Muftuler LT, Initiative ADN (2010) Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage 49(3):2123–2133

    Article  PubMed  Google Scholar 

  28. Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: {T}he effects of field strength, scanner upgrade and manufacturer. Neuroimage 32(1):180–194

    Article  PubMed  Google Scholar 

Download references

Funding

This work was partly carried out under the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project (grant number 16dm0207017h0003), funded by the Japan Agency for Medical Research and Development (AMED) and Intramural Research Grant (27-8) for Neurological and Psychiatric Disorders of the NCNP.

Author information

Authors and Affiliations

Authors

Contributions

IB designed the research, performed the statistical analysis, and drafted the manuscript. NM performed the volumetric segmentation. HM participated in the design of the study and supervised the statistical analysis. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Iman Beheshti.

Ethics declarations

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval

This study was approved by the Institutional Review Board at the National Center of Neurology and Psychiatry, Tokyo, Japan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beheshti, I., Maikusa, N. & Matsuda, H. Effects of aging on brain volumes in healthy individuals across adulthood. Neurol Sci 40, 1191–1198 (2019). https://doi.org/10.1007/s10072-019-03817-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10072-019-03817-3

Keywords

Navigation