Skip to main content
SpringerLink
Log in

Morphometry in Normal Aging

  • Protocol
  • First Online:
Brain Morphometry

Part of the book series: Neuromethods ((NM,volume 136))

  • 1359 Accesses

Abstract

Magnetic resonance imaging (MRI)-based evaluation of brain anatomy is regarded as a well-validated method for assessing the age-related trajectories of human regional brain volumes over the life span. Automated softwares such as FreeSurfer have enabled automated quantification of regional and even subregional volumes. Vowel-based morphometry is easily applicable with a short scanning time. Age-related volume reductions are observed in the perisylvian, pericentral, and cingulate cortex as well as the thalamic radiations, internal capsule, corpus callosum, cerebellum, and deep white matter. Hippocampal subfields show age-related volume decline prominently in CA1, the dentate gyrus, and the perirhinal cortex. Recent application of graph theory to structural connectivity has revealed significant network differences between young and old age groups, particularly in the default mode network.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM (2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6:67–77

    Article  PubMed  PubMed Central  Google Scholar 

  2. Jack CR Jr, Barkhof F, Bernstein MA et al (2011) Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease. Alzheimers Dement 7:474–485

    Article  PubMed  PubMed Central  Google Scholar 

  3. Geuze E, Vermetten E, Bremner JD (2005) MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol Psychiatry 10:147–159

    Article  CAS  PubMed  Google Scholar 

  4. Frisoni GB, Jack CR Jr, Bocchetta M et al (2015) The EADC-ADNI harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimers Dement 11:111–125

    Article  PubMed  Google Scholar 

  5. Fischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355

    Article  CAS  PubMed  Google Scholar 

  6. Fujishima M, Maikusa N, Nakamura K et al (2014) Mild cognitive impairment, poor episodic memory, and late-life depression are associated with cerebral cortical thinning and increased white matter hyperintensities. Front Aging Neurosci 6:306

    Article  PubMed  PubMed Central  Google Scholar 

  7. Yushkevich PA, Wang H, Pluta J et al (2010) Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI. NeuroImage 53:1208–1224

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ashburner J, Friston KJ (2000) Voxel-based morphometry--the methods. NeuroImage 11:805–821

    Article  CAS  PubMed  Google Scholar 

  9. Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. NeuroImage 14:1238–1243

    Article  CAS  PubMed  Google Scholar 

  10. Good CD, Johnsrude IS, Ashburner J et al (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14:21–36

    Article  CAS  PubMed  Google Scholar 

  11. Karas GB, Burton EJ, Rombouts SA et al (2003) A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry. NeuroImage 18:895–907

    Article  CAS  PubMed  Google Scholar 

  12. Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38:95–113

    Article  PubMed  Google Scholar 

  13. Eggert LD, Sommer J, Jansen A, Kircher T, Konrad C (2012) Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. PLoS One 7:e45081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Klein A, Andersson J, Ardekani BA et al (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46:786–802

    Article  PubMed  PubMed Central  Google Scholar 

  15. Raji CA, Lopez OL, Kuller LH, Carmichael OT, Becker JT (2009) Age, Alzheimer disease, and brain structure. Neurology 73:1899–1905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Raz N, Lindenberger U, Rodrigue KM et al (2005) Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex 15:1676–1689

    Article  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  18. Matsuda H, Ohnishi T, Asada T et al (2003) Correction for partial-volume effects on brain perfusion SPECT in healthy men. J Nucl Med 44:1243–1252

    PubMed  Google Scholar 

  19. Tisserand DJ, van Boxtel MP, Pruessner JC, Hofman P, Evans AC, Jolles J (2004) A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. Cereb Cortex 14:966–973

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  21. 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 28:1075–1087

    Article  PubMed  Google Scholar 

  22. Curiati PK, Tamashiro JH, Squarzoni P et al (2009) Brain structural variability due to aging and gender in cognitively healthy elders: results from the Sao Paulo ageing and health study. Am J Neuroradiol 30:1850–1856

    Article  CAS  PubMed  Google Scholar 

  23. Kalpouzos G, Chételat G, Baron JC et al (2009) Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiol Aging 30:112–124

    Article  CAS  PubMed  Google Scholar 

  24. Terribilli D, Schaufelberger MS, Duran FL et al (2011) Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol Aging 32:354–368

    Article  PubMed  PubMed Central  Google Scholar 

  25. Giorgio A, Watkins KE, Chadwick M et al (2010) Longitudinal changes in grey and white matter during adolescence. NeuroImage 49:94–103

    Article  CAS  PubMed  Google Scholar 

  26. Giorgio A, Santelli L, Tomassini V et al (2010) Age-related changes in grey and white matter structure throughout adulthood. NeuroImage 51:943–951

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liu H, Wang L, Geng Z et al (2016) A voxel-based morphometric study of age- and sex-related changes in white matter volume in the normal aging brain. Neuropsychiatr Dis Treat 12:453–465

    PubMed  PubMed Central  Google Scholar 

  28. Streitbürger DP, Möller HE, Tittgemeyer M, Hund-Georgiadis M, Schroeter ML, Mueller K (2012) Investigating structural brain changes of dehydration using voxel-based morphometry. PLoS One 7:e44195

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hutton C, Draganski B, Ashburner J, Weiskopf N (2009) A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. NeuroImage 48:371–380

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wu W, Brickman AM, Luchsinger J et al (2008) The brain in the age of old: the hippocampal formation is targeted differentially by diseases of late life. Ann Neurol 64:698–706

    Article  PubMed  PubMed Central  Google Scholar 

  31. Mueller SG, Stables L, AT D et al (2007) Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T. Neurobiol Aging 28:719–726

    Article  CAS  PubMed  Google Scholar 

  32. Yushkevich PA, Pluta JB, Wang H et al (2015) Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Hum Brain Mapp 36:258–287

    Article  PubMed  Google Scholar 

  33. Wisse LE, Biessels GJ, Geerlings MI (2014) Critical appraisal of the hippocampal subfield segmentation package in FreeSurfer. Front Aging Neurosci 6:261

    Article  PubMed  PubMed Central  Google Scholar 

  34. de Flores R, La Joie R, Landeau B et al (2015) Effects of age and Alzheimer's disease on hippocampal subfields: comparison between manual and FreeSurfer volumetry. Hum Brain Mapp 36:463–474

    Article  PubMed  Google Scholar 

  35. Iglesias JE, Augustinack JC, Nguyen K et al (2015) A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage 115:117–137

    Article  PubMed  PubMed Central  Google Scholar 

  36. Mueller SG, Weiner MW (2009) Selective effect of age, Apo e4, and Alzheimer's disease on hippocampal subfields. Hippocampus 19:558–564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Shing YL, Rodrigue KM, Kennedy KM et al (2011) Hippocampal subfield volumes: age, vascular risk, and correlation with associative memory. Front Aging Neurosci 3:2

    Article  PubMed  PubMed Central  Google Scholar 

  38. Raz N, Daugherty AM, Bender AR, Dahle CL, Land S (2015) Volume of the hippocampal subfields in healthy adults: differential associations with age and a pro-inflammatory genetic variant. Brain Struct Funct 220:2663–2674

    Article  CAS  PubMed  Google Scholar 

  39. Kerchner GA, Bernstein JD, Fenesy MC et al (2013) Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: a seven tesla magnetic resonance imaging study. J Neurosci 33:16666–16672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. La Joie R, Fouquet M, Mézenge F et al (2010) Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence. NeuroImage 53:506–514

    Article  PubMed  Google Scholar 

  41. Ziegler G, Dahnke R, Jäncke L, Yotter RA, May A, Gaser C (2012) Brain structural trajectories over the adult lifespan. Hum Brain Mapp 33:2377–2389

    Article  PubMed  Google Scholar 

  42. Pereira JB, Valls-Pedret C, Ros E et al (2014) Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI. Hippocampus 24:403–414

    Article  PubMed  Google Scholar 

  43. Voineskos AN, Winterburn JL, Felsky D et al (2015) Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan. Hum Brain Mapp 36:3020–3037

    Article  PubMed  Google Scholar 

  44. Daugherty AM, Bender AR, Raz N, Ofen N (2016) Age differences in hippocampal subfield volumes from childhood to late adulthood. Hippocampus 26:220–228

    Article  PubMed  Google Scholar 

  45. Chadwick MJ, Bonnici HM, Maguire EA (2014) CA3 size predicts the precision of memory recall. Proc Natl Acad Sci U S A 111:10720–10725

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bender AR, Daugherty AM, Raz N (2013) Vascular risk moderates associations between hippocampal subfield volumes and memory. J Cogn Neurosci 25:1851–1862

    Article  PubMed  Google Scholar 

  47. Mechelli A, Friston KJ, Frackowiak RS, Price CJ (2005) Structural covariance in the human cortex. J Neurosci 25:8303–8310

    Article  CAS  PubMed  Google Scholar 

  48. Lerch JP, Worsley K, Shaw WP et al (2006) Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. NeuroImage 31:993–1003

    Article  PubMed  Google Scholar 

  49. Stam CJ, Reijneveld JC (2007) Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys 1:3

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bullmore ET, Bassert DS (2011) Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 7:113–140

    Article  PubMed  Google Scholar 

  51. Hosseini SM, Hoeft F, Kesler SR (2012) GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One 7:e40709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gong G, He Y, Chen ZJ, Evans AC (2012) Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. NeuroImage 59:1239–1248

    Article  PubMed  Google Scholar 

  53. Hosseini SM, Kesler SR (2013) Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults. NeuroImage 78:402–414

    Article  PubMed  PubMed Central  Google Scholar 

  54. Tijms BM, Seriès P, Willshaw DJ, Lawrie SM (2012) Similarity-based extraction of individual networks from gray matter MRI scans. Cereb Cortex 22:1530–1541

    Article  PubMed  Google Scholar 

  55. Tijms BM, Kate MT, Wink AM et al (2016) Gray matter network disruptions and amyloid beta in cognitively normal adults. Neurobiol Aging 37:154–160

    Article  CAS  PubMed  Google Scholar 

  56. Chen ZJ, He Y, Rosa-Neto P, Gong G, Evans AC (2011) Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI. NeuroImage 56:235–245

    Article  PubMed  Google Scholar 

  57. Wu K, Taki Y, Sato K et al (2012) Age-related changes in topological organization of structural brain networks in healthy individuals. Hum Brain Mapp 33:552–568

    Article  CAS  PubMed  Google Scholar 

  58. Zhu W, Wen W, He Y, Xia A, Anstey KJ, Sachdev P (2012) Changing topological patterns in normal aging using large-scale structural networks. Neurobiol Aging 33:899–913

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was carried out under the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project, funded by the Japan Agency for Medical Research and Development (AMED).

Author information

Authors and Affiliations

  1. Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan

    Hiroshi Matsuda

Authors
  1. Hiroshi Matsuda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroshi Matsuda .

Editor information

Editors and Affiliations

  1. IRCCS Santa Lucia Foundation, Laboratory of Neuropsychiatry, Rome, Italy

    Gianfranco Spalletta

  2. Division of Neuropsychiatry Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA

    Fabrizio Piras

  3. IMT School for Advanced Studies–Lucca, Lucca, Italy

    Tommaso Gili

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Matsuda, H. (2018). Morphometry in Normal Aging. In: Spalletta, G., Piras, F., Gili, T. (eds) Brain Morphometry. Neuromethods, vol 136. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7647-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7647-8_11

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7645-4

  • Online ISBN: 978-1-4939-7647-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation