Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Publication Preview--Ahead of Print
    • Past Issue Archive
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
  • For Authors
  • About Us
    • About AJNR
    • Editors
    • American Society of Neuroradiology
  • Submit a Manuscript
  • Podcasts
    • Subscribe on iTunes
    • Subscribe on Stitcher
  • More
    • Subscribers
    • Permissions
    • Advertisers
    • Alerts
    • Feedback
  • Other Publications
    • ajnr

User menu

  • Subscribe
  • Alerts
  • Log in

Search

  • Advanced search
American Journal of Neuroradiology
American Journal of Neuroradiology

American Journal of Neuroradiology

  • Subscribe
  • Alerts
  • Log in

Advanced Search

  • Home
  • Content
    • Current Issue
    • Publication Preview--Ahead of Print
    • Past Issue Archive
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
  • For Authors
  • About Us
    • About AJNR
    • Editors
    • American Society of Neuroradiology
  • Submit a Manuscript
  • Podcasts
    • Subscribe on iTunes
    • Subscribe on Stitcher
  • More
    • Subscribers
    • Permissions
    • Advertisers
    • Alerts
    • Feedback
  • Follow AJNR on Twitter
  • Visit AJNR on Facebook
  • Follow AJNR on Instagram
  • Join AJNR on LinkedIn
  • RSS Feeds
LetterLetter

Simple Linear Regression Model Is Misleading When Used to Analyze Quantitative Diffusion Tensor Imaging Data That Include Young and Old Adults

K.M. Hasan
American Journal of Neuroradiology October 2010, 31 (9) E80; DOI: https://doi.org/10.3174/ajnr.A2184
K.M. Hasan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • References
  • PDF
Loading

I read with interest a recent article published in this journal by Wang et al.1 The authors analyzed diffusion tensor imaging (DTI) data acquired on 71 healthy young, old, and older adult brains a (20–79 years of age). The authors calculated diffusion tensor metrics such as fractional anisotropy (FA) and mean, axial, and radial diffusivities by placing regions of interest on the caudate, putamen, and globus pallidus, and they used a linear regression model to fit the scatter of age versus DTI metrics. The article highlights the importance of using the tensor eigenvalues in the interpretation of normal-aging brain data in key gray matter structures that can be used as surrogate neuroimaging markers of natural aging. On the basis of the analysis of these regions of interest, the study concluded that FA increased steadily with age in the putamen (r = 0.535, P < .001). The FA increase in the putamen was attributed primarily to a decrease in the transverse diffusivity (r = −0.451, P < .008).

The increase in striatal FA with age as reported by Wang et al is an important finding that confirms previous and recent DTI reports on both healthy children2,3 and young3,4 and older adults,5–9 or across the human lifespan.10

While a trend in striatal increase in FA versus age reported by Wang et al is consistent with several reports using different DTI analysis methods,2–11 I should also point out that the finding of reduced mean diffusivity with age is contradictory to several previous reports that compared healthy young and older adults. For example, Bhagat and Beaulieu6 and Pfefferbaum et al7 reported that putaminal tensor axial and mean diffusivities increased significantly with advancing age. Càmara et al8 reported an increase in putaminal diffusion anisotropy but a nonsignificant trend in age versus mean diffusivity.

The expected rise in the water-molecular-diffusivity trend in deep striatal gray matter can be seen when including young children and adopting nonlinear curve-fitting models.10 The striatal mean diffusivity curves across the lifespan should also mimic the transverse relaxation age trajectories.11–13 The nonlinear (eg, quadratic) model consolidates reports on healthy children and young and older adults.

I conclude that DTI quantitative reports with a relatively small population and sparse attenuation and extended age ranges should not use simple linear regression because this simple model fails to accommodate the expected decrease in diffusivity in children and the predicted rise in diffusivity as a result of increased water extracellular mobility as tissue ages.11–13

References

  1. 1.↵
    1. Wang Q,
    2. Xu X,
    3. Zhang M
    . Normal aging in the basal ganglia evaluated by eigenvalues of diffusion tensor imaging. AJNR Am J Neuroradiol 2010; 31: 516– 20
    Abstract/FREE Full Text
  2. 2.↵
    1. Mukherjee P,
    2. Miller JH,
    3. Shimony JS,
    4. et al
    . Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol 2002; 23: 1445– 56
    Abstract/FREE Full Text
  3. 3.↵
    1. Snook L,
    2. Paulson LA,
    3. Roy D,
    4. et al
    . Diffusion tensor imaging of neurodevelopment in children and young adults. Neuroimage 2005; 26: 1164– 73
    CrossRefPubMed
  4. 4.↵
    1. Lebel C,
    2. Walker L,
    3. Leemans A,
    4. et al
    . Microstructural maturation of the human brain from childhood to adulthood. Neuroimage 2008; 40: 1044– 55
    CrossRefPubMed
  5. 5.↵
    1. Abe O,
    2. Aoki S,
    3. Hayashi N,
    4. et al
    . Normal aging in the central nervous system: quantitative MR diffusion tensor analysis. Neurobiol Aging 2002; 23: 433– 41
    CrossRefPubMed
  6. 6.↵
    1. Bhagat YA,
    2. Beaulieu C
    . Diffusion anisotropy in subcortical white matter and cortical gray matter: changes with aging and the role of CSF-suppression. J Magn Reson Imaging 2004; 20: 216– 27
    CrossRefPubMed
  7. 7.↵
    1. Pfefferbaum A,
    2. Adalsteinsson E,
    3. Rohlfing T,
    4. et al
    . Diffusion tensor imaging of deep gray matter brain structures: effects of age and iron concentration. Neurobiol Aging 2010; 31: 482– 93
    CrossRefPubMed
  8. 8.↵
    1. Càmara E,
    2. Bodammer N,
    3. Rodríguez-Fornells A,
    4. et al
    . Age-related water diffusion changes in human brain: a voxel-based approach. Neuroimage 2007; 34: 1588 99
    CrossRefPubMed
  9. 9.↵
    1. Hasan KM,
    2. Halphen C,
    3. Boska MD,
    4. et al
    . Diffusion tensor metrics, T2 relaxation, and volumetry of the naturally aging human caudate nuclei in healthy young and middle-aged adults: possible implications for the neurobiology of human brain aging and disease. Magn Reson Med 2008; 59: 7– 13
    CrossRefPubMed
  10. 10.↵
    1. Hasan KM,
    2. Frye RE
    . Diffusion tensor-based regional gray matter tissue segmentation using the international consortium for brain mapping atlases. Human Brain Mapping. 2010 (in press)
  11. 11.↵
    1. Hasan KM,
    2. Walimuni IS,
    3. Abid H,
    4. et al
    . DTI, T2 relaxation and volumetry of the human brain corpus striatum across the lifespan. In: Proceedings of the 18th Meeting of the International Society for Magnetic Resonance in Medicine, Stockholm, Sweden. May 1–7, 2010: 606
  12. 12.↵
    1. Saito N,
    2. Sakai O,
    3. Ozonoff A,
    4. et al
    . Relaxo-volumetric multispectral quantitative magnetic resonance imaging of the brain over the human lifespan: global and regional aging patterns. Magn Reson Imaging 2009; 27: 895– 906. Epub 2009 Jun 10
    CrossRefPubMed
  13. 13.↵
    1. Baratti C,
    2. Barnett AS,
    3. Pierpaoli C
    . Comparative MR imaging study of brain maturation in kittens with T1, T2, and the trace of the diffusion tensor. Radiology 1999; 210: 133– 42
    CrossRefPubMed
  • Copyright © American Society of Neuroradiology
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 31 (9)
American Journal of Neuroradiology
Vol. 31, Issue 9
1 Oct 2010
  • Table of Contents
  • Index by author
Advertisement
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on American Journal of Neuroradiology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Simple Linear Regression Model Is Misleading When Used to Analyze Quantitative Diffusion Tensor Imaging Data That Include Young and Old Adults
(Your Name) has sent you a message from American Journal of Neuroradiology
(Your Name) thought you would like to see the American Journal of Neuroradiology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Simple Linear Regression Model Is Misleading When Used to Analyze Quantitative Diffusion Tensor Imaging Data That Include Young and Old Adults
K.M. Hasan
American Journal of Neuroradiology Oct 2010, 31 (9) E80; DOI: 10.3174/ajnr.A2184

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Simple Linear Regression Model Is Misleading When Used to Analyze Quantitative Diffusion Tensor Imaging Data That Include Young and Old Adults
K.M. Hasan
American Journal of Neuroradiology Oct 2010, 31 (9) E80; DOI: 10.3174/ajnr.A2184
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Purchase

Jump to section

  • Article
    • References
  • Info & Metrics
  • References
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • No citing articles found.
  • Crossref
  • Google Scholar

This article has not yet been cited by articles in journals that are participating in Crossref Cited-by Linking.

More in this TOC Section

  • Callosal Angle Narrowing in Research Data Bases of the Cognitively Impaired
  • Reply:
  • Reply:
Show more Letters

Similar Articles

Advertisement

News and Updates

  • Lucien Levy Best Research Article Award
  • Thanks to our 2021 Distinguished Reviewers
  • Press Releases

Resources

  • Evidence-Based Medicine Level Guide
  • How to Participate in a Tweet Chat
  • AJNR Podcast Archive
  • Ideas for Publicizing Your Research
  • Librarian Resources
  • Terms and Conditions

Opportunities

  • Share Your Art in Perspectives
  • Get Peer Review Credit from Publons
  • Moderate a Tweet Chat

American Society of Neuroradiology

  • Neurographics
  • ASNR Annual Meeting
  • Fellowship Portal
  • Position Statements

© 2022 by the American Society of Neuroradiology | Print ISSN: 0195-6108 Online ISSN: 1936-959X

Powered by HighWire