Skip to main content
SpringerLink
Log in

Normal Brain Aging: Prediction of Age, Sex and White Matter Hyperintensities Using a MR Image-Based Machine Learning Technique

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

Included in the following conference series:

Abstract

A better understanding of normal human brain aging is required to better study age-related neurodegeneration including cognitive impairment. We propose an automatic deep-learning method to analyze the predictive ability of magnetic resonance images with respect to age, sex and the presence of an age-related pathology (white matter hyperintensity, WMH). Experiments performed in a large dataset, containing 200 normal subjects, resulted in average accuracy rates to predict subject age (82.0%), sex (79.5%), and WMH occurrence (72.5%) when combining handcrafted texture and convolutional features. Positive and negative correlations between other extracted features and the subject characteristics (age, sex and WMH occurrence) were also observed. Even though human brain variability due to age, sex and WMH occurrence in structural magnetic resonance imaging may be subtle (and often not observable by human specialists), our results demonstrate that MR images alone contain relevant information that can better characterize the aging process and some demographic information of the population.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    http://braintumorsegmentation.org/.

References

  1. Gunning-Dixon, F.M., Brickman, A.M., et al.: Aging of cerebral white matter: a review of MRI findings. Int. J. Geriatr. Psychiatr. 24(2), 109–117 (2009)

    Article  Google Scholar 

  2. Tsang, A., Lebel, C.A., Bray, S.L., et al.: White matter structural connectivity is not correlated to cortical resting-state functional connectivity over the healthy adult lifespan. Front. Aging Neurosci. 9(144), 1–13 (2017)

    Google Scholar 

  3. Lampe, L., Kharabian-Masouleh, S., Kynast, J., et al.: Lesion location matters: the relationships between white matter hyperintensities on cognition in the healthy elderly. J. Cereb. Blood Flow Metab. (2017). https://doi.org/10.1177/0271678X17740501

  4. Lockhart, S.N., DeCarli, C.: Structural imaging measures of brain aging. Neuropsychology 24(3), 271–289 (2014)

    Article  Google Scholar 

  5. Spilt, A., Geeraedts, T., Craen, A.J., et al.: Age-related changes in normal-appearing brain tissue and white matter hyperintensities: more of the same or something else? New Engl. J. Med. 26(4), 725–729 (2005)

    Google Scholar 

  6. Leite, M., Rittner, L., Appenzeller, S.: Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging. J. Med. Imaging 2(1), 014002-1–014002-10 (2015)

    Article  Google Scholar 

  7. Dichgans, M., Wardlaw, J., Smith, E., et al.: Metacohorts for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: an initiative of the joint programme for neurodegenerative disease research. Alzheimer’s Dement. 12(12), 1235–1249 (2016)

    Article  Google Scholar 

  8. Fillmore, P.T., Phillips, M.C., Richards, J.E.: Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age. Front. Aging Neurosci. 7, 44 (2015)

    Article  Google Scholar 

  9. Griffanti, L., Zamboni, G., Khan, A., et al.: BIANCA (Brain Intensity Abnormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. NeuroImage 141, 191–205 (2016)

    Article  Google Scholar 

  10. Despotovic, I., Goossens, B., Philips, W.: MRI segmentation of the human brain: challenges, methods, and applications. Comput. Math. Methods Med. 2015(1), 1–23 (2015)

    Article  Google Scholar 

  11. Tustison, N.J., Avants, B.B., Cook, P.A., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  12. Jenkinson, M., Beckmann, C.F., Behrens, T.E., et al.: FSL. Neuroimage 62, 782–790 (2012)

    Article  Google Scholar 

  13. Nasreddine, Z.S., Phillips, N.A., Bédirian, V., et al.: The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53(4), 695–699 (2005)

    Article  Google Scholar 

  14. Bates, A.T., Divino, C.: Laparoscopic surgery in the elderly: a review of the literature. Aging Dis. 2(6), 149–155 (2015)

    Article  Google Scholar 

  15. Chen, S.C., Shoemaker, S.: Age and cohort effects: the American senior tourism market. Ann. Tour. Res. 48, 58–75 (2014)

    Article  Google Scholar 

  16. Yichuan, T.: Deep learning using linear support vector machines. In: Proceedings of International Conference on Machine Learning (2013)

    Google Scholar 

  17. Woods, R., Gonzalez, R.C.: Digital Image Processing (2000)

    Google Scholar 

  18. Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(1), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  19. Schwartz, W.R., Siqueira, F.R., Pedrini, H.: Evaluation of feature descriptors for texture classification. J. Eletron. Imaging 21(2), 1–17 (2012)

    Google Scholar 

  20. Shin, H., Roth, H., Gao, M., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)

    Article  Google Scholar 

  21. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, San Francisco, vol. 2, pp. 1137–1143 (1995)

    Google Scholar 

  22. Minka, T.: Automatic choice of dimensionality for PCA. NIPS 13, 514 (2000)

    Google Scholar 

  23. McKinney, W.: Data structures for statistical computing in Python. In: Proceedings of the 9th Python in Science Conference (2010)

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Hotchkiss Brain Institute, the Hopewell Professorship, and Coordination for the Improvement of Higher Education Personnel (CAPES, PVE 88881.062158/2014-01) for providing financial support. The Calgary Normative Study (CNS) is funded by the Canadian Institutes for Health Research (CIHR). We thank Drs. Linda B. Andersen, M. Louis Lauzon and Cheryl R. McCreary for their assistance in the CNS. Infrastructure at the Calgary Image Processing and Analysis Centre (CIPAC) was supported by the Canadian Foundation for Innovation (CFI) and the Government of Alberta.

Author information

Authors and Affiliations

  1. Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

    Mariana Bento, Roberto Souza, Marina Salluzzi & Richard Frayne

  2. Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada

    Mariana Bento & Richard Frayne

  3. Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada

    Roberto Souza, Marina Salluzzi & Richard Frayne

Authors
  1. Mariana Bento
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marina Salluzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard Frayne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mariana Bento .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bento, M., Souza, R., Salluzzi, M., Frayne, R. (2018). Normal Brain Aging: Prediction of Age, Sex and White Matter Hyperintensities Using a MR Image-Based Machine Learning Technique. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93000-8_61

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation