Skip to main content

Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks

Part of the Lecture Notes in Computer Science book series (LNIP,volume 13001)


Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work, we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer’s Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer’s disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples.


  • Deep learning
  • Biomechanical modelling
  • Neurodegeneration
  • Disease progression

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   59.99
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

Learn about institutional subscriptions


  1. 1.

  2. 2.


  1. Bae, J.B., et al.: Identification of Alzheimer’s disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging. Sci. Rep. 10(1), 1–10 (2020)

    CrossRef  Google Scholar 

  2. Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800, August 2019.,

  3. Bass, C., et al.: Image synthesis with a convolutional capsule generative adversarial network, December 2018.

  4. Bass, C., da Silva, M., Sudre, C., Tudosiu, P.D., Smith, S., Robinson, E.: ICAM: interpretable classification via disentangled representations and feature attribution mapping. In: Advances in Neural Information Processing Systems, vol. 33 (2020)

    Google Scholar 

  5. Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X., Konukoglu, E.: Visual feature attribution using wasserstein GANs, June 2018.

  6. Cardoso, M.J., et al.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34(9), 1976–1988 (2015).

    CrossRef  MathSciNet  Google Scholar 

  7. Carmichael, O., McLaren, D.G., Tommet, D., Mungas, D., Jones, R.N., Initiative, A.D.N., et al.: Coevolution of brain structures in amnestic mild cognitive impairment. NeuroImage 66, 449–456 (2013)

    CrossRef  Google Scholar 

  8. Ferreira, D., et al.: Distinct subtypes of Alzheimer’s disease based on patterns of brain atrophy: longitudinal trajectories and clinical applications. Sci. Rep. 7(1), 1–13 (2017).

    CrossRef  Google Scholar 

  9. Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)

    CrossRef  Google Scholar 

  10. Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)

    CrossRef  Google Scholar 

  11. Khan, N.M., Abraham, N., Hon, M.: Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease. IEEE Access 7, 72726–72735 (2019)

    CrossRef  Google Scholar 

  12. Khanal, B., Lorenzi, M., Ayache, N., Pennec, X.: A Biophysical Model of Shape Changes due to Atrophy in the Brain with Alzheimer’s Disease. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 41–48. Springer, Cham (2014).

    CrossRef  Google Scholar 

  13. Khanal, B., Lorenzi, M., Ayache, N., Pennec, X.: A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer’s disease. NeuroImage 134, 35–52 (2016).,

  14. Bigolin Lanfredi, R., Schroeder, J.D., Vachet, C., Tasdizen, T.: Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 738–748. Springer, Cham (2020).

    CrossRef  Google Scholar 

  15. Li, H., Habes, M., Wolk, D.A., Fan, Y.: Alzheimer’s disease neuroimaging initiative and the australian imaging biomarkers and lifestyle study of aging: a deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer’s Dement. 15(8), 1059–1070 (2019).,

  16. Pawlowski, N., Castro, D.C., Glocker, B.: Deep structural causal models for tractable counterfactual inference, June 2020. arXiv:2006.06485 [cs, stat],

  17. Rabinovici, G., et al.: Distinct MRI atrophy patterns in autopsy-proven Alzheimer’s disease and frontotemporal lobar degeneration. Am. J. Alzheimer’s Dis. Dement. & #x00AE; 22(6), 474–488 (2008)

    Google Scholar 

  18. Richman, D.P., Stewart, R.M., Hutchinson, J.W., Caviness, V.S.: Mechanical model of brain convolutional development. Sci. (New York, N.Y.) 189(4196), 18–21 (1975). doi: 10.1126/science.1135626

    Google Scholar 

  19. Rodriguez, E.K., Hoger, A., McCulloch, A.D.: Stress-dependent finite growth in soft elastic tissues. J. Biomech. 27(4), 455–467 (1994).

    CrossRef  Google Scholar 

  20. Tallinen, T., Chung, J.Y., Biggins, J.S., Mahadevan, L.: Gyrification from constrained cortical expansion. Proc. Natl. Acad. Sci. 111(35), 12667–12672 (2014).,

  21. Tallinen, T., Chung, J.Y., Rousseau, F., Girard, N., Lefèvre, J., Mahadevan, L.: On the growth and form of cortical convolutions. Nat. Phys. 12(6), 588–593 (2016).,

  22. Xu, G., Knutsen, A.K., Dikranian, K., Kroenke, C.D., Bayly, P.V., Taber, L.A.: Axons pull on the brain, but tension does not drive cortical folding. J. Biomech. Eng. 132(7), 071013 (2010).

    CrossRef  Google Scholar 

  23. Young, J.M., Yao, J., Ramasubramanian, A., Taber, L.A., Perucchio, R.: Automatic generation of user material subroutines for biomechanical growth analysis. J. Biomech. Eng. 132(10), 104505 (2010).,

Download references


The data used in this work was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Mariana Da Silva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silva, M.D., Sudre, C.H., Garcia, K., Bass, C., Cardoso, M.J., Robinson, E.C. (2021). Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87585-5

  • Online ISBN: 978-3-030-87586-2

  • eBook Packages: Computer ScienceComputer Science (R0)