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Latent Processes Governing Neuroanatomical Change in Aging and Dementia

  • Christian WachingerEmail author
  • Anna Rieckmann
  • Martin Reuter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Clinically normal aging and pathological processes cause structural changes in the brain. These changes likely occur in overlapping regions that accommodate neural systems with high susceptibility to deleterious factors. Due to the overlap, the separation between aging and pathological processes is challenging when analyzing brain structures independently. We propose to identify multivariate latent processes that govern cross-sectional and longitudinal neuroanatomical changes across the brain in aging and dementia. A discriminative representation of neuroanatomy is obtained from spectral shape descriptors in the BrainPrint. We identify latent factors by maximizing the covariance between morphological change and response variables of age and a proxy for dementia. Our results reveal cross-sectional and longitudinal patterns of change in neuroanatomy that distinguishes aging processes from disease processes. Finally, latent processes do not only yield a parsimonious model but also a significantly improved prediction accuracy.

Notes

Acknowledgement

This work was supported in part by the Faculty of Medicine at LMU (FöFoLe) and the Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B).

References

  1. 1.
    Buckner, R.L.: Memory and executive function in aging and AD. Neuron 44(1), 195–208 (2004)CrossRefGoogle Scholar
  2. 2.
    Davatzikos, C., Xu, F., An, Y., Fan, Y., Resnick, S.M.: Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132(8), 2026–2035 (2009)CrossRefGoogle Scholar
  3. 3.
    De Jong, S.: SIMPLS: an alternative approach to partial least squares regression. Chemometr. Intell. Lab. Syst. 18(3), 251–263 (1993)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., Ayache, N.: Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. Int. J. Comput. Vis. 103(1), 22–59 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Fischl, B., Salat, D.H., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  6. 6.
    Gaser, C., Franke, K., Klöppel, S., Koutsouleris, N., Sauer, H.: Brainage in mild cognitive impaired patients: predicting the conversion to Alzheimer’s disease. PloS one 8(6), e67346 (2013)CrossRefGoogle Scholar
  7. 7.
    Jagust, W.: Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron 77(2), 219–234 (2013)CrossRefGoogle Scholar
  8. 8.
    Mueller, S.G., Weiner, M.W., Thal, L.J., et al.: The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin. North Am. 15(4), 869–877 (2005)CrossRefGoogle Scholar
  9. 9.
    Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)CrossRefGoogle Scholar
  10. 10.
    Reuter, M., Wolter, F.E., Peinecke, N.: Laplace-beltrami spectra as “shape-DNA” of surfaces and solids. Comput. Aided Des. 38(4), 342–366 (2006)CrossRefGoogle Scholar
  11. 11.
    Wachinger, C., Golland, P., Kremen, W., Fischl, B., Reuter, M.: Brainprint: a discriminative characterization of brain morphology. Neuroimage 109, 232–248 (2015)CrossRefGoogle Scholar
  12. 12.
    Wold, S., Sjöström, M., Eriksson, L.: PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. Syst. 58(2), 109–130 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christian Wachinger
    • 1
    Email author
  • Anna Rieckmann
    • 2
  • Martin Reuter
    • 3
    • 4
  1. 1.Artificial Intelligence in Medical Imaging (AI-Med)KJP, LMU MünchenMunichGermany
  2. 2.Department of Radiation SciencesUmeå UniveristyUmeåSweden
  3. 3.DZNEBonnGermany
  4. 4.Department of RadiologyHarvard Medical SchoolBostonUSA

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