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Computational Modelling of Pathogenic Protein Behaviour-Governing Mechanisms in the Brain

  • Konstantinos GeorgiadisEmail author
  • Alexandra L. Young
  • Michael Hütel
  • Adeel Razi
  • Carla Semedo
  • Jonathan Schott
  • Sébastien Ourselin
  • Jason D. Warren
  • Marc Modat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)

Abstract

Most neurodegenerative diseases are caused by pathogenic proteins. Pathogenic protein behaviour is governed by neurobiological mechanisms which cause them to spread and accumulate in the brain, leading to cellular death and eventually atrophy. Patient data suggests atrophy loosely follows a number of spatiotemporal patterns, with different patterns associated with each neurodegenerative disease variant. It is hypothesised that the behaviour of different pathogenic protein variants is governed by different mechanisms, which could explain the pattern variety. Machine learning approaches take advantage of the pattern predictability for differential diagnosis and prognosis, but are unable to reveal new information on the underlying mechanisms, which are still poorly understood. We propose a framework where computational models of these mechanisms were created based on neurobiological literature. Competing hypotheses regarding the mechanisms were modelled and the outcomes evaluated against empirical data of Alzheimer’s disease. With this approach, we are able to characterise the impact of each mechanism on the neurodegenerative process. We also demonstrate how our framework could evaluate candidate therapies.

Keywords

Computational modelling Neurodegenerative disease 

Notes

Acknowlegments

This work received funding from the Engineering and Physical Sciences Research Council (EP/L016478/1), the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575), the Alzheimer’s Society UK (AS-PG-15-025), the Australian Research Council Discovery Early Career Research Award (DE170100128), the MRC (CSUB19166), the ARUK (ARUK-Network 2012-6-ICE; ARUK-PG2014-1946; ARUK-PG2017-1946), the Brain Research Trust (UCC14191), the European Union’s Horizon 2020 research and innovation programme (Grant 666992), the NIHR UCL/H Biomedical Research Centre and a Wellcome Trust Senior Clinical Fellowship [091673/Z/10/Z].

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Friston, K.J., Kahan, J., Biswal, B., Razi, A.: A DCM for resting state fMRI. Neuroimage 94, 396–407 (2014)CrossRefGoogle Scholar
  3. 3.
    Friston, K.J., et al.: Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage 128, 413–431 (2016)CrossRefGoogle Scholar
  4. 4.
    Frost, B., Diamond, M.I.: Prion-like mechanisms in neurodegenerative diseases. Nat. Rev. Neurosci. 11(3), 155–159 (2010)CrossRefGoogle Scholar
  5. 5.
    Georgiadis, K., et al.: Computational modelling of pathogenic protein spread in neurodegenerative diseases. PLoS one 13(2), e0192518 (2018)CrossRefGoogle Scholar
  6. 6.
    Karahanoğlu, F.I., et al.: Total activation: fMRI deconvolution through spatio-temporal regularization. Neuroimage 73, 121–134 (2013)CrossRefGoogle Scholar
  7. 7.
    Mandelli, M.L., et al.: Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia. Brain 139(10), 2778–2791 (2016)CrossRefGoogle Scholar
  8. 8.
    Mawuenyega, K.G.: Decreased clearance of CNS \(\beta \)-amyloid in Alzheimer’s disease. Science 330(6012), 1774 (2010)CrossRefGoogle Scholar
  9. 9.
    Proctor, C.J., Boche, D., Gray, D.A., Nicoll, J.A.: Investigating interventions in Alzheimer’s disease with computer simulation models. PloS one 8(9), e73631 (2013)CrossRefGoogle Scholar
  10. 10.
    Raj, A., LoCastro, E., Kuceyeski, A., Tosun, D., Relkin, N., Weiner, M.: Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in Alzheimer’s disease. Cell Rep. 10(3), 359–369 (2015)CrossRefGoogle Scholar
  11. 11.
    Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: SIFT: spherical-deconvolution informed filtering of tractograms. NeuroImage 67, 298–312 (2013)CrossRefGoogle Scholar
  12. 12.
    Stephan, K.E., et al.: Tractography-based priors for dynamic causal models. Neuroimage 47(4), 1628–1638 (2009)CrossRefGoogle Scholar
  13. 13.
    Tournier, J.D., Calamante, F., Connelly, A.: Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc. ISMRM 18, 1670 (2010)Google Scholar
  14. 14.
    Warren, J.D., et al.: Molecular nexopathies: a new paradigm of neurodegenerative disease. Trends Neurosci. 36(10), 561–569 (2013)CrossRefGoogle Scholar
  15. 15.
    Young, A.L., et al.: Multiple orderings of events in disease progression. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 711–722. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19992-4_56CrossRefGoogle Scholar
  16. 16.
    Zhou, J., et al.: Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73(6), 1216–1227 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Konstantinos Georgiadis
    • 1
    Email author
  • Alexandra L. Young
    • 1
  • Michael Hütel
    • 1
  • Adeel Razi
    • 3
  • Carla Semedo
    • 1
  • Jonathan Schott
    • 2
  • Sébastien Ourselin
    • 1
    • 2
    • 4
  • Jason D. Warren
    • 2
  • Marc Modat
    • 1
    • 2
    • 4
  1. 1.Centre for Medical Imaging ComputingUniversity College LondonLondonUK
  2. 2.Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
  3. 3.Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
  4. 4.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK

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