Skip to main content
SpringerLink
Log in

Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials

  • Conference paper
  • First Online:
Book cover Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2017, ML-CDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10553))

Abstract

We propose a method for selecting pre-symptomatic subjects likely to have amyloid plaques in the brain, based on the automatic analysis of neuropsychological and MRI data and using a cross-validated binary classifier. By avoiding systematic PET scan for selecting subjects, it reduces the cost of forming cohorts of subjects with amyloid plaques for clinical trials, by scanning fewer subjects but increasing the number of recruitments. We validate our method on three cohorts of subjects at different disease stages, and compare the performance of six classifiers, showing that the random forest yields good results more consistently, and that the method generalizes well when tested on an unseen data set.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

References

  1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  2. Chetelat, G., La Joie, R., Villain, N., Perrotin, A., de La Sayette, V., Eustache, F., Vandenberghe, R.: Amyloid imaging in cognitively normal individuals, at-risk populations and preclinical alzheimer’s disease. Neuroimage Clin. 2, 356–365 (2013)

    Article  Google Scholar 

  3. Chupin, M., Hammers, A., Liu, R.S.N., Colliot, O., Burdett, J., Bardinet, E., Duncan, J.S., Garnero, L., Lemieux, L.: Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3), 749–761 (2009)

    Article  Google Scholar 

  4. Doody, R.S., Thomas, R.G., Farlow, M., Iwatsubo, T., Vellas, B., Joffe, S., Kieburtz, K., Raman, R., Sun, X., Aisen, P.S., Siemers, E., Liu-Seifert, H., Mohs, R.: Phase 3 trials of solanezumab for mild-to-moderate alzheimer’s disease. N. Engl. J. Med. 370(4), 311–321 (2014)

    Article  Google Scholar 

  5. Dubois, B., Hampel, H., Feldman, H.H., Scheltens, P., Aisen, P., Andrieu, S., Bakardjian, H., Benali, H., Bertram, L., Blennow, K., Broich, K., Cavedo, E., Crutch, S., Dartigues, J.F., Duyckaerts, C., Epelbaum, S., Frisoni, G.B., Gauthier, S., Genthon, R., Gouw, A.A., et al.: Preclinical alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimer’s Dement. 12(3), 292–323 (2016)

    Article  Google Scholar 

  6. Friedman, J.: Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  7. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  8. Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)

    Article  Google Scholar 

  9. Hardy, J.A., Higgins, G.A.: Alzheimer’s disease: the amyloid cascade hypothesis. Science 256(5054), 184–185 (1992)

    Article  Google Scholar 

  10. Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)

    Article  Google Scholar 

  11. Insel, P.S., Palmqvist, S., Mackin, R.S., Nosheny, R.L., Hansson, O., Weiner, M.W., Mattsson, N.: Assessing risk for preclinical \(\upbeta \)-amyloid pathology with APOE, cognitive, and demographic information. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 4, 76–84 (2016)

    Google Scholar 

  12. Jack, C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., Trojanowski, J.Q.: Hypothetical model of dynamic biomarkers of the alzheimer’s pathological cascade. Lancet Neurol. 9(1), 119 (2010)

    Article  Google Scholar 

  13. Mielke, M.M., Wiste, H.J., Weigand, S.D., Knopman, D.S., Lowe, V.J., Roberts, R.O., Geda, Y.E., Swenson-Dravis, D.M., Boeve, B.F., Senjem, M.L., Vemuri, P., Petersen, R.C., Jack, C.R.: Indicators of amyloid burden in a population-based study of cognitively normal elderly. Neurology 79(15), 1570–1577 (2012)

    Article  Google Scholar 

  14. Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)

    Article  Google Scholar 

  15. O’Brien, J.T., Herholz, K.: Amyloid imaging for dementia in clinical practice. BMC Medicine 13, 163 (2015)

    Article  Google Scholar 

Download references

Acknowledgement

This work was partly funded by ERC grant N\(^\text {o}\)678304, H2020 EU grant N\(^\text {o}\)666992 and ANR grant ANR-10-IAIHU-06. HH is supported by the AXA Research Fund, the Fondation UPMC and the Fondation pour la Recherche sur Alzheimer, Paris, France. OC is supported by a “contrat d’interface local” from AP-HP.

Author information

Authors and Affiliations

  1. Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM) - Pitié-Salpêtrière hospital, Bvd de l’hôpital, Paris, France

    Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois, Harald Hampel & Stanley Durrleman

  2. Inria Paris, Aramis Project-team, 75013, Paris, France

    Manon Ansart, Stéphane Epelbaum, Olivier Colliot, Didier Dormont & Stanley Durrleman

  3. AP-HP, Pitié-Salpêtrière hospital, Department of Neurology, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France

    Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Bruno Dubois & Harald Hampel

  4. AP-HP, Pitié-Salpêtrière Hospital, Department of Neuroradiology, Paris, France

    Olivier Colliot & Didier Dormont

  5. AXA Research Fund and UPMC Chair, Paris, France

    Harald Hampel

Authors
  1. Manon Ansart
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stéphane Epelbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geoffroy Gagliardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olivier Colliot
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Didier Dormont
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bruno Dubois
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Harald Hampel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stanley Durrleman
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the ADNI, and the INSIGHT study group

Corresponding author

Correspondence to Manon Ansart .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. IBM Research - Almaden, San Jose, California, USA

    Tanveer Syeda-Mahmood

  5. Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

  6. IBM Research - Almaden, San Jose, USA

    Mehdi Moradi

  7. University of Queensland, Brisbane, Queensland, Australia

    Andrew Bradley

  8. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  9. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  10. Case Western Reserve University, Cleveland, Ohio, USA

    Anant Madabhushi

  11. Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  12. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  13. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  14. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ansart, M. et al. (2017). Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67558-9_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67557-2

  • Online ISBN: 978-3-319-67558-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation