Advertisement

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

  • Manon Ansart
  • Stéphane Epelbaum
  • Geoffroy Gagliardi
  • Olivier Colliot
  • Didier Dormont
  • Bruno Dubois
  • Harald Hampel
  • Stanley Durrleman
  • for the ADNI, and the INSIGHT study group
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Notes

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.

References

  1. 1.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefMATHGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Friedman, J.: Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (2000)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)CrossRefGoogle Scholar
  9. 9.
    Hardy, J.A., Higgins, G.A.: Alzheimer’s disease: the amyloid cascade hypothesis. Science 256(5054), 184–185 (1992)CrossRefGoogle Scholar
  10. 10.
    Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)CrossRefGoogle Scholar
  11. 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. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 15.
    O’Brien, J.T., Herholz, K.: Amyloid imaging for dementia in clinical practice. BMC Medicine 13, 163 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Manon Ansart
    • 1
    • 2
  • Stéphane Epelbaum
    • 1
    • 2
    • 3
  • Geoffroy Gagliardi
    • 1
    • 3
  • Olivier Colliot
    • 1
    • 2
    • 3
    • 4
  • Didier Dormont
    • 1
    • 2
    • 4
  • Bruno Dubois
    • 1
    • 3
  • Harald Hampel
    • 1
    • 3
    • 5
  • Stanley Durrleman
    • 1
    • 2
  • for the ADNI, and the INSIGHT study group
  1. 1.Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM) - Pitié-Salpêtrière hospitalParisFrance
  2. 2.Inria Paris, Aramis Project-teamParisFrance
  3. 3.AP-HP, Pitié-Salpêtrière hospital, Department of Neurology, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A)ParisFrance
  4. 4.AP-HP, Pitié-Salpêtrière Hospital, Department of NeuroradiologyParisFrance
  5. 5.AXA Research Fund and UPMC ChairParisFrance

Personalised recommendations