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Assisting the Diagnosis of Neurodegenerative Disorders Using Principal Component Analysis and TensorFlow

  • Fermín SegoviaEmail author
  • Marcelo García-Pérez
  • Juan Manuel Górriz
  • Javier Ramírez
  • Francisco Jesús Martínez-Murcia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

Neuroimaging data provides a valuable tool to assist the diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). During last years many research efforts have focused on the development of computer systems that automatically analyze neuroimaging data and allow improving the diagnosis of those diseases. This field has benefited from modern machine learning techniques, which provide a higher generalization ability, however the high dimensionality of the data is still a challenge and there is room for improvement. In this work we demonstrate a computer system based on Principal Component Analysis and TensorFlow, the machine learning library recently released by Google. The proposed system is able to successfully separate AD or PD patients from healthy subjects, as well as distinguishing between PD and other parkinsonian syndromes. The obtained results suggest that TensorFlow is a suitable environment to classify neuroimaging data and can help to improve the diagnosis of AD and Parkinsonism.

Keywords

Multivariate analysis Machine learning TensorFlow Principal component analysis Alzheimer’s disease Parkinson’s disease 

Notes

Acknowledgment

This work was supported by and the MINECO under the TEC2012-34306 and TEC2015-64718-R projects and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Projects P09-TIC-4530 and P11-TIC-7103 and a Talent Hub project granted to FS (project approved by the Andalucía Talent Hub Program launched by the Andalusian Knowledge Agency, co-funded by the European Union’s Seventh Framework Program, Marie Sklodowska-Curie actions (COFUND Grant Agreement no 291780) and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía).

References

  1. 1.
    TensorFlow - google’s latest machine learning system, open sourced for everyone. http://googleresearch.blogspot.com.es/2015/11/tensor-googles-latestmachine_9.html
  2. 2.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/, software available from tensorow.org
  3. 3.
    Bach, J., Ziegler, U., Deuschl, G., Dodel, R., Doblhammer-Reiter, G.: Projected numbers of people with movement disorders in the years 2030 and 2050. Mov. Disord. 26(12), 2286–2290 (2011)CrossRefGoogle Scholar
  4. 4.
    Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement. J. Alzheimer’s Assoc. 3(3), 186–191 (2007)CrossRefGoogle Scholar
  5. 5.
    Duin, R.: Classifiers in almost empty spaces. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 1–7 (2000)Google Scholar
  6. 6.
    Foster, N.L., Heidebrink, J.L., Clark, C.M., Jagust, W.J., Arnold, S.E., Barbas, N.R., DeCarli, C.S., Turner, R.S., Koeppe, R.A., Higdon, R., Minoshima, S.: FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain 130(10), 2616–2635 (2007)CrossRefGoogle Scholar
  7. 7.
    Fougère, C.I., Pöpperl, G., Levin, J., Wängler, B., Böning, G., Uebleis, C., Cumming, P., Bartenstein, P., Bötzel, K., Tatsch, K.: The value of the dopamine D2/3 receptor ligand 18F-Desmethoxyfallypride for the differentiation of idiopathic and nonidiopathic parkinsonian syndromes. J. Nucl. Med. 51(4), 581–587 (2010)CrossRefGoogle Scholar
  8. 8.
    Friston, K., Büchel, C.: Functional connectivity: eigenimages and multivariate analyses. In: Friston, K., Ashburner, J., Kiebel, S., Nichols, T., Penny, W. (eds.) Statistical Parametric Mapping, Chap. 37, pp. 492–507. Academic Press, London (2007)CrossRefGoogle Scholar
  9. 9.
    Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images, 1st edn. Academic Press, Amsterdam, Boston (2006)Google Scholar
  10. 10.
    Gilman, S., Wenning, G.K., Low, P.A., Brooks, D.J., Mathias, C.J., Trojanowski, J.Q., Wood, N.W., Colosimo, C., Dürr, A., Fowler, C.J., Kaufmann, H., Klockgether, T., Lees, A., Poewe, W., Quinn, N., Revesz, T., Robertson, D., Sandroni, P., Seppi, K., Vidailhet, M.: Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71(9), 670–676 (2008)CrossRefGoogle Scholar
  11. 11.
    Hughes, A.J., Daniel, S.E., Ben-Shlomo, Y., Lees, A.J.: The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 125(4), 861–870 (2002)CrossRefGoogle Scholar
  12. 12.
    Illán, I.A., Górriz, J.M., Ramírez, J., Segovia, F., Jiménez-Hoyuela, J.M., Lozano, S.J.O.: Automatic assistance to parkinson’s disease diagnosis in DaTSCAN SPECT imaging. Med. Phys. 39(10), 5971–5980 (2012)CrossRefGoogle Scholar
  13. 13.
    Koch, W., Radau, P.E., Hamann, C., Tatsch, K.: Clinical testing of an optimized software solution for an automated, observer-independent evaluation of dopamine transporter SPECT studies. J. Nucl. Med. 46(7), 1109–1118 (2005)Google Scholar
  14. 14.
    Litvan, I., Agid, Y., Calne, D., Campbell, G., Dubois, B., Duvoisin, R.C., Goetz, C.G., Golbe, L.I., Grafman, J., Growdon, J.H., Hallett, M., Jankovic, J., Quinn, N.P., Tolosa, E., Zee, D.S.: Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP international workshop. Neurology 47(1), 1–9 (1996)CrossRefGoogle Scholar
  15. 15.
    Lopez, M., Ramirez, J., Gorriz, J., Salas-Gonzalez, D., Alvarez, I., Segovia, F., Puntonet, C.G.: Automatic tool for Alzheimer’s disease diagnosis using PCA and bayesian classification rules. Electron. Lett. 45(8), 389–391 (2009)CrossRefGoogle Scholar
  16. 16.
    Murray, D.G., McSherry, F., Isaacs, R., Isard, M., Barham, P., Abadi, M.: Naiad: a timely dataflow system. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP 2013, pp. 439–455. ACM, New York (2013)Google Scholar
  17. 17.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Saxena, P., Pavel, D.G., Quintana, J.C., Horwitz, B.: An automatic threshold-based scaling method for enhancing the usefulness of Tc-HMPAO SPECT in the diagnosis of Alzheimer’s disease. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 623–630. Springer, Heidelberg (1998). doi: 10.1007/BFb0056248 CrossRefGoogle Scholar
  19. 19.
    Segovia, F., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., Álvarez, I., López, M., Chaves, R.: A comparative study of feature extraction methods for the diagnosis of Alzheimer’s disease using the ADNI database. Neurocomputing 75(1), 64–71 (2012)CrossRefGoogle Scholar
  20. 20.
    Segovia, F., Bastin, C., Salmon, E., Górriz, J.M., Ramírez, J., Phillips, C.: Combining PET images and neuropsychological test data for automatic diagnosis of Alzheimer’s disease. PLoS ONE 9(2), e88687 (2014)CrossRefGoogle Scholar
  21. 21.
    Towey, D.J., Bain, P.G., Nijran, K.S.: Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images. Nucl. Med. Commun. 32(8), 699–707 (2011)CrossRefGoogle Scholar
  22. 22.
    Trambaiolli, L.R., Lorena, A.C., Fraga, F.J., Kanda, P.A.M., Anghinah, R., Nitrini, R.: Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin. EEG Neurosci. 42(3), 160–165 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fermín Segovia
    • 1
    Email author
  • Marcelo García-Pérez
    • 1
  • Juan Manuel Górriz
    • 1
  • Javier Ramírez
    • 1
  • Francisco Jesús Martínez-Murcia
    • 1
  1. 1.Department of Signal Theory, Networking and CommunicationsUniversity of GranadaGranadaSpain

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