Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors

  • Gerard SanromaEmail author
  • Víctor Andrea
  • Oualid M. Benkarim
  • José V. Manjón
  • Pierrick Coupé
  • Oscar Camara
  • Gemma Piella
  • Miguel A. González Ballester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)


Alzheimer’s disease (AD) is characterized by a progressive decline in the cognitive functions accompanied by an atrophic process which can already be observed in the early stages using magnetic resonance images (MRI). Individualized prediction of future progression to AD, when patients are still in the mild cognitive impairment (MCI) stage, has potential impact for preventive treatment. Atrophy patterns extracted from longitudinal MRI sequences provide valuable information to identify MCI patients at higher risk of developing AD in the future. We present a novel descriptor that uses the similarity between local image patches to encode local displacements due to atrophy between a pair of longitudinal MRI scans. Using a conventional logistic regression classifier, our descriptor achieves \(76\%\) accuracy in predicting which MCI patients will progress to AD up to 3 years before conversion.


Early AD prediction Non-local patch-based label fusion Longitudinal analysis 



The first author is co-financed by the Marie Curie FP7-PEOPLE-2012-COFUND 462 Action. Grant agreement no: 600387.


  1. 1.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  2. 2.
    Cash, D.M., Frost, C., Iheme, L.O., Ünay, D., Kandemir, M., Fripp, J., Salvado, O., Bourgeat, P., Reuter, M., Fischl, B., Lorenzi, M., Frisoni, G.B., Pennec, X., Peirson, R.K., Gunter, J.L., Senjem, M.L., Jack, C.R., Guizard, N., Fonov, V.S., Collins, D.L., Modat, M., Cardoso, M.J., Leung, K.K., Wang, H., Das, S.R., Yushkevich, P.A., Malone, I.B., Fox, N.C., Schott, J.M., Ourselin, S.: Assessing atrophy measurement techniques in dementia: results from the MIRIAD atrophy challenge. NeuroImage 123, 149–164 (2015)CrossRefGoogle Scholar
  3. 3.
    Chincarini, A., Sensi, F., Rei, L., Gemme, G., Squarcia, S., Longo, R., Brun, F., Tangaro, S., Bellotti, R., Amoroso, N., Bocchetta, M., Redolfi, A., Bosco, P., Boccardi, M., Frisoni, G.B., Nobili, F.: Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. NeuroImage 125, 834–847 (2016)CrossRefGoogle Scholar
  4. 4.
    Coupé, P., Eskildsen, S.F., Manjón, J.V., Fonov, V.S., Pruessner, J.C., Allard, M., Collins, D.L.: Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage Clin. 1, 141–152 (2012)CrossRefGoogle Scholar
  5. 5.
    Coupé, P., Manjón, J.V., Fonov, V.S., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  6. 6.
    Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56, 766–781 (2011)CrossRefGoogle Scholar
  7. 7.
    Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)CrossRefGoogle Scholar
  8. 8.
    Jie, B., Liu, M., Zhang, D., Shen, D.: Temporally-constrained group sparse learning for longitudinal data analysis in Alzheimer’s disease. IEEE Trans. Biomed. Eng. 64(1), 238–249 (2015)CrossRefGoogle Scholar
  9. 9.
    Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage 104, 398–412 (2015)CrossRefGoogle Scholar
  10. 10.
    Nyúl, L.G., Udupa, J.K.: On standardizing the mr image instensity scale. Magn. Reson. Med. 42(6), 1072–1081 (1999)CrossRefGoogle Scholar
  11. 11.
    Thompson, P.M., Hayashi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E., Semple, J., Herman, D., Hong, M.S., Dittmer, S.S., Doddrell, D.M., Toga, A.W.: Dynamics of gray matter loss in Alzheimer’s disease. J. Neurosci. 23(3), 994–1005 (2003)Google Scholar
  12. 12.
    Tong, T., Gao, Q., Guerrero, R., Ledig, C., Chen, L., Rueckert, D.: A Novel grading biomarker for the prediction of conversion from Mild cognitive impairment to Alzheimer’s disease. IEEE Trans. Biomed. Eng. 64(1), 155–165 (2017)CrossRefGoogle Scholar
  13. 13.
    Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  14. 14.
    Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., Lötjönen, J.: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLOS ONE 6, 10 (2011)CrossRefGoogle Scholar
  15. 15.
    Zhu, Y., Zhu, X., Kim, M., Shen, D., Wu, G.: Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. In: MICCAI (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gerard Sanroma
    • 1
    Email author
  • Víctor Andrea
    • 1
  • Oualid M. Benkarim
    • 1
  • José V. Manjón
    • 2
  • Pierrick Coupé
    • 3
    • 4
  • Oscar Camara
    • 1
  • Gemma Piella
    • 1
  • Miguel A. González Ballester
    • 1
    • 5
  1. 1.DTICUniversitat Pompeu FabraBarcelonaSpain
  2. 2.ITACAUniversitat Politécnica de ValénciaValenciaSpain
  3. 3.University of Bordeaux, LaBRI, UMR 5800TalenceFrance
  4. 4.CNRS, LaBRI, UMR 5800TalenceFrance
  5. 5.ICREABarcelonaSpain

Personalised recommendations