Annals of Operations Research

, Volume 258, Issue 1, pp 31–57 | Cite as

Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI

  • Alper Çevik
  • Gerhard-Wilhelm Weber
  • B. Murat Eyüboğlu
  • Kader Karlı Oğuz
  • The Alzheimer’s Disease Neuroimaging Initiative
OR in Neuroscience


Neuroscience is of emerging importance along with the contributions of Operational Research to the practices of diagnosing neurodegenerative diseases with computer-aided systems based on brain image analysis. Although multiple biomarkers derived from Magnetic Resonance Imaging (MRI) data have proven to be effective in diagnosing Alzheimer’s disease (AD) and mild cognitive impairment (MCI), no specific system has yet been a part of routine clinical practice. This paper aims to introduce a fully-automated voxel-based procedure, Voxel-MARS, for detection of AD and MCI in early stages of progression. Performance was evaluated on a dataset of 508 MRI volumes gathered from the Alzheimer’s Disease Neuroimaging Initiative database. Data were transformed into a high-dimensional space through a feature extraction process. A novel 3-step feature selection procedure was applied. Multivariate Adaptive Regression Splines method was used as a classifier for the first time in the field of brain MRI analysis. The results were compared to those presented in a previous study on 28 voxel-based methods in terms of their ability to separate control normal (CN) subjects from the ones diagnosed with AD and MCI. It was observed that our method outperformed all of the others in sensitivity (83.58% in AD/CN and 78.38% in MCI/CN classification) with acceptable specificity values (over 85% in both cases). Furthermore, the method worked for discriminating MCI patients which converted to AD in 18 months (MCIc) from non-converters (MCInc) with a sensitivity outcome better than 27 of 28 methods. Overall, it was shown that the proposed method is promising in early detection of AD.


Neuroscience Operational research Image analysis Computer-aided diagnosis Pattern classification Alzheimer’s disease 



This study is based on Alper Çevik’s Ph.D. thesis. B. Murat Eyüboğlu and Gerhard-Wilhelm Weber are the thesis co-supervisors. Kader Karlı Oğuz is a member of the thesis committee. This project has been supported by the Graduate School of Natural and Applied Sciences, METU Scientific Research Fund ‘BAP 07-02-2012-101’. The authors would like to thank Dr. Güçlü Ongun, Dr. Ayşe Özmen, and Dr. Semih Kuter for their valuable comments and suggestions to improve the study. We are also greatly indebted to Ajdan Küçükçiftçi for her proofreading which improved composition of this paper. Data collection and sharing for this study was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimers Association; Alzheimers Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.


  1. Adaszewski, S., Dukart, J., Kherif, F., Frackowiak, R., & Draganski, B. (2013). How early can we predict Alzheimer’s disease using computational anatomy? Neurobiology of Aging, 34(12), 2815–2826.CrossRefGoogle Scholar
  2. Alvarez, I., Gorriz, J., Ramirez, J., Salas-Gonzalez, D., Lopez, M., Puntonet, C., et al. (2009). Alzheimer’s diagnosis using eigenbrains and support vector machines. Electronics Letters, 45(7), 342–343.CrossRefGoogle Scholar
  3. Álvarez-Miranda, E., Farhan, H., Luipersbeck, M., & Sinnl, M. (2016). A bi-objective network design approach for discovering functional modules linking Golgi apparatus fragmentation and neuronal death. Annals of Operations Research, 1–26.
  4. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95–113.CrossRefGoogle Scholar
  5. Ashburner, J. (2009). Computational anatomy with the SPM software. Magnetic Resonance Imaging, 27(8), 1163–1174. (Proceedings of the international school on magnetic resonance and brain function).CrossRefGoogle Scholar
  6. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26(3), 839–851.CrossRefGoogle Scholar
  7. Boutet, C., Chupin, M., Lehricy, S., Marrakchi-Kacem, L., Epelbaum, S., Poupon, C., et al. (2014). Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7T MRI: A feasibility study. Neuroimage: Clinical, 5, 341–348.CrossRefGoogle Scholar
  8. Chaves, R., Ramìrez, J., Górriz, J., López, M., Salas-Gonzalez, D., lvarez, I., et al. (2009). SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neuroscience Letters, 461(3), 293–297.CrossRefGoogle Scholar
  9. Chincarini, A., Bosco, P., Calvini, P., Gemme, G., Esposito, M., Olivieri, C., et al. (2011). Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage, 58(2), 469–480.CrossRefGoogle Scholar
  10. Chupin, M., Grardin, E., Cuingnet, R., Boutet, C., Lemieux, L., & Lehricy, S., et al. (2009a). Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus, 19(6), 579–587.Google Scholar
  11. Chupin, M., Hammers, A., Liu, R., Colliot, O., Burdett, J., & Bardinet, E., et al. (2009b). Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation. Neuroimage, 46(3), 749–761.Google Scholar
  12. Colliot, O., Chtelat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., et al. (2008). Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology, 248(1), 194–201.CrossRefGoogle Scholar
  13. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehricy, S., Habert, M. O., et al. (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage, 56(2), 766–781.CrossRefGoogle Scholar
  14. Davatzikos, C., Fan, Y., Wu, X., Shen, D., & Resnick, S. M. (2008). Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiology of Aging, 29(4), 514–523.CrossRefGoogle Scholar
  15. Frackowiak, R., Friston, K., Frith, C., Dolan, R., Price, C., Zeki, S., et al. (2003). Human Brain Function (2nd ed.). Cambridge: Academic Press.Google Scholar
  16. Francis, L. (2003). Martian chronicles: Is MARS better than neural networks? In: Casualty Actuarial Society Forum (pp. 75–102).Google Scholar
  17. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.CrossRefGoogle Scholar
  18. Friston, K., Holmes, A., Poline, J. B., Price, C., & Frith, C. (1996). Detecting activations in PET and fMRI: Levels of inference and power. Neuroimage, 4(3), 223–235.CrossRefGoogle Scholar
  19. Gerardin, E., Chtelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H. S., et al. (2009). Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage, 47(4), 1476–1486.CrossRefGoogle Scholar
  20. Graa, M., Termenon, M., Savio, A., Gonzalez-Pinto, A., Echeveste, J., Prez, J., et al. (2011). Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation. Neuroscience Letters, 502(3), 225–229.CrossRefGoogle Scholar
  21. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference and prediction (2nd ed.). Berlin: Springer.CrossRefGoogle Scholar
  22. Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., et al. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.CrossRefGoogle Scholar
  23. Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., et al. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology, 9(1), 119–128.CrossRefGoogle Scholar
  24. Jain, A., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37.CrossRefGoogle Scholar
  25. Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., et al. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain, 131(3), 681–689.CrossRefGoogle Scholar
  26. Li, M., Qin, Y., Gao, F., Zhu, W., & He, X. (2014). Discriminative analysis of multivariate features from structural MRI and diffusion tensor images. Magnetic Resonance Imaging, 32(8), 1043–1051.CrossRefGoogle Scholar
  27. Liu, S., Liu, S., Cai, W., Pujol ,S., Kikinis, R., & Feng, D. (2014). Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) (pp. 1015–1018).Google Scholar
  28. López, M., Ramìrez, J., Górriz, J., Álvarez, I., Salas-Gonzalez, D., Segovia, F., et al. (2011). Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer’s disease. Neurocomputing, 74(8), 1260–1271. (Selected papers from the 3rd international work-conference on the interplay between natural and artificial computation (IWINAC 2009)).CrossRefGoogle Scholar
  29. Magnin, B., Mesrob, L., Kinkingnhun, S., Plgrini-Issac, M., Colliot, O., Sarazin, M., et al. (2009). Support vector machine-based classification of Alzheimers disease from whole-brain anatomical MRI. Neuroradiology, 51(2), 73–83.CrossRefGoogle Scholar
  30. Misra, C., Fan, Y., & Davatzikos, C. (2009). Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI. Neuroimage, 44(4), 1415–1422.CrossRefGoogle Scholar
  31. Moisen, G. G., & Frescino, T. S. (2002). Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157(23), 209–225.CrossRefGoogle Scholar
  32. Morra, J., Tu, Z., Apostolova, L., Green, A., Toga, A., & Thompson, P. (2010). Comparison of adaboost and support vector machines for detecting Alzheimer’s disease through automated hippocampal segmentation. IEEE Transactions on Medical Imaging, 29(1), 30–43.CrossRefGoogle Scholar
  33. Mwangi, B., Tian, T., & Soares, J. (2014). A review of feature reduction techniques in neuroimaging. Neuroinformatics, 12(2), 229–244.CrossRefGoogle Scholar
  34. Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285–296), 23–27.Google Scholar
  35. Özmen, A., Weber, G. W., Batmaz, ì, & Kropat, E. (2011). RCMARS: Robustification of cmars with different scenarios under polyhedral uncertainty set. Communications in Nonlinear Science and Numerical Simulation, 16(12), 4780–4787. (sI:Complex Systems and Chaos with Fractionality, Discontinuity, and Nonlinearity).CrossRefGoogle Scholar
  36. Padilla, P., Gorriz, J., Ramirez, J., Chaves, R., Segovia, F., Alvarez, I., et al. (2010). Alzheimer’s disease detection in functional images using 2D Gabor wavelet analysis. Electronics Letters, 46(8), 556–558.CrossRefGoogle Scholar
  37. Padilla, P., Lopez, M., Gorriz, J., Ramirez, J., Salas-Gonzalez, D., & Alvarez, I. (2012). NMF-SVM based cad tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Transactions on Medical Imaging, 31(2), 207–216.CrossRefGoogle Scholar
  38. Park, H., Yang, J., Seo, J., & Lee, J. (2012). Dimensionality reduced cortical features and their use in the classification of Alzheimer’s disease and mild cognitive impairment. Neuroscience Letters, 529(2), 123–127.CrossRefGoogle Scholar
  39. Ramìrez, J., Górriz, J., Salas-Gonzalez, D., Romero, A., López, M., lvarez, I., et al. (2013). Computer-aided diagnosis of Alzheimers type dementia combining support vector machines and discriminant set of features. Information Sciences, 237, 59–72.CrossRefGoogle Scholar
  40. Ramìrez, J., Górriz, J., Segovia, F., Chaves, R., Salas-Gonzalez, D., López, M., et al. (2010). Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters, 472(2), 99–103.CrossRefGoogle Scholar
  41. Salas-Gonzalez, D., Górriz, J. M., Ramìrez, J., Illn, I. A., López, M., Segovia, F., Chaves, R., Padilla, P., Puntonet, C. G., & Alzheimers Disease Neuroimage Initiative, T. (2010). Feature selection using factor analysis for Alzheimers diagnosis using F18-FDG PET images. Medical Physics, 37(11), 6084–6095.Google Scholar
  42. Salas-Gonzalez, D., Górriz, J. M., Ramìrez, J., López, M., Illan, I. A., Segovia, F., et al. (2009). Analysis of SPECT brain images for the diagnosis of Alzheimer’s disease using moments and support vector machines. Neuroscience Letters, 461(1), 60–64.CrossRefGoogle Scholar
  43. Savio, A., & Graa, M. (2013). Deformation based feature selection for computer aided diagnosis of Alzheimers disease. Expert Systems with Applications, 40(5), 1619–1628.CrossRefGoogle Scholar
  44. Segovia, F., Górriz, J., Ramìrez, J., Salas-Gonzalez, D., lvarez, I., López, M., et al. (2012). A comparative study of feature extraction methods for the diagnosis of Alzheimer’s disease using the ADNI database. Neurocomputing, 75(1), 64–71.CrossRefGoogle Scholar
  45. Shih, D. T., Kim, S. B., Chen, V. C. P., Rosenberger, J. M., & Pilla, V. L. (2014). Efficient computer experiment-based optimization through variable selection. Annals of Operations Research, 216(1), 287–305.CrossRefGoogle Scholar
  46. Strickland, J. (2014). Predictive modeling and analytics. LULU Press.
  47. Suk, H. I., Lee, S. W., Shen, D., Initiative, A. D. N., et al. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage, 101, 569–582.CrossRefGoogle Scholar
  48. Tiraboschi, P., Hansen, L. A., Thal, L. J., & Corey-Bloom, J. (2004). The importance of neuritic plaques and tangles to the development and evolution of AD. Neurology, 62(11), 1984–1989.CrossRefGoogle Scholar
  49. van der Maaten, L. J., Postma, E. O., & van den Herik, H. J. (2009). Dimensionality reduction: A comparative review. Journal of Machine Learning Research, 10(1–41), 66–71.Google Scholar
  50. Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., et al. (2008). Alzheimer’s disease diagnosis in individual subjects using structural MR images: Validation studies. Neuroimage, 39(3), 1186–1197.CrossRefGoogle Scholar
  51. Weber, G. W., Batmaz, I., Köksal, G., Taylan, P., & Yerlikaya-Özkurt, F. (2012). CMARS: A new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Problems in Science and Engineering, 20(3), 371–400.CrossRefGoogle Scholar
  52. Wendy, L., & Martinez, A. R. M. (2002). Computational statistics handbook with MATLAB. London: Chapman and Hall, CRC.Google Scholar
  53. Westman, E., Simmons, A., Zhang, Y., Muehlboeck, J. S., Tunnard, C., Liu, Y., et al. (2011). Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls. Neuroimage, 54(2), 1178–1187.CrossRefGoogle Scholar
  54. Yao, P. (2009). Hybrid fuzzy SVM model using CART and MARS for credit scoring. In Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC ’09. International Conference on (Vol. 2, pp. 392–395).Google Scholar
  55. Ye, J., Chen, K., Wu, T., Li, J., Zhao, Z., Patel, R., Bae, M., Janardan, R., Liu, H., Alexander, G., & Reiman, E. (2008). Heterogeneous data fusion for Alzheimer’s disease study. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1025–1033). ACM: New York, NY, USA, KDD’08.Google Scholar
  56. Ye, J., Wu, T., Li, J., & Chen, K. (2011). Machine learning approaches for the neuroimaging study of Alzheimer’s disease. Computer, 44(4), 99–101.CrossRefGoogle Scholar
  57. Zhang, T., & Davatzikos, C. (2011). ODVBA: Optimally-discriminative voxel-based analysis. IEEE Transactions on Medical Imaging, 30(8), 1441–1454.CrossRefGoogle Scholar
  58. Zhang, W., & Goh, A. T. (2016). Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers, 7(1), 45–52.CrossRefGoogle Scholar
  59. Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage, 55(3), 856–867.CrossRefGoogle Scholar

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© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Biomedical Engineering Graduate Program, Graduate School of Natural and Applied SciencesMiddle East Technical UniversityAnkaraTurkey
  2. 2.Institute of Applied Mathematics and Biomedical Engineering Graduate ProgramMiddle East Technical UniversityAnkaraTurkey
  3. 3.Department of Electrical and Electronics Engineering and Biomedical Engineering Graduate ProgramMiddle East Technical UniversityAnkaraTurkey
  4. 4.Department of Radiology, Faculty of MedicineHacettepe UniversityAnkaraTurkey

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