, Volume 15, Issue 2, pp 115–132 | Cite as

Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease

  • Bo Cheng
  • Mingxia Liu
  • Dinggang ShenEmail author
  • Zuoyong Li
  • Daoqiang ZhangEmail author
  • the Alzheimer’s Disease Neuroimaging Initiative.
Original Article


Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer’s Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multi-domain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.


Transfer learning Multi-domain Alzheimer’s disease (AD) Feature selection 



Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI 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 Neuron Imaging at the University of California, Los Angeles. This work was supported in part by the National Natural Science Foundation of China (Nos. 61602072, 61422204 and 61473149), the Chongqing Cutting-edge and Applied Foundation Research Program (Nos. cstc2016jcyjA0063, cstc2014jcyjA1316, and cstc2014jcyjA40035), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Nos. KJ1501014, KJ1401010, and KJ1601003), the NUAA Fundamental Research Funds (No. NE2013105), and NIH grants (AG041721, AG049371, AG042599, AG053867).


  1. Association, A.s (2014). 2014 Alzheimer’s disease facts and figures. Alzheimers Dement, 10, 47–92.Google Scholar
  2. Chang, C.C., Lin, C.J., (2001). LIBSVM: a library for support vector machines
  3. Chao, L. L., Buckley, S. T., Kornak, J., Schuff, N., Madison, C., Yaffe, K., Miller, B. L., Kramer, J. H., & Weiner, M. W. (2010). ASL perfusion MRI predicts cognitive decline and conversion from MCI to dementia. Alzheimer Disease and Associated Disorders, 24, 19–27.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chen, X., Pan, W., Kwok, J. T., & Carbonell, J. G. (2009). Accelerated gradient method for multi-task sparse learning problem. In Proceeding of ninth IEEE international conference on data mining and knowledge discovery (pp. 746–751).Google Scholar
  5. Cheng, B., Zhang, D., & Shen, D. (2012). Domain transfer learning for MCI conversion prediction. In Proceeding of International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012 7510 (pp. 82–90).CrossRefGoogle Scholar
  6. Cheng, B., Zhang, D., Chen, S., Kaufer, D. I., Shen, D., & ADNI (2013). Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers. Neuroinformatics, 11, 339–353.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Cheng, B., Liu, M., Suk, H., Shen, D., & Zhang, D. (2015a). Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, 9, 913–926.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Cheng, B., Liu, M., Zhang, D., Munsell, B. C., & Shen, D. (2015b). Domain transfer learning for MCI conversion prediction. IEEE Transactions on Biomedical Engineering, 62, 1805–1817.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Chetelat, G., Landeau, B., Eustache, F., Mezenge, F., Viader, F., de la Sayette, V., Desgranges, B., & Baron, J. C. (2005). Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. NeuroImage, 27, 934–946.CrossRefPubMedGoogle Scholar
  10. Cho, Y., Seong, J. K., Jeong, Y., Shin, S. Y., & ADNI (2012). Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. NeuroImage, 59, 2217–2230.CrossRefPubMedGoogle Scholar
  11. CIT, (2012). Medical Image Processing, Analysis and Visualization (MIPAV)
  12. Coupé, P., Eskildsen, S. F., Manjón, J. V., Fonov, V. S., Pruessner, J. C., Allard, M., & Collins, D. L. (2012). Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage: Clinical, 1, 141–152.CrossRefGoogle Scholar
  13. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehericy, S., Habert, M. O., Chupin, M., Benali, H., & Colliot, O. (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage, 56, 766–781.CrossRefPubMedGoogle Scholar
  14. Da, X., Toledo, J. B., Zee, J., Wolk, D. A., Xie, S. X., Ou, Y., Shacklett, A., Parmpi, P., Shaw, L., Trojanowski, J. Q., & Davatzikos, C. (2014). Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage: Clinical, 4, 164–173.CrossRefGoogle Scholar
  15. Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q., (2011). Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 32, 2322.e2319–2322.e2327.Google Scholar
  16. DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44, 837–845.CrossRefPubMedGoogle Scholar
  17. Duan, L. X., Tsang, I. W., & Xu, D. (2012). Domain transfer multiple kernel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 465–479.CrossRefPubMedGoogle Scholar
  18. Duchesne, S., & Mouiha, A. (2011). Morphological factor estimation via high-dimensional reduction: prediction of MCI conversion to probable AD. International Journal of Alzheimer's Disease, 2011, 914085.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Eskildsen, S. F., Coupé, P., García-Lorenzo, D., Fonov, V., Pruessner, J. C., & Collins, D. L. (2013). Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. NeuroImage, 65, 511–521.CrossRefPubMedGoogle Scholar
  20. Fan, Y., Batmanghelich, N., Clark, C. M., Davatzikos, C., & Initia, A. D. N. (2008). Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage, 39, 1731–1743.CrossRefPubMedGoogle Scholar
  21. Filipovych, R., Davatzikos, C., & Initia, A. D. N. (2011). Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage, 55, 1109–1119.CrossRefPubMedGoogle Scholar
  22. Gaser, C., Franke, K., Kloppel, S., Koutsouleris, N., & Sauer, H. (2013). BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer’s disease. PloS One, 8, e67346.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Guo, X., Wang, Z., Li, K., Li, Z., Qi, Z., Jin, Z., Yao, L., & Chen, K. (2010). Voxel-based assessment of gray and whitematter volumes in Alzheimer’s disease. Neuroscience Letters, 468, 146–150.CrossRefPubMedGoogle Scholar
  24. Hinrichs, C., Singh, V., Xu, G. F., Johnson, S. C., & Neuroimaging, A. D. (2011). Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage, 55, 574–589.CrossRefPubMedGoogle Scholar
  25. Hu, K., Wang, Y., Chen, K., Hou, L., & Zhang, X. (2016). Multi-scale features extraction from baseline structure MRI for MCI patient classification and AD early diagnosis. Neurocomputing, 175, 132–145.CrossRefGoogle Scholar
  26. Jie, B., Zhang, D., Cheng, B., & Shen, D. (2015). Manifold regularized multitask feature learning for multimodality disease classification. Human Brain Mapping, 36, 489–507.CrossRefPubMedGoogle Scholar
  27. Kabani, N., MacDonald, D., Holmes, C. J., & Evans, A. (1998). A 3D atlas of the human brain. NeuroImage, 7, S717.Google Scholar
  28. Khedher, L., Ramírez, J., Górriz, J. M., Brahim, A., & Segovia, F. (2015). Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing, 151, 139–150.CrossRefGoogle Scholar
  29. Li, H., Liu, Y., Gong, P., Zhang, C., & Ye, J. (2014). Hierarchical interactions model for predicting mild cognitive impairment (MCI) to Alzheimer’s disease (AD) conversion. PloS One, 9, e82450.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Liu, J., Ji, S., Ye, J., (2009). SLEP: sparse learning with efficient projections. Arizona State University,
  31. Liu, F., Wee, C. Y., Chen, H. F., Shen, D. G., & ADNI (2014). Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. NeuroImage, 84, 466–475.CrossRefPubMedGoogle Scholar
  32. Liu, M., Zhang, D., & Shen, D., (2016a). Inherent structure based multi-view learning with multi-atlas feature representation for alzheimer's disease diagnosis. IEEE Transactions on Biomedical Engineering, 63, 1473–1482.Google Scholar
  33. Liu, M., Zhang, D., & Shen, D., (2016b). Relationship induced multi-template learning for diagnosis of alzheimer's disease and mild cognitive impairment. IEEE Transactions on Medical Imaging, 35, 1463–1474.Google Scholar
  34. 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, 1415–1422.CrossRefPubMedGoogle Scholar
  35. Moradi, E., Pepe, A., Gaser, C., Huttunen, H., & Tohka, J. (2015). Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage, 104, 398–412.CrossRefPubMedGoogle Scholar
  36. Nemirovski, A., (2005). Efficient method s in convex programming.Google Scholar
  37. Obozinski, G., Taskar, B., & Jordan, M. I. (2006). Multi-task feature selection. Statistics Department, UC Berkeley: Technical report.Google Scholar
  38. Ota, K., Oishi, N., Ito, K., Fukuyama, H., & Grp, S.-J. S. (2014). A comparison of three brain atlases for MCI prediction. Journal of Neuroscience Methods, 221, 139–150.CrossRefPubMedGoogle Scholar
  39. Ota, K., Oishi, N., Ito, K., & Fukuyama, H. (2015). Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer’s disease. Journal of Neuroscience Methods, 256, 168–183.CrossRefPubMedGoogle Scholar
  40. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.CrossRefGoogle Scholar
  41. Querbes, O., Aubry, F., Pariente, J., Lotterie, J.-A., Demonet, J.-F., Duret, V., Puel, M., Berry, I., Fort, J.-C., Celsis, P., & ADNI (2009). Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain: A Journal of Neurology, 132, 2036–2047.CrossRefGoogle Scholar
  42. Risacher, S. L., Saykin, A. J., West, J. D., Shen, L., Firpi, H. A., & McDonald, B. C. (2009). Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Current Alzheimer Research, 6, 347–361.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Müller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12.Google Scholar
  44. Sabuncu, M. R., Konukoglu, E., & ADNI (2015). Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics, 13, 31–46.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Schwartz, Y., Varoquaux, G., Pallier, C., Pinel, P., Poline, J., & Thirion, B. (2012). Improving accuracy and power with transfer learning using a meta-analytic database. In Proceeding of International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012 7512 (pp. 248–255).CrossRefGoogle Scholar
  46. Shen, D., & Davatzikos, C. (2002). HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging, 21, 1421–1439.CrossRefPubMedGoogle Scholar
  47. Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17, 87–97.CrossRefPubMedGoogle Scholar
  48. Suk, H., Lee, S. W., Shen, D., & ADNI (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Tibshirani, R. J. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, Series B, 58, 267–288.Google Scholar
  50. Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D., 2011. Robust Deformable-Surface-Based Skull-Stripping for Large-Scale Studies. In: Fichtinger, G., Martel, A., Peters, T. (Eds.), Medical Image Computing and Computer-Assisted Intervention. Springer Berlin / Heidelberg, Toronto, Canada, pp. 635–642.Google Scholar
  51. Wee, C. Y., Yap, P. T., Shen, D. G., & ADNI (2013). Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping, 34, 3411–3425.CrossRefPubMedGoogle Scholar
  52. Westman, E., Muehlboeck, J. S., & Simmons, A. (2012). Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. NeuroImage, 62, 229–238.CrossRefPubMedGoogle Scholar
  53. Westman, E., Aguilar, C., Muehlboeck, J. S., & Simmons, A. (2013). Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer’s disease and mild cognitive impairment. Brain Topography, 26, 9–23.CrossRefPubMedGoogle Scholar
  54. Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D. P., Rueckert, D., Soininen, H., & Lotjonen, J. (2011). Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PloS One, 6, e25446.CrossRefPubMedPubMedCentralGoogle Scholar
  55. Yang, J., Yan, R., Hauptmann, A.G., (2007). Cross-domain video concept detection using adaptive SVMs. Proceedings of the 15th international conference on Multimedia, 188–197.Google Scholar
  56. Ye, J., Farnum, M., Yang, E., Verbeeck, R., Lobanov, V., Raghavan, N., Novak, G., DiBernardo, A., Narayan, V.A., ADNI, (2012). Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC Neurology 12, 1471–2377–1412-1446.Google Scholar
  57. Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S. (2013). Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. NeuroImage: Clinical, 2, 735–745.CrossRefGoogle Scholar
  58. Zhang, D., Shen, D., (2011). Semi-supervised multimodal classification of Alzheimer’s disease. Proceeding of IEEE International Symposium on Biomedical Imaging 1628–1631.Google Scholar
  59. Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Transactions on Medical Imaging, 20, 45–57.CrossRefPubMedGoogle Scholar
  60. Zhang, J., Gao, Y., Munsell, B.C., & Shen, D., (2016). Detecting anatomical landmarks for fast alzheimer's disease diagnosis. IEEE Transactions on Medical Imaging. Doi:  10.1109/TMI.2016.2582386.
  61. Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., & ADNI (2011). Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage, 55, 856–867.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Zhang, D., Shen, D., & ADNI (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage, 59, 895–907.CrossRefPubMedGoogle Scholar
  63. Zhou, J., Liu, J., Narayan, V. A., Ye, J., & ADNI (2013). Modeling disease progression via multi-task learning. NeuroImage, 78, 233–248.CrossRefPubMedGoogle Scholar
  64. Zhu, X., Huang, Z., Shen, H. T., Cheng, J., & Xu, C. (2012). Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition, 45, 3003–3016.CrossRefGoogle Scholar
  65. Zhu, X., Suk, H., & Shen, D. (2014). A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage, 100, 91–105.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Bo Cheng
    • 1
  • Mingxia Liu
    • 2
    • 3
  • Dinggang Shen
    • 3
    • 4
    Email author
  • Zuoyong Li
    • 5
  • Daoqiang Zhang
    • 2
    • 5
    Email author
  • the Alzheimer’s Disease Neuroimaging Initiative.
  1. 1.Key Laboratory of Advanced Network and Intellectual TechnologyChongqing Three Gorges UniversityChongqingChina
  2. 2.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA
  4. 4.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  5. 5.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina

Personalised recommendations