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Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment

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Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.

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  • Al, N. F. E. (2008). Principal component analysis of FDG PET in amnestic MCI. European Journal of Nuclear Medicine and Molecular Imaging, 35(12), 2191–2202 (2112).

    Article  Google Scholar 

  • Apostolova, L. G., Hwang, K. S., Andrawis, J. P., Green, A. E., Babakchanian, S., Morra, J. H., et al. (2010). 3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects. Neurobiology of Aging, 31(8), 1284–1303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bouwman, F. H., van der Flier, W. M., Schoonenboom, N. S. M., van Elk, E. J., Kok, A., Rijmen, F., et al. (2007). Longitudinal changes of CSF biomarkers in memory clinic patients. Neurology, 69(10), 1006–1011.

    Article  CAS  PubMed  Google Scholar 

  • Brookmeyer, R., Johnson, E., Ziegler-Grahamm, K., Arrighi, H. M., Brookmeyer, R., & Johnson, E. (2007). O1-02-01 forecasting the global burden of Alzheimer’s disease. Alzheimers & Dementia the Journal of the Alzheimers Association, 3(3), 186–191.

    Article  Google Scholar 

  • Chang, C. C., & Lin, C. J. (2007). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 389–396.

    Google Scholar 

  • Chen, X., Pan, W., Kwok, J. T., & Carbonell, J. G. (2009). Accelerated gradient method for multi-task sparse learning problem. Proceedings of the International Conference on Data Mining, 746–751.

  • Chételat, G., Desgranges, B., Sayette, V., La, D., Viader, F., Eustache, F., & J-C, B. (2003). Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology, 60(8), 1374–1377.

    Article  PubMed  Google Scholar 

  • Dai, Z., Yan, C., Wang, Z., Wang, J., Xia, M., Li, K., et al. (2012). Discriminative analysis of early Alzheimer’s disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). NeuroImage, 59(3), 2187–2195.

    Article  PubMed  Google Scholar 

  • De, S. S., de Leon, M. J., Rusinek, H., Convit, A., Tarshish, C. Y., Roche, A., et al. (2001). Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiology of Aging, 22(4), 529–539.

    Article  Google Scholar 

  • Derflinger, S., Sorg, C., Gaser, C., Myers, N., Arsic, M., Kurz, A., et al. (2011). Grey-matter atrophy in Alzheimer’s disease is asymmetric but not lateralized. Journal of Alzheimers Disease, 25(2), 347–357.

    CAS  Google Scholar 

  • Desikan, R. S., Cabral, H. J., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., et al. (2009). Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease. Brain, 132(Part 8), 2048–2057.

    Article  PubMed  PubMed Central  Google Scholar 

  • Du, A. T., Schuff, N., Kramer, J. H., Rosen, H. J., Gorno-Tempini, M. L., Rankin, K., et al. (2007). Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain, 130(4), 1159–1166.

    Article  PubMed  PubMed Central  Google Scholar 

  • Evgeniou, T., & Pontil, M. (2004). Regularized multi—task learning. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, (pp. 109–117).

  • Fan, Y., Shen, D., Gur, R. C., Gur, R. E., & Davatzikos, C. (2007). COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Transactions on Medical Imaging, 26(1), 93–105.

    Article  PubMed  Google Scholar 

  • Fjell, A. M., Walhovd, K. N. C., Mcevoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., et al. (2010). CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease. Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(6), 2088–2101.

    Article  CAS  Google Scholar 

  • Foster, N. L., Heidebrink, J. L., Clark, C. M., Jagust, W. J., Arnold, S. E., Barbas, N. R., et al. (2007). FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain, 130(10), 2616–2635 (2620).

    Article  PubMed  Google Scholar 

  • Gerardin, E., Chételat, G. l., 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gray, K. R., Aljabar, P., Heckemann, R. A., Hammers, A., & Rueckert, D. (2012). Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. NeuroImage, 65, 167–175.

    Article  PubMed  Google Scholar 

  • Higdon, R., Foster, N. L., Koeppe, R. A., DeCarli, C. S., Jagust, W. J., Clark, C. M., et al. (2004). A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer’s disease using FDG-PET imaging. Statistics in Medicine, 23(2), 315–326. doi:10.1002/sim.1719.

    Article  PubMed  Google Scholar 

  • Hinrichs, C., Singh, V., Xu, G., & Johnson, S. C. (2011). Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage, 55(2), 574–589.

    Article  PubMed  Google Scholar 

  • Huang, S., Li, J., Ye, J., Wu, T., Chen, K., & Fleisher, A., et al. (2011). Identifying Alzheimer s disease-related brain regions from multi-modality neuroimaging data using sparse composite linear discrimination analysis. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 24. Curran Associates, Inc.

  • Jack, C. R., Jr., 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. Lancet Neurology, 9(1), 119–128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jie, B., Zhang, D., Cheng, B., & Shen, D. (2015). Manifold regularized multitask feature learning for multimodality disease classification. Human Brain Mapping, 36(2), 489–507.

    Article  PubMed  Google Scholar 

  • Kumar, A., & Daume Iii, H. (2012). Learning task grouping and overlap in multi-task learning. Computer Science - Learning.

  • Landau, S. M., Harvey DMadison, C. M., Reiman, E. M., Foster, N. L., Aisen, P. S., Petersen, R. C., et al. (2010). Comparing predictors of conversion and decline in mild cognitive impairment. Neurology, 75(3), 230–238.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Leon, M. J. D., Mosconi, L., Li, J., Santi, S. D., Yao, Y., Tsui, W. H., et al. (2007). Longitudinal CSF isoprostane and MRI atrophy in the progression to AD. Journal of Neurology, 254(12), 1666–1675.

    Article  PubMed  Google Scholar 

  • Liu, J., & Ye, J. (2010). Efficient L1/Lq norm regularization. Cambridge University Pub.

  • Liu, F., Wee, C. Y., Chen, H., & Shen, D. (2014). Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. NeuroImage, 84, 466–475.

    Article  PubMed  Google Scholar 

  • Magnin, B. t., Mesrob, L., Kinkingnéhun, S., Pélégrini-Issac, M., Colliot, O., Sarazin, M., et al. (2009). Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology, 51(2), 73–83.

    Article  PubMed  Google Scholar 

  • Mattsson, N., Zetterberg, H., Hansson, O., Andreasen, N., Parnetti, L., Jonsson, M., et al. (2009). CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA: The Journal of the American Medical Association, 302(4), 385–393.

    Article  CAS  PubMed  Google Scholar 

  • Mcevoy, L. K., Fennema-Notestine, C., Roddey, J. C., Hagler, D. J., Jr., Holland, D., Karow, D. S., et al. (2009). Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment1. Radiology, 251(1), 195–205.

    Article  PubMed  PubMed Central  Google Scholar 

  • MJ, W., Kawas, C. H., Stewart, W. F., Rudow, G. L., & Troncoso, J. C. (2004). Hippocampal neurons in pre-clinical Alzheimer’s disease. Neurobiology of Aging, 25(25), 1205–1212.

    Google Scholar 

  • Morris, J., Storandt, M., Miller, J., McKeel, D., Price, J., Rubin, E., et al. (2001). Mild cognitive impairment represents early-stage Alzheimer disease. Archives of Neurology, 58(3), 397–405.

    Article  CAS  PubMed  Google Scholar 

  • Nesterov, Y. (2003). Introductory lectures on convex optimization: a basic course. Computer Programming(Oct), 49–50.

  • Nestor, P. J., Scheltens, P., & Hodges, J. R. (2004). Advances in the early detection of Alzheimer’s disease. Nature Medicine, 10 suppl(7suppl), S34–S41.

    PubMed  Google Scholar 

  • Obozinski, G., Jordan, M., & Taskar, B. (2006). Multi-task feature selection. The Workshop of Structural Knowledge Transfer for Machine Learning in International Conference on Machine Learning, 7(2), 1693–1696.

    Google Scholar 

  • Obozinski, G., Taskar, B., & Jordan, M. I. (2010). Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, 20(2), 231–252.

    Article  Google Scholar 

  • Oliveira, P. P. D., Nitrini, R., Busatto, G., Buchpiguel, C., Sato, J. R., & Amaro, E. (2010). Use of SVM methods with surface-based cortical and volumetric subcortical measurements to detect Alzheimer’s disease. Journal of Alzheimers Disease, 19(4), 1263–1272. doi:10.3233/jad-2010-1322.

    Google Scholar 

  • Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56(3), 303–308.

    Article  CAS  PubMed  Google Scholar 

  • Poulina, S., Dautoffb, R., Morris, J., Barrett, L., & Dickersona, B. (2011). Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Research: Neuroimaging, 194(1), 7–13.

  • Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A., & Leahy, R. M. (2001). Magnetic resonance image tissue classification using a partial volume model. In Neuroimage, pp. 856–876.

  • Shaw, L. M., Vanderstichele, H., Knapik‐Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., et al. (2009). Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Annals of Neurology, 65(4), 403–413.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shen, D., & Davatzikos, C. (2002). HAMMER: hierarchical attribute matching mechanism for elastic registration. In IEEE Trans. on Medical Imaging pp. 1421–1439.

  • Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1997). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87–97.

    Article  Google Scholar 

  • Smith, & Stephen, M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155.

    Article  PubMed  Google Scholar 

  • Sole, A. D., Clerici, F., Chiti, A., Lecchi, M., Mariani, C., Maggiore, L., et al. (2008). Individual cerebral metabolic deficits in Alzheimer’s disease and amnestic mild cognitive impairment: an FDG PET study. European Journal of Nuclear Medicine and Molecular Imaging, 35(7), 1357–1366.

    Article  PubMed  Google Scholar 

  • Suk, H. I., Lee, S. W., & Shen, D. (2014). Subclass-based multi-task learning for Alzheimer’s disease diagnosis. Frontiers in Aging Neuroscience, 6(6), 168.

    PubMed  PubMed Central  Google Scholar 

  • Tibshirani, R. (1994). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58(1), 267–288.

    Google Scholar 

  • Walhovd, K. B., Fjell, A. M., Dale, A. M., Mcevoy, L. K., Brewer, J., Karow, D. S., et al. (2010). Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiology of Aging, 31(7), 1107–1121.

    Article  CAS  PubMed  Google Scholar 

  • 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(1), 229–238.

    Article  PubMed  Google Scholar 

  • Wolf, H., Jelic, V., Gertz, H. J., Nordberg, A., Julin, P., & Wahlund, L. O. (2003). A critical discussion of the role of neuroimaging in mild cognitive impairment. Acta Neurologica Scandinavica, 179(Supplement s179), 52–76.

    Article  PubMed  Google Scholar 

  • Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, 68(1), 49–67. As the access to this document is restricted, you may want to look for a different version under “Related research” (further below) orfor a different version of it.

    Article  Google Scholar 

  • Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., & Ye, J. (2012). Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data ☆. NeuroImage, 61(3), 622–632.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, D., & Shen, D. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage, 59(2), 895–907.

    Article  PubMed  Google Scholar 

  • 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(1), 45–57.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage, 55, 856–867.

    Article  PubMed  PubMed Central  Google Scholar 

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This work was supported in part by the National Natural Science Foundation of China (Nos. 61422204, 61473149, 61170151), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20123218110009), and the NUAA Fundamental Research Funds (No. NE2013105), and by NIH grants EB006733, EB008374, EB009634, MH100217, AG041721, and AG042599.

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Correspondence to Dinggang Shen or Daoqiang Zhang.

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Zu, C., Jie, B., Liu, M. et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment. Brain Imaging and Behavior 10, 1148–1159 (2016).

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