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Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease

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Abstract

Transfer learning has been successfully used in the early diagnosis of Alzheimer’s disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.

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Notes

  1. Z ∈ {0.000001, 0.00001, 0.0001, 0.0003, 0.0007, 0.001, 0.003, 0.005, 0.007, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}

References

  • Argyriou, A., Evgeniou, T., & Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73, 243–272.

    Article  Google Scholar 

  • Association, A. s. (2015). 2015 Alzheimer’s disease facts and figures. Alzheimer’s & Dement, 11, 332–384.

  • Bouwman, F. H., Schoonenboom, S. N. M., van der Flier, W. M., van Elk, E. J., Kok, A., Barkhof, F., Blankenstein, M. A., & Scheltens, P. (2007). CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment. Neurobiology of Aging, 28, 1070–1074.

    Article  CAS  Google Scholar 

  • Chang, C. C., & Lin, C. J. (2001). LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  • 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.

    Article  Google Scholar 

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

  • Cheng, B., Liu, M., Shen, D., Zuoyong, L., & Zhang, D. (2017). Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics, 15, 115–132.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Cho, Y., Seong, J. K., Jeong, Y., & Shin, S. Y. (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.

    Article  Google Scholar 

  • CIT, (2012). Medical image processing, analysis and visualization (MIPAV) http://mipav.cit.nih.gov/clickwrap.php.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • deToledo-Morrell, L., Stoub, T. R., Bulgakova, M., Wilson, R. S., Bennett, D. A., Leurgans, S., Wuu, J., & Turner, D. A. (2004). MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiology of Aging, 25, 1197–1203.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dukart, J., Sambataro, F., & Bertolino, A. (2016). Accurate prediction of conversion to Alzheimer’s disease using imaging, genetic, and neuropsychological biomarkers. Journal of Alzheimer’s disease, 49, 1143–1159.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Filipovych, R., & Davatzikos, C. (2011). Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage, 55, 1109–1119.

    Article  Google Scholar 

  • Gong, P., Ye, J., & Zhang, C. (2012). Robust Multi-Task Feature Learning. Proceeding of the 18th ACM SIGKDD conference on knowledge discovery and data mining.

  • Hao, X., Yao, X., Yan, J., Risacher, S. L., Saykin, A. J., Zhang, D., & Shen, L. (2016). Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer’s disease. Neuroinformatics, 14, 439–452.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kabani, N., MacDonald, D., Holmes, C. J., & Evans, A. (1998). A 3D atlas of the human brain. Neuroimage, 7, S717.

    Article  Google Scholar 

  • Lehmann, M., Koedam, E. L., Barnes, J., Bartlett, J. W., Barkhof, F., Wattjes, M. P., Schott, J. M., Scheltens, P., & Fox, N. C. (2012). Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers. Neurobiology of Aging.

  • Liu, F., Wee, C. Y., Chen, H. F., & Shen, D. G. (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  Google Scholar 

  • Liu, J., Chen, J., & Ye, J. (2009a). Large-scale sparse logistic regression. Proceeding of the 15th ACM SIGKDD conference on knowledge discovery and data mining.

  • Liu, J., Ji, S., & Ye, J. (2009b). Multi-task feature learning via efficient ℓ2,1 -norm minimization. UAI, 339–348.

  • Liu, J., Ji, S., & Ye, J. (2009c). SLEP: sparse learning with efficient projections. Arizona State University, http://www.public.asu.edu/~jye02/Software/SLEP.

  • Liu, M., Zhang, D., Chen, S., & Xue, H. (2016a). Joint binary classifier learning for ECOC-based Multi-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2335–2341.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Liu, M., Zhang, J., Yap, P. T., & Shen, D. (2017). View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Medical Image Analysis, 36, 123–134.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nesterov, Y. (2004). Introductory Lectures on Convex Optimization: A Basic Course. Springer Netherlands.

  • Nesterov, Y. (2007). Gradient methods for minimizing composite objective function. Center for Operations Research and Econometrics (CORE), Catholic University of Louvain, Technical Report, 76.

  • Obozinski, G., Taskar, B., & Jordan, M. I. (2006). Multi-task feature selection. Technical report, Statistics Department, UC Berkeley.

  • 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.

    Article  Google Scholar 

  • Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.

    Article  Google Scholar 

  • Pujol, O., Radeva, P., Vitria, J.,. Discriminant, E. C. O. C. (2006). A heuristic method for application dependent design of error correcting output codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1007–1012.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • 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. Proceeding of International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012 7512, 248–255.

  • Shen, D., & Davatzikos, C. (2002). HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging, 21, 1421–1439.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Suk, H., Lee, S. W., & Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101, 569–582.

    Article  Google Scholar 

  • Tibshirani, R. J. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, Series B, 58, 267–288.

    Google Scholar 

  • Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009a). MRI and CSF biomarkers in normal, MCI, and AD subjects Diagnostic discrimination and cognitive correlations. Neurology, 73, 287–293.

    Article  CAS  Google Scholar 

  • Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009b). MRI and CSF biomarkers in normal, MCI, and AD subjects predicting future clinical change. Neurology, 73, 294–301.

    Article  CAS  Google Scholar 

  • Wang, L., Wee, C. Y., Tang, X., Yap, P. T., & Shen, D. (2016). Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder. Brain Imaging and Behavior, 10, 33–40.

    Article  CAS  Google Scholar 

  • 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 G. Fichtinger, A. Martel & T. Peters (Eds.), Medical Image Computing and Computer-Assisted Intervention (pp. 635–642). Berlin / Heidelberg: Springer.

    Google Scholar 

  • Wee, C. Y., Yap, P. T., & Shen, D. (2013). Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping, 34, 3411–3425.

    Article  Google Scholar 

  • 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.

    Article  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, 229–238.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zhang, D., & Shen, D. (2012b). Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One, 3, e33182.

    Article  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  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, 45–57.

    Article  CAS  Google Scholar 

  • Zhou, J., Liu, J., Narayan, V. A., & Ye, J. (2013). Modeling disease progression via multi-task learning. NeuroImage, 78, 233–248.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhu, X., Suk, H. I., Lee, S. W., & Shen, D. (2015). Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis. Brain Imaging and Behavior, 10, 818–828.

    Article  Google Scholar 

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Acknowledgements

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 (http://www.fnih.org). 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 by the National Natural Science Foundation of China (Nos. 61602072, 61573023, 61732006, and 61473149), Chongqing Cutting-edge and Applied Foundation Research Program (Grant No. cstc2016jcyjA0063), Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant Nos. KJ1401010, KJ1601003, KJ1601015, KJ1710248, KJ1710257), NIH grants (AG041721, AG049371, AG042599, AG053867), Key Laboratory of Chongqing Municipal Institutions of Higher Education (Grant No. [2017]3), and Program of Chongqing Development and Reform Commission (Grant No. 2017[1007]).

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    Authors and Affiliations

    1. Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China

      Bo Cheng

    2. Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, 404100, China

      Bo Cheng

    3. Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA

      Mingxia Liu & Dinggang Shen

    4. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

      Daoqiang Zhang

    5. Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea

      Dinggang Shen

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    Correspondence to Daoqiang Zhang or Dinggang Shen.

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    Bo Cheng and Mingxia Liu contribute equally to this work.

    Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal -investigators/.

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    Cheng, B., Liu, M., Zhang, D. et al. Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease. Brain Imaging and Behavior 13, 138–153 (2019). https://doi.org/10.1007/s11682-018-9846-8

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