Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 1149-1157

  Yuan, Lei ^a   Wang, Yalin ^a   Thompson, Paul M ^b   Narayan, Vaibhav A ^c   Ye, Jieping ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c JOHNSON AND JOHNSON PHARMACEUTICAL RESEARCH AND DEVELOPMENT   (United States)

Author keywords

incomplete data; multi source feature learning; multi task learning

Indexed keywords

ALZHEIMER'S DISEASE; BASE CLASSIFIERS; BRAIN-IMAGING DATA; CEREBRO SPINAL FLUIDS; COGNITIVE IMPAIRMENT; DATA SOURCE; DATA TYPE; FDG-PET; FEATURE LEARNING; FLUORODEOXYGLUCOSE-POSITRON EMISSION TOMOGRAPHIES; IMAGING MODALITY; INCOMPLETE DATA; JOINT ANALYSIS; LEARNING METHODS; MULTI-MODALITY; MULTI-SOURCE FUSION; MULTISOURCES; MULTITASK LEARNING; NORMAL CONTROLS; PROTEOMICS;

CEREBROSPINAL FLUID; DATA MINING; FORECASTING; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; MOLECULAR BIOLOGY; POSITRON EMISSION TOMOGRAPHY;

NEUROIMAGING;

EID: 84866042961     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339710     Document Type: Conference Paper

References (44)

1. R. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research, 6:1817-1853, 2005.
2. A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
3. M. Braskie, A. Klunder, K. Hayashi, H. Protas, V. Kepe, K. Miller, S. Huang, J. Barrio, L. Ercoli, P. Siddarth, et al. Plaque and tangle imaging and cognition in normal aging and Alzheimer's disease. Neurobiology of Aging, 31(10):1669-1678, 2010.
4. J. Cai, E. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal of Optimization, 20(4):1956-1982, 2010.
5. E. Candes and T. Tao. The power of convex relaxation: Near-optimal matrix completion. Information Theory, IEEE Transactions on, 56(5):2053-2080, 2010.
6. R. Casanova, R. Srikanth, A. Baer, P. Laurienti, J. Burdette, S. Hayasaka, L. Flowers, F. Wood, and J. Maldjian. Biological parametric mapping: a statistical toolbox for multimodality brain image analysis. Neuroimage, 34(1):137-143, 2007.
7. P. Combettes and J. Pesquet. Proximal splitting methods in signal processing. Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pages 185-212, 2011.
8. R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, and M. Habert. Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage, 2010.
9. D. Devanand, G. Pradhaban, X. Liu, A. Khandji, S. De Santi, S. Segal, H. Rusinek, G. Pelton, L. Honig, R. Mayeux, et al. Hippocampal and entorhinal atrophy in mild cognitive impairment. Neurology, 68(11):828-836, 2007.
10. T. Dietterich. Ensemble methods in machine learning. Multiple Classifier Systems, pages 1-15, 2000.
11. Y. Fan, S. Resnick, X. Wu, and C. Davatzikos. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. Neuroimage, 41(2):277-285, 2008.
12. C. Fennema-Notestine, D. Hagler Jr, L. McEvoy, A. Fleisher, E. Wu, D. Karow, and A. Dale. Structural MRI biomarkers for preclinical and mild Alzheimer's disease. Human Brain Mapping, 30(10):3238-3253, 2009.
13. S. Gao. A shared random effect parameter approach for longitudinal dementia data with non-ignorable missing data. Statistics in Medicine, 23(2):211-219, 2004.
14. S. Hardy, H. Allore, and S. Studenski. Missing data: A special challenge in aging research. Journal of the American Geriatrics Society, 57(4):722-729, 2009.
15. T. Hastie, R. Tibshirani, G. Sherlock, M. Eisen, P. Brown, and D. Botstein. Imputing missing data for gene expression arrays. Technical Report, 1999.
16. X. Hua, B. Gutman, C. Boyle, P. Rajagopalan, A. Leow, I. Yanovsky, A. Kumar, A. Toga, C. Jack Jr, N. Schuff, et al. Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry. Neuroimage, 2011.
17. V. Ibanez, P. Pietrini, G. Alexander, M. Furey, D. Teichberg, J. Rajapakse, S. Rapoport, M. Schapiro, and B. Horwitz. Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer's disease. Neurology, 50(6):1585-1593, 1998.
18. C. Jack, V. Lowe, M. Senjem, S. Weigand, B. Kemp, M. Shiung, D. Knopman, B. Boeve, W. Klunk, C. Mathis, et al. [11C]-PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment. Brain, 131(3):665, 2008.
19. C. Jack Jr, F. Barkhof, M. Bernstein, M. Cantillon, P. Cole, C. DeCarli, B. Dubois, S. Duchesne, N. Fox, G. Frisoni, et al. Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease. Alzheimer's and Dementia, 7(4):474-485, 2011.
20. C. Jack Jr, M. Bernstein, N. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P. Britson, J. L Whitwell, C. Ward, et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4):685-691, 2008.
21. O. Kohannim, X. Hua, D. Hibar, S. Lee, Y. Chou, A. Toga, C. Jack Jr, M. Weiner, P. Thompson, et al. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiology of Aging, 31(8):1429-1442, 2010.
22. R. Kuljiš. Grand challenges in dementia 2010. Frontiers in Neurology, 1, 2010.
23. S. Landau, D. Harvey, C. Madison, R. Koeppe, E. Reiman, N. Foster, M. Weiner, and W. Jagust. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiology of Aging, 32(7):1207-1218, 2011.
24. S. Lemm, B. Blankertz, T. Dickhaus, and K. Muller. Introduction to machine learning for brain imaging. Neuroimage, 2010.
25. A. Liaw and M. Wiener. Classification and regression by randomforest. R News, 2(3):18-22, 2002.
26. J. Liu, S. Ji, and J. Ye. Multi-task feature learning via efficient L2,1-Norm minimization. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pages 339-348. AUAI Press, 2009.
27. J. Liu, L. Yuan, and J. Ye. An efficient algorithm for a class of fused lasso problems. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pages 323-332. ACM, 2010.
28. E. Martinez-Montes, P. Valdés-Sosa, F. Miwakeichi, R. Goldman, and M. Cohen. Concurrent EEG/fMRI analysis by multiway partial least squares. Neuroimage, 22(3):1023-1034, 2004.
29. S. Mueller, M. Weiner, L. Thal, R. Petersen, C. Jack, W. Jagust, J. Trojanowski, A. Toga, and L. Beckett. The Alzheimer's Disease Neuroimaging Initiative. Neuroimaging Clinics of North America, 15(4):869, 2005.
30. Y. Nesterov. Gradient methods for minimizing composite objective function. ReCALL, 76(2007076), 2007.
31. Y. Nesterov and I. Nesterov. Introductory lectures on convex optimization: A basic course, volume 87. Springer, 2004.
32. R. Palmer and D. Royall. Missing data? Plan on it! Journal of the American Geriatrics Society, 58:S343-S348, 2010.
33. E. Reiman, J. Langbaum, and P. Tariot. Alzheimer's prevention initiative: a proposal to evaluate presymptomatic treatments as quickly as possible. Biomarkers, 4(1):3-14, 2010.
34. T. Schneider. Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of Climate, 14(5):853-871, 2001.
35. J. Schott, J. Bartlett, J. Barnes, K. Leung, S. Ourselin, N. Fox, and ADNI. Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: baseline adjustment. Neurobiology of Aging, 31(8):1452, 2010.
36. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1):273-289, 2002.
37. P. Van Ness, T. Murphy, K. Araujo, M. Pisani, and H. Allore. The use of missingness screens in clinical epidemiologic research has implications for regression modeling. Journal of clinical epidemiology, 60(12):1239-1245, 2007.
38. P. Vemuri, H. Wiste, S. Weigand, L. Shaw, J. Trojanowski, M. Weiner, D. Knopman, R. Petersen, C. J. Jack, and ADNI. MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations. Neurology, 73(4):287-293, 2009.
39. Y. Wang, Y. Fan, P. Bhatt, and C. Davatzikos. High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables. Neuroimage, 50(4):1519-1535, 2010.
40. K. Worsley, J. Poline, K. Friston, and A. Evans. Characterizing the response of PET and fMRI data using multivariate linear models. Neuroimage, 6(4):305-319, 1997.
41. J. Ye, K. Chen, T. Wu, J. Li, Z. Zhao, R. Patel, M. Bae, R. Janardan, H. Liu, G. Alexander, et al. Heterogeneous data fusion for Alzheimer's disease study. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pages 1025-1033. ACM, 2008.
42. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1):49-67, 2006.
43. D. Zhang, Y. Wang, L. Zhou, H. Yuan, D. Shen, et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage, 2011.
44. J. Zhou, J. Chen, and J. Ye. MALSAR: Multi-tAsk Learning via StructurAl Regularization. Arizona State University, 2011.