The relevance voxel machine (RVoxM): A self-tuning bayesian model for informative image-based prediction

IEEE Transactions on Medical Imaging

Volumn 31, Issue 12, 2012, Pages 2290-2306

  Sabuncu, Mert R ^a,b   Van Leemput, Koen ^a,c,d  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^d AALTO UNIVERSITY   (Finland)

Author keywords

Image classification; pattern recognition

Indexed keywords

ALZHEIMER'S DISEASE; BAYESIAN MODEL; CLASSIFICATION MODELS; CORTICAL THICKNESS; FREE PARAMETERS; GRAY MATTER; HEALTHY CONTROLS; IMAGE-BASED; PREDICTIVE ACCURACY; PROBABILISTIC PREDICTION; REGRESSION MODEL; SELFTUNING; SURFACE-BASED; TRAINING PHASE;

BAYESIAN NETWORKS; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; MEDICAL IMAGING; PATTERN RECOGNITION; REGRESSION ANALYSIS;

FORECASTING;

ADOLESCENT; ADULT; AGE; AGED; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BRAIN CORTEX; CASE CONTROL STUDY; FACTUAL DATABASE; FEMALE; HISTOLOGY; HUMAN; IMAGE PROCESSING; MALE; METHODOLOGY; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; REPRODUCIBILITY;

ADOLESCENT; ADULT; AGE FACTORS; AGED; AGED, 80 AND OVER; ALGORITHMS; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CASE-CONTROL STUDIES; CEREBRAL CORTEX; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 84870489618     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2012.2216543     Document Type: Article

References (88)

1. J. Ashburner and K. J. Friston, "Voxel-based morphometry-The methods, " Neuroimage, vol. 11, no. 6, pp. 805-821, 2000.
2. C. Davatzikos, A. Genc, D. Xu, and S. M. Resnick, "Voxel-based morphometry using the RAVENS maps: Methods and validation using simulated longitudinal atrophy, " NeuroImage, vol. 14, no. 6, pp. 1361-1369, 2001. (Pubitemid 33124184)
3. M. K. Chung, K. J. Worsley, T. Paus, C. Cherif, D. L. Collins, J. N. Giedd, J. L. Rapoport, and A. C. Evans, "A unified statistical approach to deformation-based morphometry, " NeuroImage, vol. 14, no. 3, pp. 595-606, 2001. (Pubitemid 32801707)
4. B. Fischl and A. M. Dale, "Measuring the thickness of the human cerebral cortex from magnetic resonance images, " PNAS, vol. 97, no. 20, p. 11050, 2000.
5. K. J. Worsley and K. J. Friston, "Analysis of fMRI time-series revis-ited\Again, " Neuroimage, vol. 2, no. 3, pp. 173-181, 1995.
6. J. D. Haynes and G. Rees, "Decoding mental states from brain activity in humans, " Nat. Rev. Neurosci., vol. 7, no. 7, pp. 523-534, 2006. (Pubitemid 43989773)
7. T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira, X. Wang, M. Just, and S. Newman, "Learning to decode cognitive states from brain images, " Mach. Learn., vol. 57, no. 1, pp. 145-175, 2004.
8. J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini, "Distributed and overlapping representations of faces and objects in ventral temporal cortex, " Science, vol. 293, no. 5539, p. 2425, 2001.
9. J. Mourão-Miranda, A. L. W. Bokde, C. Born, H. Hampel, and M. Stetter, "Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional MRI data, " NeuroImage, vol. 28, no. 4, pp. 980-995, 2005. (Pubitemid 41713632)
10. K. A. Norman, S. M. Polyn, G. J. Detre, and J. V. Haxby, "Beyond mind-reading: Multi-voxel pattern analysis of fMRI data, " Trends Cognitive Sci., vol. 10, no. 9, pp. 424-430, 2006. (Pubitemid 44308461)
11. N. Batmanghelich, B. Taskar, and C. Davatzikos, "A general and unifying framework for feature construction, in image-based pattern classification, " in IPMI. New York: Springer, 2009, pp. 423-434.
12. C. Davatzikos, Y. Fan, X. Wu, D. Shen, and S. M. Resnick, "Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging, " Neurobiol. Aging, vol. 29, no. 4, pp. 514-523, 2008. (Pubitemid 351312821)
13. Y. Fan, D. Shen, and C. Davatzikos, "Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM, " in Proc. Med. Image Computing Computer-Assisted Inter-vent. MICCAI 2005, 2005, pp. 1-8. (Pubitemid 43828227)
14. S. Klöppel, C. M. Stonnington, C. Chu, B. Draganski, R. I. Scahill, J. D. Rohrer, N. C. Fox, C. R. Jack, J. Ashburner, and R. S. J. Frackowiak, "Automatic classification of MR scans in Alzheimer's disease, " Brain, vol. 131, no. 3, p. 681, 2008.
15. Y. Liu, L. Teverovskiy, O. Carmichael, R. Kikinis, M. Shenton, C. S. Carter, V. A. Stenger, S. Davis, H. Aizenstein, and J. T. Becker et al., "Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification, " in Proc. Med. Image Computing Computer-Assisted Intervent. MICCAI 2004, 2004, pp. 393-401. (Pubitemid 40787405)
16. K. Pohl and M. Sabuncu, "A unified framework for MR based disease classification, " in Information Processing in Medical Imaging. New York: Springer, 2009, pp. 300-313.
17. N. U. F. Dosenbach, B. Nardos, A. L. Cohen, D. A. Fair, J. D. Power, J. A. Church, S. M. Nelson, G. S. Wig, A. C. Vogel, and C. N. Lessov-Schlaggar et al., "Prediction of individual brain maturity using fMRI, " Science, vol. 329, no. 5997, p. 1358, 2010.
18. J. Ashburner, C. Hutton, R. Frackowiak, I. Johnsrude, C. Price, and K. Friston, "Identifying global anatomical differences: Deformation-based morphometry, " Human Brain Mapp., vol. 6, no. 5-6, pp. 348-357, 1998. (Pubitemid 28460591)
19. P. Golland, "Discriminative direction for kernel classifiers, " Adv. Neural Inf. Process. Syst., vol. 1, pp. 745-752, 2002.
20. Z. Lao, D. Shen, Z. Xue, B. Karacali, S. M. Resnick, and C. Da-vatzikos, "Morphological classification of brains via high-dimensional shape transformations and machine learning methods, " Neuroimage, vol. 21, no. 1, pp. 46-57, 2004. (Pubitemid 38114995)
21. P. Golland, W. E. L. Grimson, M. E. Shenton, and R. Kikinis, "Detection and analysis of statistical differences in anatomical shape, " Med. Image Anal., vol. 9, no. 1, pp. 69-86, 2005. (Pubitemid 40003895)
22. Y. Fan, D. Shen, R. C. Gur, R. E. Gur, and C. Davatzikos, "COMPARE: Classification of morphological patterns using adaptive regional elements, " IEEE Trans. Med. Imag., vol. 26, no. 1, pp. 93-105, Jan. 2007. (Pubitemid 46096354)
23. O. Yamashita, M. Sato, T. Yoshioka, F. Tong, and Y. Kamitani, "Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns, " NeuroImage, vol. 42, pp. 1414-1429, 2008.
24. F. De Martino, G. Valente, N. Staeren, J. Ashburner, R. Goebel, and E. Formisano, "Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial pat terns, " NeuroImage, vol. 43, pp. 44-58, 2008.
25. E. Janousova, M. Vounou, R. Wolz, K. Gray, D. Rueckert, and G. Mon-tana1, "Fast brain-wide search of highly discriminative regions in medical images: An application to Alzheimer's disease, " in Proc. Med. Image Understand. Anal., 2011, pp. 17-21.
26. B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini- Issac, O. Col-liot, M. Sarazin, B. Dubois, S. Lehéricy, and H. Benali, "Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI, " Neuroradiology, vol. 51, no. 2, pp. 73-83, 2009.
27. R. S. Desikan, H. J. Cabral, C. P. Hess, W. P. Dillon, C. M. Glaston-bury, M. W. Weiner, N. J. Schmansky, D. N. Greve, D. H. Salat, R. L. Buckner, and B. Fischl, "Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease, " Brain, vol. 132, pp. 2048-2057, 2009.
28. P. Vemuri, J. L. Gunter, M. L. Senjem, J. L. Whitwell, K. Kantarci, D. S. Knopman, B. F. Boeve, R. C. Petersen, and C. R. Jack, Jr., "Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies, " Neuroimage, vol. 39, no. 3, pp. 1186-1197, 2008.
29. K. Franke, G. Ziegler, S. Kloppel, and C. Gaser, "Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters, " NeuroImage, vol. 50, no. 3, pp. 883-892, 2010.
30. L. Liang, V. Cherkassky, and D. A. Rottenberg, "Spatial SVM for feature selection and fMRI activation detection, " in Int. Joint Conf. Neural Networks, 2006 (IJCNN'06), 2006, pp. 1463-1469. (Pubitemid 351369493)
31. G. Fung and J. Stoeckel, "SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information, " Knowl. Inf. Syst., vol. 11, no. 2, pp. 243-258, 2007.
32. Z. Xiang, Y. Xi, U. Hasson, and P. Ramadge, "Boosting with spatial regularization, " Adv. Neural Inf. Process. Syst., vol. 22, pp. 2107-2115, 2009.
33. R. Cuingnet, M. Chupin, H. Benali, and O. Colliot, "Spatial and anatomical regularization of SVM for brain image analysis, " Adv. Neural Inf. Process. Syst., vol. 23, pp. 460-468, 2010.
34. R. Cuingnet, C. Rosso, M. Chupin, S. Lehéricy, D. Dormont, H. Be-nali, Y. Samson, and O. Colliot, "Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, " Med. Image Anal., vol. 15, pp. 729-737, 2011.
35. V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion, "Total variation regularization for fMRI-based prediction of behaviour, " IEEE Trans. Med. Imag., vol. 30, no. 7, pp. 1328-1340, Jul. 2011.
36. N. Batmanghelich, B. Taskar, and C. Davatzikos, "Generative- discriminative basis learning for medical imaging, " IEEE Trans. Med. Imag., vol. 31, no. 1, pp. 51-69, Jan. 2012.
37. M. A. J. van Gerven, B. Cseke, F. P. de Lange, and T. Heskes, "Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior, " NeuroImage, vol. 50, pp. 150-161, 2010.
38. D. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2003.
39. R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M. O. Habert, M. Chupin, H. Benali, and O. Colliot, "Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database, " NeuroImage, vol. 56, no. 2, pp. 766-781, 2011.
40. C. Cortes and V. Vapnik, "Support-vector networks, " Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
41. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006.
42. M. R. Sabuncu and K. Van Leemput, "The Relevance Voxel Machine (RVoxM): A Bayesian method for image-based prediction, " in Proceedings of MICCAI2011, 2011, vol. 6893, pp. 99-106.
43. R. M. Neal, Bayesian Learning for Neural Networks. New York: Springer-Verlag, 1996.
44. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine, " J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001. (Pubitemid 33687203)
45. D. J. C. MacKay, "Bayesian Methods for Adaptive Models, " Ph.D. dissertation, California Inst. Technol., Pasadena, CA, 1992.
46. J. Berger, Statistical Decision Theory and Bayesian Analysis, 2nd ed. New York: Springer, 1985.
47. D. A. Harville, "Maximum likelihood approaches to variance component estimation and to related problems, " J. Am. Stat. Assoc., pp. 320-338, 1977.
48. M. A. Woodbury, Inverting modified matrices Memorandum Rep., 1950, vol. 42, p. 106,.
49. M. D'Esposito, E. Zarahn, G. K. Aguirre, and B. Rypma, "The effect of normal aging on the coupling of neural activity to the bold hemody-namic response, " Neuroimage, vol. 10, no. 1, pp. 6-14, 1999. (Pubitemid 29369889)
50. D. H. Salat, R. L. Buckner, A. Z. Snyder, D. N. Greve, R. S. R. Desikan, E. Busa, J. C. Morris, A. M. Dale, and B. Fischl, "Thinning of the cerebral cortex in aging, " Cerebral Cortex, vol. 14, no. 7, p. 721, 2004.
51. J. Ashburner, "A fast diffeomorphic image registration algorithm, " Neuroimage, vol. 38, no. 1, pp. 95-113, 2007. (Pubitemid 47440133)
52. D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, "Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, " J. Cogn. Neurosci., vol. 19, no. 9, pp. 1498-1507, 2007. (Pubitemid 47358152)
53. J. Ashburner and K. J. Friston, "Unified segmentation, " Neuroimage, vol. 26, no. 3, pp. 839-851, 2005. (Pubitemid 40825026)
54. R. A. Sperling, P. S. Aisen, L. A. Beckett, D. A. Bennett, S. Craft, A. M. Fagan, T. Iwatsubo, C. R. Jack, J. Kaye, and T. J. Montine et al., "Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging and the Alzheimer's Association workgroup, " Alzheimer's Dementia, 2011.
55. C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. A. Henson, K. J. Friston, and R. S. J. Frackowiak, "A voxel-based morphometric study of ageing in 465 normal adult human brains, " Neuroimage, vol. 14, no. 1, pp. 21-36, 2001. (Pubitemid 32552740)
56. K. A. Jobst, A. D. Smith, M. Szatmari, M. M. Esiri, A. Jaskowski, N. Hindley, B. McDonald, and A. J. Molyneux, "Rapidly progressing atrophy of medial temporal lobe in Alzheimer's disease, " Lancet, vol. 343, no. 8901, pp. 829-830, 1994. (Pubitemid 24147514)
57. A. M. Dale, B. Fischl, and M. I. Sereno, "Cortical surface-based analysis I: Segmentation and surface reconstruction, " Neuroimage, vol. 9, no. 2, pp. 179-194, 1999. (Pubitemid 29369874)
58. B. Fischl, M. I. Sereno, and A. M. Dale, "Cortical surface-based analysis II: Inflation, flattening, and a surface-based coordinate system, " Neuroimage, vol. 9, no. 2, pp. 195-207, 1999. (Pubitemid 29369875)
59. B. Fischl, M. I. Sereno, R. B. H. Tootell, and A. M. Dale, "High-resolution intersubject averaging and a coordinate system for the cortical surface, " Human Brain Mapp., vol. 8, no. 4, pp. 272-284, 1999.
60. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, pp. 27:1-27:27, 2011.
61. R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, and B. T. Hyman et al., "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, " Neuroimage, vol. 31, no. 3, pp. 968-980, 2006. (Pubitemid 44183792)
62. R. L. Buckner, A. Z. Snyder, B. J. Shannon, G. LaRossa, R. Sachs, A. F. Fotenos, Y. I. Sheline, W. E. Klunk, C. A. Mathis, and J. C. Morris et al., "Molecular, structural, and functional characterization of Alzheimer's disease: Evidence for a relationship between default activity, amyloid, and memory, " J. Neurosci., vol. 25, no. 34, p. 7709, 2005.
63. D. R. Cox, "Regression models and life-tables, " J. R. Stat. Soc. Ser. B (Methodological), pp. 187-220, 1972.
64. D. Wipf and S. Nagarajan, "A new view of automatic relevance determination, " in Advances in Neural Information Processing Systems, J. C. Platt, D. Koller, Y. Singer, and S. Roweis, Eds. Cambridge, MA: MIT Press, 2008, vol. 20, pp. 1625-1632.
65. V. Michel, E. Eger, C. Keribin, and B. Thirion, "Multiclass sparse Bayesian regression for fMRI-based prediction, " J. Biomed. Imag., pp. 2:1-2:13, Jan. 2011.
66. D. J. C. MacKay, "Comparison of approximate methods for handling hyperparameters, " Neural Computat., vol. 11, no. 5, pp. 1035-1068, 1999.
67. P. M. Williams, "Bayesian regularization and pruning using a Laplace prior, " Neural Computat., vol. 7, no. 1, pp. 117-143, 1995.
68. R. Tibshirani, "Regression shrinkage and selection via the lasso, " J. R. Stat. Soc. Series B (Methodological), pp. 267-288, 1996.
69. Y. Qi, T. P. Minka, R. W. Picard, and Z. Ghahramani, "Predictive automatic relevance determination by expectation propagation, " in Proc. 21st Int. Conf. Mach. Learn., 2004, pp. 671-678. (Pubitemid 40290867)
70. H. Zou and T. Hastie, "Regularization and variable selection via the elastic net, " J. R. Stat. Soc.: Ser. B, vol. 67, no. 2, pp. 301-320, 2005. (Pubitemid 40465877)
71. K. Friston, C. Chu, J. Mourao-Miranda, O. Hulme, G. Rees, W. Penny, and J. Ashburner, "Bayesian decoding of brain images, " NeuroImage, vol. 39, pp. 181-205, 2008. (Pubitemid 350102208)
72. F. Bach, "Exploring large feature spaces with hierarchical multiple kernel learning, " Adv. Neural Inf. Process. Syst., pp. 105-112, 2008.
73. B. P. Zhao, G. Rocha, and B. Yu, "The composite absolute penalties family for grouped and hierarchical variable selection, " Ann. Stat., vol. 37, pp. 3468-3497, 2009.
74. J. Huang, T. Zhang, and D. Metaxas, "Learning with structured spar-sity, " J. Mach. Learn. Res., vol. 12, pp. 3371-3412, 2011.
75. R. Jenatton, J.-Y. Audibert, and F. Bach, "Structured variable selection with sparsity-inducing norms, " J. Mach. Learn. Res., vol. 12, pp. 2777-2824, 2011.
76. R. Jenatton, A. Gramfort, V. Michel, G. Obozinski, F. Bach, and B. Thirion, "Multi-scale mining of fMRI data with hierarchical structured sparsity, " in IEEE Int. Workshop Pattern Recognit. Neuroimag., 2011.
77. M. Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables, " J. R. Stat. Soc.: Ser. B, vol. 68, no. 1, pp. 49-67, 2006. (Pubitemid 43415335)
78. F. R. Bach, "Consistency of the group lasso and multiple kernel learning, " J. Mach. Learn. Res., vol. 9, pp. 1179-1225, 2008.
79. M. Hebiri and S. Van De Geer, "The smooth-lasso and other-penalized methods, " Electron. J. Statist., vol. 5, pp. 1184-1226, 2011.
80. Y. Wang, Y. Fan, P. Bhatt, and C. Davatzikos, "High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables, " NeuroImage, vol. 50, pp. 1519-1535, 2010.
81. F. Bunea, Y. She, H. Ombao, A. Gongvatana, K. Devlin, and R. Cohen, "Penalized least squares regression methods and applications to neu-roimaging, " NeuroImage, vol. 55, pp. 1519-1527, 2011.
82. G. Varoquaux, A. Gramfort, and B. Thirion, "Small-sample brain mapping: Sparse recovery on spatially correlated designs with randomization and clustering, " in Proc. 29th Int. Conf. Mach. Learn., 2012.
83. Y. Saeys, I. Inza, and P. Larrañaga, "A review of feature selection techniques in bioinformatics, " Bioinformatics, vol. 23, no. 19, p. 2507, 2007.
84. J. Hua, W. D. Tembe, and E. R. Dougherty, "Performance of feature-selection methods in the classification of high-dimension data, " Pattern Recognit., vol. 42, no. 3, pp. 409-424, 2009.
85. J. Himberg and A. Hyvärinen, "Icasso: Software for investigating the reliability of ICA estimates by clustering and visualization, " in IEEE 13th Workshop Neural Netw. Signal Process., 2003, pp. 259-268.
86. Y. Saeys, T. Abeel, and Y. Van de Peer, "Robust feature selection using ensemble feature selection techniques, " Mach. Learn. Knowl. Discov. Databases, pp. 313-325, 2008.
87. C. Plant, S. J. Teipel, A. Oswald, C. Böhm, T. Meindl, J. Mourao-Miranda, A. W. Bokde, H. Hampel, and M. Ewers, "Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease, " Neuroimage, vol. 50, no. 1, pp. 162-174, 2010.
88. M. E. Tipping and A. C. Faul, "Fast marginal likelihood maximisation for sparse Bayesian models, " in Proc. 9th Int. Workshop Artif. Intell. Stat., 2003.