Multivariate models of inter-subject anatomical variability

NeuroImage

Volumn 56, Issue 2, 2011, Pages 422-439

  Ashburner, John ^a   Klöppel, Stefan ^b  

^a WELLCOME TRUST CENTRE FOR NEUROIMAGING   (United Kingdom)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALLOMETRY; ANATOMICAL VARIATION; ARTICLE; BRAIN MAPPING; COGNITION; CORRELATION ANALYSIS; DECISION MAKING; DISCRIMINANT ANALYSIS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; IMAGE PROCESSING; KERNEL METHOD; NEUROIMAGING; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; VOXEL BASED MORPHOMETRY;

ANIMALS; BRAIN; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, NEUROLOGICAL; MULTIVARIATE ANALYSIS; PATTERN RECOGNITION, AUTOMATED;

EID: 79955005414     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.03.059     Document Type: Article

References (148)

1. Adams D.C., Rohlf F.J., Slice D.E. Geometric morphometrics: ten years of progress following the revolution. Ital. J. Zool. 2004, 710(1):5-16.
2. Alexander D., Gee J., Bajcsy R. Elastic matching of diffusion tensor MRIs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1 1999, 244-249.
3. Allassonnière S., Amit Y., Trouvé A. Towards a coherent statistical framework for dense deformable template estimation. J. R. Stat. Soc. B Methodol. 2007, 690(1):3-29.
4. Amari S.I., Nagaoka H. Methods of Information Geometry 2007, AMS Bookstore.
5. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007, 380(1):95-113.
6. Ashburner J., Friston K. Multimodal image coregistration and partitioning-a unified framework. Neuroimage 1997, 60(3):209-217.
7. Ashburner J., Friston K.J. Why voxel-based morphometry should be used. Neuroimage 2001, 140(6):1238-1243.
8. Ashburner J., Friston K.J. Computing average shaped tissue probability templates. NeuroImage 2009, 450(2):333-341.
9. Ashburner J., Hutton C., Frackowiak R., Johnsrude I., Price C., Friston K. Identifying global anatomical differences: deformation-based morphometry. Hum. Brain Mapp. 1998, 60(5-6):348-357.
10. Avants B., Gee J.C. Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 2004, 23:139-150.
11. Bach, F.R., Jordan, M.I. A probabilistic interpretation of canonical correlation analysis. Dept. Statist., Univ. California, Berkeley, CA, Tech. Rep, 688, 2005.
12. Baloch S., Davatzikos C. Morphological appearance manifolds in computational anatomy: groupwise registration and morphological analysis. Neuroimage 2009, 450(1S1):73-85.
13. Baron-Cohen S. The extreme male brain theory of autism. Trends Cogn. Sci. 2002, 60(6):248-254.
14. Beckmann C.F., Smith S.M. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 2004, 230(2):137-152.
15. Beg M.F., Khan A. Computing an average anatomical atlas using LDDMM and geodesic shooting. 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro 2006, 1116-1119.
16. Beg M.F., Miller M.I., Trouvé A., Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 2005, 610(2):139-157.
17. Behrens T.E.J., Johansen-Berg H., Woolrich M.W., Smith S.M., Wheeler-Kingshott C.A.M., Boulby P.A., Barker G.J., Sillery E.L., Sheehan K., Ciccarelli O., et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 2003, 60(7):750-757.
18. Behrens T.E.J., Jenkinson M., Robson M.D., Smith S.M., Johansen-Berg H. A consistent relationship between local white matter architecture and functional specialisation in medial frontal cortex. Neuroimage 2006, 300(1):220-227.
19. Behrens T.E.J., Berg H.J., Jbabdi S., Rushworth M.F.S., Woolrich M.W. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?. Neuroimage 2007, 340(1):144-155.
20. Bell A.J., Sejnowski T.J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995, 70(6):1129-1159.
21. Bishop C.M. Variational principal components. Artificial Neural Networks. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470), volume 1 1999.
22. Bishop C.M., Lasserre J. Generative or discriminative? Getting the best of both worlds. Bayesian Stat. 2007, 8:3-24.
23. Bishop C.M., et al. Pattern Recognition and Machine Learning 2006, New York, Springer.
24. Bookstein F.L. Biometrics and brain maps: the promise of the morphometric synthesis. Neuroinformatics: an Overview of the Human Brain Project 1996.
25. Bookstein F.L. Morphometric Tools for Landmark Data: Geometry and Biology 1997, Cambridge Univ. Pr.
26. Boser B.E., Guyon I.M., Vapnik V.N. A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory 1992, 144-152. ACM New York, NY, USA.
27. Bro-Nielsen M., Cotin S. Real-time volumetric deformable models for surgery simulation using finite elements and condensation. Computer Graphics Forum, volume 15 1996, 57-66.
28. Butcher J. Alzheimer's researchers open the doors to data sharing. Lancet Neurol. 2007, 60(6):480-481.
29. Chance S.A., Esiri M.M., Crow T.J. Macroscopic brain asymmetry is changed along the antero-posterior axis in schizophrenia. Schizophr. Res. 2005, 740(2-3):163-170.
30. Chapelle O., Schölkopf B., Zien A. Semi-Supervised Learning 2006.
31. Christensen G.E., Rabbitt R.D., Miller M.I., Joshi S.C., Grenander U., Coogan T.A., Van Essen D.C. Topological properties of smooth anatomic maps. Information processing in medical imaging 1995, 101-112.
32. Cotter, C.J., Holm, D.D., Singular solutions, momentum maps and computational anatomy. Arxiv preprint nlin.SI/0605020, 2006.
33. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods 2000, Cambridge Univ Pr.
34. Csete M.E., Doyle J.C. Reverse Engineering of Biological Complexity 2002.
35. da Fontoura Costa L., Cesar R.M. Shape Analysis and Classification: Theory and Practice 2001, CRC.
36. Davatzikos C., Shen D., Lao Z., Xue Z., Karacali B. Morphological classification of medical images using nonlinear support vector machines. IEEE International Symposium on Biomedical Imaging: Nano to Macro 2004, 587-590.
37. Davatzikos C., Fan Y., Wu X., Shen D., Resnick S.M. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol. Aging 2008, 290(4):514-523.
38. Davies R., Twining C., Taylor C. Statistical Models of Shape: Optimisation and Evaluation 2008, Springer-Verlag New York Inc.
39. Dryden I.L., Mardia K.V. Statistical Shape Analysis 1998, Wiley, New York.
40. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, Wiley, New York.
41. Elstein A.S., Schwarz A. Clinical Problem Solving and Diagnostic Decision Making: Selective Review of the Cognitive Literature 2002.
42. Fan Y., Shen D., Davatzikos C. Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM. Lect. Notes Comput. Sci. 2005, 3749:1.
43. Fletcher P.C., Dolan R.J., Shallice T., Frith C.D., Frackowiak R.S.J., Friston K.J. Is multivariate analysis of PET data more revealing than the univariate approach? Evidence from a study of episodic memory retrieval. NeuroImage 1996, 30(3):209-215.
44. Friston K., Mattout J., Trujillo-Barreto N., Ashburner J., Penny W. Variational free energy and the Laplace approximation. NeuroImage 2007, 340(1):220-234.
45. Friston K., Chu C., Mourão-Miranda J., Hulme O., Rees G., Penny W., Ashburner J. Bayesian decoding of brain images. Neuroimage 2008, 390(1):181-205.
46. Friston K.J., Ashburner J. Generative and recognition models for neuroanatomy. NeuroImage 2004, 230(1):21-24.
47. Friston K.J., Holmes A.P., Worsley K.J., Poline J.P., Frith C.D., Frackowiak R.S.J., et al. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 1994, 20(4):189-210.
48. Friston K.J., Poline J.B., Holmes A.P., Frith C.D., Frackowiak R.S.J. A multivariate analysis of PET activation studies. Hum. Brain Mapp. 1996, 40(2).
49. Gerardin E., Chételat G., Chupin M., Cuingnet R., Desgranges B., Kim H.S., Niethammer M., Dubois B., Lehéricy S., Garnero L., et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. Neuroimage 2009.
50. Ghahramani Z., Beal M.J. Variational inference for Bayesian mixtures of factor analysers. Adv. Neural. Inf. Process. Syst. 2000, 12:449-455.
51. Golland P. Discriminative direction for kernel classifiers. Adv. Neural Inf. Proces. Syst. 2002, 1:745-752.
52. Golland P., Grimson W.E.L., Shenton M.E., Kikinis R. Deformation analysis for shape based classification. Lect. Notes Comput. Sci. 2001, 517-530.
53. Golland P., Grimson W.E.L., Shenton M.E., Kikinis R. Detection and analysis of statistical differences in anatomical shape. Med. Image Anal. 2005, 90(1):69-86.
54. Grenander U., Miller M. Pattern Theory: from Representation to Inference 2007, Oxford University Press, USA.
55. Grenander U., Miller M.I. Computational anatomy: an emerging discipline. Q. Appl. Math. 1998, 560(4):617-694.
56. Guimond A., Guttmann C.R.G., Warfield S.K., Westin C.F. Deformable registration of DT-MRI data based on transformation invariant tensor characteristics. Proc. ISBI, volume 2 2002.
57. Hand D.J. Measuring diagnostic accuracy of statistical prediction rules. Stat. Neerl. 2001, 550(1):3-16.
58. Hinton G.E., Osindero S., Teh Y.W. A fast learning algorithm for deep belief nets. Neural Comput. 2006, 180(7):1527-1554.
59. Hirsch J.E. Does the h index have predictive power?. Proc. Natl Acad. Sci. U. S. A. 2007, 1040(49):19193.
60. Holub A.D., Welling M., Perona P. Hybrid generative-discriminative visual categorization. Int. J. Comput. Vis. 2008, 770(1):239-258.
61. Hoyer P.O., Janzing D., Mooij J., Peters J., Scholkopf B. Nonlinear causal discovery with additive noise models. Adv. Neural. Inf. Process. Syst. 2009, 21:689-696.
62. Huxley J. Problems of Relative Growth 1993, Johns Hopkins University Press.
63. Jaakkola T., Diekhans M., Haussler D. A discriminative framework for detecting remote protein homologies. J. Comput. Biol. 2000, 70(1-2):95-114.
64. Jaynes E.T., Bretthorst G.L. Probability Theory: the Logic of Science 2003, Cambridge University Press, Cambridge UK.
65. Johansen-Berg H., Behrens T.E.J., Sillery E., Ciccarelli O., Thompson A.J., Smith S.M., Matthews P.M. Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. Cereb. Cortex 2005, 150(1):31-39.
66. Joshi S., Davis B., Jomier M., Gerig G. Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 2004, 23:151-160.
67. Kendall D.G., Barden D., Carne T.K., Le H. Shape and Shape Theory 1999, Wiley.
68. Kiebel S.J., Daunizeau J., Phillips C., Friston K.J. Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG. NeuroImage 2008, 390(2):728-741.
69. Kitano H. Computational systems biology. Nature 2002, 4200(6912):206-210.
70. Klein J.C., Behrens T.E.J., Robson M.D., Mackay C.E., Higham D.J., Johansen-Berg H. Connectivity-based parcellation of human cortex using diffusion MRI: establishing reproducibility, validity and observer independence in BA 44/45 and SMA/pre-SMA. Neuroimage 2007, 340(1):204-211.
71. Kloppel S., Stonnington C.M., Barnes J., Chen F., Chu C., Good C.D., Mader I., Anne Mitchell L., Patel A.C., Roberts C.C., et al. Accuracy of dementia diagnosis-a direct comparison between radiologists and a computerized method. Brain 2008.
72. Kloppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Jack C.R., Ashburner J., Frackowiak R.S.J. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 681(3).
73. Krim H., Yezzi A.J. Statistics and Analysis of Shapes 2006, Birkhauser.
74. Lao Z., Shen D., Xue Z., Karacali B., Resnick S.M., Davatzikos C. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage 2004, 210(1):46-57.
75. Lasserre J.A., Bishop C.M., Minka T.P. Principled hybrids of generative and discriminative models. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1 2006.
76. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 4010(6755):788-791.
77. Lele S., Richtsmeier J.T. An invariant Approach to Statistical Analysis of Shapes 2001, CRC Pr I Llc.
78. Lorenzen P., Davis B., Joshi S. Unbiased atlas formation via large deformations metric mapping. Lect. Notes Comput. Sci. 2005, 3750:411.
79. Luders E., Steinmetz H., Jancke L. Brain size and grey matter volume in the healthy human brain. Neuroreport 2002, 130(17):2371.
80. MacKay D.J.C. The evidence framework applied to classification networks. Neural Comput. 1992, 40(5):720-736.
81. MacKay D.J.C. Information Theory, Inference, and Learning Algorithms 2003, Cambridge Univ Pr.
82. Magnin B., Mesrob L., Kinkingnéhun S., Pélégrini-Issac M., Colliot O., Sarazin M., Dubois B., Lehéricy S., Benali H. Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology 2009, 510(2):73-83.
83. Makrogiannis S., Verma R., Karacali B., Davatzikos C. Anatomical equivalence class: a morphological analysis framework using a lossless shape descriptor. IEEE Trans. Med. Imaging 2007, 26(4):619-631.
84. Marcus D.S., Wang T.H., Parker J., Csernansky J.G., Morris J.C., Buckner R.L. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 2007, 190(9):1498-1507.
85. Markram H. The blue brain project. Nat. Rev. Neurosci. 2006, 70(2):153-160.
86. Marsland S., McLachlan R. A Hamiltonian particle method for diffeomorphic image registration. Lect. Notes Comput. Sci. 2007, 4584:396.
87. Martin R.D. Relative brain size and basal metabolic rate in terrestrial vertebrates. Nature 1981, 293:57-60.
88. Mazziotta J., Toga A., Evans A., Fox P., Lancaster J., Zilles K., Woods R., Paus T., Simpson G., Pike B., et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 2001, 3560:1293.
89. McIntosh A.R., Bookstein F.L., Haxby J.V., Grady C.L. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 1996, 30(3):143-157.
90. Mechelli A., Friston K.J., Frackowiak R.S., Price C.J. Structural covariance in the human cortex. J. Neurosci. 2005, 250(36):8303-8310.
91. Miller M., Banerjee A., Christensen G., Joshi S., Khaneja N., Grenander U., Matejic L. Statistical methods in computational anatomy. Stat. Meth. Med. Res. 1997, 60(3):267.
92. Miller M.I. Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms. NeuroImage 2004, 23:19-33.
93. Miller M.I., Qiu A. The emerging discipline of Computational Functional Anatomy. Neuroimage 2009, 450(1S1):16-39.
94. Miller M.I., Trouvé A., Younes L. Geodesic shooting for computational anatomy. J. Math. Imaging Vis. 2006, 240(2):209-228.
95. Miller M.I., Priebe C.E., Qiu A., Fischl B., Kolasny A., Brown T., Park Y., Ratnanather J.T., Busa E., Jovicich J., et al. Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping. Hum. Brain Mapp. 2008, 300(7):2132-2141.
96. Mitchell T.M., Hutchinson R., Niculescu R.S., Pereira F., Wang X., Just M., Newman S. Learning to decode cognitive states from brain images. Mach. Learn. 2004, 570(1):145-175.
97. Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C.R., Jagust W., Trojanowski J.Q., Toga A.W., Beckett L. Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. J. Alzheimer's Assoc. 2005, 10(1):55-66.
98. Mumford D. Neuronal architectures for pattern-theoretic problems. Large-scale neuronal theories of the brain 1994, 125-152.
99. Mumford D. Pattern theory: a unifying perspective. Perception as Bayesian inference 1996, 25-62.
100. Mumford D. Pattern theory: the mathematics of perception. Proceedings of the International Congress of Mathematicians, volume 3 2002.
101. Mumford D. Empirical statistics and stochastic models for visual signals. New Directions in Statistical Signal Processing: From Systems to Brain 2005.
102. Oishi K., Zilles K., Amunts K., Faria A., Jiang H., Li X., Akhter K., Hua K., Woods R., Toga A.W., et al. Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage 2008, 430(3):447-457.
103. Park H.J., Kubicki M., Shenton M.E., Guimond A., McCarley R.W., Maier S.E., Kikinis R., Jolesz F.A., Westin C.F. Spatial normalization of diffusion tensor MRI using multiple channels. Neuroimage 2003, 200(4):1995-2009.
104. Penny W.D., Stephan K.E., Mechelli A., Friston K.J. Comparing dynamic causal models. NeuroImage 2004, 220(3):1157-1172.
105. Pereira F., Mitchell T., Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 2009, 450(1S1):199-209.
106. Petersson K.M., Nichols T.E., Poline J.B., Holmes A.P. Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models. Philos. Trans. R. Soc. Biol. Sci. 1999, 3540(1387):1239-1260.
107. Qiu A., Miller M.I. Multi-structure network shape analysis via normal surface momentum maps. Neuroimage 2008, 420(4):1430-1438.
108. Rasmussen C.E., Quinonero-Candela J. Healing the relevance vector machine through augmentation. Machine Learning-International Workshop Then Conference, volume 22 2005, 689.
109. Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning 2006, Springer.
110. Rilling J.K. Human and nonhuman primate brains: are they allometrically scaled versions of the same design?. Evol. Anthropol.: Issues News Rev. 2006, 150(2).
111. Roweis S., Ghahramani Z. A unifying review of linear Gaussian models. Neural Comput. 1999, 110(2):305-345.
112. Ruiz-Alzola J., Westin C.F., Warfield S.K., Alberola C., Maier S., Kikinis R. Nonrigid registration of 3D tensor medical data. Med. Image Anal. 2002, 60(2):143-161.
113. Sabuncu M.R., Singer B.D., Conroy B., Bryan R.E., Ramadge P.J., Haxby J.V. Function-based intersubject alignment of human cortical anatomy. Cereb. Cortex 2010, 20:130-140.
114. Salimi-Khorshidi G., Smith S.M., Keltner J.R., Wager T.D., Nichols T.E. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage 2009, 450(3):810-823.
115. Saxe R., Brett M., Kanwisher N. Divide and conquer: a defense of functional localizers. Neuroimage 2006, 300(4):1088-1096.
116. Schmah T., Hinton G.E., Zemel R.S., Small S.L., Strother S. Generative versus discriminative training of RBMs for classification of fMRI images. NIPS 2008.
117. Schölkopf B., Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2002, the MIT Press.
118. Seeley W.W., Crawford R.K., Zhou J., Miller B.L., Greicius M.D. Neurodegenerative diseases target large-scale human brain networks. Neuron 2009, 620(1):42-52.
119. Small C.G. The Statistical Theory of Shape 1996, Springer Verlag.
120. Smith S.M., Fox P.T., Miller K.L., Glahn D.C., Fox P.M., Mackay C.E., Filippini N., Watkins K.E., Toro R., Laird A.R., et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl Acad. Sci. U. S. A. 2009, 1060(31):13040.
121. Stephan K.E., Kamper L., Bozkurt A., Burns G., Young M.P., Kotter R. Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). Philos. Trans. R. Soc. Lond. B Biol. Sci. 2001, 356:1159-1186.
122. Thompson D., Bonner J.T. On Growth and Form 1942, Cambridge University Press, Cambridge.
123. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 2001, 1:211-244.
124. Tipping M.E., Bishop C.M. Mixtures of probabilistic principal component analyzers. Neural Comput. 1999, 110(2):443-482.
125. Tipping M.E., Bishop C.M. Probabilistic principal component analysis. J. R. Stat. Soc. B Stat. Methodol. 1999, 611-622.
126. Trouvé A., Younes L. Metamorphoses through lie group action. Found. Comput. Math. 2005, 50(2):173-198.
127. Trouvé A., Yu Y. Metric similarities learning through examples: an application to shape retrieval. Lect. Notes Comput. Sci. 2001, 50-62.
128. Vaillant M., Miller M.I., Younes L., Trouvé A. Statistics on diffeomorphisms via tangent space representations. NeuroImage 2004, 23:161-169.
129. Van Horn J.D., Toga A.W. Is it time to re-prioritize neuroimaging databases and digital repositories?. Neuroimage 2009.
130. Van Leemput K., Maes F., Vandermeulen D., Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 1999, 180(10):897-908.
131. Vapnik V.N. An overview of statistical learning theory. IEEE Transact. Neural Netw. 1999, 100(5):988-999.
132. Vemuri P., Gunter J.L., Senjem M.L., Whitwell J.L., Kantarci K., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 2008, 390(3):1186-1197.
133. Wang L., Beg F., Ratnanather T., Ceritoglu C., Younes L., Morris J.C., Csernansky J.G., Miller M.I. Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. IEEE Trans. Med. Imaging 2007, 260(4):462-470.
134. Watanabe S. Knowing and Guessing: a Quantitative Study of Inference and Information 1969, John Wiley & Sons.
135. West G.B., Brown J.H. The Origin of Allometric Scaling Laws in Biology from Genomes to Ecosystems: Towards a Quantitative Unifying Theory of Biological structure and Organization 2005.
136. West G.B., Brown J.H., Enquist B.J. A general model for ontogenetic growth. Nature 2001, 4130(6856):628-631.
137. Williams C.K.I., Barber D. Bayesian classification with Gaussian processes. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 200(12):1342-1351.
138. Williams C.K.I., Rasmussen C.E. Gaussian processes for regression. Adv. Neural. Inf. Process. Syst. 1996, 8.
139. Woods R.P. Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation. NeuroImage 2003, 180(3):769-788.
140. Wright I.C., McGuire P.K., Poline J.B., Travere J.M., Murray R.M., Frith C.D., Frackowiak R.S.J., Friston K.J. A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage 1995, 20(4):244-252.
141. Xu L., Liu J., Adali T., Calhoun V.D. Source based morphometry using structural MRI phase images to identify sources of gray matter and white matter relative differences in schizophrenia versus controls. ICASSP 2008 2008, 533-536.
142. Younes L. Jacobi fields in groups of diffeomorphisms and applications. Q. Appl. Math. 2007, 650(1):113-134.
143. Younes L., Qiu A., Winslow R.L., Miller M.I. Transport of relational structures in groups of diffeomorphisms. J. Math. Imaging Vis. 2008, 320(1):41-56.
144. Younes L., Arrate F., Miller M.I. Evolutions equations in computational anatomy. Neuroimage 2009, 450(1S1):40-50.
145. Zhang H., Yushkevich P.A., Alexander D.C., Gee J.C. Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med. Image Anal. 2006, 100(5):764-785.
146. Zhang K., Sejnowski T.J. A universal scaling law between gray matter and white matter of cerebral cortex. Proc. Natl Acad. Sci. U. S. A. 2000, 970(10):5621-5626.
147. Zhu X., Goldberg A.B. Introduction to Semi-Supervised Learning. Synth. Lect. Artif. Intell. Mach. Learn. 2009, 30(1):1-130.
148. Zou K.H., O'Malley A.J., Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 2007, 1150(5):654.