SGPP: Spatial Gaussian predictive process models for neuroimaging data

NeuroImage

Volumn 89, Issue , 2014, Pages 70-80

  Hyun, Jung Won ^a   Li, Yimei ^a   Gilmore, John H ^b   Lu, Zhaohua ^b,d   Styner, Martin ^b,c   Zhu, Hongtu ^b,d  

^a ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c University of North Carolina   (United States)

^d University of North Carolina at Chapel Hill   (United States)

Author keywords

Cokriging; Functional principal component analysis; Missing data; Prediction; Simultaneous autoregressive model; Spatial Gaussian predictive process

Indexed keywords

ACCURACY; AGE; ARTICLE; CORRELATION ANALYSIS; FEMALE; HUMAN; HUMAN EXPERIMENT; INFANT; LATERAL BRAIN VENTRICLE; MALE; MORPHOMETRICS; NERVE CELL DIFFERENTIATION; NEUROIMAGING; NORMAL HUMAN; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SIMULATION; SPATIAL GAUSSIAN PREDICTIVE PROCESS; STATISTICAL ANALYSIS; STATISTICAL MODEL; CONTROLLED STUDY; FUNCTIONAL PRINCIPAL COMPONENT MODEL; PROCESS MODEL; SIMULTANEOUS AUTOREGRESSIVE MODEL; VOXEL BASED MORPHOMETRY;

COKRIGING; FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS; MISSING DATA; PREDICTION; SIMULTANEOUS AUTOREGRESSIVE MODEL; SPATIAL GAUSSIAN PREDICTIVE PROCESS;

COMPUTER SIMULATION; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFANT; LATERAL VENTRICLES; MALE; NEUROIMAGING; NORMAL DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS; SPATIAL ANALYSIS;

EID: 84892880465     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.11.018     Document Type: Article

References (58)

1. Ball T., Breckel T.P., Mutschler I., Aertsen A., Schulze-Bonhage A., Hennig J., Speck O. Variability of FMRI-response patterns at different spatial observation scales. Hum. Brain Mapp. 2012, 33:1155-1171.
2. Banerjee S., Gelfand A.E., Finley A.O., Sang H. Gaussian predictive process models for large spatial data sets. J. R. Stat. Soc. Ser. B Stat Methodol. 2008, 70:825-848.
3. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 1995, 289-300.
4. Bowman F.D. Spatio-temporal models for region of interest analyses of functional mappping experiments. J. Am. Stat. Assoc. 2007, 102:442-453.
5. Bowman F.D., Caffo B.A., Bassett S., Kilts C. Bayesian hierarchical framework for spatial modeling of fmri data. NeuroImage 2008, 39:146-156.
6. Brezger A., Fahrmeir L., Hennerfeind A. Adaptive Gaussian Markov random fields with applications in human brain mapping. J. R. Stat. Soc. Ser. C 2007, 56:327-345.
7. Carlin B.P., Banerjee S. Hierarchical multivariate car models for spatio-temporally correlated survival data. Bayesian Statistics 7 2003, 45-63. Oxford University Press, Oxford.
8. Chiou J.M., Muller H.G., Wang J.L., et al. Functional response models. Stat. Sin. 2004, 14:675-694.
9. Cressie N.A. Statistics for Spatial Data 1993, Revised edition, Wiley, New York.
10. Cressie N., Johannesson G. Fixed rank kriging for very large spatial data sets. J. R. Stat. Soc. Ser. B Stat Methodol. 2008, 70:209-226.
11. Cressie N., Wikle C. Statistics for Spatio-Temporal Data 2011, Wiley, Hoboken, NJ.
12. Derado G., Bowman F.D., Kilts C.D. Modeling the spatial and temporal dependence in fmri data. Biometrics 2010, 66:949-957.
13. Derado G., Bowman F., Zhang L. Predicting brain activity using a Bayesian spatial model. Stat. Methods Med. Res. 2013, 22:382-397.
14. Di C.Z., Crainiceanu C.M., Caffo B.S., Punjabi N.M. Multilevel functional principal component analysis. Ann. Appl. Stat. 2009, 3:458-488.
15. Evans A.C., Group, B.D.C. The NIH MRI study of normal brain development. NeuroImage 2006, 30:184-202.
16. Gelfand A.E., Vounatsou P. Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics 2003, 4:11-15.
17. Grenander U., Miller M.I. Pattern Theory From Representation to Inference 2007, Oxford University Press.
18. Greven S., Crainiceanu C., Caffo B., Reich D. Longitudinal functional principal component analysis. Electron. J. Stat. 2010, 4:1022-1054.
19. Groves A.R., Chappell M.A., Woolrich M.W. Combined spatial and non-spatial prior for inference on mri time-series. NeuroImage 2009, 45:795-809.
20. Guo Y., Bowman D.F., Kilts C. Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia. Hum. Brain Mapp. 2008, 29:1092-1109.
21. Hall P., Müller H.G., Wang J.L. Properties of principal component methods for functional and longitudinal data analysis. Ann. Stat. 2006, 34:1493-1517.
22. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2009, Springer, Hoboken, New Jersey. 2nd edition.
23. Higdon R., Foster N.L., Koeppe R.A., DeCarli C.S., Jagust W.J., Clark C.M., Barbas N.R., Arnold S.E., Turner R.S., Heidebrink J.L., et al. A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer's disease using fdg-pet imaging. Stat. Med. 2004, 23:315-326.
24. Hinrichs C., Singh V., Mukherjee L., Xu G., Chung M.K., Johnson S.C., ADNI Spatially augmented lpboosting for ad classification with evaluations on the ADNI dataset. NeuroImage 2009, 48:138-149.
25. Hyun J.W., Burman P., Paul D. A regression approach for estimating the parameters of the covariance function of a stationary spatial random process. J. Stat. Plan. Infer. 2012, 142:2330-2344.
26. Jones D.K., Symms M.R., Cercignani M., Howard R.J. The effect of filter size on VBM analyses of DT-MRI data. NeuroImage 2005, 26:546-554.
27. Kelejian H.H., Prucha I.R. Estimation of simultaneous systems of spatially interrelated cross sectional equations. J. Econ. 2004, 118:27-50.
28. Kissling W.D., Carl G. Spatial autocorrelation and the selection of simultaneous autoregressive models. Glob. Ecol. Biogeogr. 2008, 17:59-71.
29. Li Y., Zhu H., Shen D., Lin W., Gilmore J.H., Ibrahim J.G. Multiscale adaptive regression models for neuroimaging data. J. R. Stat. Soc. Ser. B 2011, 73:559-578.
30. Li Y., Gilmore J., Wang J., Styner M., Lin W.L., Zhu H.T. Two-stage multiscale adaptive regression methods of twin neuroimaging data. IEEE Trans. Med. Imaging 2012, 31:1100-1112.
31. Li Y., Gilmore J.H., Shen D., Styner M., Lin W., Zhu H.T. Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data. NeuroImage 2013, 72:91-105.
32. Marquand A., Howard M., Brammer M., Chu C., Coen S., Mourão-Miranda J. Quantitative prediction of subjective pain intensity from whole-brain fmri data using gaussian processes. NeuroImage 2010, 49:2178-2189.
33. Martino F.D., Valente G., Staeren N., Ashburner J., Goebel R., Formisano E. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage 2008, 43:44-58.
34. Myers D.E. Matrix formulation of co-kriging. J. Int. Assoc. Math. Geol. 1982, 14:249-257.
35. Penny W.D., Trujillo-Barreto N.J., Friston K.J. Bayesian fMRI time series analysis with spatial priors. NeuroImage 2005, 24:350-362.
36. Pinheiro J.C., Bates D.M. Unconstrained parametrizations for variance-covariance matrices. Stat. Comput. 1996, 6:289-296.
37. Polzehl J., Spokoiny V.G. Propagation-separation approach for local likelihood estimation. Probab. Theory Relat. Fields 2006, 135:335-362.
38. Polzehl J., Voss H.U., Tabelow K. Structural adaptive segmentation for statistical parametric mapping. NeuroImage 2010, 52:515-523.
39. Qiu P. Jump surface estimation, edge detection, and image restoration. J. Am. Stat. Assoc. 2007, 102.
40. Ryali S., Supekar K., Abrams D.A., Menon V. Sparse logistic regression for whole-brain classification of fmri data. NeuroImage 2010, 51:752-764.
41. Sang H., Huang J.Z. A full scale approximation of covariance functions for large spatial data sets. J. R. Stat. Soc. B 2012, 74:111-132.
42. Schabenberger O., Gotway C.A. Statistical Methods for Spatial Data Analysis 2004, Chapman and Hall/CRC.
43. Smith M.S., Fahrmeir L. Spatial Bayesian variable selection with application to functional magnetic resonance imaging. J. Am. Stat. Assoc. 2007, 102:417-431.
44. Stein M.L. A modeling approach for large spatial datasets. J. Korean Stat. Soc. 2008, 37:3-10.
45. Styner M., Lieberman J.A., Pantazis D., Gerig G. Boundary and medial shape analysis of the hippocampus in schizophrenia. Med. Image Anal. 2004, 8:197-203.
46. Tabelow K., Polzehl J., Spokoiny V., Voss H.U. Diffusion tensor imaging: structural adaptive smoothing. NeuroImage 2008, 39:1763-1773.
47. Uijl S.G., Leijten F.S., Arends J.B., Parra J., Van Huffelen A.C., Moons K.G. Prognosis after temporal lobe epilepsy surgery: the value of combining predictors. Epilepsia 2008, 49:1317-1323.
48. Vaden J.K., Gebregziabher M., Kuchinsky S.E., Eckert M.A. Multiple imputation of missing FMRI data in whole brain analysis original research article. NeuroImage 2012, 60:1843-1855.
49. Wall M.M. A close look at the spatial structure implied by the car and sar models. J. Stat. Plan. Infer. 2004, 121:311-324.
50. Waller L.A., Gotway C.A. Applied Spatial Statistics for Public Health Data 2004, Wiley-Interscience.
51. Worsley K.J., Taylor J.E., Tomaiuolo F., Lerch J. Unified univariate and multivariate random field theory. NeuroImage 2004, 23:189-195.
52. Yao F., Lee T. Penalized spline models for functional principal component analysis. J. R. Stat. Soc. Ser. B Stat Methodol. 2006, 68:3-25.
53. Yuan Y., Gilmore J.H., Geng X., Styner M., Chen K., Wang J.L., Zhu H. A longitudinal functional analysis framework for analysis of white matter tract statistics. LNCS7917, Information Processing in Medical Imaging 2013, 220-231. Springer, Berlin/Heidelberg. W.M. Wells, S. Joshi, K.M. Pohl (Eds.).
54. Zellner A. An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. J. Am. Stat. Assoc. 1962, 57:348-368.
55. Zhao L., Boucher M., Rosa-Neto P., Evans A. Impact of scale space search on age- and gender-related changes in mri-based cortical morphometry. Hum. Brain Mapp. 2013, 34:2113-2128.
56. Zhu H.T., Gu M., Peterson B.S. Maximum likelihood from spatial random effects models via the stochastic approximation expectation maximization algorithm. Stat. Comput. 2007, 15:163-177.
57. Zhu H., Kong L., Li R., Styner M., Gerig G., Lin W., Gilmore J.H. FADTTS: functional analysis of diffusion tensor tract statistics. NeuroImage 2011, 56:1412-1425.
58. Zipunnikov V., Caffo B., Yousem D., Davatzikos C., Schwartz B., Crainiceanu C. Functional principal components model for high-dimensional brain imaging. NeuroImage 2011, 58:772-784.