Multiscale adaptive regression models for neuroimaging data

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volumn 73, Issue 4, 2011, Pages 559-578

  Li, Yimei ^a   Zhu, Hongtu ^a   Shen, Dinggang ^a   Lin, Weili ^a   Gilmore, John H ^a   Ibrahim, Joseph G ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Kernel; Multiscale adaptive regression; Neuroimaging data; Propagation separation; Smoothing; Sphere; Test statistics

Indexed keywords

EID: 79961044921     PISSN: 13697412     EISSN: 14679868     Source Type: Journal    
DOI: 10.1111/j.1467-9868.2010.00767.x     Document Type: Article

References (31)

1. Ashburner, J. and Friston, K. J. (2000) Voxel-based morphometry: the methods. NeuroImage, 11, 805-821.
2. Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2004) Hierarchical Modeling and Analysis for Spatial Data. Boca Raton: Chapman and Hall.
3. Basser, P. J., Mattiello, J. and LeBihan, D. (1994) MR diffusion tensor spectroscopy and imaging. Biophys. J., 66, 259-267.
4. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B, 57, 289-300.
5. Besag, J. (1986) On the statistical analysis of dirty pictures (with discussion). J. R. Statist. Soc. B, 48, 259-302.
6. Bowman, F. D. (2007) Spatio-temporal models for region of interest analyses of functional mapping experiments. J. Am. Statist. Ass., 102, 442-453.
7. Chung, M. K., Robbins, S., Dalton, K. M., Davidson, R. J., Alexander, A. L. and Evans, A. C. (2005) Cortical thickness analysis in autism via heat kernel smoothing. NeuroImage, 25, 1256-1265.
8. Friston, K. J. (2007) Statistical Parametric Mapping: the Analysis of Functional Brain Images. London: Academic Press.
9. Hecke, W. V., Sijbers, J., Backer, S. D., Poot, D., Parizel, P. M. and Leemans, A. (2009). On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods. NeuroImage, 46, 692-707.
10. Huettel, S. A., Song, A. W. and McCarthy, G. (2004) Functional Magnetic Resonance Imaging. Sunderland: Sinauer Associates.
11. Jones, D. K., Symms, D. K., Cercignani, M. and Howard, R. J. (2005) The effect of filter size on VBM analyses of DT-MRI data. NeuroImage, 26, 546-554.
12. Lazar, N. (2008) The Statistical Analysis of Functional MRI Data. New York: Springer.
13. Li, Y. M., Zhu, H., Shen, D., Lin, W., Gilmore, J. H. and Ibrahim, J. G. (2010) Technical proofs for Multiscale adaptive regression models for neuroimaging data. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill. (Available from.)
14. McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd edn. London: Chapman and Hall.
15. Nichols, T. and Hayasaka, S. (2003) Controlling the family-wise error rate in functional neuroimaging: a comparative review. Statist. Meth. Med. Res., 12, 419-446.
16. Polzehl, J. and Spokoiny, V. G. (2000) Adaptive weights smoothing with applications to image restoration. J. R. Statist. Soc. B, 62, 335-354.
17. Polzehl, J. and Spokoiny, V. G. (2006) Propagation-separation approach for local likelihood estimation. Probab. Theor. Reltd Flds, 135, 335-362.
18. Polzehl, J. and Tabelow, K. (2007) fmri: a package for analyzing fmri data. R News, 7, 13-17.
19. Qiu, P. (2005) Image Processing and Jump Regression Analysis. New York: Wiley.
20. Qiu, P. (2007) Jump surface estimation, edge detection, and image restoration. J. Am. Statist. Ass., 102, 745-756.
21. Tabelow, K., Piech, V., Polzehl, J. and Voss, H. U. (2008a) High-resolution fMRI: overcoming the signal-to-noise problem. J. Neursci. Meth., 178, 357-365.
22. Tabelow, K., Polzehl, J., Spokoiny, V. and Voss, H. U. (2008b) Diffusion tensor imaging: structural adaptive smoothing. NeuroImage, 39, 1763-1773.
23. Tabelow, K., Polzehl, J., Ulug, A. M., Dyke, J. P., Watts, R., Heier, L. A. and Voss, H. U. (2008c) Accurate localization of functional brain activity using structure adaptive smoothing. IEEE Trans. Med. Imgng, 27, 531-537.
24. Tabelow, K., Polzehl, J., Voss, H. U. and Spokoiny, V. (2006) Analyzing fMRI experiments with structural adaptive smoothing procedures. NeuroImage, 33, 55-62.
25. Thompson, P. M. and Toga, A. W. (2002) A framework for computational anatomy. Comput. Visualizn Sci., 5, 13-34.
26. Worsley, K. J. (2003) Detecting activation in fMRI data. Statist. Meth. Med. Res., 12, 401-418.
27. Worsley, K. J., Taylor, J. E., Tomaiuolo, F. and Lerch, J. (2004) Unified univariate and multivariate random field theory. NeuroImage, 23, 189-195.
28. Yap, P. T., Wu, G. R., Zhu, H.T., Lin, W. L. and Shen, D. G. (2009) TIMER: tensor image morphing for elastic registration. NeuroImage, 47, 549-563.
29. Yue, Y., Loh, J. M. and Lindquist, M. A. (2010) Adaptive spatial smoothing of fMRI images. Statist. Interface, 3, 3-14.
30. Zhu, H. T., Gu, M. G. and Peterson, B. G. (2007a) Maximum likelihood from spatial random effects models via the stochastic approximation expectation maximization algorithm. Statist. Comput., 15, 163-177.
31. Zhu, H. T., Zhang, H. P., Ibrahim, J. G. and Peterson, B. S. (2007b) Statistical analysis of diffusion tensors in diffusion-weighted magnetic resonance image data (with discussion). J. Am. Statist. Ass., 102, 1085-1102.