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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 591032, 7 pages
http://dx.doi.org/10.1155/2013/591032
Research Article

Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data

1Department of Biostatistics, Graduate School of Medicine, Kurume University, Kurume 8300011, Japan
2Department of Integrated Therapy for Chronic Kidney Disease, Graduate School of Medical Sciences, Kyushu University, Fukuoka 8118582, Japan
3Biostatistics Center, Kurume University, Kurume 8300011, Japan

Received 11 January 2013; Revised 27 March 2013; Accepted 29 March 2013

Academic Editor: Shigeyuki Matsui

Copyright © 2013 Hisako Yoshida et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Taki, S. Kinomura, K. Sato et al., “Relationship between body mass index and gray matter volume in 1,428 healthy individuals,” Obesity, vol. 16, no. 1, pp. 119–124, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. C. M. Falvey, C. Rosano, E. M. Simonsick et al., “Macro- and microstructural magnetic resonance imaging indices associated with diabetes among community-dwelling older adults,” Diabetes Care, vol. 36, no. 3, pp. 677–682, 2013. View at Google Scholar
  3. E. Le Floch, V. Guillemot, V. Frouin et al., “Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares,” Neuroimage, vol. 15, pp. 11–24, 2012. View at Google Scholar
  4. J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” NeuroImage, vol. 11, no. 6, part 1, pp. 805–821, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Davatzikos, “Why voxel-based morphometric analysis should be used with great caution when characterizing group differences,” NeuroImage, vol. 23, no. 1, pp. 17–20, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Wold, “Estimation of principal components and related models by iterative least squares,” Journal of Multivariate Analysis, pp. 391–420, 1966. View at Google Scholar
  7. A. R. McIntosh, F. L. Bookstein, J. V. Haxby, and C. L. Grady, “Spatial pattern analysis of functional brain images using partial least squares,” NeuroImage, vol. 3, no. 3, pp. 143–157, 1996. View at Publisher · View at Google Scholar · View at Scopus
  8. S. de Jong and C. T. Braak, “Comments on the PLS kernel algorithm,” Journal of Chemotherapy, vol. 8, no. 2, pp. 169–174, 1994. View at Google Scholar
  9. R. J. Tibshirani, “Regression shrinkage and selection via the Lasso,” Journal of the Royal Statistical Society Series B, vol. 58, pp. 267–288, 1996. View at Google Scholar
  10. B. Efron, T. Hastie, I. Johnstone, and R. J. Tibshirani, “Least angle regression,” Annals of Statistics, vol. 32, pp. 407–451, 2004. View at Google Scholar
  11. K. A. Lê Cao, D. Rossouw, and C. Robert-Granié, “A sparse PLS: variable selection when integrating omics data,” Statistical Applications in Genetics and Molecular Biology, vol. 7, no. 1, article 35, 2008. View at Google Scholar
  12. H. Chun and S. Keleş, “Sparse partial least squares regression for simultaneous dimension reduction and variable selection,” Journal of the Royal Statistical Society Series B, vol. 72, no. 1, pp. 3–25, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Saranli and B. Baykal, “Complexity reduction in radial basis function (RBF) networks by using radial B-spline functions,” Neurocomputing, vol. 18, no. 1–3, pp. 183–194, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Tenenhaus, La Régression PLS. théorie et Pratique, Technip, Paris, France, 1998.
  15. Y. Yakushiji, Y. Nanri, T. Hirotsu et al., “Marked cerebral atrophy is correlated with kidney dysfunction in nondisabled adults,” Hypertension Research, vol. 33, no. 12, pp. 1232–1237, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Hirakata, H. Kanai, and H. Nakane, “Depressed cerebral lxygen metabolism in patients with chronic renal failure: a positron emission tomography study,” Journal of the Japanese Society for Dialysis Therapy, vol. 34, no. 7, pp. 1149–1155, 2001. View at Google Scholar
  17. K. Tsuruya, H. Yoshida, and T. Kitazono, “Possible contribution of anemia to brain atrophy in predialysis patients with chronic kidney disease,” Journal of the American Society of Nephrology Supplement A, vol. 667, 2012, SA-PO141. View at Google Scholar