Eigenanatomy improves detection power for longitudinal cortical change

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7512 LNCS, Issue , 2012, Pages 206-213

  Avants, Brian ^b   Dhillon, Paramveer ^b   Kandel, Benjamin M ^b   Cook, Philip A ^b   McMillan, Corey T ^a   Grossman, Murray ^a   Gee, James C ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; NEUROIMAGING;

DIMENSIONALITY REDUCTION; EIGEN DECOMPOSITION; EXPERIMENTAL EVIDENCE; MULTIPLE COMPARISON; OBJECTIVE FUNCTIONS; REGION OF INTEREST; REGIONS OF INTEREST; VOXEL-BASED MORPHOMETRY;

MEDICAL COMPUTING;

AGING; ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; LONGITUDINAL STUDY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

AGING; ALGORITHMS; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; LONGITUDINAL STUDIES; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

MLCS; MLOWN;

EID: 84872907909     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_26     Document Type: Conference Paper

References (21)

1. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
2. Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. Journal of Computational and Graphical Statistics 15(2), 262–286 (2006)
3. Hoyer, P.O., Dayan, P.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)
4. Guan, N., Tao, D., Luo, Z., Yuan, B.: Manifold regularized discriminative non-negative matrix factorization with fast gradient descent. IEEE Trans. Image Process. 20(7), 2030–2048 (2011)
5. Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized non-negative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. (December 2010)
6. Hosoda, K., Watanabe, M., Wersing, H., Krner, E., Tsujino, H., Tamura, H., Fujita, I.: A model for learning topographically organized parts-based representations of objects in visual cortex: topographic nonnegative matrix factorization. Neural Comput. 21(9), 2605–2633 (2009)
7. Witten, D.M., Tibshirani, R.: A framework for feature selection in clustering. J. Am. Stat. Assoc. 105(490), 713–726 (2010)
8. Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)
9. Cherkassky, V., Ma, Y.: Another look at statistical learning theory and regularization. Neural Netw. 22(7), 958–969 (2009)
10. Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441 (2008)
11. Yushkevich, P.A., Avants, B.B., Das, S.R., Pluta, J., Altinay, M., Craige, C., A.D.N.I.: Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3 t MRI data. Neuroimage 50(2), 434–445 (2010)
12. Holland, D., Dale, A.M., A.D.N.I.: Nonlinear registration of longitudinal images and measurement of change in regions of interest. Med. Image Anal. 15(4), 489–497 (2011)
13. Avants, B.B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., Gee, J.C.: The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49(3), 2457–2466 (2010)
14. Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26(3), 839–851 (2005)
15. Irwin, D.J., McMillan, C.T., Toledo, J.B., Arnold, S.A., Shaw, L.M., Wang, L.S., Trojanowski, J.Q., Lee, V.M.Y., Grossman, M.: Comparison of Cerbebrospinal Fluid Levels of Tau and ABeta1-42 in Alzheimer’s Disease and Frontotemporal Degeneration Using Two Analytical Platforms. Archives of neurology (2011) (submitted)
16. Witten, D.M., Tibshirani, R., Hastie, T.: A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3), 515–534 (2009)
17. Kim, H., Park, H.: Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23, 1495–1502 (2007)
18. Heiler, M., Schnörr, C.: Learning sparse representations by non-negative matrix factorization and sequential cone programming. Journal of Machine Learning Research 7, 1385–1407 (2006)
19. d’Aspremont, A., El Ghaoui, L., Jordan, M.I., Lanckriet, G.R.G.: A direct formulation for sparse pca using semidefinite programming. SIAM Rev. 49, 434–448 (2007)
20. Lee, K., Tak, S., Ye, J.C.: A data-driven sparse glm for fMRI analysis using sparse dictionary learning with mdl criterion. IEEE Trans. Med. Imaging 30(5), 1076–1089 (2011)
21. Sill, M., Kaiser, S., Benner, A., Kopp-Schneider, A.: Robust biclustering by sparse singular value decomposition incorporating stability selection. Bioinformatics 27(15), 2089–2097 (2011)