Prediction and interpretation of distributed neural activity with sparse models

NeuroImage

Volumn 44, Issue 1, 2009, Pages 112-122

  Carroll, Melissa K ^a   Cecchi, Guillermo A ^b   Rish, Irina ^b   Garg, Rahul ^b   Rao, A Ravishankar ^b  

^a Princeton University   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CELL ACTIVITY; CELL FUNCTION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; NERVE CELL; NERVE POTENTIAL; NEUROIMAGING; PREDICTION; PRIORITY JOURNAL;

BRAIN; BRAIN MAPPING; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL;

EID: 55149117963     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2008.08.020     Document Type: Article

References (21)

1. Calhoun, V.D., Adali, T., Hansen, L.K., Larsen, J., Pekar, J.J., 2003. ICA of Functional MRI Data: An Overview. In: 4th international symposium on Independent Component Analysis and Blind Signal Separation (ICA2003).
2. Callicott J.H., Mattay V.S., Bertolino A., Finn K., Coppola R., Frank J.A., Goldberg T.E., and Weinberger D.R. Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. Cereb. Cortex 9 1 (1999) 20-26
3. Chigirev, D., Stephens, G., The-Princeton-EBC-Team, 2006. Predicting base features with supervoxels. Abstract presented, 12th HBM meeting, Florence, Italy.
4. Cox D., and Savoy R. Functional magnetic resonance imaging (fMRI) brain reading: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19 (2003) 261-270
5. de Fockert J., Rees G., Frith C., and Lavie N. The role of working memory in visual selective attention. Science 291 (2001) 1803-1806
6. Efron B., Hastie T., Johnstone I., and Tibshirani R. Least angle regression. Ann. Statist. 32 1 (2004) 407-499
7. Fan J., and Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96 2 (2001) 1348-1360
8. Forman S., Cohen J., Fitzgerald M., Eddy W., Mintun M., and Noll D. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33 (1995) 636-647
9. Frank I., and Friedman J. A statistical view of some chemometrics regression tools. Technometrics 35 2 (1993) 109-148
10. Fu W. Penalized regression: the bridge versus the lasso. J. Comput. Graph. Stat. 7 2 (1998) 397-416
11. Haxby J., Gobbini M., Furey M., Ishai A., Schouten J., and Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293 5539 (2001) 2425-2430
12. Hoerl A., and Kennard R. Ridge regression. Encycl. Stat. Sci. 8 2 (1988) 129-136
13. Mitchell T., Hutchinson R., Niculescu R., Pereira F., Wang X., Just M., and Newman S. Learning to decode cognitive states from brain images. Mach. Learn. 57 (2004) 145-175
14. Norman K., Polyn S., Detre G., and Haxby J. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10 3 (2006) 424-430
15. Okabe A., Boots B., Sugihara K., and Chiu S. Spatial Tesselations: Concepts and Applications of Voronoi Diagrams. 2nd Ed (2000), Wiley
16. Pittsburgh-EBC-Group, 2007. PBAIC Homepage: http://www.ebc.pitt.edu/2007/competition.html.
17. Sjöstrand, K., 2005. Matlab implementation of LASSO, LARS, the elastic net and SPCA. Version 2.0. URL http://www2.imm.dtu.dk/pubdb/p.php?3897
18. Tibshirani R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc., Ser. B. 58 1 (1996) 267-288
19. Ulfarsson, M., Solo, V., 2007. Sparse variable principal component analysis with application to fMRI. In: Proc. IEEE International Symposium on Biomedical Imaging (ISBI07).
20. Weisberg S. Applied Linear Regression (1980), Wiley, New York
21. Zou H., and Hastie T. Regularization and variable selection via the Elastic Net. J. R. Stat. Soc., Ser. B. 67 2 (2005) 301-320