The effect of spatial smoothing on fMRI decoding of columnar-level organization with linear support vector machine

Journal of Neuroscience Methods

Volumn 212, Issue 2, 2013, Pages 355-361

  Misaki, Masaya ^a   Luh, Wen Ming ^a   Bandettini, Peter A ^a  

^a NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

Author keywords

Columnar level decoding; Informative spatial frequency; Multivoxel pattern analysis; Support vector machine

Indexed keywords

ACCURACY; ADULT; ARTICLE; CLINICAL ARTICLE; DATA PROCESSING; EYE DOMINANCE; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; KERNEL METHOD; MALE; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINE; VISUAL CORTEX;

ADULT; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84871017588     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2012.11.004     Document Type: Article

References (36)

1. Adams D.L., Sincich L.C., Horton J.C. Complete pattern of ocular dominance columns in human primary visual cortex. J Neurosci 2007, 27:10391-10403.
2. Bishop C.M. Pattern recognition and machine learning 2007, Springer, New York.
3. Boynton G.M. Imaging orientation selectivity: decoding conscious perception in V1. Nat Neurosci 2005, 8:541-542.
4. Brants M., Baeck A., Wagemans J., de Beeck H.P. Multiple scales of organization for object selectivity in ventral visual cortex. Neuroimage 2011, 56:1372-1381.
5. Chaimow D., Yacoub E., Ugurbil K., Shmuel A. Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns. Neuroimage 2011, 56:627-642.
6. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines 2001, Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
7. Chu C-YC. Pattern recognition and machine learning for magnetic resonance images with kernel methods. Doctoral thesis. Wellcome Trust Centre for Neuroimaging Institute of Neurology. University College London; 2009.
8. Cox D.D., Savoy R.L. Functional magnetic resonance imaging (fMRI) brain reading: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 2003, 19:261-270.
9. Cox R.W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996, 29:162-173.
10. Cristianini N., Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods 2000, Cambridge University Press, Cambridge, UK.
11. de Brecht M., Yamagishi N. Combining sparseness and smoothness improves classification accuracy and interpretability. Neuroimage 2012, 60:1550-1561.
12. DemŠar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 2006, 7:1-30.
13. Duda R.O., Hart P., Stork D.G. Pattern classification 2000, John Wiley and Sons, New York. 2nd ed.
14. Eickhoff S.B., Stephan K.E., Mohlberg H., Grefkes C., Fink G.R., Amunts K., et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 2005, 25:1325-1335.
15. Etzel J.A., Valchev N., Keysers C. The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines. Neuroimage 2011, 54:1159-1167.
16. Freeman J., Brouwer G.J., Heeger D.J., Merriam E.P. Orientation decoding depends on maps, not columns. J Neurosci 2011, 31:4792-4804.
17. Gardner J.L. Is cortical vasculature functionally organized?. Neuroimage 2010, 49:1953-1956.
18. Golland P., Fischl B. Permutation tests for classification: towards statistical significance in image-based studies. Inf Process Med Imaging 2003, 330-341.
19. Haynes J-D., Rees G. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci 2005, 8:686-691.
20. Hollander M., Wolfe D.A. Nonparametric statistical methods 1999, Wiley-Interscience, New York. 2nd ed.
21. Kamitani Y., Sawahata Y. Spatial smoothing hurts localization but not information: pitfalls for brain mappers. Neuroimage 2010, 49:1949-1952.
22. Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain. Nat Neurosci 2005, 8:679-685.
23. Kriegeskorte N., Cusack R., Bandettini P. How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter. Neuroimage 2010, 49:1965-1976.
24. Kriegeskorte N., Goebel R., Bandettini P. Information-based functional brain mapping. Proc Natl Acad Sci USA 2006, 103:3863-3868.
25. LaConte S., Strother S., Cherkassky V., Anderson J., Hu X. Support vector machines for temporal classification of block design fMRI data. Neuroimage 2005, 26:317-329.
26. Ledoit O., Wolf M. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J Empir Finance 2003, 10:603-621.
27. Misaki M., Kim Y., Bandettini P.A., Kriegeskorte N. Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage 2010, 53:103-118.
28. Mitchell T. Machine learning 1997, McGraw Hill, New York.
29. Op De Beeck H.P. Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses?. Neuroimage 2010, 49:1943-1948.
30. Op de Beeck H.P. Probing the mysterious underpinnings of multi-voxel fMRI analyses. NeuroImage 2010, 50:567-571.
31. R Core Team R: a language and environment for statistical computing 2012, R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/.
32. Schafer J., Strimmer K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 2005, 4. (article 32).
33. Schwarzkopf D.S., Schindler A., Rees G. Knowing with which eye we see: utrocular discrimination and eye-specific signals in human visual cortex. PLoS ONE 2010, 5:e13775.
34. Shmuel A., Chaimow D., Raddatz G., Ugurbil K., Yacoub E. Mechanisms underlying decoding at 7 T: ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye. Neuroimage 2010, 49:1957-1964.
35. Swisher J.D., Gatenby J.C., Gore J.C., Wolfe B.A., Moon C.H., Kim S.G., et al. Multiscale pattern analysis of orientation-selective activity in the primary visual cortex. J Neurosci 2010, 30:325-330.
36. Vapnik V. The nature of statistical learning theory 1995, NY, Springer-Verlag.