Statistical learning analysis in neuroscience: Aiming for transparency

Frontiers in Neuroscience

Volumn 4, Issue MAY, 2010, Pages 38-43

  Hanke, Michael ^a,b,c   Halchenko, Yaroslav O ^a,b   Haxby, James V ^a,b   Pollmann, Stefan ^c,d  

^a DARTMOUTH COLLEGE   (United States)

^b Dartmouth College   (United States)

^c OTTO VON GUERICKE UNIVERSITY   (Germany)

^d Center for Behavioral Brain Sciences   (Germany)

Author keywords

Machine learning; MVPA; PyMVPA; Python

Indexed keywords

EID: 79955012500     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/neuro.01.007.2010     Document Type: Article

References (30)

1. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173.
2. Friston, K., Jezzard, P., and Turner, R. (1994). Analysis of functional MRI time-series. Hum. Brain Mapp. 1, 153-171.
3. Goebel, R., Esposito, F., and Formisano, E. (2006). Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Hum. Brain Mapp. 27, 392-401.
4. Green, D. M., and Swets, J. A. (1966). Signal Detection and Recognition by Human Observers. New York, Wiley.
5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. 11, 10-18.
6. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., and Pollmann, S. (2009a). PyMVPA: a Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7, 37-53.
7. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J., and Pollmann, S. (2009b). PyMVPA: a unifying approach to the analysis of neuroscientific data. Front. Neuroinformatics 3:3. doi: 10.3389/neuro.11.003.2009.
8. Hanson, S., Matsuka, T., and Haxby, J. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a "face" area? Neuroimage 23, 156-166.
9. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., and Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425-2430.
10. Haynes, J.-D., and Rees, G. (2006). Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7, 523-534.
11. Jäkel, F., Schölkopf, B., and Wichmann, F. A. (2009). Does cognitive science need kernels? Trends Cogn. Sci. 13, 381-388.
12. Kamitani, Y., and Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679-685.
13. Kay, K. N., Naselaris, T., Prenger, R. J., and Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature 452, 352-355.
14. Kippenhahn, J. S., Barker, W. W., Pascal, S., Nagel, J., and Duara, R. (1992). Evaluation of a neural-network classifier for pet scans of normal and Alzheimer's disease subjects. J. Nucl. Med. 33, 1459-1467.
15. Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., and Bandettini, P. A. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60, 1126-1141.
16. Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., and Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535-40.
17. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature 453, 869-878.
18. McIntosh, A. R., Bookstein, F. L., Haxby, J. V., and Grady, C. L. (1996). Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3, 143-57.
19. McIntosh, A. R., Chau, W. K., and Protzner, A. B. (2004). Spatiotemporal analysis of event-related fmri data using partial least squares. Neuroimage 23, 764-75.
20. Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M., Morito, Y., Tanabe, H. C., Sadato, N., and Kamitani, Y. (2008). Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron 60, 915-29.
21. Moeller, J. R., and Strother, S. (1991). A regional covariance approach to the analysis of functional patterns in positron emission tomographic data. J. Cereb. Blood Flow Metab. 11, 121-135.
22. Mur, M., Bandettini, P. A., and Kriegeskorte, N. (2009). Revealing representational content with pattern-information fMRI - an introductory guide. Soc. Cogn. Affect. Neurosci. 4, 101-109.
23. Norman, K. A., Polyn, S. M., Detre, G. J., and Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424-430.
24. O'Toole, A. J., Jiang, F., Abdi, H., Penard, N., Dunlop, J. P., and Parent, M. A. (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J. Cogn. Neurosci. 19, 1735-1752.
25. Pereira, F., Mitchell, T., and Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45, 199-209.
26. Rex, D. E., Ma, J. Q., and Toga, A. W. (2003). The LONI pipeline processing environment. Neuroimage 19, 1033-48.
27. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., and Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, 208-219.
28. Sonnenburg, S., Raetsch, G., Schaefer, C., and Schoelkopf, B. (2006). Large scale multiple kernel learning. J. Mach. Learn. Res. 7, 1531-1565.
29. Strother, S., La Conte, S., Kai Hansen, L., Anderson, J., Zhang, J., Pulapura, S., and Rottenberg, D. (2004). Optimizing the fmri data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis. Neuroimage 23, 196-207.
30. Sun, D., van Erp, T. G., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., Hardt, M. E., Nuechterlein, K. H., Toga, A. W., and Cannon, T. D. (2009). Elucidating an mri-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms. Biol. Psychiatry 66, 1055-1060.