Sparse logistic regression for whole-brain classification of fMRI data

NeuroImage - Tập 51 - Trang 752-764 - 2010
Srikanth Ryali1, Kaustubh Supekar2,3, Daniel A. Abrams1, Vinod Menon1,4
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
2Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305, USA
3Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94305, USA
4Program in Neuroscience, Stanford University School of Medicine, Stanford, CA 94305 USA

Tài liệu tham khảo

Abrams, D.A., Bhatara, A.K., Ryali, S., Balaban, E., Levitin, D.J., Menon, V., submitted for publication. Music and speech structure engage shared brain resources but elicit different activity patterns. Bi, 2003, Dimensionality reduction via sparse support vector machines, J. Mach. Learn. Res., 1229 Carroll, 2009, Prediction and interpretation of distributed neural activity with sparse models, Neuroimage, 44, 112, 10.1016/j.neuroimage.2008.08.020 Cox, 2003, Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex, Neuroimage, 19, 261, 10.1016/S1053-8119(03)00049-1 De Martino, 2008, Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns, Neuroimage, 43, 44, 10.1016/j.neuroimage.2008.06.037 Formisano, 2008, “Who” is saying “what”? Brain-based decoding of human voice and speech, Science, 322, 970, 10.1126/science.1164318 Friederici, 2003, The role of left inferior frontal and superior temporal cortex in sentence comprehension: localizing syntactic and semantic processes, Cereb. Cortex, 13, 170, 10.1093/cercor/13.2.170 Friston, 1996, Movement-related effects in fMRI time-series, Magn. Reson. Med., 25, 346, 10.1002/mrm.1910350312 Glover, 1998, Self-navigated spiral fMRI: interleaved versus single-shot, Magn. Reson. Med., 39, 361, 10.1002/mrm.1910390305 Grosenick, 2008, Interpretable classifiers for FMRI improve prediction of purchases, IEEE Trans. Neural Syst. Rehabil. Eng., 16, 539, 10.1109/TNSRE.2008.926701 Guyon, 2003, An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157 Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389, 10.1023/A:1012487302797 Hastie, 2001 Haynes, 2005, Predicting the orientation of invisible stimuli from activity in human primary visual cortex, Nat. Neurosci., 8, 686, 10.1038/nn1445 Haynes, 2007, Reading hidden intentions in the human brain, Curr. Biol., 17, 323, 10.1016/j.cub.2006.11.072 Humphries, 2005, Response of anterior temporal cortex to syntactic and prosodic manipulations during sentence processing, Hum. Brain Mapp., 26, 128, 10.1002/hbm.20148 Kim, 2000, SVD regularization algorithm for improved high-order shimming Koelsch, 2002, Bach speaks: a cortical “language-network” serves the processing of music, NeuroImage, 17, 956, 10.1006/nimg.2002.1154 Kohavi, 1997, Wrappers for feature selection, Artif. Intell., 97, 273, 10.1016/S0004-3702(97)00043-X Kriegeskorte, 2006, Information-based functional brain mapping, Proc. Natl. Acad. Sci. U. S. A., 103, 3863, 10.1073/pnas.0600244103 Krishnapuram, 2005, Sparse multinomial logistic regression: fast algorithms and generalization bounds, IEEE Trans. Pattern Anal. Mach. Intell., 27, 957, 10.1109/TPAMI.2005.127 Levitin, 2003, Musical structure is processed in “language” areas of the brain: a possible role for Brodmann Area 47 in temporal coherence, NeuroImage, 20, 2142, 10.1016/j.neuroimage.2003.08.016 Mitchell, 2004, Learning to decode cognitive states from brain images, Mach. Learn., 57, 145, 10.1023/B:MACH.0000035475.85309.1b Mourao-Miranda, 2005, Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data, Neuroimage, 28, 980, 10.1016/j.neuroimage.2005.06.070 Mourao-Miranda, 2006, The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data, Neuroimage, 33, 1055, 10.1016/j.neuroimage.2006.08.016 Pereira, 2009, Machine learning classifiers and fMRI: a tutorial overview, Neuroimage, 45, S199, 10.1016/j.neuroimage.2008.11.007 Perkins, 2003, Grafting: fast incremental feature selection by gradient descent in function space, J. Mach. Learn. Res., 1333 Phillips, 2002, Systematic regularization of linear inverse solutions of the EEG source localization problem, Neuroimage, 17, 287, 10.1006/nimg.2002.1175 Rogalsky, 2008, Selective attention to semantic and syntactic features modulates sentence processing networks in anterior temporal cortex, Cereb. Cortex, 19, 786, 10.1093/cercor/bhn126 Tervaniemi, 2006, From air oscillations to music and speech: functional magnetic resonance imaging evidence for fine-tuned neural networks in audition, J. Neurosci., 26, 8647, 10.1523/JNEUROSCI.0995-06.2006 Tibshirani, 1996, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. B, 58, 267 Tipping, 2001, Sparse Bayesian learning and relevant vector machine, J. Mach. Learn. Res., 1, 211 van Gerven, 2009, Interpreting single trial data using groupwise regularisation, Neuroimage, 46, 665, 10.1016/j.neuroimage.2009.02.041 1991, On great speeches of the 20th century [CD]. Los Angeles:, Rhino Records Wang, 2009, A hybrid SVM-GLM approach for fMRI data analysis, Neuroimage, 46, 608, 10.1016/j.neuroimage.2009.03.016 Weston, 2003, Use of the zero norm with linear models and kernel methods, J. Mach. Learn. Res., 3, 1439 Yamashita, 2008, Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns, Neuroimage, 42, 1414, 10.1016/j.neuroimage.2008.05.050 Zou, 2005, Regularization and variable selection via the elastic net, J. R. Stat. Soc. B Stat. Methedol., 67, 301, 10.1111/j.1467-9868.2005.00503.x