References
This list aims to be a collection of literature, that is of particular interest
in the context of multivariate pattern analysis. It includes all references
cited throughout this manual, but also a number of additional manuscripts
containing descriptions of interesting analysis methods or fruitful
experiments.
- Adluru, N., Hanlon, B. M., Lutz, A., Lainhart, J. E., Alexander, A. L. & Davidson, R. J. (2013). Penalized Likelihood Phenotyping: Unifying Voxelwise Analyses and Multi-Voxel Pattern Analyses in Neuroimaging. Neuroinformatics, 1-21.
- DOI: http://dx.doi.org/10.1007/s12021-012-9175-9
Albanese, D., Visintainer, R., Merler, S., Riccadonna, S., Jurman, G. & Furlanello, C. (2012). mlpy: machine learning Python. arXiv preprint arXiv:1202.6548.
- Andersson, P., Ramsey, N. F., Viergever, M. A. & Pluim, J. P. (2013). 7T fMRI reveals feasibility of covert visual attention-based brain–computer interfacing with signals obtained solely from cortical grey matter accessible by subdural surface electrodes. Clinical neurophysiology, 124, 2191-2197.
- DOI: http://dx.doi.org/10.1016/j.clinph.2013.05.009
- Bandettini, P. A. (2009). Seven topics in functional magnetic resonance imaging. Journal of Integrative Neuroscience, 8, 371–403.
- URL: http://www.ncbi.nlm.nih.gov/pubmed/19938211
- Baumgartner, F., Hanke, M., Geringswald, F., Zinke, W., Speck, O. & Pollmann, S. (2013). Evidence for feature binding in the superior parietal lobule. NeuroImage, 68, 173-180.
- DOI: http://dx.doi.org/10.1016/j.neuroimage.2012.12.002
- Carlin, J. D., Calder, A. J., Kriegeskorte, N., Nili, H. & Rowe, J. B. (2011). A head view-invariant representation of gaze direction in anterior superior temporal sulcus. Curr Biol, 21, 1817–21.
- DOI: http://dx.doi.org/10.1016/j.cub.2011.09.025
- Carlin, J. D., Rowe, J. B., Kriegeskorte, N., Thompson, R. & Calder, A. J. (2011). Direction-Sensitive Codes for Observed Head Turns in Human Superior Temporal Sulcus. Cerebral Cortex, **, .
Keywords: pymvpa, fMRI, searchlight
DOI: http://dx.doi.org/10.1093/cercor/bhr061
URL: http://cercor.oxfordjournals.org/content/early/2011/06/27/cercor.bhr061.short
- Carter, R. M., Bowling, D. L., Reeck, C. & Huettel, S. A. (2012). A distinct role of the temporal-parietal junction in predicting socially guided decisions. Science, 337, 109-111.
- DOI: http://dx.doi.org/10.1126/science.1219681
- Chen, X., Pereira, F., Lee, W., Strother, S. & Mitchell, T. (2006). Exploring predictive and reproducible modeling with the single-subject FIAC dataset. Human Brain Mapping, 27, 452–461.
This paper illustrates the necessity to consider the stability or
reproducibility of a classifier’s feature selection as at least equally
important to it’s generalization performance.
Keywords: feature selection, feature selection stability
DOI: http://dx.doi.org/10.1002/hbm.20243
URL: http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16565951
- Clithero, J. A., Smith, D. V., Carter, R. M. & Huettel, S. A. (2010). Within- and cross-participant classifiers reveal different neural coding of information. NeuroImage.
DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.03.057
URL: http://www.ncbi.nlm.nih.gov/pubmed/20347995
- Cohen, J. (1994). The earth is round (p< 0.05). American Psychologist, 49, 997–1003.
Classical critic of null hypothesis significance testing
Keywords: hypothesis testing
URL: http://www.citeulike.org/user/mdreid/article/2643653
- Cohen, J. R., Asarnow, R. F., Sabb, F. W., Bilder, R. M., Bookheimer, S. Y., Knowlton, B. J. & Poldrack, R. A. (2010). Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals. Frontiers in Human Neuroscience, 4:47.
DOI: http://dx.doi.org/10.3389/fnhum.2010.00047
URL: http://www.ncbi.nlm.nih.gov/pubmed/20661296
- Cole, M. W., Etzel, J. A., Zacks, J. M., Schneider, W. & Braver, T. S. (2011). Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex. Frontiers in Human Neuroscience, 5.
- DOI: http://dx.doi.org/10.3389/fnhum.2011.00142
- Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y., Abdi, H. & Haxby, J. V. (2012). The Representation of Biological Classes in the Human Brain. Journal of Neuroscience, 32, 2608-2618.
DOI: http://dx.doi.org/10.1523/JNEUROSCI.5547-11.2012
URL: http://www.jneurosci.org/content/32/8/2608#aff-4
- Demšar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 7, 1–30.
This is a review of several classifier benchmark procedures.
URL: http://portal.acm.org/citation.cfm?id=1248548
- Duff, E. P., Trachtenberg, A. J., CE, C. E. M., Howard, M. A., Wilson, F., Smith, S. M. & Woolrich, M. W. (2011). Task-driven ICA feature generation for accurate and interpretable prediction using fMRI. NeuroImage, 60, 189-203.
- URL: http://www.ncbi.nlm.nih.gov/pubmed/22227050
- Efron, B., Trevor, H., Johnstone, I. & Tibshirani, R. (2004). Least Angle Regression. Annals of Statistics, 32, 407–499.
Keywords: least angle regression, LARS
DOI: http://dx.doi.org/10.1214/009053604000000067
- Ekman, M., Derrfuss, J., Tittgemeyer, M. & Fiebach, C. J. (2012). Predicting errors from reconfiguration patterns in human brain networks. Proceedings of the National Academy of Sciences, 109, 16714-16719.
- DOI: http://dx.doi.org/10.1073/pnas.1207523109
- Farrell, D., Webb, H., Johnston, M. A., Poulsen, T. A., O’Meara, F., Christensen, L. L., Beier, L., Borchert, T. V. & Nielsen, J. E. (2012). Toward Fast Determination of Protein Stability Maps: Experimental and Theoretical Analysis of Mutants of a Nocardiopsis prasina Serine Protease. Biochemistry, 51, 5339-5347.
- DOI: http://dx.doi.org/10.1021/bi201926f
- Fisher, R. A. (1925). Statistical methods for research workers. Oliver and Boyd: Edinburgh.
One of the 20th century’s most influential books on statistical methods, which
coined the term ‘Test of significance’.
Keywords: statistics, hypothesis testing
URL: http://psychclassics.yorku.ca/Fisher/Methods/
- Garcia, S. & Fourcaud-Trocmé, N. (2009). OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework. Front Neuroinformatics, 3, 14.
DOI: http://dx.doi.org/10.3389/neuro.11.014.2009
URL: http://www.ncbi.nlm.nih.gov/pubmed/19521545
- Gilliam, T., Wilson, R. C. & Clark, J. A. (2010). Scribe Identification in Medieval English Manuscripts. Proceedings of the International Conference on Pattern Recognition.
- URL: ftp://ftp.computer.org/press/outgoing/proceedings/juan/icpr10b/data/4109b880.pdf
- Gorlin, S., Meng, M., Sharma, J., Sugihara, H., Sur, M. & Sinha, P. (2012). Imaging prior information in the brain. Proceedings of the National Academy of Sciences, 109, 7935-7940.
DOI: http://dx.doi.org/10.1073/pnas.1111224109
URL: http://www.pnas.org/content/109/20/7935.abstract
- Guyon, I. & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning, 3, 1157–1182.
- URL: http://www.jmlr.org/papers/v3/guyon03a.html
- Hanke, M., Baumgartner, F. J., Ibe, P., Kaule, F. R., Pollmann, S., Speck, O., Zinke, W. & Stadler, J. (in press). A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Scientific Data.
- URL: http://www.studyforrest.org
- Hanke, M., Halchenko, Y. O., Haxby, J. V. & Pollmann, S. (2010). Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience, 4, 38–43.
Focused review article emphasizing the role of transparency to facilitate
adoption and evaluation of statistical learning techniques in neuroimaging
research.
DOI: http://dx.doi.org/10.3389/neuro.01.007.2010
Hanke, M., Halchenko, Y. O., Sederberg, P. B. & Hughes, J. M. The PyMVPA Manual. Available online at http://www.pymvpa.org/PyMVPA-Manual.pdf.
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37–53.
Introduction into the analysis of fMRI data using PyMVPA.
Keywords: PyMVPA, fMRI
DOI: http://dx.doi.org/10.1007/s12021-008-9041-y
- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. & Pollmann, S. (2009). PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. Frontiers in Neuroinformatics, 3, 3.
Demonstration of PyMVPA capabilities concerning multi-modal or
modality-agnostic data analysis.
Keywords: PyMVPA, fMRI, EEG, MEG, extracellular recordings
DOI: http://dx.doi.org/10.3389/neuro.11.003.2009
- Hanson, S. J. & Halchenko, Y. O. (2008). Brain reading using full brain support vector machines for object recognition: there is no “face” identification area. Neural Computation, 20, 486–503.
Keywords: support vector machine, SVM, feature selection, recursive feature elimination, RFE
DOI: http://dx.doi.org/10.1162/neco.2007.09-06-340
- Hanson, S. J. & Schmidt, A. (2011). High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories. NeuroImage, 54, 1715-1734.
- DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.08.02
- Hanson, S. J., Matsuka, T. & Haxby, J. V. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?. NeuroImage, 23, 156–166.
- DOI: http://dx.doi.org/10.1016/j.neuroimage.2004.05.020
- Hassabis, D., Spreng, R. N., Rusu, A. A., Robbins, C. A., Mar, R. A. & Schacter, D. L. (2013). Imagine all the people: How the brain creates and uses personality models to predict behavior. Cerebral Cortex.
- DOI: http://dx.doi.org/10.1093/cercor/bht042
- Hastie, T., Tibshirani, R. & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer: New York.
Excellent summary of virtually all techniques relevant to the field. A free PDF
version of this book is available from the authors’ website at
http://www-stat.stanford.edu/%7Etibs/ElemStatLearn/
DOI: http://dx.doi.org/10.1007/b94608
URL: http://www-stat.stanford.edu/%7Etibs/ElemStatLearn/
- Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L. & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430.
Keywords: split-correlation classifier
DOI: http://dx.doi.org/10.1126/science.1063736
- Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404–416.
DOI: http://dx.doi.org/10.1016/j.neuron.2011.08.026
URL: http://www.cell.com/neuron/abstract/S0896-6273%2811%2900781-1
- Haynes, J. & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7, 523–534.
Review of decoding studies, emphasizing the importance of ethical issues
concerning the privacy of personal thought.
DOI: http://dx.doi.org/10.1038/nrn1931
- Helfinstein, S. M., Schonberg, T., Congdon, E., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Cannon, T. D., London, E. D., Bilder, R. M. & Poldrack, R. A. (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111, 2470-2475.
DOI: http://dx.doi.org/10.1073/pnas.1321728111
URL: http://www.pnas.org/content/111/7/2470.abstract
- Hiroyuki, A., Brian, M., Li, N., Yumiko, S. & Massimo, P. (2012). Decoding Semantics across fMRI sessions with Different Stimulus Modalities: A practical MVPA Study. Frontiers in Neuroinformatics, 6.
Keywords: pymvpa, fmri
DOI: http://dx.doi.org/10.3389/fninf.2012.00024
URL: http://www.frontiersin.org/Neuroinformatics/10.3389/fninf.2012.00024/full
- Hollmann, M., Rieger, J. W., Baecke, S., Lützkendorf, R., Müller, C., Adolf, D. & Bernarding, J. (2011). Predicting decisions in human social interactions using real-time fMRI and pattern classification. PloS one, 6, e25304.
- DOI: http://dx.doi.org/10.1371/journal.pone.0025304
- Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Med, 2, e124.
Simulation study speculating that it is more likely for a research claim to be
false than true. Along the way the paper highlights aspects to keep in mind
while assessing the ‘scientific significance’ of any given study, such as,
viability, reproducibility, and results.
Keywords: hypothesis testing
DOI: http://dx.doi.org/10.1371/journal.pmed.0020124
- Jain, A. & Kemp, C. C. (2012). Improving robot manipulation with data-driven object-centric models of everyday forces. Autonomous Robots, 1-17.
DOI: http://dx.doi.org/10.1007/s10514-013-9344-1
URL: http://www.hrl.gatech.edu/pdf/improve_everyday_forces.pdf
- Jimura, K. & Poldrack, R. (2011). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia.
- DOI: http://dx.doi.org/10.1016/j.neuropsychologia.2011.11.007
- Jurica, P. & van Leeuwen, C. (2009). OMPC: an open-source MATLAB-to-Python compiler. Frontiers in Neuroinformatics, 3, 5.
- DOI: http://dx.doi.org/10.3389/neuro.11.005.2009
- Jäkel, F., Schölkopf, B. & Wichmann, F. A. (2009). Does Cognitive Science Need Kernels?. Trends in Cognitive Sciences, 13, 381–388.
A summary of the relationship of machine learning and cognitive science.
Moreover it also points out the role of kernel-based methods in this context.
Keywords: kernel methods, similarity
DOI: http://dx.doi.org/10.1016/j.tics.2009.06.002
URL: http://www.sciencedirect.com/science/article/B6VH9-4X4R9BC-1/2/e2e90008d0a8887878c72777462335fd
- Kamitani, Y. & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8, 679–685.
One of the two studies showing the possibility to read out orientation
information from visual cortex.
DOI: http://dx.doi.org/10.1038/nn1444
- Kaplan, J. T. & Meyer, K. (2012). Multivariate pattern analysis reveals common neural patterns across individuals during touch observation. Neuroimage, 60, 204-212.
- DOI: http://dx.doi.org/10.1016/j.neuroimage.2011.12.059
- Kaunitz, L. N., Kamienkowski, J. E., Olivetti, E., Murphy, B., Avesani, P. & Melcher, D. P. (2011). Intercepting the first pass: rapid categorization is suppressed for unseen stimuli. Frontiers in Perception Science, 2, 198.
Keywords: pymvpa, eeg
DOI: http://dx.doi.org/10.3389/fpsyg.2011.00198
URL: http://www.frontiersin.org/perception_science/10.3389/fpsyg.2011.00198/full
- Kienzle, W., Franz, M. O., Schölkopf, B. & Wichmann, F. A. (In press). Center-surround patterns emerge as optimal predictors for human saccade targets. Journal of Vision.
- This paper offers an approach to make sense out of feature sensitivities of
non-linear classifiers.
- Kohler, P. J., Fogelson, S. V., Reavis, E. A., Meng, M., Guntupalli, J. S., Hanke, M., Halchenko, Y. O., Connolly, A. C., Haxby, J. V. & Tse, P. U. (2013). Pattern classification precedes region-average hemodynamic response in early visual cortex. NeuroImage, 78, 249-260.
- DOI: http://dx.doi.org/10.1016/j.neuroimage.2013.04.019
- Kriegeskorte, N., Goebel, R. & Bandettini, P. A. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the USA, 103, 3863–3868.
Paper introducing the searchlight algorithm.
Keywords: searchlight
DOI: http://dx.doi.org/10.1073/pnas.0600244103
- Kriegeskorte, N., Mur, M. & Bandettini, P. A. (2008). Representational similarity analysis - connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.
- DOI: http://dx.doi.org/10.3389/neuro.06.004.2008
- Krishnapuram, B., Carin, L., Figueiredo, M. A. & Hartemink, A. J. (2005). Sparse multinomial logistic regression: fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 957–968.
Keywords: sparse multinomial logistic regression, SMLR
DOI: http://dx.doi.org/10.1109/TPAMI.2005.127
URL: http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=15943426
- Kubilius, J., Wagemans, J. & Beeck, H. O. d. (2011). Emergence of perceptual gestalts in the human visual cortex: The case of the configural superiority effect. Psychological Science, in press.
Keywords: pymvpa, fMRI
DOI: http://dx.doi.org/10.1177/0956797611417000
- LaConte, S., Strother, S., Cherkassky, V., Anderson, J. & Hu, X. (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage, 26, 317–329.
Comprehensive evaluation of preprocessing options with respect to
SVM-classifier (and others) performance on block-design fMRI data.
Keywords: SVM
DOI: http://dx.doi.org/10.1016/j.neuroimage.2005.01.048
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DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.06.052
URL: http://www.ncbi.nlm.nih.gov/pubmed/20600972
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Paper introducing Modified NIST (MNIST) dataset for performance comparisons of
character recognition performance across a variety of classifiers.
Keywords: handwritten character recognition, multilayer neural networks, MNIST, statistical learning
DOI: http://dx.doi.org/10.1109/5.726791
Legge, D. & Badii, A. (2010). An Application of Pattern Matching for the Adjustment of Quality of Service Metrics. The International Conference on Emerging Network Intelligence.
- Lescroart, M. D. & Biederman, I. (2013). Cortical representation of medial axis structure. Cerebral Cortex, 23, 629-637.
- DOI: http://dx.doi.org/10.1093/cercor/bhs046
- Liang, M., Mouraux, A., Hu, L. & Iannetti, G. (2013). Primary sensory cortices contain distinguishable spatial patterns of activity for each sense. Nature communications, 4.
- DOI: http://dx.doi.org/10.1038/ncomms2979
- Man, K., Kaplan, J. T., Damasio, A. & Meyer, K. (2012). Sight and sound converge to form modality-invariant representations in temporoparietal cortex. The Journal of Neuroscience, 32, 16629-16636.
- DOI: http://dx.doi.org/10.1523/JNEUROSCI.2342-12.2012
- Manelis, A. & Reder, L. M. (2013). He Who Is Well Prepared Has Half Won The Battle: An fMRI Study of Task Preparation. Cerebral Cortex.
DOI: http://dx.doi.org/10.1093/cercor/bht262
URL: http://cercor.oxfordjournals.org/content/early/2013/10/02/cercor.bht262.abstract
- Manelis, A., Hanson, C. & Hanson, S. J. (2010). Implicit memory for object locations depends on reactivation of encoding-related brain regions. Human Brain Mapping.
Keywords: PyMVPA, implicit memory, fMRI
DOI: http://dx.doi.org/10.1002/hbm.20992
- Manelis, A., Reder, L. M. & Hanson, S. J. (2011). Dynamic Changes In The Medial Temporal Lobe During Incidental Learning Of Object–Location Associations. Cerebral Cortex.
Keywords: pymvpa, fMRI
DOI: http://dx.doi.org/10.1093/cercor/bhr151
- Margulies, D. S., Böttger, J., Long, X., Lv, Y., Kelly, C., Schäfer, A., Goldhahn, D., Abbushi, A., Milham, M. P., Lohmann, G. & Villringer, A. (2010). Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. Magnetic Resonance Materials in Physics, Biology and Medicine, 23, 289–307.
DOI: http://dx.doi.org/10.1007/s10334-010-0228-5
URL: http://www.ncbi.nlm.nih.gov/pubmed/20972883
- McNamee, D., Rangel, A. & O’Doherty, J. P. (2013). Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex. Nature neuroscience, 16, 479-485.
- DOI: http://dx.doi.org/10.1038/nn.3337
- Merrill, J., Sammler, D., Bangert, M., Goldhahn, D., Lohmann, G., Turner, R. & Friederici, A. D. (2012). Perception of words and pitch patterns in song and speech. Frontiers in psychology, 3, 76.
- DOI: http://dx.doi.org/10.3389/fpsyg.2012.000
- Meyer, K. & Kaplan, J. T. (2011). Cross-Modal Multivariate Pattern Analysis. Journal of visualized experiments: JoVE.
- DOI: http://dx.doi.org/10.3791/3307
- Meyer, K., Kaplan, J. T., Essex, R., Damasio, H. & Damasio, A. (2011). Seeing Touch Is Correlated with Content-Specific Activity in Primary Somatosensory Cortex. Cerebral Cortex.
DOI: http://dx.doi.org/10.1093/cercor/bhq289
URL: http://www.ncbi.nlm.nih.gov/pubmed/21330469
- Meyer, K., Kaplan, J. T., Essex, R., Webber, C., Damasio, H. & Damasio, A. (2010). Predicting visual stimuli based on activity in auditory cortices. Nature Neuroscience.
- DOI: http://dx.doi.org/10.1038/nn.2533
- Mitchell, T., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M. & Newman, S. (2004). Learning to Decode Cognitive States from Brain Images. Machine Learning, 57, 145–175.
- DOI: http://dx.doi.org/10.1023/B:MACH.0000035475.85309.1b
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- DOI: http://dx.doi.org/10.1093/scan/nsn044
- Nichols, T. E. & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15, 1–25.
Overview of standard nonparametric randomization and permutation testing
applied to neuroimaging data (e.g. fMRI)
DOI: http://dx.doi.org/10.1002/hbm.1058
- Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science, 10, 424–430.
- DOI: http://dx.doi.org/10.1016/j.tics.2006.07.005
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- DOI: http://dx.doi.org/10.1162/0898929053467550
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- DOI: http://dx.doi.org/10.1162/jocn.2007.19.11.1735
- Olivetti, E., Greiner, S. & Avesani, P. (2012). Induction in Neuroscience with Classification: Issues and Solutions. Machine Learning and Interpretation in Neuroimaging, 42-50.
- DOI: http://dx.doi.org/10.1007/978-3-642-34713-9_6
Olivetti, E., Veeramachaneni, S., Greiner, S. & Avesani, P. (2010). Brain Connectivity Analysis by Reduction to Pair Classification. The 2nd IAPR International Workshop on Cognitive Information Processing.
Oosterhof, N. N., Wiestler, T., Downing, P. E. & Diedrichsen, J. (2011). A comparison of volume-based and surface-based multi-voxel pattern analysis. NeuroImage, 56, 593-600.
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
- URL: http://dl.acm.org/citation.cfm?id=1953048.2078195
- Pereira, F. & Botvinick, M. (2011). Information mapping with pattern classifiers: a comparative study. Neuroimage, 56, 476-496.
- DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.05.026
- Pereira, F., Mitchell, T. & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, 199–209.
DOI: http://dx.doi.org/10.1016/j.neuroimage.2008.11.007
URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892746/
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Analysis of slow event-related fMRI data using patter classification techniques.
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Discussion of possible scenarios where univariate and multivariate (SVM)
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Good coverage of kernel methods and associated statistical learning aspects
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First published study employing PyMVPA for MRI-based analysis of Psychosis.
Keywords: PyMVPA, psychosis, MRI
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Study using PyMVPA to perform immobilization detection to improve navigation
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- Keywords: support vector machine, SVM
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Demonstration of overfitting and introducing the bias in the error estimation
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Keywords: statistical learning, model selection, error estimation, hypothesis testing
DOI: http://dx.doi.org/10.1186/1471-2105-7-91
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Keywords: support vector machine, SVM, group analysis
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Historical excurse into the life of 10 prominent statisticians of XXth century
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Keywords: statistics, hypothesis testing
DOI: http://dx.doi.org/10.1111/j.1745-6924.2009.01167.x
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Keywords: feature selection, statistical learning
URL: http://www-stat.stanford.edu/%7Ehastie/Papers/B67.2%20(2005)%20301-320%20Zou%20%26%20Hastie.pdf