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.
Keywords: pymvpa-reference
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.
- Keywords: pymvpa-reference
- 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.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1016/j.clinph.2013.05.009
- Avants, B. B., Libon, D. J., Rascovsky, K., Boller, A., McMillan, C. T., Massimo, L., Coslett, H. B., Chatterjee, A., Gross, R. G. & Grossman, M. (2014). Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. NeuroImage, 84, 698-711.
Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1016/j.neuroimage.2013.09.048
URL: http://dx.doi.org/10.1016/j.neuroimage.2013.09.048
- Bandettini, P. A. (2009). Seven topics in functional magnetic resonance imaging. Journal of Integrative Neuroscience, 8, 371–403.
Keywords: pymvpa-reference
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.
Keywords: pymvpa
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
- Cole, M. W., Ito, T. & Braver, T. S. (2015). The Behavioral Relevance of Task Information in Human Prefrontal Cortex. Cerebral Cortex.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1093/cercor/bhv072
URL: http://dx.doi.org/10.1093/cercor/bhv072
- 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/
- Fogelson, S. V., Kohler, P. J., Miller, K. J., Granger, R. & Tse, P. U. (2014). Unconscious neural processing differs with method used to render stimuli invisible. Frontiers in Psychology, 5.
Keywords: pymvpa
DOI: http://dx.doi.org/10.3389/fpsyg.2014.00601
URL: http://dx.doi.org/10.3389/fpsyg.2014.00601
- Garcia, S. & Fourcaud-Trocmé, N. (2009). OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework. Front Neuroinformatics, 3, 14.
Keywords: pymvpa-reference
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
- Greisel, N., Seitz, S., Drory, N., Bender, R., Saglia, R. & Snigula, J. (2015). Photometric Redshifts and Model Spectral Energy Distributions of Galaxies From the SDSS-III BOSS DR10 Data. arXiv preprint arXiv:1505.01157.
Keywords: pymvpa
URL: http://arxiv.org/abs/1505.01157
- Guntupalli, J. S., Hanke, M., Halchenko, Y. O., Connolly, A. C., Ramadge, P. J. & Haxby, J. V. (2016). A Model of Representational Spaces in Human Cortex. Cerebral Cortex.
Keywords: pymvpa, hyperalignment
DOI: http://dx.doi.org/10.1093/cercor/bhw068
- Guo, B. & Meng, M. (2015). The encoding of category-specific versus nonspecific information in human inferior temporal cortex. NeuroImage.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1016/j.neuroimage.2015.04.006
URL: http://dx.doi.org/10.1016/j.neuroimage.2015.04.006
- 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.
Keywords: pymvpa
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.
Keywords: pymvpa-reference
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.
Keywords: pymvpa
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://web.stanford.edu/%7Ehastie/ElemStatLearn/
DOI: http://dx.doi.org/10.1007/b94608
URL: http://web.stanford.edu/%7Ehastie/ElemStatLearn/
- Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual review of neuroscience, 37, 435-456.
- Keywords: pymvpa-reference
- 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.
Keywords: pymvpa, hyperalignment
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
- Hebart, M. N., Görgen, K. & Haynes, J. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data. Frontiers in Neuroinformatics, 8.
- DOI: http://dx.doi.org/10.3389/fninf.2014.00088
- Heitmeyer, C. L., Pickett, M., Leonard, E. I., Archer, M. M., Ray, I., Aha, D. W. & Trafton, J. G. (2014). Building high assurance human-centric decision systems. Autom Softw Eng, 22, 159-197.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1007/s10515-014-0157-z
URL: http://dx.doi.org/10.1007/s10515-014-0157-z
- 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.
Keywords: pymvpa
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.
Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1371/journal.pone.0025304
- Huffman, D. J. & Stark, C. E. (2014). Multivariate pattern analysis of the human medial temporal lobe revealed representationally categorical cortex and representationally agnostic hippocampus. Hippocampus, 24, 1394-1403.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1002/hipo.22321
URL: http://dx.doi.org/10.1002/hipo.22321
- 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
- Jimura, K., Cazalis, F., Stover, E. R. S. & Poldrack, R. A. (2014). The neural basis of task switching changes with skill acquisition. Front. Hum. Neurosci., 8.
Keywords: pymvpa
DOI: http://dx.doi.org/10.3389/fnhum.2014.00339
URL: http://dx.doi.org/10.3389/fnhum.2014.00339
- 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
- Kasabov, N. K. (2014). NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks, 52, 62-76.
DOI: http://dx.doi.org/10.1016/j.neunet.2014.01.006
URL: http://dx.doi.org/10.1016/j.neunet.2014.01.006
- 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.
- Kim, N. Y., Lee, S. M., Erlendsdottir, M. C. & McCarthy, G. (2014). Discriminable spatial patterns of activation for faces and bodies in the fusiform gyrus. Front. Hum. Neurosci., 8.
Keywords: pymvpa
DOI: http://dx.doi.org/10.3389/fnhum.2014.00632
URL: http://dx.doi.org/10.3389/fnhum.2014.00632
- Klein, M. E. & Zatorre, R. J. (2014). Representations of Invariant Musical Categories Are Decodable by Pattern Analysis of Locally Distributed BOLD Responses in Superior Temporal and Intraparietal Sulci. Cerebral Cortex.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1093/cercor/bhu003
URL: http://dx.doi.org/10.1093/cercor/bhu003
- 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.
Keywords: pymvpa
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
- Kubilius, J., Wagemans, J. & Beeck, H. P. O. d. (2014). Encoding of configural regularity in the human visual system. Journal of Vision, 14, 11-11.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1167/14.9.11
URL: http://dx.doi.org/10.1167/14.9.11
- 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
- Laconte, S. M. (2010). Decoding fMRI brain states in real-time. NeuroImage.
Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.06.052
URL: http://www.ncbi.nlm.nih.gov/pubmed/20600972
- Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
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
- Lee, S. M. & McCarthy, G. (2014). Functional Heterogeneity and Convergence in the Right Temporoparietal Junction. Cerebral Cortex.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1093/cercor/bhu292
URL: http://dx.doi.org/10.1093/cercor/bhu292
- 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.
- Keywords: pymvpa-reference
- Lescroart, M. D. & Biederman, I. (2013). Cortical representation of medial axis structure. Cerebral Cortex, 23, 629-637.
Keywords: pymvpa
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.
Keywords: pymvpa
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.
Keywords: pymvpa
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
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Keywords: pymvpa, fMRI
DOI: http://dx.doi.org/10.1093/cercor/bhr151
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Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1007/s10334-010-0228-5
URL: http://www.ncbi.nlm.nih.gov/pubmed/20972883
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1523/JNEUROSCI.4677-14.2015
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1038/nn.3337
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- DOI: http://dx.doi.org/10.3389/fpsyg.2012.000
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Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.3791/3307
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DOI: http://dx.doi.org/10.1093/cercor/bhq289
URL: http://www.ncbi.nlm.nih.gov/pubmed/21330469
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1523/jneurosci.2062-14.2014
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Keywords: pymvpa-reference
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Overview of standard nonparametric randomization and permutation testing
applied to neuroimaging data (e.g. fMRI)
DOI: http://dx.doi.org/10.1002/hbm.1058
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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.
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1523/jneurosci.2159-13.2014
URL: http://dx.doi.org/10.1523/JNEUROSCI.2159-13.2014
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Keywords: pymvpa-reference
URL: http://dl.acm.org/citation.cfm?id=1953048.2078195
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Keywords: pymvpa-reference
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Keywords: pymvpa-reference
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Keywords: pymvpa-reference
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Analysis of slow event-related fMRI data using patter classification techniques.
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1080/17470919.2014.978026
URL: http://dx.doi.org/10.1080/17470919.2014.978026
- Pollmann, S., Zinke, W., Baumgartner, F., Geringswald, F. & Hanke, M. (2014). The right temporo-parietal junction contributes to visual feature binding. NeuroImage, 101, 289-297.
Keywords: pymvpa
DOI: http://dx.doi.org/10.1016/j.neuroimage.2014.07.021
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1162/jocn_a_00576
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Discussion of possible scenarios where univariate and multivariate (SVM)
sensitivity maps derived from the same dataset could differ. Including the
case were univariate methods would assign a substantially larger score to
some features.
Keywords: support vector machine, SVM, sensitivity
DOI: http://dx.doi.org/10.1016/j.jneumeth.2008.04.008
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1016/j.neuroimage.2014.11.014
URL: http://dx.doi.org/10.1016/j.neuroimage.2014.11.014
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1073/pnas.1404396111
URL: http://dx.doi.org/10.1073/pnas.1404396111
- Scholkopf, B. & Smola, A. (2001). Learning with Kernels: Support Vector Machines, Regularization. MIT Press: Cambridge, MA.
Good coverage of kernel methods and associated statistical learning aspects
(e.g. error bounds)
Keywords: statistical learning, kernel methods, error estimation
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Keywords: pymvpa-reference
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1093/cercor/bhu124
URL: http://dx.doi.org/10.1093/cercor/bhu124
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1162/jocn_a_00733
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Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1038/nrn2994
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Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1111/j.1756-8765.2010.01092.x
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1073/pnas.1210126110
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- Spacek, M. & Swindale, N. (2009). Python in Neuroscience. The Neuromorphic Engineer.
Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.2417/1200907.1682
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Keywords: pymvpa-reference
DOI: http://dx.doi.org/10.1016/j.neuroimage.2012.09.063
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1371/journal.pone.0063198
- 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. & Cannon, T. D. (2009). Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms. Biological Psychiatry, 66, 1055–1060.
First published study employing PyMVPA for MRI-based analysis of Psychosis.
Keywords: pymvpa, psychosis, MRI
DOI: http://dx.doi.org/10.1016/j.biopsych.2009.07.019
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Study using PyMVPA to perform immobilization detection to improve navigation
reliability of an autonomous robot.
DOI: http://dx.doi.org/10.1109/IROS.2009.5354290
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- Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer: New York.
- Keywords: support vector machine, SVM
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Demonstration of overfitting and introducing the bias in the error estimation
using cross-validation on entire dataset for performing model selection.
Keywords: statistical learning, model selection, error estimation, hypothesis testing
DOI: http://dx.doi.org/10.1186/1471-2105-7-91
URL: http://www.ncbi.nlm.nih.gov/pubmed/16504092
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1523/jneurosci.0351-14.2014
URL: http://dx.doi.org/10.1523/JNEUROSCI.0351-14.2014
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Keywords: support vector machine, SVM, group analysis
DOI: http://dx.doi.org/10.1016/j.neuroimage.2007.03.072
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Keywords: pymvpa
DOI: http://dx.doi.org/10.1016/j.neuroimage.2014.05.045
URL: http://dx.doi.org/10.1016/j.neuroimage.2014.05.045
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DOI: http://dx.doi.org/10.1016/j.neuroimage.2010.04.035
URL: http://www.ncbi.nlm.nih.gov/pubmed/20406690
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Historical excurse into the life of 10 prominent statisticians of XXth century
and their scientific contributions.
Keywords: statistics, hypothesis testing
DOI: http://dx.doi.org/10.1111/j.1745-6924.2009.01167.x
Xu, H., Lorbert, A., Ramadge, P. J., Guntupalli, J. S. & Haxby, J. V. (2012). Regularized hyperalignment of multi-set fMRI data. Proceedings of the 2012 IEEE Signal Processing Workshop.
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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