{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T04:42:57Z","timestamp":1771044177654,"version":"3.50.1"},"reference-count":31,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2011,1,1]],"date-time":"2011-01-01T00:00:00Z","timestamp":1293840000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"name":"ANR","award":["ViMAGINE ANR-08-BLAN-0250-02"],"award-info":[{"award-number":["ViMAGINE ANR-08-BLAN-0250-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2011]]},"abstract":"<jats:p><jats:italic>Inverse inference<\/jats:italic>has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, called<jats:italic>Multiclass Sparse Bayesian Regression<\/jats:italic>(<jats:italic>MCBR<\/jats:italic>), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features.<\/jats:p>","DOI":"10.1155\/2011\/350838","type":"journal-article","created":{"date-parts":[[2011,6,23]],"date-time":"2011-06-23T15:02:05Z","timestamp":1308841325000},"page":"1-13","source":"Crossref","is-referenced-by-count":8,"title":["Multiclass Sparse Bayesian Regression for fMRI-Based Prediction"],"prefix":"10.1155","volume":"2011","author":[{"given":"Vincent","family":"Michel","sequence":"first","affiliation":[{"name":"PARIETAL Team, INRIA Saclay-\u00cele-de-France, 91191 Saclay, France"},{"name":"Laboratoire de Math\u00e9matiques, Universit\u00e9 Paris-Sud 11, 91400 Orsay, France"},{"name":"CEA, DSV, I2BM, NeuroSpin, 91191 Gif-sur-Yvette, France"}]},{"given":"Evelyn","family":"Eger","sequence":"additional","affiliation":[{"name":"CEA, DSV, I2BM, NeuroSpin, 91191 Gif-sur-Yvette, 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