Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
Joint Authors
Thirion, Bertrand
Michel, Vincent
Eger, Evelyn
Keribin, Christine
Source
International Journal of Biomedical Imaging
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-06-23
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Inverse inference 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 Multiclass Sparse Bayesian Regression (MCBR), 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.
American Psychological Association (APA)
Michel, Vincent& Eger, Evelyn& Keribin, Christine& Thirion, Bertrand. 2011. Multiclass Sparse Bayesian Regression for fMRI-Based Prediction. International Journal of Biomedical Imaging،Vol. 2011, no. 2011, pp.1-13.
https://search.emarefa.net/detail/BIM-464951
Modern Language Association (MLA)
Michel, Vincent…[et al.]. Multiclass Sparse Bayesian Regression for fMRI-Based Prediction. International Journal of Biomedical Imaging No. 2011 (2011), pp.1-13.
https://search.emarefa.net/detail/BIM-464951
American Medical Association (AMA)
Michel, Vincent& Eger, Evelyn& Keribin, Christine& Thirion, Bertrand. Multiclass Sparse Bayesian Regression for fMRI-Based Prediction. International Journal of Biomedical Imaging. 2011. Vol. 2011, no. 2011, pp.1-13.
https://search.emarefa.net/detail/BIM-464951
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-464951