NDLI: A sparse variational Bayesian approach for fMRI data analysis

Content Provider	IEEE Xplore Digital Library
Author	Oikonomou, V.P. Tripoliti, E.E. Fotiadis, D.I.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Ioannina, Ioannina (Oikonomou, V.P.; Tripoliti, E.E.; Fotiadis, D.I.)
Abstract	The aim of this work is to propose a new approach for the determination of the design matrix in fMRI experiments. The design matrix embodies all available knowledge about experimentally controlled factors and potential confounds. This knowledge is expressed through the regressors of the design matrix. However, in a particular fMRI time series some of those regressors may not be present. In order to take into account this prior information a Bayesian approach based on hierarchical prior, which expresses the sparsity of the design matrix, is used over the parameters of the generalized linear model. The proposed method automatically prunes the columns of the design matrix which are irrelevant to the generation of data. The evaluation of the proposed approach on simulated and real experiments have shown higher performance compared to the conventional t-test approach.
Starting Page	1
Ending Page	6
File Size	265345
Page Count	6
File Format	PDF
ISBN	9781424428441
DOI	10.1109/BIBE.2008.4696811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-08
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Data analysis Statistical analysis Parameter estimation Magnetic resonance imaging Sparse matrices Neurons Smoothing methods Statistics Maximum likelihood estimation
Content Type	Text
Resource Type	Article

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