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Bayesian source separation of fMRI signals

Rowe, Daniel B. (2001) Bayesian source separation of fMRI signals. In: Bayesian inference and maximum entropy methods in science and engineering : 20th international workshop, Gif-sur-Yvette, France, 8-13 July 2000. American Institute of Physics Conference Proceedings. No.568. American Institute of Physics , Melville, NY, pp. 375-387. ISBN 0735400040 http://resolver.caltech.edu/CaltechAUTHORS:ROWaipcp01

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Abstract

In analyzing the results of functional magnetic resonance imaging, the identification of significant activation in voxels is a crucial task. In computing the activation level, a standard method is to select an assumed to be known reference function and perform a multiple regression of the time courses on it and a linear trend. Once the linear trend is found, the correlation between the assumed to be known reference function and the detrended observed time-course in each voxel is computed and voxels colored according to their correlation. But the most important question is: How do we choose the reference function? This paper develops a Bayesian statistical approach to determining the underlying source reference function based on Bayesian source separation, and uses it on both simulated and real fMRI data. This underlying reference function is the unobserved response due the presentation of the experimental stimulus.


Item Type:Book Section
Additional Information:© 2001 American Institute of Physics
Subject Keywords:magnetic resonance imaging; signal processing; deconvolution; Bayes methods; statistical analysis; performance evaluation
Record Number:CaltechAUTHORS:ROWaipcp01
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:ROWaipcp01
Alternative URL:http://dx.doi.org/10.1063/1.1381901
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2354
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:29 Mar 2006
Last Modified:26 Dec 2012 08:48

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