Clustered Compressed Sensing in fMRI Data Analysis Using a Bayesian Framework
Keywords:
Bayesian framework, sparse prior, clustered prior, posterior, MAP, compressive sensing, LASSO, clustered LASSO, GLM, fMRI dataAbstract
This paper provides a Bayesian method of
analyzing functional magnetic resonance imaging (fMRI) data.
Usually fMRI signals are noisy and need efficient algorithms to
estimate or detect the signals accurately. Using a Bayesian
frame work we have used two different priors: sparsity and
clusterdness in the fMRI data by using a general linear model
(GLM), which is used as a main tool in fMRI studies. This
enhances the effectiveness of the model to help analyze the data
better. So in this work we have built the Bayesian framework
needed first. Then, we have applied our analysis on synthetic
data that we made and that are well known, and the results
show that clustered compressive sampling has given better
results compared to the using of only sparse prior and/ or to the
analysis without considering the two priors. Later we have
applied it on fMRI data and the results are much better in terms
of signal to noise ratio (SNR) and intensity of images
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