Paper Info
Title
Analysis of FMRI data with drift: modified general linear model and Bayesian estimator.
Abstract
The slowly varying drift poses a major problem in the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, based on the observation that noise in fMRI is long memory fractional noise and the slowly varying drift resides in a subspace spanned only by large scale wavelets, we examine a modified general linear model (GLM) in wavelet domain under Bayesian framework. This modified model estimates the activation parameters at each scale of wavelet decomposition. Then, a model selection criterion based on the results from the modified scheme is proposed to model the drift. Results obtained from simulated as well as real fMRI data show that the proposed Bayesian estimator can accurately capture the noise structure, and hence, result in robust estimation of the parameters in GLM. Besides, the proposed model selection criterion works well and could efficiently remove the drift.
Year
DOI
Venue
2008
10.1109/TBME.2008.918563
IEEE Trans. Biomed. Engineering
Keywords
DocType
Volume
fmri data analysis,brain activity detection,functional magnetic resonance imaging (fmri),neurophysiology,long memory fractional noise,wavelet transforms,model selection,wavelet domain analysis,image denoising,biomedical mri,general linear model (glm),modified general linear model,bayes methods,fractional noise,brain,wavelet decomposition,bayesian estimator,slow varying drift,medical image processing,functional magnetic resonance imaging data,brain mapping,computer simulation,robust estimator,general linear model,estimation,long memory,data models,image analysis,linear models,parameter estimation,bayesian methods,noise,bayes theorem,magnetic resonance imaging,data analysis
Journal
55
Issue
ISSN
Citations 
5
0018-9294
11
PageRank 
References 
Authors
0.87
10
2
Name
Order
Citations
PageRank
Huaien Luo1484.99
Sadasivan Puthusserypady218127.49