DocumentCode :
3641389
Title :
Order detection for fMRI analysis: Joint estimation of downsampling depth and order by information theoretic criteria
Author :
Xi-Lin Li;Sai Ma;Vince D. Calhoun;Tülay Adalı
Author_Institution :
University of Maryland, Baltimore County, Dept. of CSEE, 21250, USA
fYear :
2011
fDate :
3/1/2011 12:00:00 AM
Firstpage :
1019
Lastpage :
1022
Abstract :
Estimation of the order of functional magnetic resonance imaging (fMRI) data is a crucial step in data-driven methods assuming a multivariate linear model. Use of information theoretic criteria for model order detection was proven useful but the sample dependence in fMRI data limits this usefulness. In this paper, we propose an iterative procedure that jointly estimates the downsampling depth and order of fMRI data, both by using information theoretic criteria. Experimental results on real-world fMRI data show reliable performance of the new method. Order analysis on auditory oddball task (AOD) data of healthy and schizophrenia subjects suggests that model order can be a promising biomarker for mental disorders.
Keywords :
"Estimation","Data models","Analytical models","Smoothing methods","Joints","Biological system modeling","Covariance matrix"
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4127-3
Type :
conf
DOI :
10.1109/ISBI.2011.5872574
Filename :
5872574
Link To Document :
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