Title
Order Detection For Fmri Analysis: Joint Estimation Of Downsampling Depth And Order By Information Theoretic Criteria
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.
Year
DOI
Venue
2011
10.1109/ISBI.2011.5872574
2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO
Keywords
Field
DocType
Order detection, fMRI data, linear model
Information theory,Data modeling,Computer vision,Pattern recognition,Functional magnetic resonance imaging,Computer science,Linear model,Oddball paradigm,Order by,Artificial intelligence,Covariance matrix,Upsampling
Conference
ISSN
Citations 
PageRank 
1945-7928
6
0.57
References 
Authors
2
4
Name
Order
Citations
PageRank
Xi-Lin Li154734.85
Sai Ma2995.83
Vince D Calhoun32769268.91
Tülay Adali41690126.40