DocumentCode :
50198
Title :
An Adaptive Regression Mixture Model for fMRI Cluster Analysis
Author :
Oikonomou, V.P. ; Blekas, K.
Author_Institution :
Dept. of Appl. Inf., TEI of Ionian Islands, Lefkas, Greece
Volume :
32
Issue :
4
fYear :
2013
fDate :
Apr-13
Firstpage :
649
Lastpage :
659
Abstract :
Functional magnetic resonance imaging (fMRI) has become one of the most important techniques for studying the human brain in action. A common problem in fMRI analysis is the detection of activated brain regions in response to an experimental task. In this work we propose a novel clustering approach for addressing this issue using an adaptive regression mixture model. The main contribution of our method is the employment of both spatial and sparse properties over the body of the mixture model. Thus, the clustering approach is converted into a maximum a posteriori estimation approach, where the expectation-maximization algorithm is applied for model training. Special care is also given to estimate the kernel scalar parameter per cluster of the design matrix by presenting a multi-kernel scheme. In addition an incremental training procedure is presented so as to make the approach independent on the initialization of the model parameters. The latter also allows us to introduce an efficient stopping criterion of the process for determining the optimum brain activation area. To assess the effectiveness of our method, we have conducted experiments with simulated and real fMRI data, where we have demonstrated its ability to produce improved performance and functional activation detection capabilities.
Keywords :
biomedical MRI; brain; expectation-maximisation algorithm; learning (artificial intelligence); medical image processing; neurophysiology; regression analysis; activated brain region detection; adaptive regression mixture model; clustering approach; expectation-maximization algorithm; fMRI cluster analysis; functional activation detection capabilities; functional magnetic resonance imaging; kernel scalar parameter; maximum a posteriori estimation approach; model parameters; model training; multikernel scheme; optimum brain activation area; simulated fMRI data; sparse properties; spatial properties; training procedure; Adaptation models; Brain modeling; Clustering algorithms; Correlation; Kernel; Linear regression; Time series analysis; Expectation-maximization (EM) algorithm; Markov random field (MRF); functional magnetic resonance imaging (fMRI) analysis; regression mixture models; sparse modeling; Algorithms; Brain; Cluster Analysis; Computer Simulation; Databases, Factual; Humans; Linear Models; Magnetic Resonance Imaging; Markov Chains;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2012.2221731
Filename :
6319412
Link To Document :
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