Activation Detection on fMRI Time Series Using Hidden Markov Model
Joint Authors
Source
Advances in Artificial Neural Systems
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-26
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM).
HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals.
Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm.
Two different activation detection methods are presented in this paper.
The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map.
The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm.
The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD).
Experimental results on both normal subject and brain tumor subject are presented.
HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.
American Psychological Association (APA)
Duan, Rong& Man, Hong. 2012. Activation Detection on fMRI Time Series Using Hidden Markov Model. Advances in Artificial Neural Systems،Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-453148
Modern Language Association (MLA)
Duan, Rong& Man, Hong. Activation Detection on fMRI Time Series Using Hidden Markov Model. Advances in Artificial Neural Systems No. 2012 (2012), pp.1-12.
https://search.emarefa.net/detail/BIM-453148
American Medical Association (AMA)
Duan, Rong& Man, Hong. Activation Detection on fMRI Time Series Using Hidden Markov Model. Advances in Artificial Neural Systems. 2012. Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-453148
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-453148