Spatio-temporal fMRI signal analysis using information theory
Name
47210830-MIT.pdf
Description
Full printable version
Size
4.86 MB
Format
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Checksum (MD5)
8bfb5170b6ca4114823804847388d999
Author(s)
Kim, Junmo, 1976-
Advisor(s)
John W. Fisher.
Alternative Title
Spatio-temporal functional magnetic resonance imaging signal analysis using information theory
Date Issued
2000
Publisher
Massachusetts Institute of Technology
Abstract
Functional MRI is a fast brain imaging technique which measures the spatio-temporal neuronal activity. The development of automatic statistical analysis techniques which calculate brain activation maps from JMRI data has been a challenging problem due to the limitation of current understanding of human brain physiology. In previous work a novel information-theoretic approach was introduced for calculating the activation map for JMRI analysis [Tsai et al , 1999]. In that work the use of mutual information as a measure of activation resulted in a nonparametric calculation of the activation map. Nonparametric approaches are attractive as the implicit assumptions are milder than the strong assumptions of popular approaches based on the general linear model popularized by Friston et al [19941. Here we show that, in addition to the intuitive information-theoretic appeal, such an application of mutual information is equivalent to a hypothesis test when the underlying densities are unknown. Furthermore we incorporate local spatial priors using the well-known Ising model thereby dropping the implicit assumption that neighboring voxel time-series are independent. As a consequence of the hypothesis testing equivalence, calculation of the activation map with local spatial priors can be formulated as mincut/maxflow graph-cutting problem. Such problems can be solved in polynomial time by the Ford and Fulkerson method. Empirical results are presented on three JMRI datasets measuring motor, auditory, and visual cortex activation. Comparisons are made illustrating the differences between the proposed technique and one based on the general linear model.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 111-112).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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