Effective connectivity analysis on functional magnetic resonance imaging of the human brain
Date Issued
2010
Date
2010
Author(s)
Chu, Ying-Hua
Abstract
The purpose of this thesis is to develop data-driven effective connectivity analysis tools to reveal causal relationships in the human brain using functional magnetic resonance imaging (fMRI) measurements. I study Granger causality (GCAR) and propose the information theory-based methods, including time delayed mutual information (MI) and transfer entropy (TE). These methods can process fMRI data using either a bivariate or a multivariate approach to respectively obtain efficient calculation or to improve the specificity of the causality detection. Also, to provide statistical inference, I propose to empirically estimate the null distribution of causality measures by the amplitude adjusted Fourier-transformed (AAFT) algorithm.
Provided with two coupled time series, My simulations show that the pair-wise GCAR, time delayed MI, and TE can distinguish the information flow, but can not avoid false causality estimation due to the potential indirect information flow. Therefore, appropriate conditioning is crucial to control the specificity of causality estimation. Simulation on the fMRI time series model suggests that compared to GCAR, TE combined with AAFT has a higher specificity (a lower type I error rate).
I also apply effective connectivity analysis to in vivo visuomotor fMRI experiments using ultra-fast magnetic inverse imaging (InI). Facilitated with a high volumetric sampling rate of 10 Hz, time delayed MI found the latency between activated brain areas in the lateralized conditions. TE further estimates potential bi-directional and uni-direction causal modulation in the lateralized visuomotor network. I conclude that the time delayed MI and TE can be useful tools to delineate causal interactions in spatiotemporal imaging of human brain during tasks and cognition.
Subjects
Granger causality
transfer entropy
functional magnetic resonance imaging (fMRI)
effective connectivity
Type
thesis
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