%0 Journal Article
%J IEEE Trans Med Imaging
%D 2018
%T A Dynamic Regression Approach for Frequency-Domain Partial Coherence and Causality Analysis of Functional Brain Networks
%A Ning, Lipeng
%A Rathi, Yogesh
%X Coherence and causality measures are often used to analyze the influence of one region on another during analysis of functional brain networks. The analysis methods usually involve a regression problem, where the signal of interest is decomposed into a mixture of regressor and a residual signal. In this paper, we revisit this basic problem and present solutions that provide the minimal-entropy residuals for different types of regression filters, such as causal, instantaneously causal, and noncausal filters. Using optimal prediction theory, we derive several novel frequency-domain expressions for partial coherence, causality, and conditional causality analysis. In particular, our solution provides a more accurate estimation of the frequency-domain causality compared with the classical Geweke causality measure. Using synthetic examples and in vivo resting-state functional magnetic resonance imaging data from the human connectome project, we show that the proposed solution is more accurate at revealing frequency-domain linear dependence among high-dimensional signals.
%B IEEE Trans Med Imaging
%V 37
%P 1957-69
%8 2018 Sep
%G eng
%N 9
%1 http://www.ncbi.nlm.nih.gov/pubmed/28816657?dopt=Abstract
%R 10.1109/TMI.2017.2739740