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A Dynamic Regression Approach for Frequency-Domain Partial Coherence and Causality Analysis of Functional Brain Networks

Institution:
Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
IEEE Engineering in Medicine and Biology Society EMBS
Publication Date:
Sep-2018
Journal:
IEEE Trans Med Imaging
Volume Number:
37
Issue Number:
9
Pages:
1957-69
Citation:
IEEE Trans Med Imaging. 2018 Sep;37(9):1957-69.
PubMed ID:
28816657
PMCID:
PMC6260816
Appears in Collections:
SPL
Sponsors:
R21 MH115280/MH/NIMH NIH HHS/United States
R01 MH097979/MH/NIMH NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
R21 MH116352/MH/NIMH NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Ning L., Rathi Y. A Dynamic Regression Approach for Frequency-Domain Partial Coherence and Causality Analysis of Functional Brain Networks. IEEE Trans Med Imaging. 2018 Sep;37(9):1957-69. PMID: 28816657. PMCID: PMC6260816.
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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.