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Dynamic Regression for Partial Correlation and Causality Analysis of Functional Brain Networks

Institution:
Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2017
Publication Date:
Sep-2017
Volume Number:
20
Issue Number:
Pt 1
Pages:
365-72
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2017 Sep;20(Pt1):365-72.
Appears in Collections:
NAC, LMI, SPL
Sponsors:
R01 MH097979/MH/NIMH NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Ning L., Rathi Y. Dynamic Regression for Partial Correlation and Causality Analysis of Functional Brain Networks. Int Conf Med Image Comput Comput Assist Interv. 2017 Sep;20(Pt1):365-72.
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We propose a general dynamic regression framework for partial correlation and causality analysis of functional brain networks. Using the optimal prediction theory, we present the solution of the dynamic regression problem by minimizing the entropy of the associated stochastic process. We also provide the relation between the solutions and the linear dependence models of Geweke and Granger and derive novel expressions for computing partial correlation and causality using an optimal prediction filter with minimum error variance. We use the proposed dynamic framework to study the intrinsic partial correlation and causal- ity between seven different brain networks using resting state functional MRI (rsfMRI) data from the Human Connectome Project (HCP) and compare our results with those obtained from standard correlation and causality measures. The results show that our optimal prediction filter explains a significant portion of the variance in the rsfMRI data at low frequencies, unlike standard partial correlation analysis.