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Article

Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm

1
Center for Psychiatric Neuroscience, Feinstein Institute of Medical Research, Manhasset, NY 11030, USA
2
Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY 11004, USA
3
Department of Mathematics, Hofstra University, Hempstead, NY 11549, USA
4
Department of Psychiatry and Behavioral Sciences, Stanford University, Paolo Alto, CA 94305, USA
5
Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
6
Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS 38677, USA
7
Departments of Physiology and Pharmacology and of Neurology, State University of New York Downstate Medical Center, Brooklyn, NY 11203, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2020, 22(6), 617; https://doi.org/10.3390/e22060617
Received: 10 May 2020 / Revised: 30 May 2020 / Accepted: 30 May 2020 / Published: 2 June 2020
(This article belongs to the Section Signal and Data Analysis)
Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses. View Full-Text
Keywords: dynamic bivariate correlation; dynamic correlation; fMRI; local field potential; sliding window; dynamic conditional correlation; functional connectivity dynamic bivariate correlation; dynamic correlation; fMRI; local field potential; sliding window; dynamic conditional correlation; functional connectivity
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MDPI and ACS Style

John, M.; Wu, Y.; Narayan, M.; John, A.; Ikuta, T.; Ferbinteanu, J. Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm. Entropy 2020, 22, 617. https://doi.org/10.3390/e22060617

AMA Style

John M, Wu Y, Narayan M, John A, Ikuta T, Ferbinteanu J. Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm. Entropy. 2020; 22(6):617. https://doi.org/10.3390/e22060617

Chicago/Turabian Style

John, Majnu, Yihren Wu, Manjari Narayan, Aparna John, Toshikazu Ikuta, and Janina Ferbinteanu. 2020. "Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm" Entropy 22, no. 6: 617. https://doi.org/10.3390/e22060617

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