{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T06:39:19Z","timestamp":1763620759533,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T00:00:00Z","timestamp":1591056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01MH109508","R01MH108654"],"award-info":[{"award-number":["R01MH109508","R01MH108654"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e22060617","type":"journal-article","created":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T04:12:09Z","timestamp":1591157529000},"page":"617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4596-245X","authenticated-orcid":false,"given":"Majnu","family":"John","sequence":"first","affiliation":[{"name":"Center for Psychiatric Neuroscience, Feinstein Institute of Medical Research, Manhasset, NY 11030, USA"},{"name":"Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY 11004, USA"},{"name":"Department of Mathematics, Hofstra University, Hempstead, NY 11549, USA"}]},{"given":"Yihren","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Hofstra University, Hempstead, NY 11549, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5348-270X","authenticated-orcid":false,"given":"Manjari","family":"Narayan","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Stanford University, Paolo Alto, CA 94305, USA"}]},{"given":"Aparna","family":"John","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0848-302X","authenticated-orcid":false,"given":"Toshikazu","family":"Ikuta","sequence":"additional","affiliation":[{"name":"Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS 38677, USA"}]},{"given":"Janina","family":"Ferbinteanu","sequence":"additional","affiliation":[{"name":"Departments of Physiology and Pharmacology and of Neurology, State University of New York Downstate Medical Center, Brooklyn, NY 11203, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., and Calhoun, V.D. 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