{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T02:35:40Z","timestamp":1791426940093,"version":"4.3.3"},"reference-count":72,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T00:00:00Z","timestamp":1552867200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Recent studies related to assessing functional connectivity (FC) in resting\u2010state functional magnetic resonance imaging have revealed that the resulting connectivity patterns exhibit considerable fluctuations (dynamic FC [dFC]). A widely applied method for quantifying dFC is the sliding window technique. According to this method, the data are divided into segments with the same length (window size) and a correlation metric is employed to assess the connectivity within these segments, whereby the window size is often empirically chosen.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we rigorously investigate the assessment of dFC using the sliding window approach. Specifically, we perform a detailed comparison between different correlation metrics, including Pearson, Spearman and Kendall correlation, Pearson and Spearman partial correlation, Mutual Information (MI), Variation of Information (VI), Kullback\u2013Leibler divergence, Multiplication of Temporal Derivatives and Inverse Covariance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using test\u2013retest datasets, we show that MI and VI yielded the most consistent results by achieving high reliability with respect to dFC estimates for different window sizes. Subsequent hypothesis testing, based on multivariate phase randomization surrogate data generation, allowed the identification of dynamic connections between the posterior cingulate cortex and regions in the frontal lobe and inferior parietal lobes, which were overall in agreement with previous studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In the case of MI and VI, a window size of at least 120\u00a0s was found to be necessary for detecting dFC for some of the previously identified dynamically connected regions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.1255","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T16:50:28Z","timestamp":1552927828000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Assessment of dynamic functional connectivity in resting\u2010state fMRI using the sliding window technique"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0056-5680","authenticated-orcid":false,"given":"Antonis D.","family":"Savva","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering National Technical University of Athens Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios D.","family":"Mitsis","sequence":"additional","affiliation":[{"name":"Department of Bioengineering McGill University Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George K.","family":"Matsopoulos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering National Technical University of Athens Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,3,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.3874-05.2006"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhs352"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2010.02.005"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/nyas.12360"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1418031112"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910340409"},{"key":"e_1_2_8_8_1","first-page":"27","article-title":"Conditional likelihood maximisation: A unifying framework for information theoretic feature selection","volume":"13","author":"Brown G.","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1196\/annals.1440.011"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.01.010"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2015.2478915"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.12.011"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.07.005"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0900234106"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2014.07.003"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601417103"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.08.035"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177013815"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.05.059"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1089\/brain.2011.0008"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.04.127"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0135058100"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.5194\/npg-11-561-2004"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.06.078"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.11.055"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2018.04.013"},{"key":"e_1_2_8_27_1","volume-title":"Functional magnetic resonance imaging","author":"Huettel S. 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