{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T09:20:33Z","timestamp":1778404833162,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET), Argentina"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen\u2013Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states.<\/jats:p>","DOI":"10.3390\/e26050432","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T08:20:31Z","timestamp":1716193231000},"page":"432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen\u2013Shannon Distance"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2852-3263","authenticated-orcid":false,"given":"Luciano","family":"Zunino","sequence":"first","affiliation":[{"name":"Centro de Investigaciones \u00d3pticas (CONICET La Plata-CIC-UNLP), 1897 Gonnet, La Plata, Argentina"},{"name":"Departamento de Ciencias B\u00e1sicas, Facultad de Ingenier\u00eda, Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.physrep.2012.01.007","article-title":"Physical approach to complex systems","volume":"515","year":"2012","journal-title":"Phys. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106307","DOI":"10.1063\/1.5036959","article-title":"Differentiating resting brain states using ordinal symbolic analysis","volume":"28","author":"Montesano","year":"2018","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"010201","DOI":"10.1088\/2632-072X\/ac7f75","article-title":"Complex systems in the spotlight: Next steps after the 2021 Nobel Prize in Physics","volume":"4","author":"Bianconi","year":"2023","journal-title":"J. Phys. Complex."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fnhum.2013.00045","article-title":"Why should cognitive neuroscientists study the brain\u2019s resting state?","volume":"7","author":"Papo","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref_5","first-page":"37","article-title":"Information gain in the brain\u2019s resting state: A new perspective on autism","volume":"7","year":"2013","journal-title":"Front. Neuroinform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11571-016-9415-z","article-title":"Complexity of resting-state EEG activity in the patients with early-stage Parkinson\u2019s disease","volume":"11","author":"Yi","year":"2017","journal-title":"Cogn. Neurodyn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.3389\/fphys.2019.01619","article-title":"Time irreversibility of resting-state activity in the healthy brain and pathology","volume":"10","author":"Zanin","year":"2020","journal-title":"Front. Physiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1158404","DOI":"10.3389\/fpsyt.2023.1158404","article-title":"The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder","volume":"14","author":"Bernardi","year":"2023","journal-title":"Front. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Meghdadi, A.H., Kari\u0107, M.S., McConnell, M., Rupp, G., Richard, C., Hamilton, J., Salat, D., and Berka, C. (2021). Resting state EEG biomarkers of cognitive decline associated with Alzheimer\u2019s disease and mild cognitive impairment. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0244180"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"128952","DOI":"10.1016\/j.physa.2023.128952","article-title":"Resting state EEG complexity as a predictor of cognitive performance","volume":"624","author":"Wan","year":"2023","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e20625","DOI":"10.1016\/j.heliyon.2023.e20625","article-title":"Early detection of Parkinson\u2019s disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography","volume":"9","author":"Lin","year":"2023","journal-title":"Heliyon"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"\u015eeker, M., \u00d6zbek, Y., Yener, G., and \u00d6zerdem, M.S. (2021). Complexity of EEG dynamics for early diagnosis of Alzheimer\u2019s disease using permutation entropy neuromarker. Comput. Methods Programs Biomed., 206.","DOI":"10.1016\/j.cmpb.2021.106116"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s11517-007-0268-9","article-title":"Analysis of eyes open, eye closed EEG signals using second-order difference plots","volume":"45","author":"Thuraisingham","year":"2017","journal-title":"Med. Bio. Eng. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1016\/j.clinph.2007.07.028","article-title":"EEG differences between eyes-closed and eyes-open resting conditions","volume":"118","author":"Barry","year":"2007","journal-title":"Clin. Neurophysiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"036017","DOI":"10.1088\/1741-2552\/aa6401","article-title":"Comparison of connectivity analyses for resting state EEG data","volume":"14","author":"Olejarczyk","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vecchio, F., Miraglia, F., Pappalettera, C., Orticoni, A., Al\u00f9, F., Judica, E., Cotelli, M., and Rossini, P.M. (2021). Entropy as measure of brain networks\u2019 complexity in eyes open and closed conditions. Symmetry, 13.","DOI":"10.3390\/sym13112178"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e6912","DOI":"10.1002\/cpe.6912","article-title":"A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach","volume":"34","author":"Khosla","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113453","DOI":"10.1016\/j.chaos.2023.113453","article-title":"Spatial permutation entropy distinguishes resting brain states","volume":"171","author":"Boaretto","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Restrepo, J.F., Mateos, D.M., and L\u00f3pez, J.M.D. (2023). A Transfer entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105181"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ricci, L., and Perinelli, A. (2022). Estimating permutation entropy variability via surrogate time series. Entropy, 24.","DOI":"10.3390\/e24070853"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"niac008","DOI":"10.1093\/nc\/niac008","article-title":"Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features","volume":"2022","author":"Walter","year":"2022","journal-title":"Neurosci. Conscious."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"046217","DOI":"10.1103\/PhysRevE.70.046217","article-title":"Detecting dynamical changes in time series using the permutation entropy","volume":"70","author":"Cao","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.eplepsyres.2007.08.002","article-title":"Predictability analysis of absence seizures with permutation entropy","volume":"77","author":"Li","year":"2007","journal-title":"Epilepsy Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"041146","DOI":"10.1103\/PhysRevE.79.041146","article-title":"Deterministic dynamics of neural activity during absence seizures in rats","volume":"79","author":"Ouyang","year":"2009","journal-title":"Phys. Rev. E"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6212","DOI":"10.3390\/e16126212","article-title":"Ordinal patterns, entropy, and EEG","volume":"16","author":"Keller","year":"2014","journal-title":"Entropy"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Keller, K., Mangold, T., Stolz, I., and Werner, J. (2017). Permutation entropy: New ideas and challenges. Entropy, 19.","DOI":"10.20944\/preprints201702.0071.v1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bandt, C. (2017). A new kind of permutation entropy used to classify sleep stages from invisible EEG microstructure. Entropy, 19.","DOI":"10.3390\/e19050197"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"045310","DOI":"10.1103\/PhysRevE.105.045310","article-title":"Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time-series analysis","volume":"105","author":"Zunino","year":"2022","journal-title":"Phys. Rev. E"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"065302","DOI":"10.1103\/PhysRevE.108.065302","article-title":"Quantifying the diversity of multiple time series with an ordinal symbolic approach","volume":"108","author":"Zunino","year":"2023","journal-title":"Phys. Rev. E"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/18.61115","article-title":"Divergence measures based on the Shannon entropy","volume":"37","author":"Lin","year":"1991","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TIT.2003.813506","article-title":"A new metric for probability distributions","volume":"49","author":"Endres","year":"2003","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.physa.2017.12.073","article-title":"Monoparametric family of metrics derived from classical Jensen\u2013Shannon divergence","volume":"495","author":"Bussandri","year":"2018","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"041905","DOI":"10.1103\/PhysRevE.65.041905","article-title":"Analysis of symbolic sequences using the Jensen-Shannon divergence","volume":"65","author":"Grosse","year":"2002","journal-title":"Phys. Rev. E"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.3390\/e14081553","article-title":"Permutation entropy and its main biomedical and econophysics applications: A review","volume":"14","author":"Zanin","year":"2012","journal-title":"Entropy"},{"key":"ref_36","first-page":"241","article-title":"Recent progress in symbolic dynamics and permutation complexity\u2014Ten years of permutation entropy","volume":"222","author":"Keller","year":"2013","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20140091","DOI":"10.1098\/rsta.2014.0091","article-title":"Ordinal symbolic analysis and its application to biomedical recordings","volume":"373","author":"Keller","year":"2015","journal-title":"Phil. Trans. R. Soc. A"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1038\/s42005-021-00696-z","article-title":"Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series","volume":"4","author":"Zanin","year":"2021","journal-title":"Commun. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"31001","DOI":"10.1209\/0295-5075\/ac6a72","article-title":"20 years of ordinal patterns: Perspectives and challenges","volume":"138","author":"Leyva","year":"2022","journal-title":"Europhys. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"080401","DOI":"10.1063\/5.0167263","article-title":"Ordinal methods: Concepts, applications, new developments, and challenges\u2014In memory of Karsten Keller (1961\u20132022)","volume":"33","author":"Rosso","year":"2023","journal-title":"Chaos"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"125081","DOI":"10.1016\/j.physa.2020.125081","article-title":"Multiscale dynamics under the lens of permutation entropy","volume":"559","author":"Olivares","year":"2020","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"046210","DOI":"10.1103\/PhysRevE.86.046210","article-title":"Distinguishing chaotic and stochastic dynamics from time series by using a multiscale symbolic approach","volume":"86","author":"Zunino","year":"2012","journal-title":"Phys. Rev. E"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1016\/j.physleta.2017.03.052","article-title":"Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions","volume":"381","author":"Zunino","year":"2017","journal-title":"Phys. Lett. A"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103108","DOI":"10.1063\/1.4964646","article-title":"Surrogate-assisted network analysis of nonlinear time series","volume":"26","author":"Laut","year":"2016","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/0167-2789(92)90102-S","article-title":"Testing for nonlinearity in time series: The method of surrogate data","volume":"58","author":"Theiler","year":"1992","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1103\/PhysRevLett.77.635","article-title":"Improved surrogate data for nonlinearity tests","volume":"77","author":"Schreiber","year":"1996","journal-title":"Phys. Rev. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2018.06.001","article-title":"Surrogate data for hypothesis testing of physical systems","volume":"748","author":"Lancaster","year":"2018","journal-title":"Phys. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"BCI2000: A general-purpose brain\u2013computer interface (BCI) system","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"119373","DOI":"10.1016\/j.neuroimage.2022.119373","article-title":"The natural frequencies of the resting human brain: An MEG-based atlas","volume":"258","author":"Capilla","year":"2022","journal-title":"NeuroImage"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1523\/JNEUROSCI.3620-10.2011","article-title":"Role of Prefrontal Cortex in Conscious Visual Perception","volume":"31","author":"Libedinsky","year":"2011","journal-title":"J. Neurosci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bandt, C. (2019). Small Order Patterns in Big Time Series: A Practical Guide. Entropy, 21.","DOI":"10.3390\/e21060613"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"033127","DOI":"10.1063\/5.0038876","article-title":"Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks","volume":"31","author":"Huang","year":"2021","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103118","DOI":"10.1063\/5.0067342","article-title":"Assessing time series irreversibility through micro-scale trends","volume":"31","author":"Zanin","year":"2021","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"043130","DOI":"10.1063\/5.0200029","article-title":"Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches","volume":"34","author":"Gancio","year":"2024","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"144101","DOI":"10.1103\/PhysRevLett.109.144101","article-title":"Revisiting Algorithms for Generating Surrogate Time Series","volume":"109","author":"Gliozzi","year":"2012","journal-title":"Phys. Rev. Lett."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/5\/432\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:45:03Z","timestamp":1760107503000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/5\/432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":56,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["e26050432"],"URL":"https:\/\/doi.org\/10.3390\/e26050432","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,20]]}}}