{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:18:15Z","timestamp":1776122295008,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Provincial Key R\\&amp;D Program (Social Development)","award":["BE2015700"],"award-info":[{"award-number":["BE2015700"]}]},{"name":"Jiangsu Provincial Key R\\&amp;D Program (Social Development)","award":["BE2016773"],"award-info":[{"award-number":["BE2016773"]}]},{"name":"Natural Science Research Major Program in Universities of Jiangsu Province","award":["16KJA310002"],"award-info":[{"award-number":["16KJA310002"]}]},{"name":"Postgraduate Research \\&amp; Practice Innovation Program of Jiangsu Province","award":["KYCX20\\_0728"],"award-info":[{"award-number":["KYCX20\\_0728"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31671006"],"award-info":[{"award-number":["31671006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771251"],"award-info":[{"award-number":["61771251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (p&lt;0.004). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (p&lt;0.001), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.<\/jats:p>","DOI":"10.3390\/e24030314","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T09:34:38Z","timestamp":1645608878000},"page":"314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1359-4819","authenticated-orcid":false,"given":"Dengxuan","family":"Bai","sequence":"first","affiliation":[{"name":"School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7342-781X","authenticated-orcid":false,"given":"Wenpo","family":"Yao","sequence":"additional","affiliation":[{"name":"Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuwang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Nanjing Vocational College of Information Technolog, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"ref_1","unstructured":"(2022, February 02). World Health Organization Schizophrenia. January 2022. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/schizophrenia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/TNSRE.2016.2551700","article-title":"Abnormal neural oscillations in schizophrenia assessed by spectral power ratio of MEG during word processing","volume":"24","author":"Xu","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1109\/TCBB.2019.2899568","article-title":"Integration of imaging (epi) genomics data for the study of schizophrenia using group sparse joint nonnegative matrix factorization","volume":"17","author":"Wang","year":"2019","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1001\/jamapsychiatry.2019.3360","article-title":"Schizophrenia\u2014An overview","volume":"77","author":"McCutcheon","year":"2020","journal-title":"JAMA Psychiatry"},{"key":"ref_5","unstructured":"(2022, February 02). NIMH.Schizophrenia. Last Revised: May 2020, Available online: https:\/\/www.nimh.nih.gov\/health\/topics\/schizophrenia\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1093\/schbul\/sby024","article-title":"Inflammation in schizophrenia: Pathogenetic aspects and therapeutic considerations","volume":"44","year":"2018","journal-title":"Schizophr. Bull."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1038\/nrd.2016.28","article-title":"Altering the course of schizophrenia: Progress and perspectives","volume":"15","author":"Millan","year":"2016","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1097\/YCO.0000000000000407","article-title":"Recovery from schizophrenia: Is it possible?","volume":"31","author":"Vita","year":"2018","journal-title":"Curr. Opin. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.nic.2020.01.002","article-title":"Magnetoencephalography for Schizophrenia","volume":"30","author":"Edgar","year":"2020","journal-title":"Neuroimaging Clin."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rojas, D.C. (2019). Review of schizophrenia research using MEG. Magnetoencephalography: From Signals to Dynamic Cortical Networks, Springer Nature.","DOI":"10.1007\/978-3-319-62657-4_41-1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.schres.2019.10.023","article-title":"Reduced parietal alpha power and psychotic symptoms: Test-retest reliability of resting-state magnetoencephalography in schizophrenia and healthy controls","volume":"215","author":"Schendel","year":"2020","journal-title":"Schizophr. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"561973","DOI":"10.3389\/fpsyt.2020.561973","article-title":"A MEG Study of Visual Repetition Priming in Schizophrenia: Evidence for Impaired High-Frequency Oscillations and Event-Related Fields in Thalamo-Occipital Cortices","volume":"11","author":"Sauer","year":"2020","journal-title":"Front. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1177\/1550059418797868","article-title":"Evoked potentials investigations of deficit versus nondeficit schizophrenia: EEG-MEG preliminary data","volume":"50","author":"Boutros","year":"2019","journal-title":"Clin. EEG Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.jpsychires.2020.07.036","article-title":"Lateralized evoked responses in parietal cortex demonstrate visual short-term memory deficits in first-episode schizophrenia","volume":"130","author":"Coffman","year":"2020","journal-title":"J. Psychiatr. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1016\/j.clinph.2017.06.246","article-title":"Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: State-of-the-art and methodological challenges","volume":"128","author":"Alamian","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101878","DOI":"10.1016\/j.nicl.2019.101878","article-title":"Test-retest reliability of time-frequency measures of auditory steady-state responses in patients with schizophrenia and healthy controls","volume":"23","author":"Roach","year":"2019","journal-title":"Neuroimage Clin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.schres.2017.07.048","article-title":"Dissociable auditory mismatch response and connectivity patterns in adolescents with schizophrenia and adolescents with bipolar disorder with psychosis: A magnetoencephalography study","volume":"193","author":"Braeutigam","year":"2018","journal-title":"Schizophr. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.nicl.2018.09.007","article-title":"MEG resting-state oscillations and their relationship to clinical symptoms in schizophrenia","volume":"20","author":"Levy","year":"2018","journal-title":"Neuroimage Clin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1093\/schbul\/sbaa010","article-title":"Localization of Early-Stage Visual Processing Deficits at Schizophrenia Spectrum Illness Onset Using Magnetoencephalography","volume":"46","author":"Sklar","year":"2020","journal-title":"Schizophr. Bull."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"438","DOI":"10.3389\/fpsyt.2021.642469","article-title":"Abnormal ERPs and Brain Dynamics Mediate Basic Self Disturbance in Schizophrenia: A Review of EEG and MEG Studies","volume":"12","author":"Hamilton","year":"2021","journal-title":"Front. Psychiatry"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.schres.2019.05.007","article-title":"Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia","volume":"209","author":"Sanfratello","year":"2019","journal-title":"Schizophr. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"554844","DOI":"10.3389\/fpsyt.2020.554844","article-title":"Neurophysiological Face Processing Deficits in Patients With Chronic Schizophrenia: An MEG Study","volume":"11","author":"Ohara","year":"2020","journal-title":"Front. Psychiatry"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1142\/S0219622013500193","article-title":"An integrated data characteristic testing scheme for complex time series data exploration","volume":"12","author":"Tang","year":"2013","journal-title":"Int. J. Inf. Technol. Decis. Mak."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/TNB.2017.2705689","article-title":"Nonlinear dynamic complexity and sources of resting-state EEG in abstinent heroin addicts","volume":"16","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Nanobiosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.3389\/fphys.2018.01213","article-title":"EEG multiscale complexity in schizophrenia during picture naming","volume":"9","author":"Lozano","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"385","DOI":"10.5455\/apd.205512","article-title":"EEG complexity and frequency in chronic residual schizophrenia","volume":"17","author":"Tan","year":"2016","journal-title":"Anatol. J. Psychiatry\/Anadolu Psikiyatr. Derg."},{"key":"ref_27","first-page":"1","article-title":"EEG signal complexity analysis for schizophrenia during rest and mental activity","volume":"28","author":"Thilakvathi","year":"2017","journal-title":"Biomed. Res. India"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1016\/j.clinph.2011.04.011","article-title":"Lempel\u2013Ziv complexity in schizophrenia: A MEG study","volume":"122","author":"Turrero","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Brookes, M.J., Hall, E.L., Robson, S.E., Price, D., Palaniyappan, L., Liddle, E.B., Liddle, P.F., Robinson, S.E., and Morris, P.G. (2015). Complexity measures in magnetoencephalography: Measuring \u201cdisorder\u201d in schizophrenia. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0120991"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Bandt, C. (2016). Permutation Entropy and Order Patterns in Long Time Series. Time Series Analysis and Forecasting, Springer.","DOI":"10.1007\/978-3-319-28725-6_5"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"014101","DOI":"10.1063\/1.5133419","article-title":"Shannon entropy and quantitative time irreversibility for different and even contradictory aspects of complex systems","volume":"116","author":"Yao","year":"2020","journal-title":"Appl. Phys. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.bspc.2015.04.002","article-title":"A permutation Lempel-Ziv complexity measure for EEG analysis","volume":"19","author":"Bai","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/LSP.2016.2542881","article-title":"Dispersion entropy: A measure for time-series analysis","volume":"23","author":"Rostaghi","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"022911","DOI":"10.1103\/PhysRevE.87.022911","article-title":"Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information","volume":"87","author":"Fadlallah","year":"2013","journal-title":"Phys. Rev. E"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/TCBB.2018.2838658","article-title":"Multiscale and multimodal analysis for computational biology","volume":"15","author":"Gao","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TCBB.2018.2846648","article-title":"Quantifying direct dependencies in biological networks by multiscale association analysis","volume":"17","author":"Shi","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"068102","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale entropy analysis of complex physiologic time series","volume":"89","author":"Costa","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112725","DOI":"10.1109\/ACCESS.2020.3000439","article-title":"Power-Law Exponent Modulated Multiscale Entropy: A Complexity Measure Applied to Physiologic Time Series","volume":"8","author":"Han","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"078704","DOI":"10.7498\/aps.63.078704","article-title":"Multiscale permutation entropy analysis of electroencephalogram","volume":"63","author":"Yao","year":"2014","journal-title":"Acta Phys. Sin."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102586","DOI":"10.1016\/j.bspc.2021.102586","article-title":"Multiscale multidimensional recurrence quantitative analysis for analysing MEG signals in patients with schizophrenia","volume":"68","author":"Bai","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.physleta.2015.09.042","article-title":"Interacting price model and fluctuation behavior analysis from Lempel\u2013Ziv complexity and multi-scale weighted-permutation entropy","volume":"380","author":"Li","year":"2016","journal-title":"Phys. Lett. A"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.physa.2014.09.058","article-title":"The experimental signals analysis for bubbly oil-in-water flow using multi-scale weighted-permutation entropy","volume":"417","author":"Chen","year":"2015","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.measurement.2019.05.002","article-title":"Composite multi-scale weighted permutation entropy and extreme learning machine based intelligent fault diagnosis for rolling bearing","volume":"143","author":"Zheng","year":"2019","journal-title":"Measurement"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1103\/PhysRevA.45.3403","article-title":"Determining embedding dimension for phase-space reconstruction using a geometrical construction","volume":"45","author":"Kennel","year":"1992","journal-title":"Phys. Rev. A"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/S0167-2789(98)00240-1","article-title":"Nonlinear dynamics, delay times, and embedding windows","volume":"127","author":"Kim","year":"1999","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.clinph.2017.10.024","article-title":"Abnormal cortical neural synchrony during working memory in schizophrenia","volume":"129","author":"Kang","year":"2018","journal-title":"Clin. Neurophysiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1007\/s11682-019-00175-8","article-title":"Abnormal synchronization of functional and structural networks in schizophrenia","volume":"14","author":"Zhu","year":"2020","journal-title":"Brain Imaging Behav."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1038\/mp.2017.170","article-title":"Widespread white matter microstructural differences in schizophrenia across 4322 individuals: Results from the ENIGMA Schizophrenia DTI Working Group","volume":"23","author":"Kelly","year":"2018","journal-title":"Mol. Psychiatry"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3208","DOI":"10.1038\/s41380-019-0509-y","article-title":"White matter abnormalities across the lifespan of schizophrenia: A harmonized multi-site diffusion MRI study","volume":"25","author":"Chunga","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1038\/20924","article-title":"Multifractality in human heartbeat dynamics","volume":"399","author":"Ivanov","year":"1999","journal-title":"Nature"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1650008","DOI":"10.1142\/S0129065716500088","article-title":"Analysis of the complexity measures in the EEG of schizophrenia patients","volume":"26","author":"Akar","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1088\/0967-3334\/30\/8\/005","article-title":"Complexity analysis of EEG in patients with schizophrenia using fractal dimension","volume":"30","author":"Raghavendra","year":"2009","journal-title":"Physiol. Meas."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"115","DOI":"10.4306\/pi.2008.5.2.115","article-title":"Nonlinear analysis of electroencephalogram in schizophrenia patients with persistent auditory hallucination","volume":"5","author":"Lee","year":"2008","journal-title":"Psychiatry Investig."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/schbul\/13.2.261","article-title":"The positive and negative syndrome scale (PANSS) for schizophrenia","volume":"13","author":"Kay","year":"1987","journal-title":"Schizophr. Bull."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"127977","DOI":"10.1016\/j.physleta.2022.127977","article-title":"Comparative analysis of the original and amplitude permutations","volume":"430","author":"Yao","year":"2022","journal-title":"Phys. Lett. A"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/3\/314\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:25:23Z","timestamp":1760135123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/3\/314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,23]]},"references-count":57,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["e24030314"],"URL":"https:\/\/doi.org\/10.3390\/e24030314","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,23]]}}}