{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T01:14:50Z","timestamp":1771463690344,"version":"3.50.1"},"reference-count":135,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science Technology","award":["NSC-102-2629-B-010-001"],"award-info":[{"award-number":["NSC-102-2629-B-010-001"]}]},{"name":"Ministry of Science Technology","award":["MOST-104-2410-H-010-004-MY2"],"award-info":[{"award-number":["MOST-104-2410-H-010-004-MY2"]}]},{"name":"Ministry of Science Technology","award":["MOST-105-2221-E-009-057"],"award-info":[{"award-number":["MOST-105-2221-E-009-057"]}]},{"name":"Ministry of Science Technology","award":["MOST-105-2321-B-010-002"],"award-info":[{"award-number":["MOST-105-2321-B-010-002"]}]},{"name":"Ministry of Science Technology","award":["MOST-106-2420-H-009-001"],"award-info":[{"award-number":["MOST-106-2420-H-009-001"]}]},{"name":"Ministry of Science Technology","award":["MOST-106-2218-E-010-004-MY3"],"award-info":[{"award-number":["MOST-106-2218-E-010-004-MY3"]}]},{"DOI":"10.13039\/501100011912","name":"Taipei Veterans General Hospital","doi-asserted-by":"publisher","award":["V100D-001"],"award-info":[{"award-number":["V100D-001"]}],"id":[{"id":"10.13039\/501100011912","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011912","name":"Taipei Veterans General Hospital","doi-asserted-by":"publisher","award":["V104C-127"],"award-info":[{"award-number":["V104C-127"]}],"id":[{"id":"10.13039\/501100011912","id-type":"DOI","asserted-by":"publisher"}]},{"name":"TVGH-NTUH","award":["VN103-05"],"award-info":[{"award-number":["VN103-05"]}]},{"name":"TVGH-NTUH","award":["VN104-03"],"award-info":[{"award-number":["VN104-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with\/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.<\/jats:p>","DOI":"10.3390\/e19120680","type":"journal-article","created":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T12:26:37Z","timestamp":1512995197000},"page":"680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0268-3920","authenticated-orcid":false,"given":"Intan","family":"Low","sequence":"first","affiliation":[{"name":"Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4020-3147","authenticated-orcid":false,"given":"Po-Chih","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan"}]},{"given":"Yu-Hsiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Brain Science, National Yang-Ming University, Taipei 11221, Taiwan"}]},{"given":"Cheng-Lin","family":"Tsai","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan"}]},{"given":"Hsiang-Tai","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 11217, Taiwan"}]},{"given":"Jen-Chuen","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Institute of Brain Science, National Yang-Ming University, Taipei 11221, Taiwan"},{"name":"Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 11217, Taiwan"}]},{"given":"Li-Fen","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan"},{"name":"Institute of Brain Science, National Yang-Ming University, Taipei 11221, Taiwan"},{"name":"Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 11217, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5581-850X","authenticated-orcid":false,"given":"Yong-Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan"},{"name":"Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"key":"ref_1","unstructured":"Harold Merskey, N.B. (2002). Classification of Chronic Pain: Descriptions of Chronic Pain Syndromes and Definitions of Pain Terms, IASP Press. [2nd ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1097\/j.pain.0000000000000160","article-title":"A classification of chronic pain for ICD-11","volume":"156","author":"Treede","year":"2015","journal-title":"Pain"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Baliki, M.N., Schnitzer, T.J., Bauer, W.R., and Apkarian, A.V. (2011). Brain morphological signatures for chronic pain. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0026010"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Smith, D., Wilkie, R., Uthman, O., Jordan, J.L., and McBeth, J. (2014). Chronic pain and mortality: A systematic review. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0099048"},{"key":"ref_5","first-page":"489","article-title":"Primary dysmenorrhea","volume":"60","author":"Coco","year":"1999","journal-title":"Am. Fam. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1097\/01.AOG.0000230214.26638.0c","article-title":"Primary Dysmenorrhea: Advances in Pathogenesis and Management","volume":"108","author":"Dawood","year":"2006","journal-title":"Obstet. Gynecol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1093\/humupd\/dmv039","article-title":"What we know about primary dysmenorrhea today: A critical review","volume":"21","author":"Iacovides","year":"2015","journal-title":"Hum. Reprod. Updat."},{"key":"ref_8","first-page":"154","article-title":"Dysmenorrhea","volume":"30","author":"Dawood","year":"1985","journal-title":"J. Reprod. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Proctor, M.L., Smith, C.A., Farquhar, C.M., and Stones, R.W. (2002). Transcutaneous electrical nerve stimulation and acupuncture for primary dysmenorrhoea. Cochrane Database Syst. Rev., CD002123.","DOI":"10.1002\/14651858.CD002123"},{"key":"ref_10","unstructured":"IASP Taxonomy Working Group (2017, September 12). Visceral and Other Syndromes of the Trunk Apart from Spinal and Radicular Pain. Classification of Chronic Pain, 2nd Edition (Revised). Available online: https:\/\/www.iasp-pain.org\/files\/Content\/ContentFolders\/Publications2\/ClassificationofChronicPain\/Part_II-F.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, L.-C., Tu, C.-H., Chen, L.-F., Shen, H.-D., Chao, H.-T., Lin, M.-W., and Hsieh, J.-C. (2014). Association of brain-derived neurotrophic factor gene VAL66MET polymorphism with primary dysmenorrhea. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0112766"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.jpag.2014.01.108","article-title":"Is there a relationship between mood disorders and dysmenorrhea?","volume":"27","author":"Sahin","year":"2014","journal-title":"J. Pediatr. Adolesc. Gynecol."},{"key":"ref_13","first-page":"1","article-title":"Changes in functional connectivity of pain modulatory systems in women with primary dysmenorrhea","volume":"157","author":"Wei","year":"2015","journal-title":"Pain"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1586\/ern.12.41","article-title":"Abnormal endogenous pain modulation is a shared characteristic of many chronic pain conditions","volume":"12","author":"Staud","year":"2012","journal-title":"Expert Rev. Neurother."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1002\/ejp.639","article-title":"High prevalence of incidental brain findings in primary dysmenorrhoea","volume":"19","author":"Li","year":"2015","journal-title":"Eur. J. Pain"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1002\/ejp.753","article-title":"Altered regional cortical thickness and subcortical volume in women with primary dysmenorrhoea","volume":"20","author":"Liu","year":"2016","journal-title":"Eur. J. Pain"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.pain.2010.05.026","article-title":"Brain morphological changes associated with cyclic menstrual pain","volume":"150","author":"Tu","year":"2010","journal-title":"Pain"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1016\/j.pain.2013.05.022","article-title":"Menstrual pain is associated with rapid structural alterations in the brain","volume":"154","author":"Tu","year":"2013","journal-title":"Pain"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, P., Liu, Y., Wang, G., Li, R., Wei, Y., Fan, Y., Yu, Y., Deng, D., and Qin, W. (2017). Changes of functional connectivity of the anterior cingulate cortex in women with primary dysmenorrhea. Brain Imaging Behav., 1\u20138.","DOI":"10.1007\/s11682-017-9730-y"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.neuroimage.2009.03.080","article-title":"Abnormal cerebral metabolism during menstrual pain in primary dysmenorrhea","volume":"47","author":"Tu","year":"2009","journal-title":"Neuroimage"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.1016\/j.pain.2011.03.029","article-title":"Dysmenorrhoea is associated with central changes in otherwise healthy women","volume":"152","author":"Vincent","year":"2011","journal-title":"Pain"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"23639","DOI":"10.1038\/srep23639","article-title":"The BDNF Val66Met polymorphism is associated with the functional connectivity dynamics of pain modulatory systems in primary dysmenorrhea","volume":"6","author":"Wei","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39906","DOI":"10.1038\/srep39906","article-title":"The OPRM1 A118G polymorphism modulates the descending pain modulatory system for individual pain experience in young women with primary dysmenorrhea","volume":"7","author":"Wei","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24543","DOI":"10.1038\/srep24543","article-title":"Dynamic changes of functional pain connectome in women with primary dysmenorrhea","volume":"6","author":"Wu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_25","first-page":"27","article-title":"Intensity dependence of auditory evoked potentials in primary dysmenorrhea","volume":"151","author":"Zhang","year":"2017","journal-title":"J. Pain"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroimage.2016.09.040","article-title":"Decoding the perception of endogenous pain from resting-state MEG","volume":"144","author":"Kuo","year":"2017","journal-title":"Neuroimage"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1016\/j.clinph.2005.06.011","article-title":"Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field","volume":"116","author":"Stam","year":"2005","journal-title":"Clin. Neurophysiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2014.00409","article-title":"Network complexity as a measure of information processing across resting-state networks: Evidence from the Human Connectome Project","volume":"8","author":"McDonough","year":"2014","journal-title":"Front. Hum. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5033","DOI":"10.1073\/pnas.91.11.5033","article-title":"A measure for brain complexity: Relating functional segregation and integration in the nervous system","volume":"91","author":"Tononi","year":"1994","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1098\/rstb.2000.0560","article-title":"The labile brain. I. Neuronal transients and nonlinear coupling","volume":"355","author":"Friston","year":"2000","journal-title":"Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1098\/rstb.2000.0561","article-title":"The labile brain. II. Transients, complexity and selection","volume":"355","author":"Friston","year":"2000","journal-title":"Philos. Trans. R. Soc. Lond. B Biol. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.neuroimage.2013.04.055","article-title":"Bottom up modeling of the connectome: Linking structure and function in the resting brain and their changes in aging","volume":"80","author":"Nakagawa","year":"2013","journal-title":"Neuroimage"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.neubiorev.2013.02.015","article-title":"Moment-to-moment brain signal variability: A next frontier in human brain mapping?","volume":"37","author":"Garrett","year":"2013","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart. Circ. Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.pnpbp.2012.09.015","article-title":"Is mental illness complex? From behavior to brain","volume":"45","author":"Yang","year":"2013","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6","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_37","doi-asserted-by":"crossref","first-page":"1","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_38","doi-asserted-by":"crossref","unstructured":"Hu, M., and Liang, H. (2017). Multiscale Entropy: Recent Advances. Complexity and Nonlinearity in Cardiovascular Signals, Springer International Publishing.","DOI":"10.1007\/978-3-319-58709-7_4"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.3390\/e17053110","article-title":"The multiscale entropy algorithm and its variants: A review","volume":"17","year":"2015","journal-title":"Entropy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1142\/S0218348X07003691","article-title":"Multiscale entropy analysis of EEG from patients under different pathological conditions","volume":"15","author":"Park","year":"2007","journal-title":"Fractals"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Heisz, J.J., and McIntosh, A.R. (2013). Applications of EEG neuroimaging data: Event-related potentials, spectral power, and multiscale entropy. J. Vis. Exp., 1\u20138.","DOI":"10.3791\/50131-v"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.pnpbp.2013.07.022","article-title":"Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer\u2019s disease","volume":"47","author":"Yang","year":"2013","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.neuroimage.2010.02.009","article-title":"Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: A multiscale entropy analysis","volume":"51","author":"Takahashi","year":"2010","journal-title":"Neuroimage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1007\/s11517-016-1598-2","article-title":"EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery","volume":"55","author":"Liu","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"978","DOI":"10.3390\/e14060978","article-title":"Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery","volume":"14","author":"Liu","year":"2012","journal-title":"Entropy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1016\/j.clinph.2010.03.025","article-title":"Assessment of EEG dynamical complexity in Alzheimer\u2019s disease using multiscale entropy","volume":"121","author":"Mizuno","year":"2010","journal-title":"Clin. Neurophysiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1098\/rsta.2008.0197","article-title":"Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer\u2019s disease","volume":"367","author":"Hornero","year":"2009","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.nicl.2017.10.035","article-title":"Pre-treatment EEG signal variability is associated with treatment success in depression","volume":"17","author":"Jaworska","year":"2018","journal-title":"NeuroImage Clin."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"23073","DOI":"10.1038\/srep23073","article-title":"On the estimation of brain signal entropy from sparse neuroimaging data","volume":"6","author":"Grandy","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Polizzotto, N.R., Takahashi, T., Walker, C.P., and Cho, R.Y. (2016). Wide range multiscale entropy changes through development. Entropy, 18.","DOI":"10.3390\/e18010012"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1016\/j.clinph.2011.05.004","article-title":"Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis","volume":"122","author":"Catarino","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"McIntosh, A.R., Kovacevic, N., and Itier, R.J. (2008). Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development. PLoS Comput. Biol., 4.","DOI":"10.1371\/journal.pcbi.1000106"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ijpsycho.2010.05.010","article-title":"Linear and nonlinear analyses of EEG dynamics during non-painful somatosensory processing in chronic pain patients","volume":"77","author":"Sitges","year":"2010","journal-title":"Int. J. Psychophysiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.clinph.2008.12.043","article-title":"Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis","volume":"120","author":"Takahashi","year":"2009","journal-title":"Clin. Neurophysiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.bandc.2014.10.006","article-title":"The association of physical activity to neural adaptability during visuo-spatial processing in healthy elderly adults: A multiscale entropy analysis","volume":"92","author":"Wang","year":"2014","journal-title":"Brain Cogn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TBME.2008.919872","article-title":"Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer\u2019s disease","volume":"55","author":"Hornero","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hu, P.-C., Kuo, P.-C., Chen, L.-F., and Chen, Y.-S. (2014). Objective assessment of menstrual pain scale from resting brain signals. Digest of Technical Papers\u2014IEEE International Conference on Consumer Electronics, IEEE.","DOI":"10.1109\/ICCE-TW.2014.6904040"},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1152\/jn.00648.2010","article-title":"Brain noise is task dependent and region specific","volume":"104","author":"Misic","year":"2010","journal-title":"J. Neurophysiol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.neurobiolaging.2012.05.004","article-title":"Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: A multiscale entropy analysis","volume":"34","author":"Yang","year":"2013","journal-title":"Neurobiol. Aging"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1002\/hbm.22763","article-title":"Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness","volume":"36","author":"Yang","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0197-4580(01)00266-4","article-title":"What is physiologic complexity and how does it change with aging and disease?","volume":"23","author":"Goldberger","year":"2002","journal-title":"Neurobiol. Aging"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jad.2016.10.016","article-title":"Neural complexity as a potential translational biomarker for psychosis","volume":"216","author":"Hager","year":"2017","journal-title":"J. Affect. Disord."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.medengphy.2006.11.006","article-title":"Extraction of spectral based measures from MEG background oscillations in Alzheimer\u2019s disease","volume":"29","author":"Poza","year":"2007","journal-title":"Med. Eng. Phys."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1088\/0967-3334\/27\/11\/004","article-title":"Analysis of electroencephalograms in Alzheimer\u2019s disease patients with multiscale entropy","volume":"27","author":"Escudero","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/0304-3959(75)90044-5","article-title":"The McGill Pain Questionnaire: Major properties and scoring methods","volume":"1","author":"Melzack","year":"1975","journal-title":"Pain"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Melzack, R. (1983). The McGill Pain Questionnaire. Pain Measurement and Assessment, Raven Press.","DOI":"10.1037\/t04167-000"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/1477-7525-1-72","article-title":"Cultural issues in using the SF-36 Health Survey in Asia: Results from Taiwan","volume":"1","author":"Tseng","year":"2003","journal-title":"Heal. Qual. Life Outcomes"},{"key":"ref_69","unstructured":"Ware, J.E., Snow, K.K., Kosinski, M., and Gandek, B. (1993). SF-36 Health Survey: Manual and Interpretation Guide, Health Institute, Tufts Medical Center."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1037\/0022-006X.56.5.766","article-title":"Clinical reliabilities and validities of the Basic Personality Inventory","volume":"56","author":"Holden","year":"1988","journal-title":"J. Consult. Clin. Psychol."},{"key":"ref_71","unstructured":"Spielberger, C.D., Gorsuch, R.L., Lushene, R., Vagg, P.R., and Jacobs, G.A. (1983). Manual for the State-Trait Anxiety Inventory (form Y), Consulting Psychologists Press."},{"key":"ref_72","unstructured":"Beck, A.T., Rush, A.J., Shaw, B.F., and Emery, G. (1979). Cognitive Therapy of Depression, Guilford Press."},{"key":"ref_73","unstructured":"Beck, A.T., and Steer, R.A. (1993). Manual for the Beck Anxiety Inventory, Psychological Corporation."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1037\/1040-3590.7.4.524","article-title":"The pain catastrophizing scale: Development and validation","volume":"7","author":"Sullivan","year":"1995","journal-title":"Psychol. Assess."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/BF02534144","article-title":"Signal-space projection method for separating MEG or EEG into components","volume":"35","author":"Uusitalo","year":"1997","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/TBME.2006.878115","article-title":"Maximum contrast beamformer for electromagnetic mapping of brain activity","volume":"53","author":"Chen","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_77","unstructured":"Tikhonov, A.N., and Arsenin, V.Y. (1977). Solution of Ill-Posed Problems, VH Winston."},{"key":"ref_78","first-page":"H1643","article-title":"Physiological time-series analysis: What does regularity quantify?","volume":"266","author":"Pincus","year":"1994","journal-title":"Am. J. Physiol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1093\/cercor\/bht030","article-title":"Spatiotemporal dependency of age-related changes in brain signal variability","volume":"24","author":"McIntosh","year":"2014","journal-title":"Cereb. Cortex"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/TIT.1976.1055501","article-title":"On the Complexity of Finite Sequences","volume":"22","author":"Lempel","year":"1976","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1093\/bja\/77.2.179","article-title":"Spectral edge frequency of the electroencephalogram to monitor \u201cdepth\u201d of anaesthesia with isoflurane or propofol","volume":"77","author":"Schwender","year":"1996","journal-title":"Br. J. Anaesth."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"Landeau","year":"2002","journal-title":"Neuroimage"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1155\/2000\/421719","article-title":"Stereotaxic display of Brain lesions","volume":"12","author":"Rorden","year":"2000","journal-title":"Behav. Neurol."},{"key":"ref_85","unstructured":"Alem\u00e1n-G\u00f3mez, Y., Melie-Garc\u00eda, L., and Vald\u00e9s-Hernandez, P. (2006, January 11\u201315). IBASPM: Toolbox for automatic parcellation of brain structures. Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping, Florence, Italy."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"He, Y., Wang, J., Wang, L., Chen, Z.J., Yan, C., Yang, H., Tang, H., Zhu, C., Gong, Q., and Zang, Y. (2009). Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0005226"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1093\/cercor\/bhq237","article-title":"Anxiety Dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest","volume":"21","author":"Kim","year":"2011","journal-title":"Cereb. Cortex"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1093\/scan\/nsr051","article-title":"The balance between feeling and knowing: Affective and cognitive empathy are reflected in the brain\u2019s intrinsic functional dynamics","volume":"7","author":"Cox","year":"2012","journal-title":"Soc. Cogn. Affect. Neurosci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1196\/annals.1440.011","article-title":"The brain\u2019s default network: Anatomy, function, and relevance to disease","volume":"1124","author":"Buckner","year":"2008","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1152\/jn.00783.2009","article-title":"Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization","volume":"103","author":"VanDijk","year":"2010","journal-title":"J. Neurophysiol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1002\/hbm.20531","article-title":"Functional connectivity of default mode network components: Correlation, anticorrelation, and causality","volume":"30","author":"Uddin","year":"2009","journal-title":"Hum. Brain Mapp."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Raichle, M.E. (2011). The restless brain. Brain Connect., 1.","DOI":"10.1089\/brain.2011.0019"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.tics.2011.11.007","article-title":"Large-scale brain systems in ADHD: Beyond the prefrontal-striatal model","volume":"16","author":"Castellanos","year":"2012","journal-title":"Trends Cogn. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1073\/pnas.0504136102","article-title":"The human brain is intrinsically organized into dynamic, anticorrelated functional networks","volume":"102","author":"Fox","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/hbm.20113","article-title":"Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis","volume":"26","author":"Fransson","year":"2005","journal-title":"Hum. Brain Mapp."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1093\/cercor\/bhn014","article-title":"Functional coactivation map of the human brain","volume":"18","author":"Toro","year":"2008","journal-title":"Cereb. Cortex"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s10072-011-0636-y","article-title":"Resting-state brain networks: Literature review and clinical applications","volume":"32","author":"Rosazza","year":"2011","journal-title":"Neurol. Sci."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Song, X.W., Dong, Z.Y., Long, X.Y., Li, S.F., Zuo, X.N., Zhu, C.Z., He, Y., Yan, C.G., and Zang, Y.F. (2011). REST: A Toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0025031"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Tewarie, P., Schoonheim, M.M., Stam, C.J., van derMeer, M.L., vanDijk, B.W., Barkhof, F., Polman, C.H., and Hillebrand, A. (2013). Cognitive and clinical dysfunction, altered MEG resting-state networks and thalamic atrophy in multiple sclerosis. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0069318"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1073\/pnas.0135058100","article-title":"Functional connectivity in the resting brain: A network analysis of the default mode hypothesis","volume":"100","author":"Greicius","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1038\/nrn.2016.113","article-title":"Mind-wandering as spontaneous thought: A dynamic framework","volume":"17","author":"Christoff","year":"2016","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1523\/JNEUROSCI.5587-06.2007","article-title":"Dissociable intrinsic connectivity networks for salience processing and executive control","volume":"27","author":"Seeley","year":"2007","journal-title":"J. Neurosci."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/1073858409354384","article-title":"The dynamical balance of the brain at rest","volume":"17","author":"Deco","year":"2011","journal-title":"Neuroscientist"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.neuroimage.2010.06.016","article-title":"Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition","volume":"53","author":"Spreng","year":"2010","journal-title":"Neuroimage"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3758\/s13415-011-0083-5","article-title":"Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions","volume":"12","author":"Niendam","year":"2012","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref_106","first-page":"150","article-title":"Dorsal and ventral attention systems","volume":"20","author":"Vossel","year":"2014","journal-title":"Neuroscience"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neuron.2008.04.017","article-title":"The reorienting system of the human brain: From environment to theory of mind","volume":"58","author":"Corbetta","year":"2008","journal-title":"Neuron"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1016\/j.neuroimage.2005.08.035","article-title":"fMRI resting state networks define distinct modes of long-distance interactions in the human brain","volume":"29","author":"DeLuca","year":"2006","journal-title":"Neuroimage"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.neuron.2013.09.038","article-title":"Intrinsic coupling modes: Multiscale interactions in ongoing brain activity","volume":"80","author":"Engel","year":"2013","journal-title":"Neuron"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"486","DOI":"10.5812\/ijem.3505","article-title":"Normality tests for statistical analysis: A guide for non-statisticians","volume":"10","author":"Ghasemi","year":"2012","journal-title":"Int. J. Endocrinol. Metab."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.jneumeth.2007.03.024","article-title":"Nonparametric statistical testing of EEG- and MEG-data","volume":"164","author":"Maris","year":"2007","journal-title":"J. Neurosci. Methods"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Xia, M., Wang, J., and He, Y. (2013). BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0068910"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2147\/NAN.S73471","article-title":"Measuring entropy in functional neuroscience: Pathophysiological and clinical applications","volume":"5","author":"Chung","year":"2016","journal-title":"Neurosci. Neuroecon."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.1109\/JSEN.2013.2271735","article-title":"Entropic measures of EEG complexity in alzheimer\u2019s disease through a multivariate multiscale approach","volume":"13","author":"Labate","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/978-3-319-04129-2_17","article-title":"EEG complexity modifications and altered compressibility in mild cognitive impairment and Alzheimer\u2019s Disease","volume":"Volume 26","author":"Labate","year":"2014","journal-title":"Smart Innovation, Systems and Technologies"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1177\/107385840100700510","article-title":"Brain function, nonlinear coupling, and neuronal transients","volume":"7","author":"Friston","year":"2001","journal-title":"Neuroscientist"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1007\/s00429-015-1161-1","article-title":"Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms","volume":"221","author":"Hemington","year":"2016","journal-title":"Brain Struct. Funct."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Baliki, M.N., Mansour, A.R., Baria, A.T., and Apkarian, A.V. (2014). Functional reorganization of the default mode network across chronic pain conditions. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0106133"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.yhbeh.2007.11.007","article-title":"The resting frontal alpha asymmetry across the menstrual cycle: A magnetoencephalographic study","volume":"54","author":"Hwang","year":"2008","journal-title":"Horm. Behav."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.yhbeh.2008.10.008","article-title":"Female menstrual phases modulate human prefrontal asymmetry: A magnetoencephalographic study","volume":"55","author":"Hwang","year":"2009","journal-title":"Horm. Behav."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1093\/brain\/aws371","article-title":"The stress model of chronic pain: Evidence from basal cortisol and hippocampal structure and function in humans","volume":"136","author":"Roy","year":"2013","journal-title":"Brain"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.berh.2011.02.005","article-title":"Central pain mechanisms in chronic pain states\u2013maybe it is all in their head","volume":"25","author":"Phillips","year":"2011","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/nrn.2016.162","article-title":"Structural plasticity and reorganisation in chronic pain","volume":"18","author":"Kuner","year":"2016","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.aat.2013.06.011","article-title":"Thalamus and pain","volume":"51","author":"Yen","year":"2013","journal-title":"Acta Anaesthesiol. Taiwanica"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1126\/science.277.5328.968","article-title":"Pain affect encoded in human anterior cingulate but not somatosensory cortex","volume":"277","author":"Rainville","year":"1997","journal-title":"Science"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1038\/nrn3516","article-title":"Cognitive and emotional control of pain and its disruption in chronic pain","volume":"14","author":"Bushnell","year":"2013","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_127","first-page":"1","article-title":"EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks","volume":"2015","author":"Liu","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Valencia, J.F., Melia, U.S.P., Vallverd\u00fa, M., Borrat, X., Jospin, M., Jensen, E.W., Porta, A., Gamb\u00fas, P.L., and Caminal, P. (2016). Assessment of nociceptive responsiveness levels during sedation-analgesia by entropy analysis of EEG. Entropy, 18.","DOI":"10.3390\/e18030103"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jneumeth.2016.09.004","article-title":"The multiscale entropy: Guidelines for use and interpretation in brain signal analysis","volume":"273","author":"Courtiol","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Ahmed, M.U., Li, L., Cao, J., and Mandic, D.P. (2011, January 6\u201318). Multivariate multiscale entropy for brain consciousness analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6090185"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Dos Santos Pinheiro, E.S., de Queir\u00f3s, F.C., Montoya, P., Santos, C.L., do Nascimento, M.A., Ito, C.H., Silva, M., Nunes Santos, D.B., Benevides, S., and Miranda, J.G.V. (2016). Electroencephalographic patterns in chronic pain: A systematic review of the literature. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0149085"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.tics.2016.12.001","article-title":"Brain Rhythms of Pain","volume":"21","author":"Ploner","year":"2016","journal-title":"Trends Cogn. Sci."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1109\/TBME.2002.804582","article-title":"Quantifying physiological data with Lempel-Ziv complexity\u2014Certain issues","volume":"49","author":"Nagarajan","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1109\/18.705554","article-title":"On the universality of the LZ-based decoding algorithm","volume":"44","author":"Lapidoth","year":"1998","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/10.966601","article-title":"EEG complexity as a measure of depth of anesthesia for patients","volume":"48","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/19\/12\/680\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:53:33Z","timestamp":1760208813000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/19\/12\/680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,11]]},"references-count":135,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["e19120680"],"URL":"https:\/\/doi.org\/10.3390\/e19120680","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,11]]}}}