{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:31:15Z","timestamp":1772137875715,"version":"3.50.1"},"reference-count":50,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"the Scientific and Technological Innovation 2030","award":["2021ZD0204300"],"award-info":[{"award-number":["2021ZD0204300"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2025,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Understanding the neural mechanisms underlying consciousness during anesthesia is critical for advancing anesthesiology and neuroscience. However, given the high variability in individual sensitivity to anesthetic agents, accurately elucidating the relationship between individual characteristics and drug responses is also crucial for ensuring clinical anesthesia safety.\n                    <jats:italic>Approach.<\/jats:italic>\n                    This study utilized high-density EEG data from 20 participants under various propofol-induced sedation states. We stratified participants into low- and high-sensitivity cohorts based on their behavioral responsiveness to standardized auditory stimuli during sedation. Then the metrics such as permutation entropy (PE), phase-lag entropy (PLE), and permutation cross mutual information (PCMI) were analyzed to evaluate neural complexity, the diversity of connectivity, and information integration. Machine learning models, including support vector machines (SVM), were applied to classify individual sensitivity to propofol, with SHapley Additive exPlanations (SHAP) analysis providing feature interpretability.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Subjects were divided into high-performance (low-sensitivity) group and low-performance (high-sensitivity) group based on the accuracy of their responses to auditory stimuli. In the moderate sedation, the high-performance group exhibited elevated PE, increased PLE in alpha band and the decreased PLE in beta band, and decreased PCMI in alpha band. In the resting-state, we extracted 18 metrics that were significantly different between the two groups. Using these resting-state metrics as features, the SVM model achieved an accuracy of 87.5% \u00b1 0.06% in classifying individuals into high- or low-sensitivity groups. SHAP analysis results indicated that the features, including the PLE value of temporal in alpha band (\n                    <jats:italic>\u03b1<\/jats:italic>\n                    -PLET) and the PCMI value of frontal\u2013parietal in beta band (\n                    <jats:italic>\u03b2<\/jats:italic>\n                    -PCMIFP), were identified as robust predictors of propofol sensitivity, with high weights across various models.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This study highlights the differential neural dynamics induced by propofol across performance groups. This study highlights that resting-state metrics can predict individual sensitivity to propofol. Our findings provide preliminary insights into the potential utility of pre-anesthesia brain state assessments in predicting individual propofol sensitivity, which may contribute to the development of more precise personalized anesthesia plans.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/add0e6","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:51:34Z","timestamp":1745607094000},"page":"036004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating individual sensitivity to propofol through EEG complexity and information integration: from neural dynamics to precision anesthesia"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4509-9457","authenticated-orcid":true,"given":"Xing","family":"Jin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2467-9877","authenticated-orcid":true,"given":"Zhenhu","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-0308","authenticated-orcid":true,"given":"Fu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1359-5130","authenticated-orcid":true,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"jneadd0e6bib1","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s12264-021-00633-8","article-title":"Propofol-induced anesthesia alters corticocortical functional connectivity in the human brain: an EEG source space analysis","volume":"37","author":"Zhao","year":"2021","journal-title":"Neurosci. Bull."},{"key":"jneadd0e6bib2","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1007\/s11571-021-09775-x","article-title":"Selective corticocortical connectivity suppression during propofol-induced anesthesia in healthy volunteers","volume":"16","author":"Wang","year":"2022","journal-title":"Cogn. Neurodynamics"},{"key":"jneadd0e6bib3","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1109\/JBHI.2023.3321099","article-title":"Towards real-world applications of personalized anesthesia using policy constraint Q learning for propofol infusion control","volume":"28","author":"Cai","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadd0e6bib4","doi-asserted-by":"publisher","first-page":"6067","DOI":"10.3758\/s13428-023-02335-7","article-title":"SHAP value-based ERP analysis (SHERPA): increasing the sensitivity of EEG signals with explainable AI methods","volume":"56","author":"Sylvester","year":"2024","journal-title":"Behav. Res. Methods"},{"key":"jneadd0e6bib5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/0013-4694(50)90014-9","article-title":"Automatic electroencephalographic control of general anesthesia","volume":"2","author":"Bickford","year":"1950","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"jneadd0e6bib6","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1097\/00000542-195109000-00004","article-title":"The clinical application of automatic anesthesia","volume":"12","author":"De","year":"1951","journal-title":"Anesthesiology"},{"key":"jneadd0e6bib7","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1093\/bja\/62.3.290","article-title":"Closed-loop feedback control of propofol anaesthesia by quantitative EEG analysis in humans","volume":"62","author":"Schwilden","year":"1989","journal-title":"Br. J. Anaesth."},{"key":"jneadd0e6bib8","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s12630-010-9335-z","article-title":"A randomized controlled trial demonstrates that a novel closed-loop propofol system performs better hypnosis control than manual administration","volume":"57","author":"Hemmerling","year":"2010","journal-title":"Can. J. Anaesth."},{"key":"jneadd0e6bib9","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1093\/bja\/ael120","article-title":"Depth of anaesthesia monitoring: what\u2019s available, what\u2019s validated and what\u2019s next?","volume":"97","author":"Bruhn","year":"2006","journal-title":"Br. J. Anaesth."},{"key":"jneadd0e6bib10","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1213\/ANE.0b013e3182973687","article-title":"Closed-loop control of anesthesia: a primer for anesthesiologists","volume":"117","author":"Dumont","year":"2013","journal-title":"Anesth. Analg."},{"key":"jneadd0e6bib11","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.conb.2017.04.011","article-title":"Neural oscillations demonstrate that general anesthesia and sedative states are neurophysiologically distinct from sleep","volume":"44","author":"Akeju","year":"2017","journal-title":"Curr. Opin. Neurobiol."},{"key":"jneadd0e6bib12","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1097\/ALN.0000000000000841","article-title":"Clinical electroencephalography for anesthesiologists: part I: background and basic signatures","volume":"123","author":"Purdon","year":"2015","journal-title":"Anesthesiology"},{"key":"jneadd0e6bib13","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2023.1173304","article-title":"Permutation entropy is not an age-independent parameter for EEG-based anesthesia monitoring","volume":"15","author":"Hight","year":"2023","journal-title":"Front. Aging Neurosci."},{"key":"jneadd0e6bib14","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/7\/4\/046010","article-title":"Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia","volume":"7","author":"Li","year":"2010","journal-title":"J. Neural Eng."},{"key":"jneadd0e6bib15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s12871-020-00964-5","article-title":"Monitoring of anesthetic depth and EEG band power using phase lag entropy during propofol anesthesia","volume":"20","author":"Shin","year":"2020","journal-title":"BMC Anesthesiol."},{"key":"jneadd0e6bib16","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s10877-015-9738-z","article-title":"A comparison of different synchronization measures in electroencephalogram during propofol anesthesia","volume":"30","author":"Liang","year":"2016","journal-title":"J. Clin. Monit. Comput."},{"key":"jneadd0e6bib17","doi-asserted-by":"publisher","first-page":"4980","DOI":"10.1002\/hbm.23708","article-title":"Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness","volume":"38","author":"Lee","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"jneadd0e6bib18","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1097\/ALN.0000000000003015","article-title":"Information integration and mesoscopic cortical connectivity during propofol anesthesia","volume":"132","author":"Liang","year":"2020","journal-title":"Anesthesiology"},{"key":"jneadd0e6bib19","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1038\/nrn.2016.44","article-title":"Integrated information theory: from consciousness to its physical substrate","volume":"17","author":"Tononi","year":"2016","journal-title":"Nat. Rev. Neurosci."},{"key":"jneadd0e6bib20","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.neuron.2011.03.018","article-title":"Experimental and theoretical approaches to conscious processing","volume":"70","author":"Dehaene","year":"2011","journal-title":"Neuron"},{"key":"jneadd0e6bib21","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s12264-024-01297-w","article-title":"Accurate machine learning-based monitoring of anesthesia depth with EEG recording","volume":"41","author":"Tu","year":"2025","journal-title":"Neurosci. Bull."},{"key":"jneadd0e6bib22","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40708-024-00241-y","article-title":"Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features","volume":"11","author":"Li","year":"2024","journal-title":"Brain Inform."},{"key":"jneadd0e6bib23","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on explainable artificial intelligence (XAI): toward medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"jneadd0e6bib24","doi-asserted-by":"publisher","first-page":"306","DOI":"10.3390\/brainsci14040306","article-title":"Evaluation of the relation between ictal EEG features and XAI explanations","volume":"14","author":"Sanchez-Hernandez","year":"2024","journal-title":"Brain Sci."},{"key":"jneadd0e6bib25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004669","article-title":"Brain connectivity dissociates responsiveness from drug exposure during propofol-induced transitions of consciousness","volume":"12","author":"Chennu","year":"2016","journal-title":"PLoS Comput. Biol."},{"key":"jneadd0e6bib26","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1038\/s41598-024-82089-0","article-title":"Stationarity assessment of resting state condition via permutation entropy on EEG recordings","volume":"15","author":"Perinelli","year":"2025","journal-title":"Sci. Rep."},{"key":"jneadd0e6bib27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10877-024-01258-8","article-title":"Entropy of difference works similarly to permutation entropy for the assessment of anesthesia and sleep EEG despite the lower computational effort","volume":"38","author":"Edthofer","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"key":"jneadd0e6bib28","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.clinph.2014.05.012","article-title":"Tracking the coupling of two electroencephalogram series in the isoflurane and remifentanil anesthesia","volume":"126","author":"Liang","year":"2015","journal-title":"Clin. Neurophysiol."},{"key":"jneadd0e6bib29","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad618b","article-title":"Permutation conditional mutual information to quantify TMS-evoked cortical connectivity in disorders of consciousness","volume":"21","author":"Zhao","year":"2024","journal-title":"J. Neural Eng."},{"key":"jneadd0e6bib30","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/bioengineering12010025","article-title":"Spatial cognitive EEG feature extraction and classification based on MSSECNN and PCMI","volume":"12","author":"Wan","year":"2024","journal-title":"Bioengineering"},{"key":"jneadd0e6bib31","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/TBME.2019.2920983","article-title":"Direct interaction on specific frequency bands in functional corticomuscular coupling","volume":"67","author":"Xie","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"jneadd0e6bib32","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TBME.2019.2912840","article-title":"Neuromodulation effects of ultrasound stimulation under different parameters on mouse motor cortex","volume":"67","author":"Wang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"jneadd0e6bib33","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1213\/ANE.0000000000002864","article-title":"Correlation coefficients: appropriate use and interpretation","volume":"126","author":"Schober","year":"2018","journal-title":"Anesth. Analg."},{"key":"jneadd0e6bib34","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2207831120","article-title":"Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness","volume":"120","author":"Weiner","year":"2023","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"jneadd0e6bib35","doi-asserted-by":"publisher","first-page":"E1142","DOI":"10.1073\/pnas.1221180110","article-title":"Electroencephalogram signatures of loss and recovery of consciousness from propofol","volume":"110","author":"Purdon","year":"2013","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"jneadd0e6bib36","doi-asserted-by":"publisher","first-page":"E3377","DOI":"10.1073\/pnas.1210907109","article-title":"Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness","volume":"109","author":"Lewis","year":"2012","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"jneadd0e6bib37","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1016\/j.cub.2011.10.017","article-title":"Cortical hypersynchrony predicts breakdown of sensory processing during loss of consciousness","volume":"21","author":"Supp","year":"2011","journal-title":"Curr. Biol."},{"key":"jneadd0e6bib38","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.conb.2010.02.015","article-title":"Beta-band oscillations\u2013signalling the status quo?","volume":"20","author":"Engel","year":"2010","journal-title":"Curr. Opin. Neurobiol."},{"key":"jneadd0e6bib39","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1089\/brain.2012.0107","article-title":"General anesthesia and human brain connectivity","volume":"2","author":"Hudetz","year":"2012","journal-title":"Brain Connect."},{"key":"jneadd0e6bib40","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.neuroimage.2011.04.020","article-title":"Brain functional integration decreases during propofol-induced loss of consciousness","volume":"57","author":"Schrouff","year":"2011","journal-title":"Neuroimage"},{"key":"jneadd0e6bib41","doi-asserted-by":"publisher","first-page":"14529","DOI":"10.1073\/pnas.95.24.14529","article-title":"A neuronal model of a global workspace in effortful cognitive tasks","volume":"95","author":"Dehaene","year":"1998","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"jneadd0e6bib42","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.tins.2007.02.001","article-title":"New vistas for alpha-frequency band oscillations","volume":"30","author":"Palva","year":"2007","journal-title":"Trends Neurosci."},{"key":"jneadd0e6bib43","doi-asserted-by":"publisher","first-page":"7619","DOI":"10.1523\/JNEUROSCI.1443-09.2009","article-title":"Efficiency of functional brain networks and intellectual performance","volume":"29","author":"van den Heuvel","year":"2009","journal-title":"J. Neurosci."},{"key":"jneadd0e6bib44","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1056\/NEJMra0808281","article-title":"General anesthesia, sleep, and coma","volume":"363","author":"Brown","year":"2010","journal-title":"New Engl. J. Med."},{"key":"jneadd0e6bib45","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1097\/ALN.0b013e31829103f5","article-title":"Disruption of frontal-parietal communication by ketamine, propofol, and sevoflurane","volume":"118","author":"Lee","year":"2013","journal-title":"Anesthesiology"},{"key":"jneadd0e6bib46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2025.121084","article-title":"Spatiospectral dynamics of electroencephalography patterns during propofol-induced alterations of consciousness states","volume":"309","author":"Li","year":"2025","journal-title":"Neuroimage"},{"key":"jneadd0e6bib47","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fneur.2014.00023","article-title":"Temporal lobe epilepsy and surgery selectively alter the dorsal, not the ventral, default-mode network","volume":"5","author":"Doucet","year":"2014","journal-title":"Front. Neurol."},{"key":"jneadd0e6bib48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neubiorev.2017.04.015","article-title":"Self-awareness and the medial temporal lobe in neurodegenerative diseases","volume":"78","author":"Chavoix","year":"2017","journal-title":"Neurosci. Biobehav. Rev."},{"key":"jneadd0e6bib49","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.nicl.2014.05.008","article-title":"Linking DMN connectivity to episodic memory capacity: what can we learn from patients with medial temporal lobe damage?","volume":"5","author":"McCormick","year":"2014","journal-title":"Neuroimage Clin."},{"key":"jneadd0e6bib50","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1016\/j.neuron.2020.01.026","article-title":"Conscious processing and the global neuronal workspace hypothesis","volume":"105","author":"Mashour","year":"2020","journal-title":"Neuron"}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T06:46:26Z","timestamp":1746513986000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/add0e6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":50,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,5,6]]},"published-print":{"date-parts":[[2025,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/add0e6","relation":{"has-review":[{"id-type":"doi","id":"10.1088\/1741-2552\/ADD0E6\/v2\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/1741-2552\/ADD0E6\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/1741-2552\/ADD0E6\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/1741-2552\/ADD0E6\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/1741-2552\/ADD0E6\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/1741-2552\/ADD0E6\/v2\/decision1","asserted-by":"object"}]},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,6]]},"assertion":[{"value":"Evaluating individual sensitivity to propofol through EEG complexity and information integration: from neural dynamics to precision anesthesia","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-01-28","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-04-25","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-06","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}