{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:30:04Z","timestamp":1792510204859,"version":"4.5.0"},"reference-count":71,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T00:00:00Z","timestamp":1585267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"DOI":"10.3389\/fnins.2020.00191","type":"journal-article","created":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T09:34:06Z","timestamp":1585301646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":75,"title":["Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Baoyu","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengwan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaizhong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baojuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2020,3,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1111\/a.12724","article-title":"Insula and amygdala resting-state functional connectivity differentiate bipolar from unipolar depression.","volume":"136","author":"Ambrosi","year":"2017","journal-title":"Acta Psychiatr. Scand."},{"key":"B2","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.nicl.2016.02.018","article-title":"Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity.","volume":"16","author":"Bhaumik","year":"2016","journal-title":"Neuroimage Clin."},{"key":"B3","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.nicl.2016.02.018","article-title":"Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity.","volume":"16","author":"Bhaumik","year":"2017","journal-title":"Neuroimage Clin."},{"key":"B4","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.jad.2015.11.041","article-title":"Dynamic functional-structural coupling within acute functional state change phases: evidence from a depression recognition study.","volume":"191","author":"Bi","year":"2016","journal-title":"J. Affect. Disord."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1016\/j.neuroimage.2010.02.052","article-title":"BOLD correlates of EEG topography reveal rapid resting-state network dynamics.","volume":"52","author":"Britz","year":"2010","journal-title":"Neuroimage"},{"key":"B6","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.neuron.2014.10.015","article-title":"The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery.","volume":"84","author":"Calhoun","year":"2014","journal-title":"Neuron"},{"key":"B7","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1111\/pcn.12106","article-title":"Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis.","volume":"68","author":"Cao","year":"2014","journal-title":"Psychiatry Clin. Neurosci."},{"key":"B8","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/j.neuroimage.2017.09.065","article-title":"Dynamic functional connectivity and brain metastability during altered states of consciousness.","volume":"180","author":"Cavanna","year":"2017","journal-title":"Neuroimage"},{"key":"B9","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neuroimage.2009.12.011","article-title":"Time-frequency dynamics of resting-state brain connectivity measured with fMRI.","volume":"50","author":"Chang","year":"2010","journal-title":"Neuroimage"},{"key":"B10","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.neuroimage.2013.01.049","article-title":"EEG correlates of time-varying BOLD functional connectivity.","volume":"72","author":"Chang","year":"2013","journal-title":"Neuroimage"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: a library for support vector machines.","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"B12","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1002\/ajmg.b.32633","article-title":"Voxel-wise brain-wide functional connectivity abnormalities in first-episode, drug-naive patients with major depressive disorder.","volume":"177","author":"Cui","year":"2018","journal-title":"Am. J. Med. Genet."},{"key":"B13","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1002\/hbm.23215","article-title":"Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder.","volume":"37","author":"Demirtas","year":"2016","journal-title":"Hum. Brain Mapp."},{"key":"B14","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1038\/nrn3027","article-title":"Neural mechanisms of the cognitive model of depression.","volume":"12","author":"Disner","year":"2011","journal-title":"Nat. Rev. Neurosci."},{"key":"B15","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1016\/j.neuroimage.2017.10.022","article-title":"Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis.","volume":"180","author":"Du","year":"2018","journal-title":"Neuroimage"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101719","article-title":"Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation.","volume":"22","author":"Fan","year":"2019","journal-title":"Neuroimage Clin."},{"key":"B17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-1-907673-79-5_2","article-title":"Depression in different types of patients","author":"Friedman","year":"2014","journal-title":"Handbook of Depression."},{"key":"B18","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neuroimage.2018.06.003","article-title":"Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism.","volume":"190","author":"Fu","year":"2019","journal-title":"NeuroImage"},{"key":"B19","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jocn.2019.02.007","article-title":"Is occipital bending a structural biomarker of risk for depression and sensitivity to treatment?","volume":"63","author":"Fullard","year":"2019","journal-title":"J. Clin. Neurosci."},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00038","article-title":"Multivariate classification of major depressive disorder using the effective connectivity and functional connectivity.","volume":"12","author":"Geng","year":"2018","journal-title":"Front. Neurosci."},{"key":"B21","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.biopsych.2014.08.009","article-title":"Depression, neuroimaging and connectomics: a selective overview.","volume":"77","author":"Gong","year":"2015","journal-title":"Biol. Psychiatry"},{"key":"B22","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1002\/hbm.24812","article-title":"Altered inter- and intrahemispheric functional connectivity dynamics in autistic children.","volume":"41","author":"Guo","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"B23","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.biopsych.2015.02.020","article-title":"Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience.","volume":"78","author":"Hamilton","year":"2015","journal-title":"Biol. Psychiatry"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1002\/aur.2020","article-title":"Dynamic functional connectivity analysis reveals decreased variability of the default-mode network in developing autistic brain.","volume":"11","author":"He","year":"2018","journal-title":"Autism Res."},{"key":"B25","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1093\/cercor\/bhx335","article-title":"Reconfiguration of cortical networks in MDD uncovered by multiscale community detection with fMRI.","volume":"28","author":"He","year":"2018","journal-title":"Cereb. Cortex"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2018.00508","article-title":"Within-network connectivity in the salience network after attention bias modification training in residual depression: report from a preregistered clinical trial.","volume":"12","author":"Hilland","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"B27","doi-asserted-by":"publisher","first-page":"9283","DOI":"10.1073\/pnas.1408296111","article-title":"Recovery of consciousness is mediated by a network of discrete metastable activity states.","volume":"111","author":"Hudson","year":"2014","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"B28","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.neuroimage.2013.05.079","article-title":"Dynamic functional connectivity: promise, issues, and interpretations.","volume":"80","author":"Hutchison","year":"","journal-title":"Neuroimage"},{"key":"B29","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1002\/hbm.22058","article-title":"Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques.","volume":"34","author":"Hutchison","year":"","journal-title":"Hum. Brain Mapp."},{"key":"B30","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.media.2018.03.013","article-title":"Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.","volume":"47","author":"Jie","year":"2018","journal-title":"Med. Image Anal."},{"key":"B31","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1001\/jamapsychiatry.2015.0071","article-title":"Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity.","volume":"72","author":"Kaiser","year":"2015","journal-title":"JAMA Psychiatry"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1038\/npp.2015.352","article-title":"Dynamic resting-state functional connectivity in major depression.","volume":"41","author":"Kaiser","year":"2016","journal-title":"Neuropsychopharmacology"},{"key":"B33","doi-asserted-by":"publisher","first-page":"2955","DOI":"10.1093\/brain\/awx233","article-title":"Abnormal intrinsic brain functional network dynamics in Parkinson\u2019s disease.","volume":"140","author":"Kim","year":"2017","journal-title":"Brain"},{"key":"B34","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.jad.2012.03.024","article-title":"Why ruminators won\u2019t stop: the structural and resting state correlates of rumination and its relation to depression.","volume":"141","author":"K\u00fchn","year":"2012","journal-title":"J. Affect. Disord."},{"key":"B35","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.neuroimage.2018.09.028","article-title":"Normative pathways in the functional connectome.","volume":"184","author":"Leming","year":"2019","journal-title":"NeuroImage"},{"key":"B36","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1111\/acps.12752","article-title":"Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression.","volume":"136","author":"Li","year":"2017","journal-title":"Acta Psychiatr. Scand."},{"key":"B37","doi-asserted-by":"publisher","first-page":"4105","DOI":"10.1002\/hbm.24235","article-title":"Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation.","volume":"39","author":"Liao","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"B38","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1002\/hbm.23430","article-title":"Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure.","volume":"38","author":"Liu","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"B39","doi-asserted-by":"crossref","first-page":"133","DOI":"10.31887\/DCNS.2018.20.2\/smarek","article-title":"The frontoparietal network: function, electrophysiology, and importance of individual precision mapping.","volume":"20","author":"Marek","year":"2018","journal-title":"Dialogues Clin. Neurosci."},{"key":"B40","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1016\/j.neuroimage.2019.02.001","article-title":"Sliding window correlation analysis: modulating window shape for dynamic brain connectivity in resting state.","volume":"189","author":"Mokhtari","year":"2019","journal-title":"Neuroimage"},{"key":"B41","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.neubiorev.2015.07.014","article-title":"Resting-state functional connectivity in major depressive disorder: a review.","volume":"56","author":"Mulders","year":"2015","journal-title":"Neurosci. Biobehav. Rev."},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.65","article-title":"Major depressive disorder.","volume":"2","author":"Otte","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"B43","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.pnpbp.2018.03.020","article-title":"Transdiagnostic and diagnosis-specific dynamic functional connectivity anchored in the right anterior insula in major depressive disorder and bipolar depression.","volume":"85","author":"Pang","year":"2018","journal-title":"Prog. Neuropsychopharmacol. Biol. Psychiatry"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.664","article-title":"Relation between patterns of intrinsic network connectivity, cognitive functioning, and symptom presentation in trauma-exposed patients with major depressive disorder.","volume":"7","author":"Parlar","year":"2017","journal-title":"Brain Behav."},{"key":"B45","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.neuroimage.2016.02.074","article-title":"Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states.","volume":"133","author":"Shakil","year":"2016","journal-title":"NeuroImage"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1002\/hbm.23976","article-title":"Spatiotemporal, metabolic, and therapeutic characterization of altered functional connectivity in major depressive disorder.","volume":"39","author":"Sheng","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"B47","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neuron.2016.09.018","article-title":"The dynamics of functional brain networks: integrated network states during cognitive task performance.","volume":"92","author":"Shine","year":"2016","journal-title":"Neuron"},{"key":"B48","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1111\/j.1749-6632.2010.05888.x","article-title":"The human connectome: a complex network.","volume":"1224","author":"Sporns","year":"2011","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"B49","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.pnpbp.2018.08.008","article-title":"Cortical thickness in major depressive disorder: a systematic review and meta-analysis.","volume":"88","author":"Suh","year":"2019","journal-title":"Prog. Neuropsychopharmacol. Biol. Psychiatry"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00827","article-title":"TPH-2 gene polymorphism in major depressive disorder patients with early-wakening symptom.","volume":"12","author":"Tao","year":"2018","journal-title":"Front. Neurosci."},{"key":"B51","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1002\/hbm.22140","article-title":"Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually.","volume":"34","author":"Thompson","year":"2013","journal-title":"Hum. Brain Mapp."},{"key":"B52","doi-asserted-by":"publisher","first-page":"e2706","DOI":"10.1212\/WNL.0000000000007607","article-title":"Abnormal thalamocortical network dynamics in migraine.","volume":"92","author":"Tu","year":"2019","journal-title":"Neurology"},{"key":"B53","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1017\/s003329171900028x","article-title":"Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model.","volume":"50","author":"Wang","year":"2019","journal-title":"Psychol Med."},{"key":"B54","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.bbr.2012.02.011","article-title":"Abnormal default-mode network in angiotensin converting enzyme D allele carriers with remitted geriatric depression.","volume":"230","author":"Wang","year":"2012","journal-title":"Behav. Brain Res."},{"key":"B55","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/S0140-6736(12)61552-9","article-title":"Cognitive behavioural therapy as an adjunct to pharmacotherapy for primary care based patients with treatment resistant depression: results of the CoBalT randomised controlled trial.","volume":"381","author":"Wiles","year":"2013","journal-title":"Lancet"},{"key":"B56","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.jad.2012.11.019","article-title":"Abnormal functional connectivity of the default mode network in remitted late-onset depression.","volume":"147","author":"Wu","year":"2013","journal-title":"J. Affect. Disord."},{"key":"B57","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.pscychresns.2017.03.012","article-title":"Functional network connectivity alterations in schizophrenia and depression.","volume":"263","author":"Wu","year":"2017","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"B58","doi-asserted-by":"publisher","first-page":"4392","DOI":"10.1073\/pnas.1216856110","article-title":"Time-varying functional network information extracted from brief instances of spontaneous brain activity.","volume":"110","author":"Xiao","year":"2013","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"B59","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1002\/jmri.26749","article-title":"A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors.","volume":"50","author":"Xu","year":"","journal-title":"J. Magn. Reson. Imaging."},{"key":"B60","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1002\/jmri.26327","article-title":"Quantitative identification of non-muscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis.","volume":"49","author":"Xu","year":"","journal-title":"J. Magn. Reson. Imaging"},{"key":"B61","doi-asserted-by":"publisher","first-page":"9078","DOI":"10.1073\/pnas.1900390116","article-title":"Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.","volume":"116","author":"Yan","year":"2019","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"B62","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s12021-016-9299-4","article-title":"DPABI: data processing & analysis for (resting-state) brain imaging.","volume":"14","author":"Yan","year":"2016","journal-title":"Neuroinformatics"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2019.00052","article-title":"Common and specific functional activity features in schizophrenia, major depressive disorder, and bipolar disorder.","volume":"10","author":"Yang","year":"2019","journal-title":"Front. psychiatry"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0179638","article-title":"Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression.","volume":"12","author":"Yoshida","year":"2017","journal-title":"PLoS One"},{"key":"B65","doi-asserted-by":"publisher","first-page":"8582","DOI":"10.1073\/pnas.1900801116","article-title":"Childhood trauma history is linked to abnormal brain connectivity in major depression.","volume":"116","author":"Yu","year":"2019","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"B66","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1093\/brain\/aws059","article-title":"Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis.","volume":"135","author":"Zeng","year":"2012","journal-title":"Brain"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00197","article-title":"Diagnosis of brain diseases via multi-scale time-series model.","volume":"13","author":"Zhang","year":"2019","journal-title":"Front. Neurosci."},{"key":"B68","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jad.2019.05.067","article-title":"Treatment-na\u00efve first episode depression classification based on high-order brain functional network.","volume":"256","author":"Zheng","year":"2019","journal-title":"J. Affect. Disord."},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2018.00339","article-title":"Aberrant dynamic functional network connectivity and graph properties in major depressive disorder.","volume":"9","author":"Zhi","year":"2018","journal-title":"Front. Psychiatry"},{"key":"B70","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.jad.2017.04.040","article-title":"Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples.","volume":"218","author":"Zhong","year":"2017","journal-title":"J. Affect. Disord."},{"key":"B71","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jad.2015.12.081","article-title":"Abnormal functional connectivity density in first-episode, drug-naive adult patients with major depressive disorder.","volume":"194","author":"Zou","year":"2016","journal-title":"J. Affect. Disord."}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/article\/10.3389\/fnins.2020.00191\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T07:15:39Z","timestamp":1615014939000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/article\/10.3389\/fnins.2020.00191\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,27]]},"references-count":71,"alternative-id":["10.3389\/fnins.2020.00191"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2020.00191","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,27]]},"article-number":"191"}}