{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T13:56:18Z","timestamp":1794491778266,"version":"4.9.1"},"reference-count":89,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"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."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.01, specificity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001), graph theory (sensitivity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.05, specificity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.01), <jats:italic>n<\/jats:italic>\u2009&amp;gt;\u2009100 (sensitivity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001, specificity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001), simens equipment (sensitivity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.01, specificity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001), 3.0T field strength (Sensitivity: <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001, specificity: <jats:italic>p<\/jats:italic>\u2009=\u20090.04), and Beck Depression Inventory (BDI) (sensitivity: <jats:italic>p<\/jats:italic>\u2009=\u20090.04, specificity: <jats:italic>p<\/jats:italic>\u2009=\u20090.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (<jats:italic>n<\/jats:italic>\u2009&amp;gt;\u2009100: sensitivity: 0.71, specificity: 0.72, <jats:italic>n<\/jats:italic>\u2009&amp;lt;\u2009100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS\/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS\/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2023.1174080","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T07:53:58Z","timestamp":1695369238000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Yanjing","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijie","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.maturitas.2014.03.005","article-title":"Prevention of depression in older age","volume":"79","author":"Almeida","year":"2014","journal-title":"Maturitas"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1001\/archpsyc.1961.01710120031004","article-title":"An inventory for measuring depression","volume":"4","author":"Beck","year":"1961","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref3","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":"ref4","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":"ref5","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1038\/nrn3621","article-title":"DSM-5 and RDoC: progress in psychiatry research?","volume":"14","author":"Casey","year":"2013","journal-title":"Nat. Rev. Neurosci."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S2215-0366(15)00471-X","article-title":"Cross-trial prediction of treatment outcome in depression: a machine learning approach","volume":"3","author":"Chekroud","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jad.2022.03.079","article-title":"Regional amplitude abnormities in the major depressive disorder: a resting-state fMRI study and support vector machine analysis","volume":"308","author":"Chen","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1109\/EMBC44109.2020.9175685","article-title":"Visualizing functional network connectivity difference between healthy control and major depressive disorder using an explainable machine-learning method","volume":"2020","author":"Chun","year":"2020","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"110465","DOI":"10.1016\/j.pnpbp.2021.110465","article-title":"Resting-state fMRI in depressive and (hypo) manic mood states in bipolar disorders: a systematic review","volume":"113","author":"Claeys","year":"2022","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1038\/nn.4499","article-title":"Computational approaches to fMRI analysis","volume":"20","author":"Cohen","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1002\/mrm.22159","article-title":"Disease state prediction from resting state functional connectivity","volume":"62","author":"Craddock","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.neuroimage.2008.06.037","article-title":"Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns","volume":"43","author":"De Martino","year":"2008","journal-title":"NeuroImage"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"2253","DOI":"10.1017\/S0033291711000596","article-title":"Neural correlates of perception of emotional facial expressions in out-patients with mild-to-moderate depression and anxiety. A multicenter fMRI study","volume":"41","author":"Demenescu","year":"2011","journal-title":"Psychol. Med."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1038\/nm.4246","article-title":"Resting-state connectivity biomarkers define neurophysiological subtypes of depression","volume":"23","author":"Drysdale","year":"2017","journal-title":"Nat. Med."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12916-017-0849-x","article-title":"The new field of 'precision psychiatry'","volume":"15","author":"Fernandes","year":"2017","journal-title":"BMC Med."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1038\/s41386-021-01020-7","article-title":"Systematic misestimation of machine learning performance in neuroimaging studies of depression","volume":"46","author":"Flint","year":"2021","journal-title":"Neuropsychopharmacology"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e24","DOI":"10.1017\/S2045796019000088","article-title":"Translating the BDI and BDI-II into the HAMD and vice versa with equipercentile linking","volume":"29","author":"Furukawa","year":"2019","journal-title":"Epidemiol. Psychiatr. Sci."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"38","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":"ref20","doi-asserted-by":"publisher","first-page":"9108108","DOI":"10.1155\/2019\/9108108","article-title":"Resting-state functional network scale effects and statistical significance-based feature selection in machine learning classification","volume":"2019","author":"Guo","year":"2019","journal-title":"Comput. Math. Methods Med."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"4820935","DOI":"10.1155\/2017\/4820935","article-title":"Machine-learning classifier for patients with major depressive disorder: multifeature approach based on a high-order minimum spanning tree functional brain network","volume":"2017","author":"Guo","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1136\/jnnp.23.1.56","article-title":"A rating scale for depression","volume":"23","author":"Hamilton","year":"1960","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.neuron.2015.05.025","article-title":"A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives","volume":"87","author":"Haynes","year":"2015","journal-title":"Neuron"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jad.2016.04.042","article-title":"Psychoeducation in bipolar disorder with a SIMPLe smartphone application: feasibility, acceptability and satisfaction","volume":"200","author":"Hidalgo-Mazzei","year":"2016","journal-title":"J. Affect. Disord."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fpsyt.2013.00095","article-title":"Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging","volume":"4","author":"Iwabuchi","year":"2013","journal-title":"Front. Psych."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"S260","DOI":"10.1016\/j.biopsych.2020.02.673","article-title":"Classification between major depressive disorder and healthy controls using functional brain network topology","volume":"87","author":"Jacob","year":"2020","journal-title":"Biol. Psychiatry"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1109\/TNNLS.2021.3106299","article-title":"A survey on brain effective connectivity network learning","volume":"34","author":"Ji","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"90452","DOI":"10.18632\/oncotarget.19860","article-title":"Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases","volume":"8","author":"Jing","year":"2017","journal-title":"Oncotarget"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"4997","DOI":"10.1002\/hbm.25175","article-title":"Identifying resting-state effective connectivity abnormalities in drug-na\u00efve major depressive disorder diagnosis via graph convolutional networks","volume":"41","author":"Jun","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.biopsych.2016.10.028","article-title":"Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern Recognition studies","volume":"82","author":"Kambeitz","year":"2017","journal-title":"Biol. Psychiatry"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"177","DOI":"10.3389\/fpsyt.2016.00177","article-title":"Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example","volume":"7","author":"Kassraian-Fard","year":"2016","journal-title":"Front. Psych."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.3389\/fnins.2018.01018","article-title":"Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification","volume":"12","author":"Kazeminejad","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1038\/mp.2015.198","article-title":"Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports","volume":"21","author":"Kessler","year":"2016","journal-title":"Mol. Psychiatry"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1177\/1971400915576311","article-title":"Functional neuroimaging: fundamental principles and clinical applications","volume":"28","author":"Khanna","year":"2015","journal-title":"Neuroradiol. J."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.mri.2019.05.031","article-title":"Machine learning in resting-state fMRI analysis","volume":"64","author":"Khosla","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1046\/j.1525-1497.2001.016009606.x","article-title":"The PHQ-9: validity of a brief depression severity measure","volume":"16","author":"Kroenke","year":"2001","journal-title":"J. Gen. Intern. Med."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"8459","DOI":"10.1007\/s12652-021-03612-z","article-title":"Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda","volume":"14","author":"Kumar","year":"2022","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"296","DOI":"10.7326\/0003-4819-122-4-199502150-00010","article-title":"Functional magnetic resonance imaging of the brain","volume":"122","author":"Le Bihan","year":"1995","journal-title":"Ann. Intern. Med."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.gpb.2018.11.005","article-title":"Functional neuroimaging in the new era of big data","volume":"17","author":"Li","year":"2019","journal-title":"Genom. Proteom. Bioinformat."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1142\/S0219519421400686","article-title":"Automated recognition of depression from fewer-shot leaning in resting-state FMRI with REHO using deep convolutional neural network","volume":"21","author":"Li","year":"2021","journal-title":"J. Mech. Med. Biol."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.bbi.2019.06.012","article-title":"Sex differences in depressive-like behaviour may relate to imbalance of microglia activation in the hippocampus","volume":"81","author":"Liu","year":"2019","journal-title":"Brain Behav. Immun."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"e41282","DOI":"10.1371\/journal.pone.0041282","article-title":"Changes in community structure of resting state functional connectivity in unipolar depression","volume":"7","author":"Lord","year":"2012","journal-title":"PLoS One"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"ITC65","DOI":"10.7326\/AITC202105180","article-title":"Depression","volume":"174","author":"Mccarron","year":"2021","journal-title":"Ann. Intern. Med."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"b2535","DOI":"10.1136\/bmj.b2535","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"339","author":"Moher","year":"2009","journal-title":"BMJ"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11606-007-0428-5","article-title":"Recognition of depression by non-psychiatric physicians\u2014a systematic literature review and Meta-analysis","author":"Monica","year":"2008","journal-title":"J. Gen. Intern. Med."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s10844-021-00653-w","article-title":"Depression detection from sMRI and rs-fMRI images using machine learning","volume":"57","author":"Mousavian","year":"2021","journal-title":"J. Intell. Inf. Syst."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1002\/jmri.22806","article-title":"Prediction of illness severity in patients with major depression using structural MR brain scans","volume":"35","author":"Mwangi","year":"2012","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"400","DOI":"10.3389\/fpsyt.2020.00400","article-title":"Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI","volume":"11","author":"Nakano","year":"2020","journal-title":"Front. Psych."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1093\/eurpub\/ckv134","article-title":"Lifetime socio-economic position and depression: an analysis of the influence of cognitive function, behaviour and inflammatory markers","volume":"25","author":"Osler","year":"2015","journal-title":"Eur. J. Pub. Health"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1056\/NEJMcp1712493","article-title":"Depression in the primary care setting","volume":"380","author":"Park","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1002\/gps.4262","article-title":"Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction","volume":"30","author":"Patel","year":"2015","journal-title":"Int. J. Geriatr. Psychiatry"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1046\/j.1525-1497.1999.06278.x","article-title":"Depression among high utilizers of medical care","volume":"14","author":"Pearson","year":"1999","journal-title":"J. Gen. Intern. Med."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"S199","DOI":"10.1016\/j.neuroimage.2008.11.007","article-title":"Machine learning classifiers and fMRI: a tutorial overview","volume":"45","author":"Pereira","year":"2009","journal-title":"NeuroImage"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/S0893-133X(99)00154-2","article-title":"A method to quantify rater bias in antidepressant trials","volume":"22","author":"Petkova","year":"2000","journal-title":"Neuropsychopharmacology"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1515\/prilozi-2017-0014","article-title":"BDI in the assessment of depression in different medical conditions","volume":"38","author":"Pop-Jordanova","year":"2017","journal-title":"Pril (Makedon Akad Nauk Umet Odd Med Nauki)"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.jad.2022.01.105","article-title":"Consistency checks to improve measurement with the Hamilton rating scale for depression (HAM-D)","volume":"302","author":"Rabinowitz","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref57","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","article-title":"Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence","volume":"32","author":"Rajalakshmi","year":"2018","journal-title":"Eye (Lond.)"},{"key":"ref58","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.nicl.2016.07.012","article-title":"Accuracy of automated classification of major depressive disorder as a function of symptom severity","volume":"12","author":"Ramasubbu","year":"2016","journal-title":"Neuroimage Clin."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.conb.2019.02.006","article-title":"Machine learning and big data in psychiatry: toward clinical applications","volume":"55","author":"Rutledge","year":"2019","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"2604","DOI":"10.1109\/JBHI.2020.3043427","article-title":"Classification of adolescent major depressive disorder via static and dynamic connectivity","volume":"25","author":"Sen","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"4179","DOI":"10.1093\/cercor\/bhx273","article-title":"Meta-Connectomic analysis reveals commonly disrupted functional architectures in network modules and connectors across brain disorders","volume":"28","author":"Sha","year":"2018","journal-title":"Cereb. Cortex"},{"key":"ref62","first-page":"308","article-title":"Learning connectivity patterns via graph kernels for fMRI-based depression diagnostics [C]","author":"Sharaev","year":"2018"},{"key":"ref63","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1021\/acschemneuro.1c00256","article-title":"Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: a multicentral study","volume":"12","author":"Shi","year":"2021","journal-title":"ACS Chem. Neurosci."},{"key":"ref64","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/980972.980978","article-title":"Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n)","volume":"5","author":"Simon","year":"2003","journal-title":"ACM Sigkdd Explorat. Newslett."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"e1005180","DOI":"10.1371\/journal.pcbi.1005180","article-title":"Topology, cross-frequency, and same-frequency band interactions shape the generation of phase-amplitude coupling in a neural mass model of a cortical column","volume":"12","author":"Sotero","year":"2016","journal-title":"PLoS Comput. Biol."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"869","DOI":"10.3389\/fpsyt.2019.00869","article-title":"Cross-validation of functional MRI and paranoid-depressive scale: results from multivariate analysis","volume":"10","author":"Stoyanov","year":"2019","journal-title":"Front. Psych."},{"key":"ref67","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s00702-016-1673-8","article-title":"Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample","volume":"124","author":"Sundermann","year":"2017","journal-title":"J. Neural Transm. (Vienna)"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"101704","DOI":"10.1016\/j.artmed.2019.101704","article-title":"Machine learning and big data: implications for disease modeling and therapeutic discovery in psychiatry","volume":"99","author":"Tai","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref69","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1016\/j.jpsychires.2013.02.016","article-title":"A comparative analysis between site-based and centralized ratings and patient self-ratings in a clinical trial of major depressive disorder","volume":"47","author":"Targum","year":"2013","journal-title":"J. Psychiatr. Res."},{"key":"ref70","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1017\/S0033291707001730","article-title":"Measuring depression: comparison and integration of three scales in the GENDEP study","volume":"38","author":"Uher","year":"2008","journal-title":"Psychol. Med."},{"key":"ref71","doi-asserted-by":"publisher","first-page":"345","DOI":"10.7326\/0003-4819-134-5-200103060-00007","article-title":"The cost-utility of screening for depression in primary care","volume":"134","author":"Valenstein","year":"2001","journal-title":"Ann. Intern. Med."},{"key":"ref72","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","article-title":"Cross-validation failure: small sample sizes lead to large error bars","volume":"180","author":"Varoquaux","year":"2017","journal-title":"NeuroImage"},{"key":"ref73","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2016.10.038","article-title":"Assessing and tuning brain decoders: cross-validation, caveats, and guidelines","volume":"145","author":"Varoquaux","year":"2017","journal-title":"NeuroImage"},{"key":"ref74","first-page":"618","article-title":"A weighted Discriminative Dictionary Learning Method for Depression Disorder Classification using fMRI Data [C]","author":"Wang","year":"2016"},{"key":"ref75","doi-asserted-by":"publisher","first-page":"e1165","DOI":"10.1038\/tp.2017.117","article-title":"Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder","volume":"7","author":"Wang","year":"2017","journal-title":"Transl. Psychiatry"},{"key":"ref76","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.pscychresns.2013.09.008","article-title":"Identifying major depressive disorder using Hurst exponent of resting-state brain networks","volume":"214","author":"Wei","year":"2013","journal-title":"Psychiatry Res."},{"key":"ref77","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TMI.2019.2957097","article-title":"Multi-hypergraph learning-based brain functional connectivity analysis in fMRI data","volume":"39","author":"Xiao","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref79","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1016\/j.jad.2021.08.120","article-title":"The integrated understanding of structural and functional connectomes in depression: a multimodal meta-analysis of graph metrics","volume":"295","author":"Xu","year":"2021","journal-title":"J. Affect. Disord."},{"key":"ref80","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3389\/fnins.2020.00191","article-title":"Quantitative identification of major depression based on resting-state dynamic functional connectivity: a machine learning approach","volume":"14","author":"Yan","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref81","doi-asserted-by":"publisher","first-page":"e0179638","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":"ref82","doi-asserted-by":"publisher","first-page":"e68250","DOI":"10.1371\/journal.pone.0083943","article-title":"Convergent and divergent functional connectivity patterns in schizophrenia and depression","volume":"8","author":"Yu","year":"2013","journal-title":"PLoS One"},{"key":"ref83","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1002\/hbm.22278","article-title":"Unsupervised classification of major depression using functional connectivity MRI","volume":"35","author":"Zeng","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref84","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":"ref85","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.bpsc.2019.12.015","article-title":"Data-driven approaches to neuroimaging analysis to enhance psychiatric diagnosis and therapy","volume":"5","author":"Zhang","year":"2020","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimag."},{"key":"ref86","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/JBHI.2020.2983456","article-title":"Multi-scale time-series kernel-based learning method for brain disease diagnosis","volume":"25","author":"Zhang","year":"2021","journal-title":"IEEE J. Biomed. 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