{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:56:06Z","timestamp":1787021766615,"version":"build-2736575974"},"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32541016, 62436005, U25A20540"],"award-info":[{"award-number":["32541016, 62436005, U25A20540"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82330043"],"award-info":[{"award-number":["82330043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473303"],"award-info":[{"award-number":["62473303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Major depressive disorder (MDD) remains clinically diagnosed based on subjective symptoms rather than objective neurobiological markers, which limits diagnostic accuracy and the ability to tailor treatment. We present an ensemble hybrid framework that integrates graph neural networks (GNN) with unsupervised clustering to classify and subtype MDD using resting-state functional connectivity (rs-fMRI) profiles. A GNN was trained to distinguish MDD from healthy controls using functional connectivity derived brain graphs, and the resulting subject level embeddings were clustered to uncover subtype structure. We evaluated the approach on two public multisite cohorts, REST-meta-MDD (China; N\u2009=\u20091,604; 17 sites) and SRPBS (Japan; N\u2009=\u2009446; 4 sites), using leave-one-site-out cross-validation and cross-national transfer. The classifier achieved 0.73 leave-one-site-out accuracy on REST-meta-MDD and retained 0.78 sensitivity when transferred from the Chinese to the Japanese cohort, outperforming BrainIB and CI GNN under the same protocol. To mitigate site related confounds, we applied a standardized preprocessing pipeline and ComBat harmonization. Clustering consistently identified three MDD subtypes with distinct connectivity signatures involving the default mode network and cerebellum, the insula-cingulum temporal circuit, and frontostriatal circuitry. These findings provide a reproducible and biologically interpretable stratification of MDD. Prospective studies will be needed to link these subtypes to treatment response and other clinically meaningful outcomes.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001261","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T18:42:24Z","timestamp":1772649744000},"page":"e0001261","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Functional connectivity\u2013based classification and subtyping of major depression for precision mental health: An ensemble graph neural network 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Syst"},{"issue":"12","key":"pdig.0001261.ref022","doi-asserted-by":"crossref","first-page":"101081","DOI":"10.1016\/j.patter.2024.101081","article-title":"An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network","volume":"5","author":"S Liu","year":"2024","journal-title":"Patterns (N Y)"},{"issue":"18","key":"pdig.0001261.ref023","doi-asserted-by":"crossref","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":"C-G Yan","year":"2019","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"pdig.0001261.ref024","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":"N Tzourio-Mazoyer","year":"2002","journal-title":"Neuroimage"},{"issue":"1","key":"pdig.0001261.ref025","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting batch effects in microarray expression data using empirical Bayes methods","volume":"8","author":"WE Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"pdig.0001261.ref026","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","article-title":"Harmonization of cortical thickness measurements across scanners and sites","volume":"167","author":"J-P Fortin","year":"2018","journal-title":"Neuroimage"},{"key":"pdig.0001261.ref027","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.neuroimage.2017.08.047","article-title":"Harmonization of multi-site diffusion tensor imaging data","volume":"161","author":"J-P Fortin","year":"2017","journal-title":"Neuroimage"},{"issue":"11","key":"pdig.0001261.ref028","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.1002\/hbm.24241","article-title":"Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data","volume":"39","author":"M Yu","year":"2018","journal-title":"Hum Brain Mapp"},{"key":"pdig.0001261.ref029","article-title":"Categorical reparameterization with Gumbel-Softmax","author":"E Jang","year":"2017","journal-title":"ICLR"},{"key":"pdig.0001261.ref030","unstructured":"Maddison CJ, Mnih A, Teh YW. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. 2017."},{"key":"pdig.0001261.ref031","first-page":"9240","article-title":"GNNExplainer: Generating Explanations for Graph Neural Networks","volume":"32","author":"R Ying","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"pdig.0001261.ref032","article-title":"How powerful are graph neural networks?. arXiv preprint","author":"K Xu","year":"2018"},{"issue":"6","key":"pdig.0001261.ref033","doi-asserted-by":"crossref","first-page":"6870","DOI":"10.1109\/TPAMI.2020.2999032","article-title":"Second-Order Pooling for Graph Neural Networks","volume":"45","author":"Z Wang","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pdig.0001261.ref034","unstructured":"Yu J. Recognizing predictive substructures with subgraph information bottleneck. 2021."},{"key":"pdig.0001261.ref035","unstructured":"Tian Y. What makes for good views for contrastive learning. 2020."},{"issue":"1","key":"pdig.0001261.ref036","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TIT.2014.2370058","article-title":"Measures of Entropy From Data Using Infinitely Divisible Kernels","volume":"61","author":"LG Sanchez Giraldo","year":"2015","journal-title":"IEEE Trans Inform Theory"},{"key":"pdig.0001261.ref037","doi-asserted-by":"crossref","unstructured":"Yu S, Giraldo L, Jenssen R, Principe JC. Multivariate extension of matrix-based Renyi\u2019s \u03b1-order entropy functional. 2018.","DOI":"10.1109\/TPAMI.2019.2932976"},{"key":"pdig.0001261.ref038","unstructured":"Belghazi MI. MINE: Mutual Information Neural Estimation. 2018."},{"issue":"6191","key":"pdig.0001261.ref039","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Machine learning. Clustering by fast search and find of density peaks","volume":"344","author":"A Rodriguez","year":"2014","journal-title":"Science"},{"key":"pdig.0001261.ref040","first-page":"364","article-title":"RBF SVM","volume":"19","author":"Makara","year":"2013","journal-title":"Journal of Institute of Control, Robotics and Systems"},{"key":"pdig.0001261.ref041","first-page":"2541","article-title":"On model selection consistency of lasso","volume":"7","author":"Z Peng","year":"2006","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"pdig.0001261.ref042","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"pdig.0001261.ref043","article-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"TN Kipf","year":"2016"},{"key":"pdig.0001261.ref044","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"P Velickovic","year":"2017","journal-title":"Stat"},{"issue":"8","key":"pdig.0001261.ref045","doi-asserted-by":"crossref","first-page":"9127","DOI":"10.1609\/aaai.v36i8.20898","article-title":"ProtGNN: Towards Self-Explaining Graph Neural Networks","volume":"36","author":"Z Zhang","year":"2022","journal-title":"AAAI"},{"key":"pdig.0001261.ref046","doi-asserted-by":"crossref","first-page":"102233","DOI":"10.1016\/j.media.2021.102233","article-title":"BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis","volume":"74","author":"X Li","year":"2021","journal-title":"Med Image Anal"},{"key":"pdig.0001261.ref047","doi-asserted-by":"crossref","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"CC Chang","year":"2011","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"7","key":"pdig.0001261.ref048","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1038\/s41380-023-01977-5","article-title":"Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies","volume":"28","author":"S Gallo","year":"2023","journal-title":"Mol Psychiatry"},{"issue":"10","key":"pdig.0001261.ref049","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.5665\/sleep.6154","article-title":"Sleep-Wake Differences in Relative Regional Cerebral Metabolic Rate for Glucose among Patients with Insomnia Compared with Good Sleepers","volume":"39","author":"DB Kay","year":"2016","journal-title":"Sleep"},{"issue":"4","key":"pdig.0001261.ref050","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1097\/WNP.0000000000000377","article-title":"Structure and Function of the Human Insula","volume":"34","author":"LQ Uddin","year":"2017","journal-title":"J Clin 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learning for neuroimaging: a validation study","volume":"8","author":"SM Plis","year":"2014","journal-title":"Front Neurosci"},{"key":"pdig.0001261.ref055","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.ebiom.2019.08.023","article-title":"Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data","volume":"47","author":"W Yan","year":"2019","journal-title":"EBioMedicine"},{"issue":"11","key":"pdig.0001261.ref056","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.biopsych.2020.02.016","article-title":"Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises","volume":"88","author":"J Sui","year":"2020","journal-title":"Biol Psychiatry"},{"issue":"2020","key":"pdig.0001261.ref057","article-title":"Population graph-based multi-model ensemble method for diagnosing autism spectrum disorder","volume":"20","author":"Z Rakhimberdina","year":"2020","journal-title":"Sensors (Basel)"},{"key":"pdig.0001261.ref058","first-page":"815","article-title":"Linear Graph Convolutional Model for Diagnosing Brain Disorders","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Data Science","author":"Z Rakhimberdina","year":"2020"},{"key":"pdig.0001261.ref059","first-page":"70","article-title":"Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI","author":"D Yao","year":"2019","journal-title":"Springer International Publishing"},{"key":"pdig.0001261.ref060","first-page":"799","article-title":"Interpretable multimodality embedding of cerebral cortex using attention graph network for identifying bipolar disorder","author":"H Yang","year":"2019","journal-title":"Springer International 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