{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:55:23Z","timestamp":1790297723605,"version":"4.1.0"},"reference-count":116,"publisher":"Elsevier BV","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4702702"],"award-info":[{"award-number":["2022YFB4702702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Patterns"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.patter.2024.101081","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T18:06:27Z","timestamp":1730743587000},"page":"101081","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"title":["An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network"],"prefix":"10.1016","volume":"5","author":[{"given":"Shuyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuequan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziji","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoxi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhe","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patter.2024.101081_bib1","series-title":"Assessing Impairment: From Theory to Practice","first-page":"167","article-title":"The Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition (DSM-5) Model of Impairment","author":"Joyce-Beaulieu","year":"2016"},{"key":"10.1016\/j.patter.2024.101081_bib4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neulet.2016.09.049","article-title":"Personalized medicine: Prediction of disease vulnerability in mood disorders","volume":"669","author":"Prendes-Alvarez","year":"2018","journal-title":"Neurosci. Lett."},{"key":"10.1016\/j.patter.2024.101081_bib5","series-title":"Diagnostic and Statistical Manual of Mental","author":"Association","year":"2013"},{"key":"10.1016\/j.patter.2024.101081_bib6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41572-023-00454-1","article-title":"Major depressive disorder","volume":"9","author":"Marx","year":"2023","journal-title":"Nat. Rev. Dis. Primers"},{"key":"10.1016\/j.patter.2024.101081_bib7","doi-asserted-by":"crossref","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.\u00a0Neurol. Neurosurg. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib8","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1111\/j.2044-8260.1967.tb00530.x","article-title":"Development of a Rating Scale for Primary Depressive Illness","volume":"6","author":"HAMILTON","year":"1967","journal-title":"Br. J. Soc. Clin. Psychol."},{"key":"10.1016\/j.patter.2024.101081_bib9","doi-asserted-by":"crossref","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":"10.1016\/j.patter.2024.101081_bib10","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.jad.2017.08.060","article-title":"The psychometric properties of depression screening tools in primary healthcare settings: A systematic review","volume":"225","author":"El-Den","year":"2018","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2022.103977","article-title":"Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites","volume":"78","author":"Qin","year":"2022","journal-title":"EBioMedicine"},{"key":"10.1016\/j.patter.2024.101081_bib12","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1038\/mp.2016.60","article-title":"Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group","volume":"22","author":"Schmaal","year":"2017","journal-title":"Mol. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib13","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1038\/s41380-019-0477-2","article-title":"White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group","volume":"25","author":"van Velzen","year":"2020","journal-title":"Mol. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.brainres.2019.146546","article-title":"Hippocampal volume and cell number in depression, schizophrenia, and suicide subjects","volume":"1727","author":"Chen","year":"2020","journal-title":"Brain Res."},{"key":"10.1016\/j.patter.2024.101081_bib15","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.biopsych.2021.06.024","article-title":"Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety","volume":"91","author":"Goldstein-Piekarski","year":"2022","journal-title":"Biol. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.ebiom.2018.09.010","article-title":"Abnormal amygdala resting-state functional connectivity in adults and adolescents with major depressive disorder: A comparative meta-analysis","volume":"36","author":"Tang","year":"2018","journal-title":"EBioMedicine"},{"key":"10.1016\/j.patter.2024.101081_bib17","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/S0140-6736(21)02141-3","article-title":"Time for united action on depression: a Lancet\u2013World Psychiatric Association Commission","volume":"399","author":"Herrman","year":"2022","journal-title":"Lancet"},{"key":"10.1016\/j.patter.2024.101081_bib18","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41380-022-01806-1","article-title":"Molecular pathways of major depressive disorder converge on the synapse","volume":"28","author":"Fries","year":"2023","journal-title":"Mol. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib19","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":"Yan","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.patter.2024.101081_bib20","doi-asserted-by":"crossref","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 Psychiatr."},{"key":"10.1016\/j.patter.2024.101081_bib21","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1176\/appi.ajp.2019.18111271","article-title":"Identification of Common Neural Circuit Disruptions in Emotional Processing Across Psychiatric Disorders","volume":"177","author":"McTeague","year":"2020","journal-title":"Am. J. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib22","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1126\/science.aab2358","article-title":"Brain disorders? Precisely","volume":"348","author":"Insel","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.patter.2024.101081_bib23","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/S2215-0366(15)00579-9","article-title":"Precision psychiatry: a neural circuit taxonomy for depression and anxiety","volume":"3","author":"Williams","year":"2016","journal-title":"Lancet Psychiatr."},{"key":"10.1016\/j.patter.2024.101081_bib24","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1148\/radiol.2016152149","article-title":"Psychoradiology: the frontier of neuroimaging in psychiatry","volume":"281","author":"Lui","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.patter.2024.101081_bib25","doi-asserted-by":"crossref","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.\u00a0Intell. Inf. Syst."},{"key":"10.1016\/j.patter.2024.101081_bib26","doi-asserted-by":"crossref","first-page":"904","DOI":"10.3389\/fneur.2019.00904","article-title":"Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI","volume":"10","author":"Hojjati","year":"2019","journal-title":"Front. Neurol."},{"key":"10.1016\/j.patter.2024.101081_bib27","doi-asserted-by":"crossref","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":"10.1016\/j.patter.2024.101081_bib28","doi-asserted-by":"crossref","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":"10.1016\/j.patter.2024.101081_bib29","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/npp.2017.36","article-title":"Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach","volume":"42","author":"Christian","year":"2017","journal-title":"Neuropsychopharmacology"},{"key":"10.1016\/j.patter.2024.101081_bib30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.pscychresns.2015.07.001","article-title":"Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression","volume":"233","author":"Sato","year":"2015","journal-title":"Psychiatry Res."},{"key":"10.1016\/j.patter.2024.101081_bib31","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1111\/cns.13048","article-title":"Machine learning in major depression: From classification to treatment outcome prediction","volume":"24","author":"Gao","year":"2018","journal-title":"CNS Neurosci. Ther."},{"key":"10.1016\/j.patter.2024.101081_bib32","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s10278-018-0093-8","article-title":"Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network","volume":"31","author":"Akhavan Aghdam","year":"2018","journal-title":"J.\u00a0Digit. Imaging"},{"key":"10.1016\/j.patter.2024.101081_bib33","doi-asserted-by":"crossref","first-page":"779","DOI":"10.3389\/fnins.2020.00779","article-title":"A survey on deep learning for neuroimaging-based brain disorder analysis","volume":"14","author":"Zhang","year":"2020","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.patter.2024.101081_bib34","series-title":"Aiding Alzheimer's Disease Diagnosis Using Graph Convolutional Networks Based on rs-fMRI Data. 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China","first-page":"1","author":"Qin","year":"2022"},{"key":"10.1016\/j.patter.2024.101081_bib35","doi-asserted-by":"crossref","unstructured":"Li, X., Zhou, Y., Dvornek, N.C., Zhang, M., Zhuang, J., Ventola, P., and Duncan, J.S. (2020). Pooling regularized graph neural network for fmri biomarker analysis. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020. Part VII 23, 625\u2013635. https:\/\/doi.org\/10.1007\/978-3-030-59728-3_61","DOI":"10.1007\/978-3-030-59728-3_61"},{"key":"10.1016\/j.patter.2024.101081_bib36","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","article-title":"Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer\u2019s disease","volume":"48","author":"Parisot","year":"2018","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patter.2024.101081_bib37","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.neuroimage.2017.12.052","article-title":"Metric learning with spectral graph convolutions on brain connectivity networks","volume":"169","author":"Ktena","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102233","article-title":"BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis","volume":"74","author":"Li","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patter.2024.101081_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105823","article-title":"MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder","volume":"148","author":"Pan","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.patter.2024.101081_bib40","doi-asserted-by":"crossref","first-page":"7749","DOI":"10.1109\/TCYB.2022.3197127","article-title":"MS2 -GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection","volume":"53","author":"Chen","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patter.2024.101081_bib41","first-page":"117","article-title":"Multi-view\u00a0multi-graph embedding for brain network clustering analysis","volume":"32","author":"Liu","year":"2018","journal-title":"Proceedings of the AAAI conference on artificial intelligence AAAI 2018"},{"key":"10.1016\/j.patter.2024.101081_bib42","first-page":"639","article-title":"Dynamic Spectral Graph Convolution Networks with Assistant Task Training for Early MCI Diagnosis","volume":"2019","author":"Xing","year":"2019","journal-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI"},{"key":"10.1016\/j.patter.2024.101081_bib43","doi-asserted-by":"crossref","first-page":"7275","DOI":"10.1109\/TNNLS.2022.3154755","article-title":"Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification","volume":"35","author":"Chen","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patter.2024.101081_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2022.113041","article-title":"The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder","volume":"167","author":"Pitsik","year":"2023","journal-title":"Chaos, Solit. Fractals"},{"key":"10.1016\/j.patter.2024.101081_bib45","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/978-3-030-32254-0_54","article-title":"Graph Neural Network for Interpreting Task-fMRI Biomarkers","author":"Li","year":"2019","journal-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"},{"key":"10.1016\/j.patter.2024.101081_bib46","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neuroimage.2013.04.081","article-title":"Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes","volume":"80","author":"Yan","year":"2013","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib47","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.patter.2024.101081_bib48","series-title":"International Conference on Learning Representations - ICLR 2019","first-page":"1","article-title":"How Powerful Are Graph Neural Networks?","author":"Xu","year":"2019"},{"key":"10.1016\/j.patter.2024.101081_bib49","series-title":"International Conference on Learning Representations -ICLR 2018","first-page":"1","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"10.1016\/j.patter.2024.101081_bib50","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1007\/978-3-030-59728-3_55","article-title":"Edge-Variational Graph Convolutional Networks for Uncertainty-Aware Disease Prediction","author":"Huang","year":"2020","journal-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"},{"key":"10.1016\/j.patter.2024.101081_bib51","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TMI.2022.3219260","article-title":"Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks","volume":"42","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patter.2024.101081_bib52","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TMI.2024.3392988","article-title":"Contrastive Graph Pooling for Explainable Classification of Brain Networks","volume":"43","author":"Xu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patter.2024.101081_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105090","article-title":"Autism spectrum disorder diagnosis using the relational graph attention network","volume":"85","author":"Gu","year":"2023","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.patter.2024.101081_bib54","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.neunet.2023.11.004","article-title":"Preserving specificity in federated graph learning for fMRI-based neurological disorder identification","volume":"169","author":"Zhang","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.patter.2024.101081_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106147","article-title":"CI-GNN: A Granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis","volume":"172","author":"Zheng","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.patter.2024.101081_bib56","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/978-3-031-16452-1_36","article-title":"Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis","author":"Cui","year":"2022","journal-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"},{"key":"10.1016\/j.patter.2024.101081_bib57","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1126\/science.1222939","article-title":"Synaptic Dysfunction in Depression: Potential Therapeutic Targets","volume":"338","author":"Duman","year":"2012","journal-title":"Science"},{"key":"10.1016\/j.patter.2024.101081_bib58","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.biopsych.2017.10.030","article-title":"The Multifaceted Role of the Ventromedial Prefrontal Cortex in Emotion, Decision Making, Social Cognition, and Psychopathology","volume":"83","author":"Hiser","year":"2018","journal-title":"Biol. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib59","doi-asserted-by":"crossref","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":"10.1016\/j.patter.2024.101081_bib60","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1176\/appi.ajp.2020.20030280","article-title":"Toward circuit mechanisms of pathophysiology in depression","volume":"177","author":"Spellman","year":"2020","journal-title":"Am. J. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib61","article-title":"Altered cerebellar functional connectivity with intrinsic connectivity networks in adults with major depressive disorder","volume":"7","author":"Liu","year":"2012","journal-title":"PLoS One"},{"key":"10.1016\/j.patter.2024.101081_bib62","doi-asserted-by":"crossref","DOI":"10.1002\/brb3.738","article-title":"Relationship between cerebellar structure and emotional memory in depression","volume":"7","author":"Xu","year":"2017","journal-title":"Brain Behav."},{"key":"10.1016\/j.patter.2024.101081_bib63","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128098","article-title":"A review of graph theory-based diagnosis of neurological disorders based on EEG and MRI","volume":"599","author":"Yan","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patter.2024.101081_bib64","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40537-023-00876-4","article-title":"A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions","volume":"11","author":"Khemani","year":"2024","journal-title":"J.\u00a0Big Data"},{"key":"10.1016\/j.patter.2024.101081_bib65","doi-asserted-by":"crossref","first-page":"5833","DOI":"10.1109\/TPAMI.2022.3209686","article-title":"Graph Neural Networks in Network Neuroscience","volume":"45","author":"Bessadok","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patter.2024.101081_bib66","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/RBME.2021.3122522","article-title":"Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review","volume":"16","author":"Li","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.patter.2024.101081_bib67","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1038\/s41551-022-00942-x","article-title":"Graph representation learning in biomedicine and healthcare","volume":"6","author":"Li","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.patter.2024.101081_bib68","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.inffus.2021.10.012","article-title":"Deep learning for depression recognition with audiovisual cues: A review","volume":"80","author":"He","year":"2022","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patter.2024.101081_bib69","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41398-019-0680-6","article-title":"The rise and fall of MRI studies in major depressive disorder","volume":"9","author":"Zhuo","year":"2019","journal-title":"Transl. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib70","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TCSS.2023.3263128","article-title":"Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey","volume":"10","author":"Hasib","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.patter.2024.101081_bib71","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.pscychresns.2019.01.004","article-title":"Hippocampal functional connectivity-based discrimination between bipolar and major depressive disorders","volume":"284","author":"Fateh","year":"2019","journal-title":"Psychiatr. Res. Neuroimaging"},{"key":"10.1016\/j.patter.2024.101081_bib72","doi-asserted-by":"crossref","DOI":"10.1093\/scan\/nsac064","article-title":"Right amygdala-right precuneus connectivity is associated with childhood trauma in major depression patients and healthy controls","volume":"18","author":"Fan","year":"2023","journal-title":"Soc. Cogn. Affect. Neurosci."},{"key":"10.1016\/j.patter.2024.101081_bib73","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/s41398-022-01976-0","article-title":"Altered hippocampus and amygdala subregion connectome hierarchy in major depressive disorder","volume":"12","author":"Jacob","year":"2022","journal-title":"Transl. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib74","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jad.2022.01.087","article-title":"An aberrant hippocampal subregional network, rather than structure, characterizes major depressive disorder","volume":"302","author":"Shengli","year":"2022","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib75","doi-asserted-by":"crossref","DOI":"10.1016\/j.biopsycho.2021.108192","article-title":"Dysfunction of the anterior and intermediate hippocampal functional network in major depressive disorders across the adult lifespan","volume":"165","author":"Hu","year":"2021","journal-title":"Biol. Psychol."},{"key":"10.1016\/j.patter.2024.101081_bib76","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00406-009-0045-x","article-title":"Hippocampus, glucocorticoids and neurocognitive functions in patients with first-episode major depressive disorders","volume":"260","author":"Kaymak","year":"2010","journal-title":"Eur. Arch. Psychiatry Clin. Neurosci."},{"key":"10.1016\/j.patter.2024.101081_bib77","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.jad.2010.03.004","article-title":"Subregional hippocampal deformations in major depressive disorder","volume":"126","author":"Cole","year":"2010","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib78","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1007\/BF02831120","article-title":"Volumetric MRI analysis of the amygdala and hippocampus in subjects with major depression","volume":"24","author":"Jun","year":"2004","journal-title":"Curr. Med. Sci."},{"key":"10.1016\/j.patter.2024.101081_bib79","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.jad.2011.05.057","article-title":"Hippocampal atrophy in first episode depression: A meta-analysis of magnetic resonance imaging studies","volume":"134","author":"Cole","year":"2011","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib80","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1741-7015-2-2","article-title":"Hippocampal volume in early onset depression","volume":"2","author":"MacMaster","year":"2004","journal-title":"BMC Med."},{"key":"10.1016\/j.patter.2024.101081_bib81","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.nicl.2018.07.002","article-title":"Common and distinct abnormal frontal-limbic system structural and functional patterns in patients with major depression and bipolar disorder","volume":"20","author":"Chen","year":"2018","journal-title":"Neuroimage. Clin."},{"key":"10.1016\/j.patter.2024.101081_bib82","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.966087","article-title":"Cerebral blood flow in adolescents with drug-naive, first-episode major depressive disorder: An arterial spin labeling study based on voxel-level whole-brain analysis","volume":"16","author":"Xiong","year":"2022","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.patter.2024.101081_bib83","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1186\/s12888-022-03780-y","article-title":"Transdiagnostic alterations in neural emotion regulation circuits\u00a0\u2013 neural substrates of cognitive reappraisal in patients with depression and post-traumatic stress disorder","volume":"22","author":"Keller","year":"2022","journal-title":"BMC Psychiatr."},{"key":"10.1016\/j.patter.2024.101081_bib84","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.jad.2020.06.077","article-title":"Brain functional abnormalities in the amygdala subregions is associated with anxious depression","volume":"276","author":"Qiao","year":"2020","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib85","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.jad.2021.08.143","article-title":"Brain functional alterations in MDD patients with somatic symptoms: A resting-state fMRI study","volume":"295","author":"Liu","year":"2021","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib86","doi-asserted-by":"crossref","first-page":"272","DOI":"10.3389\/fneur.2020.00272","article-title":"Altered Resting-State Functional Connectivity of Multiple Networks and Disrupted Correlation With Executive Function in Major Depressive Disorder","volume":"11","author":"Liu","year":"2020","journal-title":"Front. Neurol."},{"key":"10.1016\/j.patter.2024.101081_bib87","article-title":"A Comparative Study of Regional Homogeneity of Resting-State fMRI Between the Early-Onset and Late-Onset Recurrent Depression in Adults","volume":"13","author":"Sun","year":"2022","journal-title":"Front. Psychol."},{"key":"10.1016\/j.patter.2024.101081_bib88","doi-asserted-by":"crossref","first-page":"555","DOI":"10.2147\/NDT.S348683","article-title":"Amplitude of Low-Frequency Oscillations in First-Episode Drug-Naive Patients with Major Depressive Disorder: A Resting State Functional Magnetic Resonance Imaging Study","volume":"18","author":"Zhang","year":"2022","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"10.1016\/j.patter.2024.101081_bib89","doi-asserted-by":"crossref","DOI":"10.1016\/j.nicl.2023.103445","article-title":"Frequency-dependent and time-variant alterations of neural activity in post-stroke depression: A resting-state fMRI study","volume":"38","author":"Wu","year":"2023","journal-title":"Neuroimage. Clin."},{"key":"10.1016\/j.patter.2024.101081_bib90","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-77136-5","article-title":"Lesions in the right Rolandic operculum are associated with self-rating affective and apathetic depressive symptoms for post-stroke patients","volume":"10","author":"Sutoko","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.patter.2024.101081_bib91","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2018.00634","article-title":"Cerebellar Contributions to Major Depression","volume":"9","author":"Depping","year":"2018","journal-title":"Front. Psychiatry"},{"key":"10.1016\/j.patter.2024.101081_bib92","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.jad.2023.10.003","article-title":"Altered brain regional homogeneity is associated with cognitive dysfunction in first-episode drug-naive major depressive disorder: A resting-state fMRI study","volume":"343","author":"Ni","year":"2023","journal-title":"J.\u00a0Affect. Disord."},{"key":"10.1016\/j.patter.2024.101081_bib93","author":"Saloni Dattani"},{"key":"10.1016\/j.patter.2024.101081_bib94","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.nicl.2015.11.003","article-title":"Studying depression using imaging and machine learning methods","volume":"10","author":"Patel","year":"2016","journal-title":"Neuroimage. Clin."},{"key":"10.1016\/j.patter.2024.101081_bib96","series-title":"Feature Selection and Imbalanced Data Handling for Depression Detection. held in Cham","first-page":"349","author":"Mousavian","year":"2018"},{"key":"10.1016\/j.patter.2024.101081_bib97","series-title":"Conference and Workshop on Neural Information Processing Systems - NeurIPS","first-page":"1025","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017"},{"key":"10.1016\/j.patter.2024.101081_bib98","unstructured":"Chen J., Ma T., Xiao C. (2018). FastGCN: fast learning with graph convolutional networks via importance sampling. Preprint at arXiv, 1801.10247."},{"key":"10.1016\/j.patter.2024.101081_bib99","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/s41597-021-01004-8","article-title":"A multi-site, multi-disorder resting-state magnetic resonance image database","volume":"8","author":"Tanaka","year":"2021","journal-title":"Sci. Data"},{"key":"10.1016\/j.patter.2024.101081_bib100","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1093\/psyrad\/kkac005","article-title":"The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder","volume":"2","author":"Chen","year":"2022","journal-title":"Psychoradiology"},{"key":"10.1016\/j.patter.2024.101081_bib101","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s10517-018-4161-3","article-title":"Spontaneous Changes in Functional Connectivity of Independent Components of fMRI Signal in Healthy Volunteers at Rest and in Subjects with Mild Depression","volume":"165","author":"Bezmaternykh","year":"2018","journal-title":"Bull. Exp. Biol. Med."},{"key":"10.1016\/j.patter.2024.101081_bib102","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.nurpra.2008.09.020","article-title":"International Classification of Diseases 10th Revision (ICD-10)","volume":"5","author":"Disantostefano","year":"2009","journal-title":"J.\u00a0Nurse Pract."},{"key":"10.1016\/j.patter.2024.101081_bib103","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s12021-016-9299-4","article-title":"DPABI: Data Processing\u00a0& Analysis for (Resting-State) Brain Imaging","volume":"14","author":"Yan","year":"2016","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.patter.2024.101081_bib104","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neuroimage.2013.08.048","article-title":"Methods to detect, characterize, and remove motion artifact in resting state fMRI","volume":"84","author":"Power","year":"2014","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib105","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib106","doi-asserted-by":"crossref","first-page":"4234","DOI":"10.1038\/s41598-021-83491-8","article-title":"Cellular correlates of gray matter volume changes in magnetic resonance morphometry identified by two-photon microscopy","volume":"11","author":"Asan","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.patter.2024.101081_bib107","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1089\/brain.2020.0815","article-title":"Relationship between Dynamic Blood-Oxygen-Level-Dependent Activity and Functional Network Connectivity: Characterization of Schizophrenia Subgroups","volume":"11","year":"2021","journal-title":"Brain Connect."},{"key":"10.1016\/j.patter.2024.101081_bib108","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroimage.2015.07.075","article-title":"Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas","volume":"122","author":"Rolls","year":"2015","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib109","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1002\/hbm.21333","article-title":"A whole brain fMRI atlas generated via spatially constrained spectral clustering","volume":"33","author":"Craddock","year":"2012","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.patter.2024.101081_bib110","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1177\/02698811211050550","article-title":"Relationship between depression, prefrontal creatine and grey matter volume","volume":"35","author":"Faulkner","year":"2021","journal-title":"J.\u00a0Psychopharmacol."},{"key":"10.1016\/j.patter.2024.101081_bib111","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res."},{"key":"10.1016\/j.patter.2024.101081_bib112","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neuroimage.2013.04.081","article-title":"Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes","volume":"80","author":"Yan","year":"2013","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib113","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":"Fortin","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.patter.2024.101081_bib114","first-page":"1","article-title":"Semi-supervised Classification with Graph Convolutional Networks","author":"Kipf","year":"2017","journal-title":"International Conference on Learning Representations 2017 (ICLR)"},{"key":"10.1016\/j.patter.2024.101081_bib115","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"3538","article-title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","volume":"32","author":"Li","year":"2018"},{"key":"10.1016\/j.patter.2024.101081_bib116","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.patter.2024.101081_bib117","first-page":"10943","article-title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","author":"Luan","year":"2019","journal-title":"33rd Conference on Neural Information Processing Systems (NeurIPS)"},{"key":"10.1016\/j.patter.2024.101081_bib118","series-title":"International conference on algorithmic learning theory 3734","first-page":"63","article-title":"Measuring Statistical Dependence with Hilbert-Schmidt Norms","author":"Gretton","year":"2005"},{"key":"10.1016\/j.patter.2024.101081_bib119","series-title":"Software","article-title":"LGMF-GNN. figshare","author":"Liu","year":"2024"}],"container-title":["Patterns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S266638992400240X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S266638992400240X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T19:58:31Z","timestamp":1779825511000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S266638992400240X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":116,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["S266638992400240X"],"URL":"https:\/\/doi.org\/10.1016\/j.patter.2024.101081","relation":{},"ISSN":["2666-3899"],"issn-type":[{"value":"2666-3899","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network","name":"articletitle","label":"Article Title"},{"value":"Patterns","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patter.2024.101081","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"101081"}}