{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T06:30:32Z","timestamp":1786516232072,"version":"build-2736575974"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Chronic kidney disease (CKD) requiring maintenance dialysis is accompanied by cognitive vulnerability and distributed brain-network dysfunction. This study evaluated whether complementary resting-state functional magnetic resonance imaging (rs-fMRI) connectome views could support subject-level characterization of CKD-related neurocognitive brain dysfunction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed a de-identified rs-fMRI dataset of 100 participants, including 52 patients with CKD receiving maintenance dialysis and 48 normal controls (NCs). The CKD group had lower Mini-Mental State Examination and Montreal Cognitive Assessment scores. AAL116 regional time series were represented as three 116 \u00d7 116 graph views: Pearson correlation, sparse representation, and Granger causality mapping. We developed MTGAF, a multiplex graph transformer combining modality-specific graph encoding, graph-transformer refinement, disease-aware readout, cross-modal attention, and adaptive fusion. Performance was evaluated with participant-level stratified 5-fold cross-validation and compared with BrainNetTF, BrainGNN, SVM, Random Forest, single-modality variants, and additional component-ablation variants. Robustness was assessed using pooled confusion matrices, bootstrap confidence intervals, calibration summaries, paired comparisons, and ROI importance analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>MTGAF achieved accuracy 0.8800 \u00b1 0.0678, macro-F1 0.8796 \u00b1 0.0680, and AUC 0.9297 \u00b1 0.0437. The pooled confusion matrix contained 42 true negatives, 46 true positives, 6 false positives, and 6 false negatives. Bootstrap 95% CIs were 0.8100\u20130.9400 for accuracy, 0.8095\u20130.9396 for macro-F1, and 0.8696\u20130.9770 for pooled AUC. The full PC\u2013SR\u2013GCM model achieved higher accuracy and macro-F1 than single-modality variants. ROI importance highlighted cingulate, temporoparietal, sensorimotor, visual-association, frontostriatal, posterior cingulate, and temporal-association systems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Multiplex modeling of complementary connectome information provides signals for characterizing CKD-related neurocognitive brain-network dysfunction. MTGAF is a research-stage imaging-AI framework for individualized brain-risk characterization in dialysis-treated CKD and should be further evaluated in larger multi-center cohorts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1906443","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:55:02Z","timestamp":1786514102000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiplex functional connectome graph transformer for cognitive vulnerability in dialysis-treated chronic kidney disease"],"prefix":"10.3389","volume":"17","author":[{"given":"Junjie","family":"Zhai","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiachang","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangdong","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Chongqing Jiaotong University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.kint.2023.10.016","article-title":"Executive summary of the KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease: known knowns and known unknowns","volume":"105","author":"Levin","year":"2024","journal-title":"Kidney Int"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1159\/000338135","article-title":"Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis","volume":"35","author":"Etgen","year":"2012","journal-title":"Am J Nephrol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.4103\/1319-2442.103561","article-title":"Assessment of cognitive dysfunction in kidney disease","volume":"23","author":"Nasser","year":"2012","journal-title":"Saudi J Kidney Dis Transplant"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-014-1160-z","article-title":"Cognitive impairment in chronic kidney disease: Clinical findings, risk factors and consequences for patient care","volume":"121","author":"Hermann","year":"2014","journal-title":"J Neural Transm"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1097\/MNH.0000000000001017","article-title":"The impact of chronic kidney disease on cognitive function","volume":"33","author":"P\u00e9pin","year":"2024","journal-title":"Curr Opin Nephrol Hypertension"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2015.12.028","article-title":"Cognition in people with end-stage kidney disease treated with hemodialysis: A systematic review and meta-analysis","volume":"67","author":"Bronas","year":"2016","journal-title":"Am J Kidney Dis"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1111\/ene.15928","article-title":"Cognitive disorders in patients with chronic kidney disease: Approaches to prevention and treatment","volume":"30","author":"Madero","year":"2023","journal-title":"Eur J Neurol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.xkme.2023.100613","article-title":"Cognitive impairment, frailty, and adverse outcomes among prevalent hemodialysis recipients: Results from a large prospective cohort study in the United Kingdom","volume":"5","author":"Anderson","year":"2023","journal-title":"Kidney Med"},{"key":"B9","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1053\/j.ajkd.2023.03.012","article-title":"Blood pressure, incident cognitive impairment, and severity of CKD: Findings from the chronic renal insufficiency cohort (CRIC) study","volume":"82","author":"Babroudi","year":"2023","journal-title":"Am J Kidney Dis"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s10157-025-02798-2","article-title":"Dialysis modality and cognitive outcomes in chronic kidney disease: A systematic review and meta-analysis","volume":"30","author":"Malik","year":"2026","journal-title":"Clin Exp Nephrol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ekir.2021.04.005","article-title":"Hemodialysis patients have impaired cerebrovascular reactivity to CO2 compared to chronic kidney disease patients and healthy controls: A pilot study","volume":"6","author":"Slessarev","year":"2021","journal-title":"Kidney Int Rep"},{"key":"B12","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1111\/sdi.13057","article-title":"Mini-mental state examination predicts mortality in patients on chronic hemodialysis","volume":"36","author":"Bossola","year":"2023","journal-title":"Semin Dialysis"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-160834","article-title":"Association of kidney function biomarkers with brain MRI findings: The BRINK study","volume":"55","author":"Vemuri","year":"2017","journal-title":"J Alzheimer\u2019s Dis"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/nrneph.2015.131","article-title":"Kidney-brain crosstalk in the acute and chronic setting","volume":"11","author":"Bugnicourt","year":"2015","journal-title":"Nat Rev Nephrol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.13130816","article-title":"Aberrant default-mode functional connectivity in patients with end-stage renal disease: a resting-state functional MR imaging study","volume":"271","author":"Ni","year":"2014","journal-title":"Radiology"},{"key":"B16","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1186\/s12882-019-1435-6","article-title":"Alterations of default mode functional connectivity in individuals with end-stage renal disease and mild cognitive impairment","volume":"20","author":"Lu","year":"2019","journal-title":"BMC Nephrol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000025633","article-title":"Alteration of brain connectivity in neurologically asymptomatic patients with chronic kidney disease","volume":"100","author":"Lee","year":"2021","journal-title":"Medicine"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.734410","article-title":"Changes of brain functional connectivity in end-stage renal disease patients receiving peritoneal dialysis without cognitive decline","volume":"8","author":"Chang","year":"2021","journal-title":"Front Med"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-020-00293-8","article-title":"Altered resting-state functional networks in patients with hemodialysis: A graph-theoretical based study","volume":"15","author":"Jin","year":"2021","journal-title":"Brain Imaging Behav"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0098346","article-title":"Structural and functional brain alterations in end stage renal disease patients on routine hemodialysis: a voxel-based morphometry and resting state functional connectivity study","volume":"9","author":"Qiu","year":"2014","journal-title":"PloS One"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1007\/s11011-014-9568-6","article-title":"Altered brain functional connectivity in hemodialysis patients with end-stage renal disease: a resting-state functional MR imaging study","volume":"31","author":"Zheng","year":"2014","journal-title":"Metab Brain Dis"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.01032","article-title":"Preferentially disrupted core hubs within the default-mode network in patients with end-stage renal disease: A resting-state functional magnetic resonance imaging study","volume":"11","author":"Ma","year":"2020","journal-title":"Front Neurol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"390","DOI":"10.3988\/jcn.2020.16.3.390","article-title":"Alterations in structural and functional connectivities in patients with end-stage renal disease","volume":"16","author":"Park","year":"2020","journal-title":"J Clin Neurol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.semnephrol.2021.09.008","article-title":"Decreased neural connectivity in the default mode network among youth and young adults with chronic kidney disease","volume":"41","author":"Herrington","year":"2021","journal-title":"Semin Nephrol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.852822","article-title":"Abnormal dynamic functional connectivity in patients with end-stage renal disease","volume":"16","author":"Li","year":"2022","journal-title":"Front Neurosci"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.70947","article-title":"Investigating brain functional connectivity and its correlation with cognitive dysfunction in chronic kidney disease patients via resting-state fMRI","volume":"15","author":"Liu","year":"2025","journal-title":"Brain Behav"},{"key":"B27","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1186\/s12882-025-04109-z","article-title":"Early detection of cognitive impairment in end-stage renal disease patients undergoing hemodialysis: Insights from resting-state functional connectivity analysis","volume":"26","author":"Liu","year":"2025","journal-title":"BMC Nephrol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2025.1738198","article-title":"Reduced global BOLD-CSF coupling in chronic kidney disease-related cognitive impairment: A resting-state functional MRI study","volume":"16","author":"Yang","year":"2026","journal-title":"Front Neurol"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2025.1585354","article-title":"Altered interhemispheric functional connectivity in end-stage renal disease patients receiving hemodialysis without cognitive impairment","volume":"16","author":"Xue","year":"2025","journal-title":"Front Neurol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2025.1510321","article-title":"Cognitive impairment assessed by static and dynamic changes of spontaneous brain activity during end stage renal disease patients on early hemodialysis","volume":"16","author":"Wu","year":"2025","journal-title":"Front Neurol"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.3598","article-title":"Multiparameter neuroimaging study of neurovascular coupling changes in patients with end-stage renal disease","volume":"14","author":"Sun","year":"2024","journal-title":"Brain Behav"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1080\/0886022X.2023.2217276","article-title":"Altered spontaneous brain activities in maintenance hemodialysis patients with cognitive impairment and the construction of cognitive function prediction models","volume":"45","author":"Sun","year":"2023","journal-title":"Renal Failure"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13040628","article-title":"Altered resting-state functional networks in nondialysis patients with stage 5 chronic kidney disease: A graph-theoretical analysis","volume":"13","author":"Song","year":"2023","journal-title":"Brain Sci"},{"key":"B34","doi-asserted-by":"publisher","first-page":"7398","DOI":"10.1038\/s41598-026-38807-x","article-title":"Resting-state fMRI reveals immediate hemodialysis-related changes in cognitive function and brain network connectivity in end-stage renal disease","volume":"16","author":"Du","year":"2026","journal-title":"Sci Rep"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2004.11.017","article-title":"Mapping directed influence over the brain using Granger causality and fMRI","volume":"25","author":"Roebroeck","year":"2005","journal-title":"NeuroImage"},{"key":"B36","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.jneumeth.2011.10.031","article-title":"A review of multivariate methods for multimodal fusion of brain imaging data","volume":"204","author":"Sui","year":"2012","journal-title":"J Neurosci Methods"},{"key":"B37","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neuroimage.2014.05.018","article-title":"General overview on the merits of multimodal neuroimaging data fusion","volume":"102","author":"Uludag","year":"2014","journal-title":"NeuroImage"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.07.006","article-title":"Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation","volume":"64","author":"Zhang","year":"2020","journal-title":"Inf Fusion"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100019","article-title":"Multimodal radiology AI","volume":"1","author":"Yan","year":"2023","journal-title":"Meta-Radiology"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1002\/cns.70951","article-title":"Identification of major depressive disorder using multiple functional connection patterns","volume":"32","author":"Ruan","year":"2026","journal-title":"CNS Neurosci Ther"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100046","article-title":"A comprehensive survey of complex brain network representation","volume":"1","author":"Tang","year":"2023","journal-title":"Meta-Radiology"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2025.100184","article-title":"Predicting efficacy of electroconvulsive therapy for adolescent major depressive disorder using a dual-branch graph attention network fusing multi-modal MRI","volume":"3","author":"Zhang","year":"2025","journal-title":"Meta-Radiology"},{"key":"B43","doi-asserted-by":"publisher","first-page":"100216","DOI":"10.1016\/j.metrad.2026.100216","article-title":"Constrained multiview contrastive learning for jointly supervised representation learning","author":"Dai","year":"2026","journal-title":"Meta-Radiology"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2026.100217","article-title":"MLLIFA: Multi-level learning and interactive fusion algorithm combined with large foundation models for Alzheimer\u2019s disease diagnosis and etiology extraction","volume":"4","author":"Fang","year":"2026","journal-title":"Meta-Radiology"},{"key":"B45","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"International Conference on Learning Representations","author":"Kipf","year":"2017"},{"key":"B46","doi-asserted-by":"crossref","DOI":"10.1109\/ISBI.2019.8759531","article-title":"Graph convolutional neural networks for Alzheimer\u2019s disease classification","volume-title":"Proceedings of the IEEE International Symposium on Biomedical Imaging","author":"Song","year":"2019"},{"key":"B47","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1162\/netn_a_00171","article-title":"Graph convolutional network for fMRI analysis based on connectivity neighborhood","volume":"5","author":"Wang","year":"2021","journal-title":"Network Neurosci"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104096","article-title":"Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction","volume":"127","author":"Jiang","year":"2020","journal-title":"Comput Biol Med"},{"key":"B49","doi-asserted-by":"publisher","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":"B50","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-215497","article-title":"Predicting brain amyloid-beta PET grades with graph convolutional networks based on functional MRI and multi-level functional connectivity","volume":"86","author":"Li","year":"2022","journal-title":"J Alzheimer\u2019s Dis"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0212470","article-title":"Graph-to-signal transformation based classification of functional connectivity brain networks","volume":"14","author":"Munia","year":"2019","journal-title":"PloS One"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3171778","article-title":"Brain connectivity based graph convolutional networks and its application to infant age prediction","volume":"41","author":"Li","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"B53","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"B54","article-title":"A generalization of transformer networks to graphs","volume-title":"Arxiv Preprint Arxiv:2012.09699","author":"Dwivedi","year":"2020"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhad377","article-title":"Comparison of the effects of peritoneal dialysis and hemodialysis on spontaneous brain activity in CKD patients: An rs-fMRI study","volume":"34","author":"Zhang","year":"2024","journal-title":"Cereb Cortex"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2017.00055","article-title":"Remodeling Pearson\u2019s Correlation for functional brain network estimation and autism spectrum disorder identification","volume":"11","author":"Li","year":"2017","journal-title":"Front Neuroinf"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2934230","article-title":"Toward a better estimation of functional brain network for mild cognitive impairment identification: A transfer learning view","volume":"24","author":"Li","year":"2020","journal-title":"IEEE J BioMed Health Inf"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.21037\/qims-24-1265","article-title":"Brain surface area and function alterations are correlated with cognition in patients with end-stage renal disease","volume":"15","author":"Wang","year":"2025","journal-title":"Quantitative Imaging Med Surg"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2025.100136","article-title":"Applications of resting-state fMRI and machine learning in cognitive impairment in type 2 diabetes mellitus: A scoping review","volume":"3","author":"Sun","year":"2025","journal-title":"Meta-Radiology"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2025.100149","article-title":"Potential neural mechanisms and imaging changes in type 2 diabetes with cognitive impairment","volume":"3","author":"Wang","year":"2025","journal-title":"Meta-Radiology"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1186\/1753-4631-1-3","article-title":"Graph theoretical analysis of complex networks in the brain","volume":"1","author":"Stam","year":"2007","journal-title":"Nonlinear BioMed Phys"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.31887\/DCNS.2013.15.3\/osporns","article-title":"Structure and function of complex brain networks","volume":"15","author":"Sporns","year":"2013","journal-title":"Dialogues Clin Neurosci"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2015.02.011","article-title":"The relation between structural and functional connectivity patterns in complex brain networks","volume":"103","author":"Stam","year":"2016","journal-title":"Int J Psychophysiol"},{"key":"B64","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s44387-025-00067-x","article-title":"Rethinking functional brain connectome analysis: Do graph deep learning models help","volume":"2","author":"Han","year":"2026","journal-title":"NPJ Artif Intell"},{"key":"B65","first-page":"47","article-title":"On the limits of applying graph transformers for brain connectome classification","volume-title":"Proceedings on \u201cI Can\u2019t Believe It\u2019s Not Better: Challenges in Applied Deep Learning\u201d at ICLR 2025 Workshops","author":"Rangel","year":"2025"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.3390\/jcdd13060228","article-title":"Electrocardiographic alterations combined with hematological, biochemical, and metabolic profiles predict prognosis in kawasaki disease","volume":"13","author":"Wang","year":"2026","journal-title":"J Cardiovasc Dev Dis"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1014223","article-title":"Partial domain adaptation enables cross domain cell type annotation between scrna-seq and snrna-seq","volume":"22","author":"Chen","year":"2026","journal-title":"PloS Comput Biol"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2026.3662418","article-title":"Test-time few-shot object detection via dynamic prototype fusion","volume":"56","author":"Wu","year":"2026","journal-title":"IEEE Trans Cybern"},{"key":"B69","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100003","article-title":"A review of uncertainty estimation and its application in medical imaging","volume":"1","author":"Zou","year":"2023","journal-title":"Meta-Radiology"}],"container-title":["Frontiers in Psychiatry"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2026.1906443\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:55:04Z","timestamp":1786514104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2026.1906443\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":69,"alternative-id":["10.3389\/fpsyt.2026.1906443"],"URL":"https:\/\/doi.org\/10.3389\/fpsyt.2026.1906443","relation":{},"ISSN":["1664-0640"],"issn-type":[{"value":"1664-0640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]},"article-number":"1906443"}}