{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:31:48Z","timestamp":1772137908137,"version":"3.50.1"},"reference-count":60,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Science and Technology for the People of Qingdao City","award":["23-2-8-smjk-2-nsh"],"award-info":[{"award-number":["23-2-8-smjk-2-nsh"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing brain imaging data, ignoring other non-imaging data (e.g., genetic, clinical information, etc.) that may have underlying disease information. In addition, imaging data acquired from different devices may exhibit varying degrees of heterogeneity, potentially resulting in numerous noisy connections during network construction. To address these challenges, this study proposes a Multimodal Multiview Bilinear Graph Convolution (MMBGCN) framework for disease risk prediction. Firstly, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) features are extracted from magnetic resonance imaging (MRI), and non-imaging information is combined with the features extracted from MRI to construct a multimodal shared adjacency matrix. The shared adjacency matrix is then used to construct the multiview network so that the effect of potential disease information in the non-imaging information on the model can be considered. Finally, the MRI features extracted by the multiview network are weighted to reduce noise, and then the spatial pattern is restored by bilinear convolution. The features of the recovered spatial patterns are then combined with the genetic information for disease prediction. The proposed method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments demonstrate the superior performance of the proposed framework and its ability to outperform other related algorithms. The average classification accuracy in the binary classification task in this study is 89.6%. The experimental results demonstrate that the method proposed in this study facilitates research on MCI diagnosis using multimodal data.<\/jats:p>","DOI":"10.1088\/2057-1976\/ada8af","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T17:48:46Z","timestamp":1736531326000},"page":"025011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis"],"prefix":"10.1088","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3382-7815","authenticated-orcid":true,"given":"Guanghui","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benzheng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"bpexada8afbib1","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","article-title":"Alzheimer's disease","volume":"397","author":"Scheltens","year":"2021","journal-title":"The Lancet"},{"key":"bpexada8afbib2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s41398-023-02361-1","article-title":"Global prevalence of mild cognitive impairment among older adults living in nursing homes: a meta-analysis and systematic review of epidemiological surveys","volume":"13","author":"Chen","year":"2023","journal-title":"Transl."},{"key":"bpexada8afbib3","doi-asserted-by":"publisher","DOI":"10.2196\/54538","article-title":"Integrating biomarkers from virtual reality and magnetic resonance imaging for the early detection of mild cognitive impairment using a multimodal learning approach: validation study","volume":"26","author":"Park","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"bpexada8afbib4","doi-asserted-by":"publisher","DOI":"10.1002\/trc2.12385","article-title":"Alzheimer's disease drug development pipeline: 2023","volume":"9","author":"Cummings","year":"2023","journal-title":"Alzheimer's & Dementia: Translational Research & Clinical Interventions"},{"key":"bpexada8afbib5","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1305565","article-title":"Automatic detection of mild cognitive impairment based on deep learning and radiomics of mr imaging","volume":"11","author":"Yang","year":"2024","journal-title":"Frontiers in Medicine"},{"key":"bpexada8afbib6","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.neuroimage.2009.07.011","article-title":"Optimizing power to track brain degeneration in alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an adni study of 515 subjects","volume":"48","author":"Hua","year":"2009","journal-title":"Neuroimage"},{"key":"bpexada8afbib7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0048973","article-title":"A longitudinal study of atrophy in amnestic mild cognitive impairment and normal aging revealed by cortical thickness","volume":"7","author":"Disease Neuroimaging Initiative","year":"2012","journal-title":"PLoS One"},{"key":"bpexada8afbib8","doi-asserted-by":"publisher","first-page":"2322\u2013e19","DOI":"10.1016\/j.neurobiolaging.2010.05.023","article-title":"Prediction of mci to ad conversion, via mri, csf biomarkers, and pattern classification","volume":"32","author":"Davatzikos","year":"2011","journal-title":"Neurobiology of aging"},{"key":"bpexada8afbib9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116459","article-title":"A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in alzheimers disease","volume":"208","author":"Alzheimers Disease Neuroimaging Initiative","year":"2020","journal-title":"Neuroimage"},{"key":"bpexada8afbib10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-37500-7","article-title":"Multimodal multitask learning for predicting mci to ad conversion using stacked polynomial attention network and adaptive exponential decay","volume":"13","author":"Ho","year":"2023","journal-title":"Sci. Rep."},{"key":"bpexada8afbib11","doi-asserted-by":"publisher","first-page":"5648","DOI":"10.3390\/s23125648","article-title":"An optimized deep learning model for predicting mild cognitive impairment using structural mri","volume":"23","author":"Alyoubi","year":"2023","journal-title":"Sensors"},{"key":"bpexada8afbib12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002254","article-title":"Apoe-related risk of mild cognitive impairment and dementia for prevention trials: an analysis of four cohorts","volume":"14","author":"Qian","year":"2017","journal-title":"PLoS medicine"},{"key":"bpexada8afbib13","doi-asserted-by":"publisher","first-page":"3254","DOI":"10.1038\/s41598-020-74399-w","article-title":"Multimodal deep learning models for early detection of alzheimers disease stage","volume":"11","author":"Venugopalan","year":"2021","journal-title":"Sci. Rep."},{"key":"bpexada8afbib14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107782","article-title":"Multi-modal imaging genetics data fusion by deep auto-encoder and self-representation network for alzheimer's disease diagnosis and biomarkers extraction","volume":"130","author":"Jiao","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"bpexada8afbib15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2024.3439530","article-title":"Modality-aware discriminative fusion network for integrated analysis of brain imaging genomics","author":"Sheng","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"bpexada8afbib16","doi-asserted-by":"publisher","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":"bpexada8afbib17","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/978-3-031-43907-0_60","article-title":"Graph convolutional network with morphometric similarity networks for schizophrenia classification","author":"Hye","year":"2023"},{"key":"bpexada8afbib18","doi-asserted-by":"publisher","first-page":"2968","DOI":"10.1109\/JBHI.2023.3262948","article-title":"Gccn: Graph capsule convolutional network for progressive mild cognitive impairment prediction and pathogenesis identification based on imaging genetic data","volume":"27","author":"Shang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"bpexada8afbib19","author":"Kipf","year":"2016","journal-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint"},{"key":"bpexada8afbib20","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/ICDM.2018.00092","article-title":"Integrative analysis of patient health records and neuroimages via memory-based graph convolutional network","volume":"2018","author":"Zhang","year":"2018"},{"key":"bpexada8afbib21","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1109\/BIBM52615.2021.9669504","article-title":"Interpretable temporal graph neural network for prognostic prediction of alzheimers disease using longitudinal neuroimaging data","volume":"2021","author":"Kim","year":"2021"},{"key":"bpexada8afbib22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102883","article-title":"Hypergraph-regularized multimodal learning by graph diffusion for imaging genetics based alzheimers disease diagnosis","volume":"89","author":"Wang","year":"2023","journal-title":"Medical Image Analysis"},{"key":"bpexada8afbib23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109106","article-title":"Multi-scale enhanced graph convolutional network for mild cognitive impairment detection","volume":"134","author":"Lei","year":"2023","journal-title":"Pattern Recognit."},{"key":"bpexada8afbib24","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/TCBB.2023.3335369","article-title":"Graph-based fusion of imaging, genetic and clinical data for degenerative disease diagnosis","volume":"21","author":"Guo","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"bpexada8afbib25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3412846","article-title":"Dynamic graph learning convolutional networks for semi-supervised classification","volume":"17","author":"Fu","year":"2021","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"bpexada8afbib26","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","article-title":"The alzheimers disease neuroimaging initiative","volume":"15","author":"Mueller","year":"2005","journal-title":"Neuroimaging Clinics of North America"},{"key":"bpexada8afbib27","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1101\/2022.06.11.495736","article-title":"Cat-a computational anatomy toolbox for the analysis of structural mri data","volume":"06","author":"(Alzheimers Disease Neuroimaging Initiative)","year":"2022","journal-title":"biorxiv"},{"key":"bpexada8afbib28","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1109\/TCSS.2022.3216483","article-title":"Mmgk: Multimodality multiview graph representations and knowledge embedding for mild cognitive impairment diagnosis","volume":"11","author":"Liu","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"bpexada8afbib29","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105669","article-title":"A hierarchical attention-based multimodal fusion framework for predicting the progression of alzheimers disease","volume":"88","author":"Lu","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"bpexada8afbib30","doi-asserted-by":"publisher","first-page":"4424","DOI":"10.1609\/aaai.v33i01.33014424","article-title":"Geniepath: Graph neural networks with adaptive receptive paths","volume":"33","author":"Liu","year":"2019"},{"key":"bpexada8afbib31","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume":"80","author":"Xu","year":"2018"},{"key":"bpexada8afbib32","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-030-32251-9_24","article-title":"Integrating heterogeneous brain networks for predicting brain disease conditions","author":"Zhang","year":"2019"},{"key":"bpexada8afbib33","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"bpexada8afbib34","doi-asserted-by":"publisher","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 alzheimers disease","volume":"48","author":"Parisot","year":"2018","journal-title":"Medical image analysis"},{"key":"bpexada8afbib35","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/ICCV.2015.170","article-title":"Bilinear cnn models for fine-grained visual recognition","author":"Lin","year":"2015","journal-title":"In Proceedings of the IEEE international conference on computer vision"},{"key":"bpexada8afbib36","doi-asserted-by":"publisher","first-page":"4514","DOI":"10.1109\/TNNLS.2020.3018790","article-title":"Inductive structure consistent hashing via flexible semantic calibration","volume":"32","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"bpexada8afbib37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102952","article-title":"Graph convolutional network with sample and feature weights for alzheimers disease diagnosis","volume":"59","author":"Zeng","year":"2022","journal-title":"Information Processing & Management"},{"key":"bpexada8afbib38","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/a17010024","article-title":"Entropy and the kullback-leibler divergence for bayesian networks: Computational complexity and efficient implementation","volume":"17","author":"Scutari","year":"2024","journal-title":"Algorithms"},{"key":"bpexada8afbib39","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101929","article-title":"Cortical graph neural network for ad and mci diagnosis and transfer learning across populations","volume":"23","author":"Alzheimers Disease Neuroimage Initiative","year":"2019","journal-title":"NeuroImage: Clinical"},{"key":"bpexada8afbib40","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1007\/s11280-023-01179-7","article-title":"Structure-adaptive graph neural network with temporal representation and residual connections","volume":"26","author":"Bi","year":"2023","journal-title":"World Wide Web"},{"key":"bpexada8afbib41","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1109\/TMI.2021.3051604","article-title":"A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity","volume":"40","author":"Yao","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexada8afbib42","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.1007\/s10489-022-03528-x","article-title":"Maximum mutual information for feature extraction from graph-structured data: Application to alzheimers disease classification","volume":"53","author":"Yang","year":"2023","journal-title":"Appl. Intell."},{"key":"bpexada8afbib43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101625","article-title":"Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of alzheimer's disease","volume":"60","author":"Alzheimer's Disease Neuroimaging Initiative","year":"2020","journal-title":"Medical Image Anal."},{"key":"bpexada8afbib44","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1109\/TMI.2021.3063150","article-title":"Relation-induced multi-modal shared representation learning for alzheimers disease diagnosis","volume":"40","author":"Ning","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexada8afbib45","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1109\/TMI.2020.2976825","article-title":"Deep spatial-temporal feature fusion from adaptive dynamic functional connectivity for mci identification","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexada8afbib46","doi-asserted-by":"publisher","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":"bpexada8afbib47","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/TMI.2023.3294967","article-title":"Mapping multi-modal brain connectome for brain disorder diagnosis via cross-modal mutual learning","volume":"43","author":"Yang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"bpexada8afbib48","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1093\/bioinformatics\/btz967","article-title":"Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment","volume":"36","author":"Bi","year":"2020","journal-title":"Bioinformatics"},{"key":"bpexada8afbib49","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"bpexada8afbib50","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.5555\/2627435.2670313","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"The journal of machine learning research"},{"key":"bpexada8afbib51","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1016\/j.neuroimage.2010.08.063","article-title":"Network modelling methods for fmri","volume":"54","author":"Smith","year":"2011","journal-title":"Neuroimage"},{"key":"bpexada8afbib52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104963","article-title":"Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks","volume":"139","author":"Yang","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"bpexada8afbib53","doi-asserted-by":"publisher","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 alzheimers disease","volume":"48","author":"Parisot","year":"2018","journal-title":"Medical image analysis"},{"key":"bpexada8afbib54","doi-asserted-by":"publisher","first-page":"10","DOI":"10.48550\/arXiv.1710.10903","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"stat"},{"key":"bpexada8afbib55","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"bpexada8afbib56","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0068910","article-title":"Brainnet viewer: a network visualization tool for human brain connectomics","volume":"8","author":"Xia","year":"2013","journal-title":"PloS one"},{"key":"bpexada8afbib57","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3390\/brainsci12010080","article-title":"Multi-modal feature selection with feature correlation and feature structure fusion for mci and ad classification","volume":"12","author":"Jiao","year":"2022","journal-title":"Brain Sciences"},{"key":"bpexada8afbib58","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2021.671351","article-title":"Altered functional connectivity of the basal nucleus of meynert in subjective cognitive impairment, early mild cognitive impairment, and late mild cognitive impairment","volume":"13","author":"Xu","year":"2021","journal-title":"Frontiers in Aging Neuroscience"},{"key":"bpexada8afbib59","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1002\/hbm.26146","article-title":"Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for alzheimer's disease and mild cognitive impairment","volume":"44","author":"Shi","year":"2023","journal-title":"Human brain mapping"},{"key":"bpexada8afbib60","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102678","article-title":"Diagnosis of alzheimers disease by joining dual attention cnn and mlp based on structural mris, clinical and genetic data","volume":"145","author":"Alzheimers Disease Neuroimaging Initiative","year":"2023","journal-title":"Artif. Intell. Med."}],"container-title":["Biomedical Physics &amp; Engineering Express"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T15:21:08Z","timestamp":1737559268000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2057-1976\/ada8af"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1,22]]},"published-print":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.1088\/2057-1976\/ada8af","relation":{"has-review":[{"id-type":"doi","id":"10.1088\/2057-1976\/ADA8AF\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/ADA8AF\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/ADA8AF\/v2\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/ADA8AF\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/ADA8AF\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2057-1976\/ADA8AF\/v2\/decision1","asserted-by":"object"}]},"ISSN":["2057-1976"],"issn-type":[{"value":"2057-1976","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,22]]},"assertion":[{"value":"Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis","name":"article_title","label":"Article Title"},{"value":"Biomedical Physics & Engineering Express","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-09-28","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-10","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-22","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}