乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T06:04:00Z","timestamp":1732341840422,"version":"3.28.0"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.ins.2024.121622","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T21:39:05Z","timestamp":1731101945000},"page":"121622","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis"],"prefix":"10.1016","volume":"691","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3180-7347","authenticated-orcid":false,"given":"Weiping","family":"Ding","sequence":"first","affiliation":[]},{"given":"Tao","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Jiashuang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Hengrong","family":"Ju","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5921-8512","authenticated-orcid":false,"given":"Shu","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.ins.2024.121622_br0010","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TMI.2022.3218745","article-title":"Braingb: a benchmark for brain network analysis with graph neural networks","volume":"42","author":"Cui","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.ins.2024.121622_br0020","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1109\/TMI.2022.3186797","article-title":"Stacked topological preserving dynamic brain networks representation and classification","volume":"41","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.ins.2024.121622_br0030","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1109\/TMI.2023.3342047","article-title":"Ordinal pattern tree: a new representation method for brain network analysis","volume":"43","author":"Ma","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.ins.2024.121622_br0040","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1093\/brain\/aww143","article-title":"Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders","volume":"139","author":"Zhang","year":"2016","journal-title":"Brain"},{"issue":"38","key":"10.1016\/j.ins.2024.121622_br0050","doi-asserted-by":"crossref","first-page":"23904","DOI":"10.1073\/pnas.2002645117","article-title":"The emergence of a functionally flexible brain during early infancy","volume":"117","author":"Yin","year":"2020","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.ins.2024.121622_br0060","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.schres.2017.09.035","article-title":"The instability of functional connectivity in patients with schizophrenia and their siblings: a dynamic connectivity study","volume":"195","author":"Guo","year":"2018","journal-title":"Schizophr. Res."},{"key":"10.1016\/j.ins.2024.121622_br0070","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","article-title":"Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment","volume":"146","author":"Kawahara","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.ins.2024.121622_br0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101709","article-title":"Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis","volume":"63","author":"Jie","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ins.2024.121622_br0090","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."},{"issue":"9","key":"10.1016\/j.ins.2024.121622_br0100","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TMI.2021.3077079","article-title":"Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural mri","volume":"40","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.ins.2024.121622_br0110","doi-asserted-by":"crossref","first-page":"13261","DOI":"10.1007\/s10489-022-04203-x","article-title":"An evolving graph convolutional network for dynamic functional brain network","volume":"53","author":"Wang","year":"2023","journal-title":"Appl. Intell."},{"issue":"6","key":"10.1016\/j.ins.2024.121622_br0120","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/TMI.2024.3363014","article-title":"Spatio-temporal graph hubness propagation model for dynamic brain network classification","volume":"43","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ins.2024.121622_br0130","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.neuroimage.2013.05.079","article-title":"Dynamic functional connectivity: promise, issues, and interpretations","volume":"80","author":"Hutchison","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.ins.2024.121622_br0140","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.neuroimage.2017.08.006","article-title":"Task-based dynamic functional connectivity: recent findings and open questions","volume":"180","author":"Gonzalez-Castillo","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.ins.2024.121622_br0150","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.eswa.2017.12.005","article-title":"A multi-attribute fusion approach extending Dempster\u2013Shafer theory for combinatorial-type evidences","volume":"96","author":"Sun","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2024.121622_br0160","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.ins.2022.04.060","article-title":"How to aggregate uncertain and incomplete cognitive evaluation information in lung cancer treatment plan selection? A method based on Dempster-Shafer theory","volume":"603","author":"Fang","year":"2022","journal-title":"Inf. Sci."},{"issue":"5","key":"10.1016\/j.ins.2024.121622_br0170","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","article-title":"A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises","volume":"109","author":"Zhou","year":"2021","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.ins.2024.121622_br0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102430","article-title":"Sspnet: an interpretable 3d-cnn for classification of schizophrenia using phase maps of resting-state complex-valued fmri data","volume":"79","author":"Lin","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ins.2024.121622_br0190","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.ebiom.2019.08.023","article-title":"Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site fmri data","volume":"47","author":"Yan","year":"2019","journal-title":"eBioMedicine"},{"key":"10.1016\/j.ins.2024.121622_br0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102370","article-title":"Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity","volume":"77","author":"Huang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ins.2024.121622_br0210","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.ins.2022.07.041","article-title":"Fc\u2013hat: hypergraph attention network for functional brain network classification","volume":"608","author":"Ji","year":"2022","journal-title":"Inf. Sci."},{"issue":"3","key":"10.1016\/j.ins.2024.121622_br0220","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/TMI.2023.3327283","article-title":"A multi-graph cross-attention-based region-aware feature fusion network using multi-template for brain disorder diagnosis","volume":"43","author":"Ma","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ins.2024.121622_br0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102679","article-title":"Sd-cnn: a static-dynamic convolutional neural network for functional brain networks","volume":"83","author":"Huang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ins.2024.121622_br0240","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ins.2021.09.035","article-title":"Incomplete multi-modal brain image fusion for epilepsy classification","volume":"582","author":"Zhu","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2024.121622_br0250","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.ins.2022.08.017","article-title":"Robust multi-view learning via adaptive regression","volume":"610","author":"Jiang","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2024.121622_br0260","series-title":"Workshop on Clinical Image-Based Procedures","first-page":"54","article-title":"Convolutional redistribution network for multi-view medical image diagnosis","author":"Zhou","year":"2022"},{"key":"10.1016\/j.ins.2024.121622_br0270","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"3975","article-title":"Rfnet: region-aware fusion network for incomplete multi-modal brain tumor segmentation","author":"Ding","year":"2021"},{"key":"10.1016\/j.ins.2024.121622_br0280","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"20707","article-title":"Multimodal dynamics: dynamical fusion for trustworthy multimodal classification","author":"Han","year":"2022"},{"issue":"12","key":"10.1016\/j.ins.2024.121622_br0290","doi-asserted-by":"crossref","first-page":"9236","DOI":"10.1109\/TPAMI.2021.3125995","article-title":"Af: an association-based fusion method for multi-modal classification","volume":"44","author":"Liang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.ins.2024.121622_br0300","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1214\/aoms\/1177698950","article-title":"Upper and lower probabilities induced by a multivalued mapping","volume":"38","author":"Dempster","year":"1967","journal-title":"Ann. Math. Stat."},{"issue":"2","key":"10.1016\/j.ins.2024.121622_br0310","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/j.2517-6161.1968.tb00722.x","article-title":"A generalization of Bayesian inference","volume":"30","author":"Dempster","year":"1968","journal-title":"J. R. Stat. Soc., Ser. B, Methodol."},{"issue":"9","key":"10.1016\/j.ins.2024.121622_br0320","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.ress.2010.07.014","article-title":"Model validation under epistemic uncertainty","volume":"96","author":"Sankararaman","year":"2011","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"2","key":"10.1016\/j.ins.2024.121622_br0330","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1109\/TPAMI.2022.3171983","article-title":"Trusted multi-view classification with dynamic evidential fusion","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ins.2024.121622_br0340","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ijar.2022.08.013","article-title":"Deep evidential fusion network for medical image classification","volume":"150","author":"Xu","year":"2022","journal-title":"Int. J. Approx. Reason."},{"key":"10.1016\/j.ins.2024.121622_br0350","first-page":"7585","article-title":"Trusted multi-view deep learning with opinion aggregation","volume":"36","author":"Liu","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"2","key":"10.1016\/j.ins.2024.121622_br0360","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/TII.2022.3206343","article-title":"Uncertainty-aware multiview deep learning for internet of things applications","volume":"19","author":"Xu","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2\u20133","key":"10.1016\/j.ins.2024.121622_br0370","first-page":"165","article-title":"Generalized Dirichlet distribution in Bayesian analysis","volume":"97","author":"Wong","year":"1998","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.ins.2024.121622_br0380","series-title":"2012 15th International Conference on Information Fusion","first-page":"1225","article-title":"Interpretation and fusion of hyper opinions in subjective logic","author":"J\u00f8sang","year":"2012"},{"key":"10.1016\/j.ins.2024.121622_br0390","first-page":"1377","article-title":"Dparsf: a matlab toolbox for \u201cpipeline\u201d data analysis of resting-state fmri","volume":"4","author":"Yan","year":"2010","journal-title":"Front. Syst. Neurosci."},{"issue":"3","key":"10.1016\/j.ins.2024.121622_br0400","doi-asserted-by":"crossref","first-page":"238","DOI":"10.2307\/1403797","article-title":"Discriminatory analysis. Nonparametric discrimination: consistency properties","volume":"57","author":"Fix","year":"1989","journal-title":"Int. Stat. Rev."},{"key":"10.1016\/j.ins.2024.121622_br0410","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119136","article-title":"Fast svm classifier for large-scale classification problems","volume":"642","author":"Wang","year":"2023","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.ins.2024.121622_br0420","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1038\/s41467-021-23774-w","article-title":"Mogonet integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"issue":"4","key":"10.1016\/j.ins.2024.121622_br0430","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1016\/j.neuroimage.2011.01.047","article-title":"Stochastic tractography study of inferior frontal gyrus anatomical connectivity in schizophrenia","volume":"55","author":"Kubicki","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.ins.2024.121622_br0440","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00401-008-0404-0","article-title":"The thalamus and schizophrenia: current status of research","volume":"117","author":"Byne","year":"2009","journal-title":"Acta Neuropathol."},{"issue":"5183","key":"10.1016\/j.ins.2024.121622_br0450","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1126\/science.7939669","article-title":"Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging","volume":"266","author":"Andreasen","year":"1994","journal-title":"Science"},{"issue":"7","key":"10.1016\/j.ins.2024.121622_br0460","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1176\/appi.ajp.158.7.1105","article-title":"Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia","volume":"158","author":"Perlstein","year":"2001","journal-title":"Am. J. Psychiatr."},{"issue":"11","key":"10.1016\/j.ins.2024.121622_br0470","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1176\/ajp.2006.163.11.1969","article-title":"Dysfunctional prefrontal regional specialization and compensation in schizophrenia","volume":"163","author":"Tan","year":"2006","journal-title":"Am. J. Psychiatr."},{"key":"10.1016\/j.ins.2024.121622_br0480","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2021.118635","article-title":"Identification of community structure-based brain states and transitions using functional mri","volume":"244","author":"Bian","year":"2021","journal-title":"NeuroImage"},{"issue":"4","key":"10.1016\/j.ins.2024.121622_br0490","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s12559-022-10014-6","article-title":"Hybrid high-order brain functional networks for schizophrenia-aided diagnosis","volume":"14","author":"Xin","year":"2022","journal-title":"Cogn. Comput."}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025524015366?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025524015366?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T02:58:18Z","timestamp":1732330698000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025524015366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":49,"alternative-id":["S0020025524015366"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2024.121622","relation":{},"ISSN":["0020-0255"],"issn-type":[{"type":"print","value":"0020-0255"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2024.121622","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"121622"}}