{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T04:02:39Z","timestamp":1794024159460,"version":"4.8.0"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"Horizon 2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004147","name":"Tsinghua University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012338","name":"The Alan Turing Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000735","name":"University of Cambridge","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000288","name":"The Royal Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000288","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.patcog.2025.112595","type":"journal-article","created":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T05:29:17Z","timestamp":1760246957000},"page":"112595","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"PC","title":["Brain foundation models with hypergraph dynamic adapter for brain disease analysis"],"prefix":"10.1016","volume":"172","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0887-7408","authenticated-orcid":false,"given":"Zhongying","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lipei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelica I.","family":"Aviles-Rivero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoe","family":"Kourtzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carola-Bibiane","family":"Sch\u00f6nlieb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2025.112595_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109061","article-title":"Conditional GAN with 3D discriminator for MRI generation of Alzheimer\u2019s disease progression","volume":"133","author":"Jung","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112595_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103301","article-title":"BrainSegFounder: towards 3D foundation models for neuroimage segmentation","volume":"97","author":"Cox","year":"2024","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.patcog.2025.112595_bib0003","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.patcog.2025.112595_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111646","article-title":"Vision-language foundation model for generalizable nasal disease diagnosis using unlabeled endoscopic records","volume":"165","author":"Liu","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112595_bib0005","series-title":"ICLR","article-title":"NeuroLM: a universal multi-task foundation model for bridging the gap between language and EEG signals","author":"Jiang","year":"2025"},{"key":"10.1016\/j.patcog.2025.112595_bib0006","first-page":"39249","article-title":"EEGPT: pretrained transformer for universal and reliable representation of EEG signals","volume":"37","author":"Wang","year":"2024","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2025.112595_bib0007","unstructured":"J. Cheng, J. Ye, Z. Deng, J. Chen, T. Li, H. Wang, Y. Su, Z. Huang, J. Chen, L. Jiang, et al., SAM-Med2D, (2023). arXiv preprint arXiv: 2308.16184."},{"key":"10.1016\/j.patcog.2025.112595_bib0008","article-title":"SAM-Med3D: a vision foundation model for general-purpose segmentation on volumetric medical images","author":"Wang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2025.112595_bib0009","series-title":"ICLR","article-title":"BrainLM: a foundation model for brain activity recordings","author":"Caro","year":"2024"},{"key":"10.1016\/j.patcog.2025.112595_bib0010","article-title":"A foundation model for generalized brain MRI analysis","author":"Tak","year":"2024","journal-title":"medRxiv"},{"key":"10.1016\/j.patcog.2025.112595_bib0011","series-title":"ICLR","article-title":"Large brain model for learning generic representations with tremendous EEG data in BCI","author":"Jiang","year":"2024"},{"key":"10.1016\/j.patcog.2025.112595_bib0012","unstructured":"C.A. Barbano, M. Brunello, B. Dufumier, M. Grangetto, Anatomical foundation models for brain MRIs, (2024). arXiv preprint arXiv: 2408.07079."},{"issue":"1","key":"10.1016\/j.patcog.2025.112595_bib0013","doi-asserted-by":"crossref","first-page":"5210","DOI":"10.1038\/s41598-024-56001-9","article-title":"Automated detection of Alzheimer\u2019s disease: a multi-modal approach with 3D MRI and amyloid PET","volume":"14","author":"Castellano","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.patcog.2025.112595_bib0014","series-title":"ICCV","first-page":"4015","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.patcog.2025.112595_bib0015","series-title":"AAAI","first-page":"3558","article-title":"Hypergraph neural networks","volume":"33","author":"Feng","year":"2019"},{"issue":"3","key":"10.1016\/j.patcog.2025.112595_bib0016","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","article-title":"Hgnn+: general hypergraph neural networks","volume":"45","author":"Gao","year":"2022","journal-title":"IEEE Trans. on Pattern Anal. and Mach. Intell."},{"key":"10.1016\/j.patcog.2025.112595_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111544","article-title":"Multi-modal hypergraph contrastive learning for medical image segmentation","volume":"165","author":"Jing","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112595_bib0018","doi-asserted-by":"crossref","first-page":"3304","DOI":"10.1109\/JBHI.2024.3490664","article-title":"Hypercomplex graph neural network: towards deep intersection of multi-modal brain networks","volume":"29","author":"Yang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.patcog.2025.112595_bib0019","doi-asserted-by":"crossref","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":"10.1016\/j.patcog.2025.112595_bib0020","doi-asserted-by":"crossref","first-page":"509","DOI":"10.3389\/fnins.2019.00509","article-title":"Diagnosis of Alzheimer\u2019s disease via multi-modality 3D convolutional neural network","volume":"13","author":"Huang","year":"2019","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.patcog.2025.112595_bib0021","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.inffus.2020.08.023","article-title":"Heterogeneous data fusion for predicting mild cognitive impairment conversion","volume":"66","author":"Shen","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2025.112595_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101850","article-title":"Multi-scale graph-based grading for Alzheimer\u2019s disease prediction","volume":"67","author":"Hett","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2025.112595_bib0023","series-title":"2023 IEEE International Conference on Multimedia and Expo (ICME)","first-page":"192","article-title":"Early diagnosis of Alzheimer\u2019s disease based on multimodal hypergraph attention network","author":"Li","year":"2023"},{"key":"10.1016\/j.patcog.2025.112595_bib0024","series-title":"MICCAI","first-page":"547","article-title":"Visual-attribute prompt learning for progressive mild cognitive impairment prediction","author":"Kang","year":"2023"},{"key":"10.1016\/j.patcog.2025.112595_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109438","article-title":"Multi-scale multimodal deep learning framework for Alzheimer\u2019s disease diagnosis","volume":"184","author":"Abdelaziz","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"10.1016\/j.patcog.2025.112595_bib0026","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.3174\/ajnr.A5667","article-title":"Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas","volume":"39","author":"Chang","year":"2018","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.patcog.2025.112595_bib0027","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1007\/s10278-017-0009-z","article-title":"Residual deep convolutional neural network predicts MGMT methylation status","volume":"30","author":"Korfiatis","year":"2017","journal-title":"J. Digit. Imaging"},{"issue":"5","key":"10.1016\/j.patcog.2025.112595_bib0028","doi-asserted-by":"crossref","first-page":"845","DOI":"10.3174\/ajnr.A7029","article-title":"MRI-based deep-learning method for determining glioma MGMT promoter methylation status","volume":"42","author":"Yogananda","year":"2021","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.patcog.2025.112595_bib0029","unstructured":"U. Baid, S. Ghodasara, S. Mohan, M. Bilello, E. Calabrese, E. Colak, K. Farahani, J. Kalpathy-Cramer, F.C. Kitamura, S. Pati, et al., The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification, (2021). arXiv preprint arXiv: 2107.02314."},{"key":"10.1016\/j.patcog.2025.112595_bib0030","series-title":"MICCAI Brainlesion Workshop","first-page":"345","article-title":"A deep learning approach to glioblastoma radiogenomic classification using brain MRI","author":"Emchinov","year":"2021"},{"issue":"1","key":"10.1016\/j.patcog.2025.112595_bib0031","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.11.1.014503","article-title":"MGMT Promoter methylation prediction based on multiparametric MRI via vision graph neural network","volume":"11","author":"Hu","year":"2024","journal-title":"J. Med. Imaging"},{"issue":"2","key":"10.1016\/j.patcog.2025.112595_bib0032","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1002\/mp.12059","article-title":"A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer","volume":"44","author":"Hugo","year":"2017","journal-title":"Med. Phys."},{"issue":"6","key":"10.1016\/j.patcog.2025.112595_bib0033","article-title":"The University of California San Francisco preoperative diffuse glioma MRI dataset","volume":"4","author":"Calabrese","year":"2022","journal-title":"Radiol. : Artif. Intell."},{"issue":"1","key":"10.1016\/j.patcog.2025.112595_bib0034","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1038\/s41597-022-01401-7","article-title":"A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms","volume":"9","author":"Liew","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.patcog.2025.112595_bib0035","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TIP.2022.3186531","article-title":"Dynamic instance domain adaptation","volume":"31","author":"Deng","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2025.112595_bib0036","series-title":"CVPR","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.patcog.2025.112595_bib0037","series-title":"ICCV","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.patcog.2025.112595_bib0038","series-title":"NeurIPS","article-title":"PyTorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"issue":"18","key":"10.1016\/j.patcog.2025.112595_bib0039","doi-asserted-by":"crossref","first-page":"4545","DOI":"10.1002\/cncr.27441","volume":"118","author":"Lechapt-Zalcman","year":"2012","journal-title":"Cancer"},{"key":"10.1016\/j.patcog.2025.112595_bib0040","series-title":"ICLR","first-page":"10096","article-title":"EfficientNetV2: smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.patcog.2025.112595_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102989","article-title":"MGMT Promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models","volume":"90","author":"Saeed","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2025.112595_bib0042","unstructured":"S. Chen, K. Ma, Y. Zheng, Med3D: transfer learning for 3d medical image analysis, (2019). arXiv preprint arXiv: 1904.00625."},{"key":"10.1016\/j.patcog.2025.112595_bib0043","series-title":"MICCAI Brainlesion Workshop","first-page":"311","article-title":"3D MRI Brain tumor segmentation using autoencoder regularization","author":"Myronenko","year":"2018"},{"key":"10.1016\/j.patcog.2025.112595_bib0044","series-title":"MICCAI","first-page":"416","article-title":"SwinUNETR-V2: stronger swin transformers with stagewise convolutions for 3d medical image segmentation","author":"He","year":"2023"},{"key":"10.1016\/j.patcog.2025.112595_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105162","article-title":"Population-based GCN method for diagnosis of Alzheimer\u2019s disease using brain metabolic or volumetric features","volume":"86","author":"Zhang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.patcog.2025.112595_bib0046","series-title":"MICCAI","first-page":"3","article-title":"Biophysics informed pathological regularisation for brain tumour segmentation","author":"Zhang","year":"2024"},{"issue":"5","key":"10.1016\/j.patcog.2025.112595_bib0047","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TMI.2021.3055428","article-title":"Multi-site infant brain segmentation algorithms: the iSeg-2019 challenge","volume":"40","author":"Sun","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2025.112595_bib0048","unstructured":"J. Ma, Z. Yang, S. Kim, B. Chen, M. Baharoon, A. Fallahpour, R. Asakereh, H. Lyu, B. Wang, MedSAM2: megment anything in 3D medical images and videos, (2025). arXiv preprint arXiv: 2504.03600."},{"key":"10.1016\/j.patcog.2025.112595_bib0049","first-page":"110746","article-title":"Segvol: universal and interactive volumetric medical image segmentation","volume":"37","author":"Du","year":"2024","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2025.112595_bib0050","unstructured":"F. Isensee, M. Rokuss, L. Kr\u00e4mer, S. Dinkelacker, A. Ravindran, F. Stritzke, B. Hamm, T. Wald, M. Langenberg, C. Ulrich, et al., nninteractive: redefining 3d promptable segmentation, (2025). arXiv preprint arXiv: 2503.08373."},{"key":"10.1016\/j.patcog.2025.112595_bib0051","series-title":"IJCAI","first-page":"2635","article-title":"Dynamic hypergraph neural networks","author":"Jiang","year":"2019"},{"issue":"1","key":"10.1016\/j.patcog.2025.112595_bib0052","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neuroimage.2011.12.071","article-title":"Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer\u2019s disease","volume":"60","author":"Gray","year":"2012","journal-title":"NeuroImage"},{"issue":"4","key":"10.1016\/j.patcog.2025.112595_bib0053","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/JBHI.2018.2857217","article-title":"Multilevel feature representation of FDG-PET brain images for diagnosing Alzheimer\u2019s disease","volume":"23","author":"Pan","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.patcog.2025.112595_bib0054","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1002\/hbm.24783","article-title":"Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer\u2019s dementia score","volume":"41","author":"Yee","year":"2020","journal-title":"Hum. Brain Mapp."},{"issue":"6","key":"10.1016\/j.patcog.2025.112595_bib0055","doi-asserted-by":"crossref","DOI":"10.1088\/1402-4896\/add2a6","article-title":"Deep learning with guided attention for early diagnosis of Alzheimer\u2019s disease","volume":"100","author":"Hoang","year":"2025","journal-title":"Phys. Scr."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320325012580?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320325012580?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T02:21:59Z","timestamp":1765419719000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320325012580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":55,"alternative-id":["S0031320325012580"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2025.112595","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Brain foundation models with hypergraph dynamic adapter for brain disease analysis","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2025.112595","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"112595"}}