{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:39:08Z","timestamp":1792611548516,"version":"4.5.0"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"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":["Medical Image Analysis"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.media.2024.103140","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:50:37Z","timestamp":1709815837000},"page":"103140","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":30,"special_numbering":"C","title":["Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis"],"prefix":"10.1016","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6735-1217","authenticated-orcid":false,"given":"Haiyan","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjie","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-670X","authenticated-orcid":false,"given":"Manhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.media.2024.103140_b1","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1093\/brain\/awaa160","article-title":"MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide","volume":"143","author":"Bashyam","year":"2020","journal-title":"Brain"},{"issue":"4","key":"10.1016\/j.media.2024.103140_b2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1111\/jcpp.12838","article-title":"Annual research review: not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain","volume":"59","author":"Batalle","year":"2018","journal-title":"J. Child Psychol. Psychiatry"},{"issue":"9832","key":"10.1016\/j.media.2024.103140_b3","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1016\/S0140-6736(12)60820-4","article-title":"National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications","volume":"379","author":"Blencowe","year":"2012","journal-title":"Lancet"},{"key":"10.1016\/j.media.2024.103140_b4","article-title":"Graph transformer geometric learning of brain networks using multimodal MR images for brain age estimation","author":"Cai","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2024.103140_b5","doi-asserted-by":"crossref","DOI":"10.3389\/fped.2020.559658","article-title":"Quiet sleep organization of very preterm infants is correlated with postnatal maturation","volume":"8","author":"Cailleau","year":"2020","journal-title":"Front. Pediatr."},{"issue":"6","key":"10.1016\/j.media.2024.103140_b6","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1111\/j.1469-8749.1970.tb07862.x","article-title":"The estimation of the postmenstrual age: a comprehensive review","volume":"12","author":"Casaer","year":"1970","journal-title":"Dev. Med. Child Neurol."},{"issue":"8","key":"10.1016\/j.media.2024.103140_b7","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.3390\/diagnostics11081384","article-title":"Transmed: Transformers advance multi-modal medical image classification","volume":"11","author":"Dai","year":"2021","journal-title":"Diagnostics"},{"key":"10.1016\/j.media.2024.103140_b8","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"5","key":"10.1016\/j.media.2024.103140_b9","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1542\/peds.2004-1915","article-title":"Age terminology during the perinatal period","volume":"114","author":"Engle","year":"2004","journal-title":"Pediatrics"},{"key":"10.1016\/j.media.2024.103140_b10","first-page":"1","article-title":"Novel metrics to characterize temporal lobe of very preterm infants on term-equivalent brain MRI","author":"Erdei","year":"2023","journal-title":"Pediatr. Res."},{"key":"10.1016\/j.media.2024.103140_b11","first-page":"1","article-title":"Age-related vulnerability of the human brain connectome","author":"Filippi","year":"2023","journal-title":"Mol. Psychiatry"},{"issue":"5","key":"10.1016\/j.media.2024.103140_b12","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1203\/pdr.0b013e318045be99","article-title":"Assessment of infant brain development with frequency-domain near-infrared spectroscopy","volume":"61","author":"Franceschini","year":"2007","journal-title":"Pediatr. Res."},{"key":"10.1016\/j.media.2024.103140_b13","article-title":"Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth","volume":"25","author":"Galdi","year":"2020","journal-title":"NeuroImage: Clin."},{"issue":"10","key":"10.1016\/j.media.2024.103140_b14","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1038\/13158","article-title":"Brain development during childhood and adolescence: a longitudinal MRI study","volume":"2","author":"Giedd","year":"1999","journal-title":"Nature Neurosci."},{"issue":"11","key":"10.1016\/j.media.2024.103140_b15","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1093\/cercor\/bhr327","article-title":"Longitudinal development of cortical and subcortical gray matter from birth to 2 years","volume":"22","author":"Gilmore","year":"2012","journal-title":"Cerebral Cortex"},{"key":"10.1016\/j.media.2024.103140_b16","doi-asserted-by":"crossref","unstructured":"Girshick, R., 2015. Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision. ICCV, pp. 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"10.1016\/j.media.2024.103140_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11357-023-00924-0","article-title":"Brain age prediction across the human lifespan using multimodal MRI data","author":"Guan","year":"2023","journal-title":"GeroScience"},{"issue":"1","key":"10.1016\/j.media.2024.103140_b18","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/TMI.2021.3108910","article-title":"Global-local transformer for brain age estimation","volume":"41","author":"He","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2024.103140_b19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.media.2024.103140_b20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"2","key":"10.1016\/j.media.2024.103140_b21","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1002\/mrm.26462","article-title":"A dedicated neonatal brain imaging system","volume":"78","author":"Hughes","year":"2017","journal-title":"Magn. Reson. Med."},{"issue":"5","key":"10.1016\/j.media.2024.103140_b22","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1056\/NEJMra2303347","article-title":"Defining the neurologic consequences of preterm birth","volume":"389","author":"Inder","year":"2023","journal-title":"N. Engl. J. Med."},{"issue":"2","key":"10.1016\/j.media.2024.103140_b23","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1006\/nimg.2002.1132","article-title":"Improved optimization for the robust and accurate linear registration and motion correction of brain images","volume":"17","author":"Jenkinson","year":"2002","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.media.2024.103140_b24","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"Fsl","volume":"62","author":"Jenkinson","year":"2012","journal-title":"Neuroimage"},{"issue":"7","key":"10.1016\/j.media.2024.103140_b25","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1038\/35081509","article-title":"Functional brain development in humans","volume":"2","author":"Johnson","year":"2001","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.media.2024.103140_b26","series-title":"International Conference on Machine Learning","first-page":"5583","article-title":"Vilt: Vision-and-language transformer without convolution or region supervision","author":"Kim","year":"2021"},{"issue":"11","key":"10.1016\/j.media.2024.103140_b27","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"issue":"11","key":"10.1016\/j.media.2024.103140_b28","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1016\/S1474-4422(09)70257-1","article-title":"Imaging biomarkers of outcome in the developing preterm brain","volume":"8","author":"Ment","year":"2009","journal-title":"Lancet Neurol."},{"key":"10.1016\/j.media.2024.103140_b29","series-title":"Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging","first-page":"366","article-title":"Multimodal region-based transformer for the classification and prediction of Alzheimer\u2019s disease","volume":"Vol. 12036","author":"Mueller","year":"2022"},{"key":"10.1016\/j.media.2024.103140_b30","series-title":"2015 IEEE International Conference on Systems, Man, and Cybernetics","first-page":"2273","article-title":"Neonatal brain age estimation using manifold learning regression analysis","author":"Nakano","year":"2015"},{"key":"10.1016\/j.media.2024.103140_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101871","article-title":"Accurate brain age prediction with lightweight deep neural networks","volume":"68","author":"Peng","year":"2021","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/j.media.2024.103140_b32","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1093\/brain\/awaa137","article-title":"Development and validation of an interpretable deep learning framework for Alzheimer\u2019s disease classification","volume":"143","author":"Qiu","year":"2020","journal-title":"Brain"},{"issue":"6","key":"10.1016\/j.media.2024.103140_b33","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1002\/hbm.25323","article-title":"Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders","volume":"42","author":"Rokicki","year":"2021","journal-title":"Hum. Brain Mapp."},{"issue":"5","key":"10.1016\/j.media.2024.103140_b34","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.jpeds.2015.08.013","article-title":"Neonatal magnetic resonance imaging pattern of brain injury as a biomarker of childhood outcomes following a trial of hypothermia for neonatal hypoxic-ischemic encephalopathy","volume":"167","author":"Shankaran","year":"2015","journal-title":"J. Pediatr."},{"issue":"4","key":"10.1016\/j.media.2024.103140_b35","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0018746","article-title":"Infant brain atlases from neonates to 1-and 2-year-olds","volume":"6","author":"Shi","year":"2011","journal-title":"PLoS One"},{"key":"10.1016\/j.media.2024.103140_b36","series-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013"},{"key":"10.1016\/j.media.2024.103140_b37","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"issue":"3","key":"10.1016\/j.media.2024.103140_b38","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.media.2024.103140_b39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroimage.2016.05.029","article-title":"Prediction of brain maturity in infants using machine-learning algorithms","volume":"136","author":"Smyser","year":"2016","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2024.103140_b40","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.neuroimage.2018.07.057","article-title":"Neonatal brain injury and aberrant connectivity","volume":"185","author":"Smyser","year":"2019","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.media.2024.103140_b41","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1111\/j.1460-9592.2011.03534.x","article-title":"Predicting weight using postmenstrual age\u2013neonates to adults","volume":"21","author":"Sumpter","year":"2011","journal-title":"Pediatr. Anesth."},{"key":"10.1016\/j.media.2024.103140_b42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.media.2024.103140_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2022.119319","article-title":"Predicting age and clinical risk from the neonatal connectome","volume":"257","author":"Taoudi-Benchekroun","year":"2022","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2024.103140_b44","unstructured":"Tournier, J.-D., Hughes, E., Tusor, N., Sotiropoulos, S.N., Jbabdi, S., Andersson, J., Rueckert, D., Edwards, A.D., Hajnal, J.V., 2015. Data-driven optimisation of multi-shell HARDI. In: Proc. of ISMRM. Vol. 23, p. 2897."},{"issue":"6","key":"10.1016\/j.media.2024.103140_b45","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4ITK: improved N3 bias correction","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2024.103140_b46","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2024.103140_b47","first-page":"174","article-title":"Geometric deep learning for post-menstrual age prediction based on the neonatal white matter cortical surface","author":"Vosylius","year":"2020"},{"key":"10.1016\/j.media.2024.103140_b48","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.neuroimage.2015.05.083","article-title":"The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI)","volume":"124","author":"Walker","year":"2016","journal-title":"Neuroimage"},{"issue":"7","key":"10.1016\/j.media.2024.103140_b49","doi-asserted-by":"crossref","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":"10.1016\/j.media.2024.103140_b50","series-title":"International Workshop on Machine Learning in Medical Imaging","first-page":"84","article-title":"Modeling life-span brain age from large-scale dataset based on multi-level information fusion","author":"Zhao","year":"2023"},{"issue":"9","key":"10.1016\/j.media.2024.103140_b51","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1109\/TMI.2020.2975344","article-title":"Hi-net: hybrid-fusion network for multi-modal MR image synthesis","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524000653?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524000653?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T18:56:02Z","timestamp":1761245762000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841524000653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":51,"alternative-id":["S1361841524000653"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2024.103140","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2024.103140","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"103140"}}