{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T10:18:02Z","timestamp":1788085082692,"version":"build-2784847793"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012331","name":"Agentschap Innoveren en Ondernemen","doi-asserted-by":"publisher","award":["HBC.2019.2579"],"award-info":[{"award-number":["HBC.2019.2579"]}],"id":[{"id":"10.13039\/100012331","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012331","name":"Agentschap Innoveren en Ondernemen","doi-asserted-by":"publisher","award":["HBC.2021.0500"],"award-info":[{"award-number":["HBC.2021.0500"]}],"id":[{"id":"10.13039\/100012331","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["V412023N"],"award-info":[{"award-number":["V412023N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["12A6U25N"],"award-info":[{"award-number":["12A6U25N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["1805620N"],"award-info":[{"award-number":["1805620N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004418","name":"Vrije Universiteit Brussel","doi-asserted-by":"publisher","award":["SRP85"],"award-info":[{"award-number":["SRP85"]}],"id":[{"id":"10.13039\/501100004418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004418","name":"Vrije Universiteit Brussel","doi-asserted-by":"publisher","award":["IOFPOC57"],"award-info":[{"award-number":["IOFPOC57"]}],"id":[{"id":"10.13039\/501100004418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003243","name":"Ministerstvo Zdravotnictv\u00ed Cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["RVO-VFN 64165"],"award-info":[{"award-number":["RVO-VFN 64165"]}],"id":[{"id":"10.13039\/501100003243","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031478","name":"NextGenerationEU","doi-asserted-by":"publisher","award":["LX22NPO5107"],"award-info":[{"award-number":["LX22NPO5107"]}],"id":[{"id":"10.13039\/100031478","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Federated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We propose FLightcase, a new FL toolbox tailored for brain research, and evaluate it on a real-world FL network to predict the cognitive status in patients with multiple sclerosis (MS) from brain magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We first trained a DenseNet neural network to predict age from T1-weighted brain MRI on three open-source datasets: IXI (586 images), SALD (491 images), and CamCAN (653 images). These were distributed across the three centres in our FL network: Brussels (BE), Greifswald (DE), and Prague (CZ). We benchmarked this federated model with a centralised version. The best-performing brain age model was then fine-tuned to predict performance on the symbol digit modalities test (SDMT) of patients with MS (Brussels: 96 images, Greifswald: 756 images, Prague: 2,424 images). Shallow transfer learning (TL) was compared with deep transfer learning, in which weights were updated either in the last layer or across the entire network, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Federated training outperformed centralised training, predicting age with a mean absolute error (MAE) of 6.08 versus 7.02. Federated training yielded Pearson correlations (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u2009.001) between true and predicted age of0.88 (IXI, Brussels), 0.91 (SALD, Greifswald), and 0.93 (CamCAN, Prague). Fine-tuning of the centralised model to SDMT was most successful with a deep TL paradigm (MAE\u2009=\u20099.19) compared to shallow TL (MAE\u2009=\u200911.05). Across Brussels, Greifswald, and Prague, deep TL predicted SDMT with MAEs of 10.71, 9.67, and 8.98, respectively, and yielded Pearson correlations between true and predicted SDMT of.25 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.282), 0.40 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), and 0.50 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Real-world federated learning using FLightcase is feasible for neuroimaging research in MS, enabling access to large MS imaging databases without sharing data. The federated SDMT-decoding model is promising and could be improved in the future by adopting FL algorithms that address the non-IID data issue and consider other imaging modalities. We hope our detailed real-world experiments and open-source distribution of FLightcase will prompt researchers to move beyond simulated FL environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1691088","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T13:05:53Z","timestamp":1773407153000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Real-world federated learning for brain imaging scientists"],"prefix":"10.3389","volume":"8","author":[{"given":"Stijn","family":"Denissen","sequence":"first","affiliation":[{"name":"AIMS Lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel","place":["Brussels, Belgium"]},{"name":"Department of Radiology, First Faculty of Medicine, Charles University, General University Hospital","place":["Prague, Czechia"]},{"name":"icometrix","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorne","family":"Laton","sequence":"additional","affiliation":[{"name":"AIMS Lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel","place":["Brussels, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Grothe","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Medicine Greifswald","place":["Greifswald, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuela","family":"Vaneckova","sequence":"additional","affiliation":[{"name":"Department of Radiology, First Faculty of Medicine, Charles University, General University Hospital","place":["Prague, Czechia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1\u0161","family":"Uher","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Medicine Greifswald","place":["Greifswald, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mat\u011bj","family":"Kudrna","sequence":"additional","affiliation":[{"name":"Department of Radiology, First Faculty of Medicine, Charles University, General University Hospital","place":["Prague, Czechia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dana","family":"Hor\u00e1kov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University, General University Hospital","place":["Prague, Czechia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Baijot","sequence":"additional","affiliation":[{"name":"AIMS Lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel","place":["Brussels, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iris-Katharina","family":"Penner","sequence":"additional","affiliation":[{"name":"Department of Neurology, Inselspital, Bern University Hospital, University of Bern","place":["Bern, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Kirsch","sequence":"additional","affiliation":[{"name":"Institute for Diagnostic Radiology and Neuroradiology, University Medicine of Greifswald","place":["Greifswald, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji\u0159\u00ed","family":"Mot\u00fdl","sequence":"additional","affiliation":[{"name":"Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University, General University Hospital","place":["Prague, Czechia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maarten","family":"De Vos","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (ESAT), KU Leuven","place":["Leuven, Belgium"]},{"name":"Development and Regeneration, KU Leuven","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver Y.","family":"Ch\u00e9n","sequence":"additional","affiliation":[{"name":"D\u00e9partement M\u00e9decine de Laboratoire et Pathologie (DMLP), Centre Hospitalier Universitaire Vaudois (CHUV)","place":["Lausanne, Switzerland"]},{"name":"Facult\u00e9 de Biologie et de M\u00e9decine (FBM), Universit\u00e9 de Lausanne","place":["Lausanne, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeroen","family":"Van Schependom","sequence":"additional","affiliation":[{"name":"AIMS Lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel","place":["Brussels, Belgium"]},{"name":"Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel","place":["Brussels, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana Maria","family":"Sima","sequence":"additional","affiliation":[{"name":"AIMS Lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel","place":["Brussels, Belgium"]},{"name":"icometrix","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Nagels","sequence":"additional","affiliation":[{"name":"AIMS Lab, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel","place":["Brussels, Belgium"]},{"name":"St Edmund Hall, University of Oxford","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"24574","DOI":"10.1038\/s41598-024-75370-9","article-title":"Deep learning based automatic detection and dipole estimation of epileptic discharges in MEG: a multi-center study","volume":"14","author":"Hirano","year":"2024","journal-title":"Sci Rep"},{"key":"B2","doi-asserted-by":"publisher","first-page":"22388","DOI":"10.1038\/s41598-023-49514-2","article-title":"Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms","volume":"13","author":"Joo","year":"2023","journal-title":"Sci Rep"},{"key":"B3","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"B4","doi-asserted-by":"publisher","first-page":"10930","DOI":"10.1038\/s41598-021-90428-8","article-title":"Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images","volume":"11","author":"Ranjbarzadeh","year":"2021","journal-title":"Sci Rep"},{"key":"B5","doi-asserted-by":"publisher","first-page":"11735","DOI":"10.1038\/s41598-024-61798-6","article-title":"A deep learning model for brain segmentation across pediatric and adult populations","volume":"14","author":"Simarro","year":"2024","journal-title":"Sci Rep"},{"key":"B6","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.inffus.2023.03.007","article-title":"Deep learning for brain age estimation: a systematic review","volume":"96","author":"Tanveer","year":"2023","journal-title":"Inform Fusion"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e71774","DOI":"10.7554\/eLife.71774","article-title":"The OpenNeuro resource for sharing of neuroscience data","volume":"10","author":"Markiewicz","year":"2021","journal-title":"Elife"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1186\/1471-2458-14-1144","article-title":"A systematic review of barriers to data sharing in public health","volume":"14","author":"van Panhuis","year":"2014","journal-title":"BMC Public Health"},{"key":"B10","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","author":"McMahan","year":"2017"},{"key":"B11","author":"Li","year":"2020"},{"key":"B12","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1007\/s12021-022-09570-x","article-title":"Decentralized brain age estimation using MRI data","volume":"20","author":"Basodi","year":"2022","journal-title":"Neuroinform"},{"key":"B13","author":"Beutel","year":"2022"},{"key":"B14","doi-asserted-by":"publisher","first-page":"7615","DOI":"10.1038\/s41467-024-51172-5","article-title":"An international study presenting a federated learning AI platform for pediatric brain tumors","volume":"15","author":"Lee","year":"2024","journal-title":"Nat Commun"},{"key":"B15","doi-asserted-by":"publisher","first-page":"7346","DOI":"10.1038\/s41467-022-33407-5","article-title":"Federated learning enables big data for rare cancer boundary detection","volume":"13","author":"Pati","year":"2022","journal-title":"Nat Commun"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.44","article-title":"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments","volume":"3","author":"Gorgolewski","year":"2016","journal-title":"Sci Data"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/imag_a_00103","article-title":"The past, present, and future of the brain imaging data structure (BIDS)","volume":"2","author":"Poldrack","year":"2024","journal-title":"Imaging Neuroscience"},{"key":"B18","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1055\/s-0035-1555115","article-title":"The impact of age on cognition","volume":"36","author":"Murman","year":"2015","journal-title":"Semin Hear"},{"key":"B19","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.1111\/ene.15473","article-title":"Brain age as a surrogate marker for cognitive performance in multiple sclerosis","volume":"29","author":"Denissen","year":"2022","journal-title":"Eur J Neurol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1038\/nrneurol.2015.157","article-title":"Magnims consensus guidelines on the use of MRI in multiple sclerosis\u2014establishing disease prognosis and monitoring patients","volume":"11","author":"Wattjes","year":"2015","journal-title":"Nat Rev Neurol"},{"key":"B21","author":"Paszke","year":"2019"},{"key":"B22","volume-title":"Pandas-Dev\/Pandas: Pandas","year":"2024"},{"key":"B23","author":"Cardoso","year":"2022"},{"key":"B24","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"B25","author":"da Costa-Luis","year":"2024"},{"key":"B26","volume-title":"Nipy\/Nibabel: 5.3.1","author":"Brett","year":"2024"},{"key":"B27","first-page":"111","article-title":"Paramiko","author":"Zadka","year":"2019"},{"key":"B28","year":""},{"key":"B29","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput Sci Eng"},{"key":"B30","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"Scipy 1.0: fundamental algorithms for scientific computing in python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat Methods"},{"key":"B31","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"B32","year":""},{"key":"B33","year":""},{"key":"B34","author":"Huang","year":"2018"},{"key":"B35","doi-asserted-by":"publisher","first-page":"118871","DOI":"10.1016\/j.neuroimage.2022.118871","article-title":"Accurate brain-age models for routine clinical MRI examinations","volume":"249","author":"Wood","year":"2022","journal-title":"Neuroimage"},{"key":"B36","author":"Roca","year":"2025"},{"key":"B37","doi-asserted-by":"publisher","first-page":"e076747","DOI":"10.1002\/alz.076747","article-title":"Federated BrainAge estimation from MRI: a proof of concept","volume":"19","author":"Mateus","year":"2023","journal-title":"Alzheimer\u2019s Dement"},{"key":"B38","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1186\/s12883-014-0204-1","article-title":"The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing","volume":"14","author":"Shafto","year":"2014","journal-title":"BMC Neurol"},{"key":"B39","year":""},{"key":"B40","doi-asserted-by":"publisher","first-page":"180134","DOI":"10.1038\/sdata.2018.134","article-title":"Structural and functional brain scans from the cross-sectional southwest university adult lifespan dataset","volume":"5","author":"Wei","year":"2018","journal-title":"Sci Data"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1212\/WNL.33.11.1444","article-title":"Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)","volume":"33","author":"Kurtzke","year":"1983","journal-title":"Neurology"},{"key":"B42","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1177\/1352458517690821","article-title":"Validity of the symbol digit modalities test as a cognition performance outcome measure for multiple sclerosis","volume":"23","author":"Benedict","year":"2017","journal-title":"Mult Scler"},{"key":"B43","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1177\/1352458514537012","article-title":"Reduced information processing speed as primum movens for cognitive decline in MS","volume":"21","author":"Van Schependom","year":"2015","journal-title":"Mult Scler"},{"key":"B44","doi-asserted-by":"publisher","first-page":"4952","DOI":"10.1002\/hbm.24750","article-title":"Automated brain extraction of multisequence MRI using artificial neural networks","volume":"40","author":"Isensee","year":"2019","journal-title":"Hum Brain Mapp"},{"key":"B45","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","article-title":"A reproducible evaluation of ANTs similarity metric performance in brain image registration","volume":"54","author":"Avants","year":"2011","journal-title":"Neuroimage"},{"key":"B46","doi-asserted-by":"publisher","first-page":"012143","DOI":"10.1088\/1742-6596\/1651\/1\/012143","article-title":"Cancer image classification based on DenseNet model","volume":"1651","author":"Zhong","year":"2020","journal-title":"J Phys: Conf Ser"},{"key":"B47","doi-asserted-by":"publisher","first-page":"103319","DOI":"10.1016\/j.nicl.2023.103319","article-title":"Age-level bias correction in brain age prediction","volume":"37","author":"Zhang","year":"2023","journal-title":"Neuroimage Clin"},{"key":"B48","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1007\/s11682-019-00184-7","article-title":"Brain asymmetry differences between Chinese and Caucasian populations: a surface-based morphometric comparison study","volume":"14","author":"Lou","year":"2020","journal-title":"Brain Imaging Behav"},{"key":"B49","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1002\/hbm.23994","article-title":"Brain structure differences between Chinese and Caucasian cohorts: a comprehensive morphometry study","volume":"39","author":"Tang","year":"2018","journal-title":"Hum Brain Mapp"},{"key":"B50","author":"Qi","year":"2023"},{"key":"B51","doi-asserted-by":"publisher","first-page":"101871","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"},{"key":"B52","doi-asserted-by":"publisher","author":"La Rosa","year":"2024","DOI":"10.1162\/imag_a_00487"},{"key":"B53","author":"Li","year":"2020"},{"key":"B54","author":"Iyer","year":"2024"},{"key":"B55","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1108\/IJWIS-04-2022-0080","article-title":"Open problems in medical federated learning","volume":"18","author":"Yoo","year":"2022","journal-title":"Int J Web Inform Syst"},{"key":"B56","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1080\/08989621.2020.1774373","article-title":"International scientific collaborative activities and barriers to them in eight societies","volume":"27","author":"Matthews","year":"2020","journal-title":"Account Res"},{"key":"B57","doi-asserted-by":"publisher","first-page":"108104","DOI":"10.1016\/j.cmpb.2024.108104","article-title":"A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients","volume":"248","author":"Vo","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"B58","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1109\/TMI.2024.3450855","article-title":"Multi-Modal federated learning for cancer staging over non-IID datasets with unbalanced modalities","volume":"44","author":"Borazjani","year":"2025","journal-title":"IEEE Transact Med Imaging"},{"key":"B59","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s13042-022-01647-y","article-title":"A survey on federated learning: challenges and applications","volume":"14","author":"Wen","year":"2023","journal-title":"Int J Mach Learn Cyber"},{"key":"B60","doi-asserted-by":"publisher","first-page":"214001","DOI":"10.1088\/1361-6560\/ac97d9","article-title":"Openfl: the open federated learning library","volume":"67","author":"Foley","year":"2022","journal-title":"Phys Med Biol"},{"key":"B61","doi-asserted-by":"publisher","first-page":"365","DOI":"10.3389\/fnins.2016.00365","article-title":"Coinstac: a privacy enabled model and prototype for leveraging and processing decentralized brain imaging data","volume":"10","author":"Plis","year":"2016","journal-title":"Front Neurosci"},{"key":"B62","doi-asserted-by":"publisher","first-page":"112201","DOI":"10.1016\/j.knosys.2024.112201","article-title":"Fedga: a greedy approach to enhance federated learning with non-IID data","volume":"301","author":"Cong","year":"2024","journal-title":"Knowl Based Syst"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2025.3531360","article-title":"A comprehensive survey of foundation models in medicine","volume":"19","author":"Khan","year":"2025","journal-title":"IEEE Rev Biomed Eng"},{"key":"B64","first-page":"2023.04.22.23288741","author":"Denissen","year":"2023"},{"key":"B65","doi-asserted-by":"publisher","first-page":"117689","DOI":"10.1016\/j.neuroimage.2020.117689","article-title":"Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal","volume":"228","author":"Dinsdale","year":"2021","journal-title":"NeuroImage"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1691088\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T13:05:55Z","timestamp":1773407155000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1691088\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":65,"alternative-id":["10.3389\/fdgth.2026.1691088"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1691088","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]},"article-number":"1691088"}}