{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T06:30:12Z","timestamp":1766212212047,"version":"3.48.0"},"reference-count":82,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The development of foundation models for brain MRI depends critically on the scale, diversity, and consistency of available data, yet systematic assessments of these factors remain scarce. In this study, we analyze 54 publicly accessible brain MRI datasets encompassing over 538,031 scans to provide a structured, multi-level overview tailored to foundation model development. At the dataset level, we characterize modality composition, disease coverage, and dataset scale, revealing strong imbalances between large healthy cohorts and smaller clinical populations. At the image level, we quantify voxel spacing, orientation, and intensity distributions across 14 representative datasets, demonstrating substantial heterogeneity that can influence representation learning. We then perform a quantitative evaluation of preprocessing variability, examining how intensity normalization, bias field correction, skull stripping, spatial registration, and interpolation alter voxel statistics and geometry. While these steps improve within-dataset consistency, residual differences persist between datasets. Finally, a feature-space case study using a 3D DenseNet121 shows measurable residual covariate shift after standardized preprocessing, confirming that harmonization alone cannot eliminate inter-dataset bias. Together, these analyses provide a unified characterization of variability in public brain MRI resources and emphasize the need for preprocessing-aware and domain-adaptive strategies in the design of generalizable brain MRI foundation models.<\/jats:p>","DOI":"10.3390\/jimaging11120454","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T16:42:05Z","timestamp":1766076125000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6766-4966","authenticated-orcid":false,"given":"Minh Sao Khue","family":"Luu","sequence":"first","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2477-0827","authenticated-orcid":false,"given":"Margaret V.","family":"Benedichuk","sequence":"additional","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia"}]},{"given":"Ekaterina I.","family":"Roppert","sequence":"additional","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia"}]},{"given":"Roman M.","family":"Kenzhin","sequence":"additional","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8931-9848","authenticated-orcid":false,"given":"Bair N.","family":"Tuchinov","sequence":"additional","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/S1474-4422(18)30499-X","article-title":"Global, regional, and national burden of neurological disorders, 1990\u20132016: A systematic analysis for the Global Burden of Disease Study 2016","volume":"18","author":"Feigin","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_2","unstructured":"Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., and Brunskill, E. (2021). On the Opportunities and Risks of Foundation Models. arXiv."},{"key":"ref_3","unstructured":"Azad, B., Azad, R., Eskandari, S., Bozorgpour, A., Kazerouni, A., Rekik, I., and Merhof, D. (2023). Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., and Chen, H. (2024). Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions. arXiv.","DOI":"10.1109\/RBME.2024.3496744"},{"key":"ref_5","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":"ref_6","first-page":"379","article-title":"A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts","volume":"15012","author":"Linguraru","year":"2024","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cox, J., Liu, P., Stolte, S.E., Yang, Y., Liu, K., See, K.B., Ju, H., and Fang, R. (2024). BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation. arXiv.","DOI":"10.1016\/j.media.2024.103301"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1038\/s41597-024-04159-2","article-title":"MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities","volume":"11","author":"Aydin","year":"2024","journal-title":"Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1002\/jmri.29101","article-title":"A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research","volume":"59","author":"Dishner","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"116450","DOI":"10.1016\/j.neuroimage.2019.116450","article-title":"Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan","volume":"208","author":"Pomponio","year":"2020","journal-title":"NeuroImage"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"vdac099","DOI":"10.1093\/noajnl\/vdac099","article-title":"The current state of glioma data registries","volume":"4","author":"Yearley","year":"2022","journal-title":"Neuro-Oncol. Adv."},{"key":"ref_12","unstructured":"Andaloussi, M.A., Maser, R., Hertel, F., Lamoline, F., and Husch, A.D. (2024). Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., and Rozycki, M. (2018). Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. arXiv."},{"key":"ref_15","unstructured":"Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., and Pati, S. (2021). The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bonato, B., Nanni, L., and Bertoldo, A. (2025). Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012\u20132025). Sensors, 25.","DOI":"10.3390\/s25061838"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1038\/s41597-025-05250-y","article-title":"MSLesSeg: Baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset","volume":"12","author":"Guarnera","year":"2025","journal-title":"Sci. Data"},{"key":"ref_18","unstructured":"Muslim, A.M. (2025, June 15). Brain MRI Dataset of Multiple Sclerosis with Consensus Manual Lesion Segmentation and Patient Meta Information. Available online: https:\/\/data.mendeley.com\/datasets\/8bctsm8jz7\/1."},{"key":"ref_19","unstructured":"Commowick, O., Cervenansky, F., Cotton, F., and Dojat, M. (October, January 27). MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure. Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France. Available online: https:\/\/inria.hal.science\/hal-03358968."},{"key":"ref_20","unstructured":"Maleki, N., Amiruddin, R., Moawad, A.W., Yordanov, N., Gkampenis, A., Fehringer, P., Umeh, F., Chukwurah, C., Memon, F., and Petrovic, B. (2025). Analysis of the MICCAI Brain Tumor Segmentation\u2014Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI. arXiv."},{"key":"ref_21","unstructured":"Adewole, M., Rudie, J.D., Gbadamosi, A., Toyobo, O., Raymond, C., Zhang, D., Omidiji, O., Akinola, R., Suwaid, M.A., and Emegoakor, A. (2023). The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa). arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1148\/ryai.240528","article-title":"The BraTS-Africa Dataset: Expanding the Brain Tumor Segmentation (BraTS) Data to Capture African Populations","volume":"7","author":"Adewole","year":"2025","journal-title":"Radiol. Artif. Intell."},{"key":"ref_23","unstructured":"LaBella, D., Adewole, M., Alonso-Basanta, M., Altes, T., Anwar, S.M., Baid, U., Bergquist, T., Bhalerao, R., Chen, S., and Chung, V. (2023). The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma. arXiv."},{"key":"ref_24","unstructured":"LaBella, D., Baid, U., Khanna, O., McBurney-Lin, S., McLean, R., Nedelec, P., Rashid, A., Tahon, N.H., Altes, T., and Bhalerao, R. (2024). Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/s41597-024-03350-9","article-title":"A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation","volume":"11","author":"LaBella","year":"2024","journal-title":"Sci. Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1038\/s41597-022-01875-5","article-title":"ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset","volume":"9","author":"Hanning","year":"2022","journal-title":"Sci. Data"},{"key":"ref_27","unstructured":"P\u00e9rez-Garc\u00eda, F., Rodionov, R., Alim-Marvasti, A., Sparks, R., Duncan, J., and Ourselin, S. (2025, June 15). EPISURG: A Dataset of Postoperative Magnetic Resonance Images (MRI) for Quantitative Analysis of Resection Neurosurgery for Refractory Epilepsy. Available online: https:\/\/rdr.ucl.ac.uk\/articles\/dataset\/EPISURG_a_dataset_of_postoperative_magnetic_resonance_images_MRI_for_quantitative_analysis_of_resection_neurosurgery_for_refractory_epilepsy\/9996158\/1."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","article-title":"Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults","volume":"22","author":"Marcus","year":"2010","journal-title":"J. Cogn. Neurosci."},{"key":"ref_30","unstructured":"(2025, June 15). IXI Dataset. Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom. Available online: https:\/\/brain-development.org\/ixi-dataset\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Badea, L., Onu, M., Wu, T., Roceanu, A., and Bajenaru, O. (2017). Exploring the reproducibility of functional connectivity alterations in Parkinson\u2019s disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188196"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"s13742\u2013016\u20130150\u20135","DOI":"10.1186\/s13742-016-0150-5","article-title":"The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data","volume":"5","author":"Puccio","year":"2016","journal-title":"Gigascience"},{"key":"ref_33","unstructured":"Gr\u00f8vik, E., Yi, D., Iv, M., Tong, E., Rubin, D.L., and Zaharchuk, G. (2025, June 15). BrainMetShare. Available online: https:\/\/aimi.stanford.edu\/datasets\/brainmetshare."},{"key":"ref_34","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":"ref_35","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1006\/cbmr.1996.0014","article-title":"AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages","volume":"29","author":"Cox","year":"1996","journal-title":"Comput. Biomed. Res."},{"key":"ref_36","doi-asserted-by":"crossref","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":"ref_37","unstructured":"Dascal, A., Koepp, M., Royer, J., Chen, J., Arafat, T., Caciagli, L., Bernasconi, N., Hopewell, R., Soucy, J.P., and Hsiao, C.H.H. (2025, June 15). An Open Dataset of Cerebral Tau Deposition in Young Healthy Adults Based on [18F]MK6240 Positron Emission Tomography. Available online: https:\/\/osf.io\/znt9d\/overview."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/mp.2013.78","article-title":"The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism","volume":"19","author":"Yan","year":"2014","journal-title":"Mol. Psychiatry"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"170010","DOI":"10.1038\/sdata.2017.10","article-title":"Enhancing studies of the connectome in autism using the autism brain imaging data exchange II","volume":"4","author":"Chen","year":"2017","journal-title":"Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7350","DOI":"10.1002\/alz.14166","article-title":"Overview of ADNI MRI","volume":"20","author":"Jack","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_42","unstructured":"Snoek, L., Miesen, M.V.D., Leij, A.V.D., Beemsterboer, T., Eigenhuis, A., and Scholte, S. (2025, June 15). AOMIC-ID1000. Available online: https:\/\/doi.org\/10.18112\/OPENNEURO.DS003097.V1.2.1."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/s41597-021-00870-6","article-title":"The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses","volume":"8","author":"Snoek","year":"2021","journal-title":"Sci. Data"},{"key":"ref_44","unstructured":"Snoek, L., Miesen, M.V.D., Leij, A.V.D., Beemsterboer, T., Eigenhuis, A., and Scholte, S. (2025, June 15). AOMIC-PIOP1. Available online: https:\/\/openneuro.org\/datasets\/ds002785\/versions\/2.0.0."},{"key":"ref_45","unstructured":"Snoek, L., Miesen, M.V.D., Leij, A.V.D., Beemsterboer, T., Eigenhuis, A., and Scholte, S. (2025, June 15). AOMIC-PIOP2. Available online: https:\/\/openneuro.org\/datasets\/ds002790\/versions\/2.0.0."},{"key":"ref_46","unstructured":"Gibson, M., Newman-Norlund, R., Bonilha, L., Fridriksson, J., Hickok, G., Hillis, A.E., Den Ouden, D.-B., and Rorden, C. (2025, June 15). Aphasia Recovery Cohort (ARC) Dataset. Available online: https:\/\/openneuro.org\/datasets\/ds004884\/versions\/1.0.1."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1038\/s41597-024-03819-7","article-title":"The Aphasia Recovery Cohort, an open-source chronic stroke repository","volume":"11","author":"Gibson","year":"2024","journal-title":"Sci. Data"},{"key":"ref_48","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":"ref_49","unstructured":"Lloyd, W.K., Morriss, J., Macdonald, B., Joanknecht, K., Nihouarn, J., and Reekum, C.M.V. (2025, June 15). Emotion Regulation in the Ageing Brain, University of Reading, BBSRC. Available online: https:\/\/openneuro.org\/datasets\/ds002366\/versions\/1.1.0."},{"key":"ref_50","unstructured":"Verdier, M.C.d., Saluja, R., Gagnon, L., LaBella, D., Baid, U., Tahon, N.H., Foltyn-Dumitru, M., Zhang, J., Alafif, M., and Baig, S. (2024). The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.neuroimage.2017.08.021","article-title":"An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement","volume":"170","author":"Souza","year":"2018","journal-title":"NeuroImage"},{"key":"ref_52","unstructured":"Park, D., Hennessee, J., Smith, E.T., Chan, M., Katen, C., Wig, G., Rodrigue, K., and Kennedy, K. (2025, June 15). The Dallas Lifespan Brain Study. Available online: https:\/\/openneuro.org\/datasets\/ds004856\/versions\/1.2.0."},{"key":"ref_53","first-page":"1268","article-title":"EDEN2020 Human Brain MRI Datasets for Brain Glioma Patients","volume":"42","author":"Castellano","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_54","unstructured":"Buckner, R.L., Roffman, J.L., Smoller, J.W., and Neuroinformatics Research Group (2014). Brain Genomics Superstruct Project (GSP)."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"giw011","DOI":"10.1093\/gigascience\/giw011","article-title":"The Healthy Brain Network Serial Scanning Initiative: A resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions","volume":"6","author":"Potler","year":"2017","journal-title":"GigaScience"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn Human Connectome Project: An overview","volume":"80","author":"Smith","year":"2013","journal-title":"NeuroImage"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"118585","DOI":"10.1016\/j.neuroimage.2021.118585","article-title":"The intracranial tumor segmentation challenge: Contour tumors on brain MRI for radiosurgery","volume":"244","author":"Lu","year":"2021","journal-title":"NeuroImage"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13550-021-00830-6","article-title":"CERMEP-IDB-MRXFDG: A database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research","volume":"11","author":"Jung","year":"2021","journal-title":"EJNMMI Res."},{"key":"ref_59","unstructured":"Taylor, P.N., Wang, Y., Simpson, C., Janiukstyte, V., Horsley, J., Leiberg, K., Little, B., Clifford, H., Adler, S., and Vos, S.B. (2025, June 15). The Imaging Database for Epilepsy And Surgery (IDEAS). Available online: https:\/\/openneuro.org\/datasets\/ds005602\/versions\/1.0.0."},{"key":"ref_60","unstructured":"Seminowicz, D., Burrowes, S., Kearson, A., Zhang, J., Krimmel, S., Samawi, L., Furman, A., and Keaser, M. (2025, June 15). MBSR. Available online: https:\/\/openneuro.org\/datasets\/ds005016\/versions\/1.1.1."},{"key":"ref_61","unstructured":"Vassantachart, A., Cao, Y., Shen, Z., Cheng, K., Gribble, M., Ye, J.C., Zada, G., Hurth, K., Mathew, A., and Guzman, S. (2025, June 15). Segmentation and Classification of Grade I and II Meningiomas from Magnetic Resonance Imaging: An Open Annotated Dataset (Meningioma-SEG-CLASS). Available online: https:\/\/www.cancerimagingarchive.net\/collection\/meningioma-seg-class\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"eadd3607","DOI":"10.1126\/sciadv.add3607","article-title":"SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry","volume":"9","author":"Iglesias","year":"2023","journal-title":"Sci. Adv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1038\/s41597-022-01682-y","article-title":"An Open MRI Dataset For Multiscale Neuroscience","volume":"9","author":"Royer","year":"2022","journal-title":"Sci. Data"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gong, Z., Xu, T., Peng, N., Cheng, X., Niu, C., Wiestler, B., Hong, F., and ei Bran Li, B. (2025, June 15). A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors. Available online: https:\/\/dataverse.harvard.edu\/dataset.xhtml?persistentId=doi:10.7910\/DVN\/KUUEWC.","DOI":"10.1038\/s41597-024-03634-0"},{"key":"ref_65","unstructured":"Pappalardo, F., Russo, G., Di Salvatore, V., Battiato, S., Guarnera, F., and Rondinella, A. (2025, June 15). MSValid Data Collection. Available online: https:\/\/zenodo.org\/records\/10875606."},{"key":"ref_66","unstructured":"Evans, J.W., Nugent, A.C., and Zarate, C.A. (2025, June 15). NIMH Ketamine Mechanism of Action Study. Available online: https:\/\/openneuro.org\/datasets\/ds005917\/versions\/1.0.1."},{"key":"ref_67","unstructured":"Nugent, A.C., Thomas, A.G., Mahoney, M., Gibbons, A., Smith, J., Charles, A., Shaw, J.S., Stout, J.D., Namyst, A.M., and Basavaraj, A. (2025, June 15). The NIMH Healthy Research Volunteer Dataset. Available online: https:\/\/openneuro.org\/datasets\/ds005752\/versions\/2.1.0."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1038\/s41597-022-01623-9","article-title":"The NIMH intramural healthy volunteer dataset: A comprehensive MEG, MRI, and behavioral resource","volume":"9","author":"Nugent","year":"2022","journal-title":"Sci. Data"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1177\/19714009241242658","article-title":"Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors","volume":"37","author":"Filimonova","year":"2024","journal-title":"Neuroradiol. J."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1002\/acn3.644","article-title":"The Parkinson\u2019s progression markers initiative (PPMI)\u2014Establishing a PD biomarker cohort","volume":"5","author":"Marek","year":"2018","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"ref_71","unstructured":"Schmainda, K.M., Prah, M.A., Connelly, J.M., and Rand, S.D. (2025, June 15). Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs (QIN-BRAIN-DSC-MRI). Available online: https:\/\/www.cancerimagingarchive.net\/collection\/qin-brain-dsc-mri\/."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Juvekar, P., Dorent, R., K\u00f6gl, F., Torio, E., Barr, C., Rigolo, L., Galvin, C., Jowkar, N., Kazi, A., and Haouchine, N. (2025, June 15). The Brain Resection Multimodal Imaging Database (ReMIND). Available online: https:\/\/www.cancerimagingarchive.net\/collection\/remind\/.","DOI":"10.1038\/s41597-024-03295-z"},{"key":"ref_73","unstructured":"Rorden, C., Absher, J., and Newman-Norlund, R. (2025, June 15). Stroke Outcome Optimization Project (SOOP). Available online: https:\/\/openneuro.org\/datasets\/ds004889\/versions\/1.1.2."},{"key":"ref_74","unstructured":"Bilder, R., Poldrack, R., Cannon, T., London, E., Freimer, N., Congdon, E., Karlsgodt, K., and Sabb, F. (2025, June 15). UCLA Consortium for Neuropsychiatric Phenomics LA5c Study. Available online: https:\/\/openneuro.org\/datasets\/ds000030\/versions\/1.0.0."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"160110","DOI":"10.1038\/sdata.2016.110","article-title":"A phenome-wide examination of neural and cognitive function","volume":"3","author":"Poldrack","year":"2016","journal-title":"Sci. Data"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Fields, B.K.K., Calabrese, E., Mongan, J., Cha, S., Hess, C.P., Sugrue, L.P., Chang, S.M., Luks, T.L., Villanueva-Meyer, J.E., and Rauschecker, A.M. (2024). The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma MRI Dataset. Radiology: Artificial Intelligence, Radiological Society of North America (RSNA).","DOI":"10.1148\/ryai.230182"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e230126","DOI":"10.1148\/ryai.230126","article-title":"The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset","volume":"6","author":"Rudie","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_78","unstructured":"Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., and Hess, C. (2025, June 15). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM). Available online: https:\/\/www.cancerimagingarchive.net\/collection\/ucsf-pdgm\/."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., and Landray, M. (2015). UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med., 12.","DOI":"10.1371\/journal.pmed.1001779"},{"key":"ref_80","unstructured":"Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J.D., Flores Santamaria, N., Fathi Kazerooni, A., and Pati, S. (2025, June 15). Multi-Parametric Magnetic Resonance Imaging (mpMRI) Scans for de novo Glioblastoma (GBM) Patients from the University of Pennsylvania Health System (UPENN-GBM). Available online: https:\/\/www.cancerimagingarchive.net\/collection\/upenn-gbm\/."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/s41597-022-01560-7","article-title":"The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics","volume":"9","author":"Bakas","year":"2022","journal-title":"Sci. Data"},{"key":"ref_82","unstructured":"Kuijf, H., Biesbroek, M., de Bresser, J., Heinen, R., Chen, C., van der Flier, W., Viergever, M., and Biessels, G.J. (2025, June 15). Data of the White Matter Hyperintensity (WMH) Segmentation Challenge. Available online: https:\/\/dataverse.nl\/dataset.xhtml?persistentId=doi:10.34894\/AECRSD."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/12\/454\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T05:13:17Z","timestamp":1766207597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/12\/454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,18]]},"references-count":82,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["jimaging11120454"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11120454","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2025,12,18]]}}}