Abstract
In this paper, we introduce a novel structural holistic Atlas (holiAtlas) of human brain anatomy based on multimodal and high-resolution MRI that covers several anatomical levels from the organ level to the substructure level, using a new protocol for dense labelling generated from the fusion of multiple local protocols at different scales. This atlas was constructed by averaging images and segmentations of 75 healthy subjects from the Human Connectome Project database. Specifically, 3T MR images of T1, T2 and WMn (White Matter nulled) contrasts at 0.125 mm3 resolution were selected for this project. The images of these 75 subjects were nonlinearly registered and averaged using symmetric group-wise normalisation to construct the atlas. At the finest level, the proposed atlas has 350 different labels derived from 7 distinct delineation protocols. These labels were grouped at multiple scales, offering a coherent and consistent holistic representation of the brain across different levels of detail. This multiscale and multimodal atlas can be used to develop new ultra-high-resolution segmentation methods, potentially improving the early detection of neurological disorders. We make it publicly available to the scientific community.
Data availability
The generated holistic atlas, together with the multiscale label definitions, is publicly available through the following links: https://volbrain.net/public/data/holiatlas_v1.0.zip and https://zenodo.org/records/15690524 under a Creative Commons license.
References
Evans, A. C. et al. 3D statistical neuroanatomical models from 305 MRI volumes. IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, 3, 1813–1817 (1993).
Coupé, P. et al. Lifespan neurodegeneration of the human brain in multiple sclerosis. Hum Brain Mapp. 44(17), 5602–5611 (2023).
Planche, V. et al. Anatomical MRI staging of frontotemporal dementia variants. Alzheimer’s Dement. 19, 3283–3294 (2023b).
Planche, V. et al. Staging of progressive supranuclear palsy-Richardson syndrome using MRI brain charts for the human lifespan. Brain Commun. 6(2), fcae055 (2024).
Talairach, J. & Tournoux, P. Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: an Approach To Cerebral Imaging (Thieme Medical Publishers, Inc., 1988).
Mazziotta, J. et al. A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos. Trans. R Soc. Lond. B Biol. Sci. 29 (1412), 1293–1322 (2001).
Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R. & Collins, D. L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47 (1), S102 (2009).
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R. & McKinstry Rand Collins, D. L. Unbiased average age-appropriate atlases for pediatric studies, NeuroImage, 54(1), 313–327 (2011).
Hammers, A. et al. Three-dimensional maximum probability atlas of the human brain, with particular reference to the Temporal lobe. Hum. Brain Mapp. 19 (4), 224–247 (2003).
Shattuck, D. W. et al. Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage 39 (3), 1064–1080 (2007).
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15 (1), 273–289 (2002).
Rolls, E. T., Joliot, M. & Tzourio-Mazoyer, N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage 122, 1–5 (2015).
Rolls, E. T., Huang, C. C., Lin, C. P., Feng, J. & Joliot, M. Automated anatomical labelling atlas 3. Neuroimage 206, 116189 (2020).
Fan, L. et al. The human brainnetome atlas: A new brain atlas based on connectional architecture. Cereb. Cortex. 26 (8), 3508–3526 (2016).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31 (3), 968–980 (2006).
Klein, A. et al. Mindboggling morphometry of human brains. PLoS Comput. Biol. 13 (2), e1005350 (2017).
Hawrylycz, M. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
Amunts, K. et al. BigBrain: an ultrahigh-resolution human brain model. Science 340, 1472–1475 (2013).
Amunts, K., Mohlberg, H., Bludau, S. & Zilles, K. Julich-Brain: A 3D probabilistic atlas of the human. Brain’s Cytoarchitecture Sci. 369, 988–992 (2020).
Lüsebrink, F. et al. Comprehensive ultrahigh resolution whole brain in vivo MRI dataset as a human Phantom. Sci. Data. 8 (1), 138 (2021).
Stucht, D. et al. Highest resolution in vivo human brain MRI using prospective motion correction. PLoS One. 10 (7), e0133921 (2015).
Singh, D. et al. Emerging trends in fast MRI using Deep-Learning reconstruction on undersampled k-Space data: A systematic review. Bioengineering 26 (9), 1012 (2023).
Zhanxiong Wu, X., Chen, S., Xie, J. & Shen, Y. Z. Super-resolution of brain MRI images based on denoising diffusion probabilistic model. Biomed. Signal Process. Control. 85, 104901 (2023).
Grover, J. et al. Super-resolution neural networks improve the Spatiotemporal resolution of adaptive MRI-guided radiation therapy. Commun. Med. 4, 64 (2024).
Schira, M. M. et al. HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations. Brain Struct. Funct. 228, 1849–1863 (2023).
Casamitjana, A. et al. A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation. bioRxiv, 2024.02.05.579016v1 (2024).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536 (7615), 171–178 (2016).
Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Robles, M. & Collins, L. Adaptive Non-Local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging. 31, 192–203 (2010).
Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging. 29 (6), 1310–1320 (2010).
Avants, B. B. et al. A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).
Manjón, J. V. et al. Robust MRI brain tissue parameter Estimation by multistage outlier rejection. Magn. Reson. Med. 59 (4), 866–873 (2008).
Dar, S. U. et al. Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE Trans. Med. Imaging. 38, 2375–2388 (2019).
Manjon, J. V., Romero, J. E. & Coupé, P. Deep learning based MRI contrast synthesis using full volume prediction. Biomedical Phys. Eng. Express. 8, 1 (2021).
Manjón, J. V. et al. Deep ICE: A deep learning approach for MRI intracranial cavity extraction. (2020). https://arxiv.org/abs/2001.05720.
Manjón, J. V. et al. vol2Brain: A new online pipeline for whole brain MRI analysis. Front. Neuroinformatics. 16, 862805 (2022).
Billot, B. et al. Automated segmentation of the hypothalamus and associated subunits in brain MRI, NeuroImage, 223, 117287. https://doi.org/10.1016/j.neuroimage.2020.117287 (2020).
Mangin, J. F. et al. A framework to study the cortical folding patterns. Neuroimage 23, S129–S138 (2004).
Perrot, M., Rivière, D. & Mangin, J. F. Cortical sulci recognition and Spatial normalization. Med. Image. Anal. 15 (4), 529–550 (2011).
Rivière, D. et al. Browsing multiple subjects when the atlas adaptation cannot be achieved via a warping strategy. Front. Neuroinformatics. 16, 7 (2022).
Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U S A. 97, 11050–11055 (2000).
Iglesias, J. E. et al. Brainstem: bayesian segmentation of brainstem structures in MRI. NeuroImage 113, 184–195 (2015).
Saygin, Z. M. et al. (joint 1st authors), High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage, 155, 370–382 (2017).
Iglesias, J. E. et al. Hippocampus: A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage 115, 117–137 (2015).
Manjón, J. V. et al. pBrain: A novel pipeline for Parkinson related brain structure segmentation. Neuroimage:Clinical 25, 102184 (2020).
Romero, J. E., Coupé, P. & Manjón, J. V. HIPS: A new hippocampus subfield segmentation method. Neuroimage 163, 286–295 (2017).
Romero, J. E. et al. CERES: A new cerebellum lobule segmentation method. Neuroimage 147, 916–924 (2017).
Carass, A. et al. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance imaging. Neuroimage 183, 30099076 (2018).
Yushkevich, P. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31 (3), 1116–1128 (2006).
Morell-Ortega, S. et al. DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI. Neuroimage 308, 121063 (2025).
Jena, R., Chaudhari, P. & Gee, J. C. FireANTs: adaptive riemannian optimization for multi-Scale Diffeomorphic Matching. (2025). https://arxiv.org/abs/2404.01249
Bazin, P. L., Alkemade, A., Mulder, M. J., Henry, A. G. & Forstmann, B. U. Multi-contrast Anat. Subcortical Struct. Parcellation eLife 9:e59430. (2020).
Bazin, P. L. et al. Automated parcellation and atlasing of the human subcortex with ultra-high resolution quantitative MRI. Imaging Neuroscience 3, imag_a_00560 (2025).
Coupé, P. et al. Lifespan changes of the human brain in alzheimer’s disease. Sci. Rep. 9, 3998 (2019).
Planche, V. et al. Structural progression of alzheimer’s disease over decades: the MRI staging scheme. Brain Commun. 4 (3), 109 (2022).
Gonzalez-Rodriguez, M. et al. Human amygdala involvement in alzheimer’s disease revealed by Stereological and dia-PASEF analysis. Brain Pathol. 33 (5), e13180 (2023).
West, M. J., Kawas, C. H., Martin, L. J. & Troncoso, J. C. The CA1 region of the human hippocampus is a hot spot in alzheimer’s disease. Ann. N Y Acad. Sci. 908, 255–259 (2000).
Kril, J. J., Hodges, J. & Halliday, G. Relationship between hippocampal volume and CA1 neuron loss in brains of humans with and without alzheimer’s disease. Neurosci. Lett. 361, 9–12 (2004).
Tanner, J. J., McFarland, N. R. & Price, C. C. Striatal and hippocampal atrophy in idiopathic parkinson’s disease patients without dementia: A morphometric analysis. Front. Neurol. 8, 139 (2017).
Coupé, P. et al. AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation, NeuroImage, 219, 117026 (2020).
Tustison, N. J. et al. Large-scale evaluation of ants and freesurfer cortical thickness measurements. Neuroimage 1, 99:166–179 (2014).
Winterburn, J. L. et al. A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. NeuroImage 74, 254–265 (2013).
Acknowledgements
This work has been developed thanks to the projects PID2020-118608RB-I00 and PID2023-152127OB-I00 of the Ministerio de Ciencia e Innovacion de España. This work also benefited from the support of the projects DeepvolBrain, HoliBrain and FOLDDICO of the French National Research Agency (ANR-18-CE45-0013, ANR-23-CE45-0020-01 and ANR-20-CHIA-0027-01). Finally, this study received financial support from the French government in the framework of the University of Bordeaux’s France 2030 program / RRI “IMPACT, the PEPR StratifyAging and the IHU VBHI (ANR-23-IAHU-0001). We thank the support of ITACA (Institute of Information and Communication Technologies) at UPV (Universitat Politècnica de València).
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Author contributionJosé V. Manjón: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration and Funding acquisition.Sergio Morell-Ortega: Software, Validation, Investigation, Data curation, Writing - Review & Editing.Marina Ruiz-Perez: Software, Validation, Investigation, Data curation, Writing - Review & Editing, Visualization.Boris Mansencal: Methodology, Software, Validation, Formal analysis, Investigation, Writing - Review & EditingEdern Le Bot: Software, Validation, Investigation, Data curation, Writing - Review & Editing, Visualization.Marien Gadea: Conceptualization, Validation, Formal analysis, Investigation, Resources, Data curation, Review & Editing, Visualization, Supervision and Funding acquisition.Enrique Lanuza: Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationGwenaelle Catheline: Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationThomas Tourdias: Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationVincent Planche: Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationRemi Giraud: Software, Validation, Formal analysis, Investigation, Writing - Review & EditingDenis Rivière: Software, Validation, Formal analysis, Investigation, Writing - Review & Editing.Jean-Francois Mangin: Software, Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationNicole Labra-Avila: Software, Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationRoberto Vivo-Hernando: Formal analysis, Investigation, Writing - Review & Editing.Gregorio Rubio: Formal analysis, Investigation, Writing - Review & Editing.Fernando Aparici: Validation, Formal analysis, Investigation, Data curation, Writing - Review & Editing, VisualizationMaria de la Iglesia-Vaya: Formal analysis, Investigation, Resources, Data curation, Writing - Review & Editing, Visualization.Pierrick Coupé: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration and Funding acquisition.
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Manjón, J.V., Morell-Ortega, S., Ruiz-Perez, M. et al. Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40186-2
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DOI: https://doi.org/10.1038/s41598-026-40186-2