Data availability:
The raw data used in this Article (MRI, histology, segmentations and so on) can be downloaded from https://doi.org/10.5522/04/24243835. An online tool to interactively explore the 3D reconstructed data can be found at https://github-pages.ucl.ac.uk/NextBrain. This website also includes links to videos, publications, code and other resources. The segmentation of the ex vivo scan can be found at https://openneuro.org/datasets/ds005422/versions/1.0.1. The databases used in the aging study are freely accessible online: OpenBHB (https://baobablab.github.io/bhb/) and aHCP (https://www.humanconnectome.org/study/hcp-lifespan-aging). The ADNI dataset used in the Alzheimer’s disease study is freely accessible with registration at https://adni.loni.usc.edu/data-samples/adni-data/. The atlases used in the Supplementary Information for comparison can be found online: Mai-Paixinos (https://www.thehumanbrain.info/brain/sections.php) and Allen (https://atlas.brain-map.org/).Code availability:
The code used in this Article for 3D histology reconstruction can be downloaded from https://github.com/acasamitjana/ERC_reconstruction and used and distributed freely. The segmentation tool is provided as Python code and is integrated in our neuroimaging toolkit ‘FreeSurfer’: https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation. The source code is available on GitHub: https://github.com/freesurfer/freesurfer/tree/dev/mri_histo_util .Extended data figures and tables are available online at: https://www.nature.com/articles/s41586-025-09708-2#Sec33 .Supplementary information is available online at: https://www.nature.com/articles/s41586-025-09708-2#Sec34 .In human neuroimaging, brain atlases are essential for segmenting regions of interest (ROIs) and comparing subjects in a common coordinate frame. State-of-the-art atlases derived from histology1,2,3 provide exquisite three-dimensional cytoarchitectural maps but lack probabilistic labels throughout the whole brain: that is, the likelihood of each location belonging to a given ROI. Here we present NextBrain, a probabilistic histological atlas of the whole human brain. We developed artificial intelligence-enabled methods to align roughly 10,000 histological sections from five whole brain hemispheres into three-dimensional volumes and to produce delineations for 333 ROIs on these sections. We also created a companion Bayesian tool for automatic segmentation of these ROIs in magnetic resonance imaging (MRI) scans. We showcase two applications of the atlas: segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer’s disease using in vivo MRI. We publicly release raw and aligned data, an online visualization tool, the atlas, the segmentation tool, and ground truth delineations for a high-resolution ex vivo hemisphere used in validation. By enabling researchers worldwide to automatically analyse brain MRIs at a higher level of granularity, NextBrain holds promise to increase the specificity of findings and accelerate our quest to understand the human brain in health and disease.Data collection and sharing for the ADNI data used in this article was funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health grant no. U01 AG024904) and DOD ADNI (Department of Defense grant no. W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This research has been primarily funded by the European Research Council awarded to J.E.I. (Starting grant no. 677697, project ‘BUNGEE-TOOLS’). A.C. is supported by the POSTDOC-UdG203 grant from Universitat de Girona. M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (grant no. AS-JF-19a-004-517). O.P. is supported by a grant from the Lundbeck foundation (grant no. R360–2021–39). M.M. is supported by the Italian National Institute of Health with a Starting Grant and by the Wellcome Trust through a Sir Henry Wellcome Fellowship (grant no. 213722/Z/18/Z). B.L.E. is supported by the Chen Institute MGH Research Scholar Award. Further support was provided by NIH grant nos. 1RF1MH123195, 1R01AG070988, 1UM1MH130981, 1RF1AG080371 and 1R21NS109627
