Casamitjana, Adrià;
Mancini, Matteo;
Robinson, Eleanor;
Peter, Loïc;
Annunziata, Roberto;
Althonayan, Juri;
Crampsie, Shauna;
... Iglesias, Juan Eugenio; + view all
(2025)
A probabilistic histological atlas of the human brain for MRI segmentation.
Nature
10.1038/s41586-025-09708-2.
(In press).
Preview |
Text
Casamitjana,Nature2025.pdf - Published Version Download (24MB) | Preview |
Abstract
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.
| Type: | Article |
|---|---|
| Title: | A probabilistic histological atlas of the human brain for MRI segmentation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41586-025-09708-2 |
| Publisher version: | https://doi.org/10.1038/s41586-025-09708-2 |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Keywords: | Biomedical engineering, Biophysical models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216719 |
Archive Staff Only
 |
View Item |
