TY - JOUR
KW - Biomedical Engineering
KW - Biophysical models
AU - Adrià Casamitjana
AU - Matteo Mancini
AU - Eleanor Robinson
AU - Loïc Peter
AU - Roberto Annunziata
AU - Juri Althonayan
AU - Shauna Crampsie
AU - Emily Blackburn
AU - Benjamin Billot
AU - Alessia Atzeni
AU - Oula Puonti
AU - Yaël Balbastre
AU - Peter Schmidt
AU - James Hughes
AU - Jean C. Augustinack
AU - Brian L. Edlow
AU - Lilla Zöllei
AU - David L. Thomas
AU - Dorit Kliemann
AU - Martina Bocchetta
AU - Catherine Strand
AU - Janice L. Holton
AU - Zane Jaunmuktane
AU - Juan Eugenio Iglesias
AB - 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 histology1–3 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.
BT - Nature
DA - 2025-11-05
DO - 10.1038/s41586-025-09708-2
LA - en
N2 - 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 histology1–3 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.
PY - 2025
SP - 1
EP - 8
T2 - Nature
TI - A probabilistic histological atlas of the human brain for MRI segmentation
UR - https://www.nature.com/articles/s41586-025-09708-2
Y2 - 2025-11-10
SN - 1476-4687
ER -