An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain

Brain atlases are indispensable tools for quantifying cellular composition across mouse brain regions. The widely used Common Coordinate Framework version 3 (CCFv3) from the Allen Institute delineates over 600 anatomical regions but lacks coverage of the most rostral and caudal brain areas, includin...

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Published inImaging neuroscience (Cambridge, Mass.) Vol. 3
Main Authors Piluso, Sébastien, Verasztó, Csaba, Carey, Harry, Delattre, Émilie, L’Yvonnet, Thibaud, Colnot, Éloïse, Romani, Armando, Bjaalie, Jan G., Markram, Henry, Keller, Daniel
Format Journal Article
LanguageEnglish
Published 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA MIT Press 21.05.2025
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ISSN2837-6056
2837-6056
DOI10.1162/imag_a_00565

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Summary:Brain atlases are indispensable tools for quantifying cellular composition across mouse brain regions. The widely used Common Coordinate Framework version 3 (CCFv3) from the Allen Institute delineates over 600 anatomical regions but lacks coverage of the most rostral and caudal brain areas, including the main olfactory bulb, cerebellum, and medulla. Additionally, the CCFv3 does not include annotations for key cerebellar layers, and its Nissl-stained reference volume is misaligned, limiting its efficiency. To overcome these limitations, we developed the Blue Brain Project (BBP) CCFv3 augmented atlas (CCFv3 ), which includes a fully annotated mouse brain and an improved Nissl-stained reference volume aligned with the CCFv3 . This enhanced atlas also features the central nervous system annotation. Building on this enhanced resource, we aligned 734 Nissl-stained brains to generate an average Nissl template at 10 µm resolution. This new atlas version enabled the construction of the first comprehensive model of cell distribution across the whole mouse central nervous system. This open-access resource broadens the applicability of brain atlases, supporting advancements in alignment accuracy, cell type mapping, and multimodal data integration.
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ISSN:2837-6056
2837-6056
DOI:10.1162/imag_a_00565