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001 | 1043717 | ||
005 | 20250716202230.0 | ||
024 | 7 | _ | |a 10.25493/GF7X-2AU |2 doi |
037 | _ | _ | |a FZJ-2025-03003 |
100 | 1 | _ | |a Kuckertz, Anika |0 P:(DE-Juel1)178766 |b 0 |u fzj |
245 | _ | _ | |a Atlas of muscarinic M2 receptor distributions in the rat brain (v2) |
260 | _ | _ | |c 2025 |b EBRAINS |
336 | 7 | _ | |a MISC |2 BibTeX |
336 | 7 | _ | |a Dataset |b dataset |m dataset |0 PUB:(DE-HGF)32 |s 1752680214_22848 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a Chart or Table |0 26 |2 EndNote |
336 | 7 | _ | |a Dataset |2 DataCite |
336 | 7 | _ | |a DATA_SET |2 ORCID |
336 | 7 | _ | |a ResearchData |2 DINI |
520 | _ | _ | |a Recent technological and methodological advances have facilitated the implementation and standardization of functional magnetic resonance imaging (fMRI) studies in rodents. Integration of such fMRI data with maps coding for the cyto- and receptor architectonic organization of the brain, as well as of its structural connectivity patterns, will facilitate the advance of our understanding of the biological underpinnings of functional networks and accelerate translational research using rat models. This dataset provides a high-resolution three-dimensional (3D) reconstruction of receptor autoradiographs coding for the distribution of the cholinergic muscarinic M2 receptor throughout the entire rat brain. The autoradiographs were obtained by means of in vitro receptor autoradiography using the specific agonist [3H]oxotremorine-M and digitization of the ensuing autoradiographs to enable their densitometric analysis. The pipeline BrainBuilder, developed for the 3D reconstruction of 2D multimodal histological datasets, was used for the reconstruction. The resulting 3D volume was registered to the Waxholm Space rat brain atlas (version 4), thus enabling its integration with fMRI data. |
536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
536 | _ | _ | |a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015) |0 G:(DE-HGF)InterLabs-0015 |c InterLabs-0015 |x 2 |
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588 | _ | _ | |a Dataset connected to DataCite |
650 | _ | 7 | |a Neuroscience |2 Other |
700 | 1 | _ | |a Palomero-Gallagher, Nicola |0 P:(DE-Juel1)131701 |b 1 |e Corresponding author |u fzj |
700 | 1 | _ | |a Funck, Thomas |0 P:(DE-Juel1)181092 |b 2 |u fzj |
773 | _ | _ | |a 10.25493/GF7X-2AU |
909 | C | O | |o oai:juser.fz-juelich.de:1043717 |p openaire |p VDB |p ec_fundedresources |
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914 | 1 | _ | |y 2025 |
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980 | _ | _ | |a UNRESTRICTED |
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