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| Hauptseite > Publikationsdatenbank > Neurotransmitter receptor densities per neuron across macaque cortex (v1.0) > print |
| Hauptseite > Publikationsdatenbank > Neurotransmitter receptor densities per neuron across macaque cortex (v1.0) > print |
| 001 | 1018264 | ||
| 005 | 20250728202331.0 | ||
| 024 | 7 | _ | |a 10.25493/5HK3-S8M |2 doi |
| 037 | _ | _ | |a FZJ-2023-04660 |
| 100 | 1 | _ | |a Froudist-Walsh, Sean |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Neurotransmitter receptor densities per neuron across macaque cortex (v1.0) |
| 260 | _ | _ | |c 2023 |b EBRAINS |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Dataset |b dataset |m dataset |0 PUB:(DE-HGF)32 |s 1753700036_3161 |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 Neurotransmitter receptors constitute key molecules in signal transduction, and the degree to which they modulate neural activity is dependent on the number of receptors expressed on the neuronal membrane. Receptors for classical neurotransmitters are heterogeneously distributed throughout the macaque monkey cerebral cortex, as are neuronal densities. We applied in vitro receptor autoradiography to quantify the densities of 14 different receptor types in 109 cytoarchitectonically identified areas located throughout the macaque cortex. We used previously published neuron density data ([Collins et al., Proc Natl Acad Sci 107: 15927–15932, 2010](https://doi.org/10.1073/pnas.1010356107)) to estimate the receptor density per neuron in each area. This data can be used to build biologically informed neuronal computational models. |
| 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 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Neuroscience |2 Other |
| 700 | 1 | _ | |a Niu, Meiqi |0 P:(DE-Juel1)171512 |b 1 |
| 700 | 1 | _ | |a Rapan, Lucija |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Palomero-Gallagher, Nicola |0 P:(DE-Juel1)131701 |b 3 |
| 700 | 1 | _ | |a Palomero-Gallagher, Nicola |0 P:(DE-Juel1)131701 |b 4 |
| 773 | _ | _ | |a 10.25493/5HK3-S8M |
| 909 | C | O | |o oai:juser.fz-juelich.de:1018264 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)171512 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)131701 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)131701 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
| 980 | _ | _ | |a dataset |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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