Home > Publications database > Mapping cyto- and receptor architectonics to understand brain function and connectivity > print

001     911496
005     20240116084314.0
024 7 _ |a 10.1016/j.biopsych.2022.09.014
|2 doi
024 7 _ |a 0006-3223
|2 ISSN
024 7 _ |a 1873-2402
|2 ISSN
024 7 _ |a 2128/33836
|2 Handle
024 7 _ |a 36567226
|2 pmid
024 7 _ |a WOS:000927447100001
|2 WOS
037 _ _ |a FZJ-2022-04758
082 _ _ |a 610
100 1 _ |a Zachlod, Daniel
|0 P:(DE-Juel1)158043
|b 0
|e Corresponding author
245 _ _ |a Mapping cyto- and receptor architectonics to understand brain function and connectivity
260 _ _ |a Amsterdam [u.a.]
|c 2023
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1705312783_8312
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a This review focuses on cytoarchitectonics and receptor architectonics as biological correlates of function and connectivity. It introduces the 3-dimensional cytoarchitectonic probabilistic maps of cortical areas and nuclei of the Julich-Brain Atlas, available at EBRAINS, to study structure-function relationships. The maps are linked to the BigBrain as microanatomical reference model and template space. The siibra software tool suite enables programmatic access to the maps and to receptor architectonic data that are anchored to brain areas. Such cellular and molecular data are tools for studying magnetic resonance connectivity including modeling and simulation. At the end, we highlight perspectives of the Julich-Brain as well as methodological considerations. Thus, microstructural maps as part of a multimodal atlas help elucidate the biological correlates of large-scale networks and brain function with a high level of anatomical detail, which provides a basis to study brains of patients with psychiatric disorders.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|0 G:(DE-HGF)POF4-5251
|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 JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)
|0 G:(DE-Juel1)JL SMHB-2021-2027
|c JL SMHB-2021-2027
|x 2
536 _ _ |a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
|0 G:(DE-HGF)InterLabs-0015
|c InterLabs-0015
|x 3
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Palomero-Gallagher, Nicola
|0 P:(DE-Juel1)131701
|b 1
|u fzj
700 1 _ |a Dickscheid, Timo
|0 P:(DE-Juel1)165746
|b 2
|u fzj
700 1 _ |a Amunts, Katrin
|0 P:(DE-Juel1)131631
|b 3
|u fzj
773 _ _ |a 10.1016/j.biopsych.2022.09.014
|g p. S0006322322015955
|0 PERI:(DE-600)1499907-9
|n 5
|p 471-479
|t Biological psychiatry
|v 93
|y 2023
|x 0006-3223
856 4 _ |u https://juser.fz-juelich.de/record/911496/files/1-s2.0-S0006322322015955-main.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:911496
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p ec_fundedresources
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)158043
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)131701
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)165746
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)131631
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)POF4-5251
|x 0
914 1 _ |y 2022
915 p c |a APC keys set
|0 PC:(DE-HGF)0000
|2 APC
915 p c |a Local Funding
|0 PC:(DE-HGF)0001
|2 APC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-28
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-28
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-28
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2023-08-25
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-08-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-08-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-08-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2023-08-25
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b BIOL PSYCHIAT : 2022
|d 2023-08-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-08-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2023-08-25
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2023-08-25
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b BIOL PSYCHIAT : 2022
|d 2023-08-25
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-1-20090406
|k INM-1
|l Strukturelle und funktionelle Organisation des Gehirns
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-1-20090406
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21