000888142 001__ 888142
000888142 005__ 20220930130258.0
000888142 0247_ $$2doi$$a10.1016/j.neuroimage.2020.117574
000888142 0247_ $$2ISSN$$a1053-8119
000888142 0247_ $$2ISSN$$a1095-9572
000888142 0247_ $$2Handle$$a2128/26641
000888142 0247_ $$2altmetric$$aaltmetric:94730829
000888142 0247_ $$2pmid$$a33221453
000888142 0247_ $$2WOS$$aWOS:000608035900049
000888142 037__ $$aFZJ-2020-04716
000888142 082__ $$a610
000888142 1001_ $$0P:(DE-Juel1)176736$$aJankovic-Rapan, Lucija$$b0
000888142 245__ $$aMultimodal 3D atlas of the macaque monkey motor and premotor cortex
000888142 260__ $$aOrlando, Fla.$$bAcademic Press$$c2021
000888142 3367_ $$2DRIVER$$aarticle
000888142 3367_ $$2DataCite$$aOutput Types/Journal article
000888142 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1609332152_21990
000888142 3367_ $$2BibTeX$$aARTICLE
000888142 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000888142 3367_ $$00$$2EndNote$$aJournal Article
000888142 520__ $$aIn the present study we reevaluated the parcellation scheme of the macaque frontal agranular cortex by implementing quantitative cytoarchitectonic and multireceptor analyses, with the purpose to integrate and reconcile the discrepancies between previously published maps of this region.We applied an observer-independent and statistically testable approach to determine the position of cytoarchitectonic borders. Analysis of the regional and laminar distribution patterns of 13 different transmitter receptors confirmed the position of cytoarchitectonically identified borders. Receptor densities were extracted from each area and visualized as its “receptor fingerprint”. Hierarchical and principal components analyses were conducted to detect clusters of areas according to the degree of (dis)similarity of their fingerprints. Finally, functional connectivity pattern of each identified area was analyzed with areas of prefrontal, cingulate, somatosensory and lateral parietal cortex and the results were depicted as “connectivity fingerprints” and seed-to-vertex connectivity maps.We identified 16 cyto- and receptor architectonically distinct areas, including novel subdivisions of the primary motor area 4 (i.e. 4a, 4p, 4m) and of premotor areas F4 (i.e. F4s, F4d, F4v), F5 (i.e. F5s, F5d, F5v) and F7 (i.e. F7d, F7i, F7s). Multivariate analyses of receptor fingerprints revealed three clusters, which first segregated the subdivisions of area 4 with F4d and F4s from the remaining premotor areas, then separated ventrolateral from dorsolateral and medial premotor areas. The functional connectivity analysis revealed that medial and dorsolateral premotor and motor areas show stronger functional connectivity with areas involved in visual processing, whereas 4p and ventrolateral premotor areas presented a stronger functional connectivity with areas involved in somatomotor responses.For the first time, we provide a 3D atlas integrating cyto- and multi-receptor architectonic features of the macaque motor and premotor cortex. This atlas constitutes a valuable resource for the analysis of functional experiments carried out with non-human primates, for modeling approaches with realistic synaptic dynamics, as well as to provide insights into how brain functions have developed by changes in the underlying microstructure and encoding strategies during evolution.
000888142 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x0
000888142 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000888142 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x2
000888142 588__ $$aDataset connected to CrossRef
000888142 7001_ $$00000-0003-4070-067X$$aFroudist-Walsh, Sean$$b1
000888142 7001_ $$0P:(DE-Juel1)171512$$aNiu, Meiqi$$b2
000888142 7001_ $$0P:(DE-HGF)0$$aXu, Ting$$b3
000888142 7001_ $$0P:(DE-Juel1)181092$$aFunck, Thomas$$b4
000888142 7001_ $$0P:(DE-Juel1)131714$$aZilles, Karl$$b5
000888142 7001_ $$0P:(DE-Juel1)131701$$aPalomero-Gallagher, Nicola$$b6$$eCorresponding author
000888142 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2020.117574$$gp. 117574 -$$p117574$$tNeuroImage$$v226$$x1053-8119$$y2021
000888142 8564_ $$uhttps://juser.fz-juelich.de/record/888142/files/Invoice_OAD0000084492.pdf
000888142 8564_ $$uhttps://juser.fz-juelich.de/record/888142/files/1-s2.0-S1053811920310594-main.pdf$$yOpenAccess
000888142 8767_ $$8OAD0000084492$$92020-11-30$$d2020-12-07$$eAPC$$jZahlung erfolgt$$zBelegnr. 1200160602
000888142 909CO $$ooai:juser.fz-juelich.de:888142$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire$$pdnbdelivery
000888142 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176736$$aForschungszentrum Jülich$$b0$$kFZJ
000888142 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171512$$aForschungszentrum Jülich$$b2$$kFZJ
000888142 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181092$$aForschungszentrum Jülich$$b4$$kFZJ
000888142 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131714$$aForschungszentrum Jülich$$b5$$kFZJ
000888142 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131701$$aForschungszentrum Jülich$$b6$$kFZJ
000888142 9131_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x0
000888142 9141_ $$y2020
000888142 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-08-28
000888142 915__ $$0LIC:(DE-HGF)CCBYNCND4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
000888142 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEUROIMAGE : 2018$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000888142 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNEUROIMAGE : 2018$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-08-28
000888142 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2020-08-28$$wger
000888142 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-08-28
000888142 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
000888142 980__ $$ajournal
000888142 980__ $$aVDB
000888142 980__ $$aUNRESTRICTED
000888142 980__ $$aI:(DE-Juel1)INM-1-20090406
000888142 980__ $$aAPC
000888142 9801_ $$aAPC
000888142 9801_ $$aFullTexts